Multi-Institutional RCT Results
Julie Cachia
2024-10-21
These are results for the multi-institutional study involving students from UW, Foothill College, and Chapman University. Data collected in the Fall/Winter of 2024.
# Cronbach's alphas and correlations
## Depression T1 r = .65
cor.test(merged_data$PHQ_4_1_T1, merged_data$PHQ_4_2_T1, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_1_T1 and merged_data$PHQ_4_2_T1
## t = 18.726, df = 482, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5941793 0.6977154
## sample estimates:
## cor
## 0.6489416## Depression T2 r = .68
cor.test(merged_data$PHQ_4_1_T2, merged_data$PHQ_4_2_T2, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_1_T2 and merged_data$PHQ_4_2_T2
## t = 18.34, df = 389, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6239246 0.7307849
## sample estimates:
## cor
## 0.6809632## Depression T3 r = .65
cor.test(merged_data$PHQ_4_1_T3, merged_data$PHQ_4_2_T3, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_1_T3 and merged_data$PHQ_4_2_T3
## t = 16.029, df = 353, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5844304 0.7054651
## sample estimates:
## cor
## 0.6490361## Depression T4 r = .68
cor.test(merged_data$PHQ_4_1_T4, merged_data$PHQ_4_2_T4, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_1_T4 and merged_data$PHQ_4_2_T4
## t = 17.312, df = 347, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6201027 0.7333368
## sample estimates:
## cor
## 0.680766## Anxiety T1 r = .73
cor.test(merged_data$PHQ_4_3_T1, merged_data$PHQ_4_4_T1, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_3_T1 and merged_data$PHQ_4_4_T1
## t = 23.768, df = 482, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6906645 0.7730883
## sample estimates:
## cor
## 0.7345747## Anxiety T2 r = .75
cor.test(merged_data$PHQ_4_3_T2, merged_data$PHQ_4_4_T2, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_3_T2 and merged_data$PHQ_4_4_T2
## t = 22.273, df = 389, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7015770 0.7892364
## sample estimates:
## cor
## 0.748661## Anxiety T3 r = .74
cor.test(merged_data$PHQ_4_3_T3, merged_data$PHQ_4_4_T3, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_3_T3 and merged_data$PHQ_4_4_T3
## t = 20.548, df = 353, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6866606 0.7820171
## sample estimates:
## cor
## 0.7380014## Anxiety T4 r = .73
cor.test(merged_data$PHQ_4_3_T4, merged_data$PHQ_4_4_T4, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$PHQ_4_3_T4 and merged_data$PHQ_4_4_T4
## t = 19.981, df = 347, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6785656 0.7767750
## sample estimates:
## cor
## 0.7314409## loneliness T1 .74
loneliness_T1 <- merged_data |>
dplyr::select(loneliness_1_T1, loneliness_2_T1, loneliness_3_T1)
alpha_pos <- psych::alpha(loneliness_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = loneliness_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.74 0.67 0.49 2.9 0.02 1.9 0.55 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.7 0.74 0.78
## Duhachek 0.7 0.74 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## loneliness_1_T1 0.74 0.74 0.59 0.59 2.8 0.024 NA 0.59
## loneliness_2_T1 0.63 0.63 0.46 0.46 1.7 0.033 NA 0.46
## loneliness_3_T1 0.59 0.59 0.42 0.42 1.5 0.037 NA 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## loneliness_1_T1 484 0.77 0.77 0.57 0.50 1.9 0.66
## loneliness_2_T1 484 0.82 0.82 0.69 0.59 1.8 0.67
## loneliness_3_T1 484 0.85 0.84 0.73 0.62 1.9 0.70
##
## Non missing response frequency for each item
## 1 2 3 miss
## loneliness_1_T1 0.28 0.55 0.16 0
## loneliness_2_T1 0.31 0.53 0.16 0
## loneliness_3_T1 0.32 0.50 0.18 0## loneliness T2 .76
loneliness_T2 <- merged_data |>
dplyr::select(loneliness_1_T2, loneliness_2_T2, loneliness_3_T2)
alpha_pos <- psych::alpha(loneliness_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = loneliness_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.76 0.76 0.69 0.52 3.3 0.018 1.7 0.53 0.55
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.76 0.8
## Duhachek 0.73 0.76 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## loneliness_1_T2 0.72 0.73 0.57 0.57 2.6 0.025 NA 0.57
## loneliness_2_T2 0.71 0.71 0.55 0.55 2.4 0.027 NA 0.55
## loneliness_3_T2 0.61 0.62 0.45 0.45 1.6 0.035 NA 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## loneliness_1_T2 391 0.81 0.80 0.64 0.56 1.8 0.65
## loneliness_2_T2 391 0.80 0.81 0.66 0.58 1.7 0.60
## loneliness_3_T2 391 0.86 0.85 0.75 0.66 1.7 0.68
##
## Non missing response frequency for each item
## 1 2 3 miss
## loneliness_1_T2 0.35 0.53 0.13 0.2
## loneliness_2_T2 0.41 0.52 0.07 0.2
## loneliness_3_T2 0.40 0.47 0.13 0.2## loneliness T3 .81
loneliness_T3 <- merged_data |>
dplyr::select(loneliness_1_T3, loneliness_2_T3, loneliness_3_T3)
alpha_pos <- psych::alpha(loneliness_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = loneliness_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.74 0.58 4.2 0.015 1.7 0.56 0.57
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.81 0.83
## Duhachek 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## loneliness_1_T3 0.78 0.78 0.64 0.64 3.6 0.020 NA 0.64
## loneliness_2_T3 0.73 0.73 0.57 0.57 2.7 0.025 NA 0.57
## loneliness_3_T3 0.69 0.70 0.53 0.53 2.3 0.028 NA 0.53
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## loneliness_1_T3 355 0.83 0.83 0.68 0.61 1.7 0.67
## loneliness_2_T3 355 0.85 0.85 0.74 0.66 1.7 0.63
## loneliness_3_T3 355 0.87 0.87 0.78 0.69 1.7 0.67
##
## Non missing response frequency for each item
## 1 2 3 miss
## loneliness_1_T3 0.40 0.47 0.12 0.27
## loneliness_2_T3 0.44 0.48 0.09 0.27
## loneliness_3_T3 0.43 0.45 0.12 0.27## loneliness T4 .81
loneliness_T4 <- merged_data |>
dplyr::select(loneliness_1_T4, loneliness_2_T4, loneliness_3_T4)
alpha_pos <- psych::alpha(loneliness_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = loneliness_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.75 0.58 4.2 0.015 1.7 0.57 0.57
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.81 0.84
## Duhachek 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## loneliness_1_T4 0.81 0.81 0.68 0.68 4.3 0.017 NA 0.68
## loneliness_2_T4 0.73 0.73 0.57 0.57 2.7 0.025 NA 0.57
## loneliness_3_T4 0.67 0.67 0.50 0.50 2.0 0.030 NA 0.50
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## loneliness_1_T4 349 0.81 0.81 0.64 0.59 1.8 0.65
## loneliness_2_T4 349 0.85 0.86 0.76 0.67 1.7 0.65
## loneliness_3_T4 349 0.89 0.88 0.81 0.72 1.8 0.69
##
## Non missing response frequency for each item
## 1 2 3 miss
## loneliness_1_T4 0.36 0.52 0.12 0.28
## loneliness_2_T4 0.39 0.50 0.11 0.28
## loneliness_3_T4 0.39 0.46 0.15 0.28## stress
stress <- merged_data |>
dplyr::select(contains("Perceived_Stress_")) |>
mutate(
Perceived_Stress_1_T1_scored = Perceived_Stress_1_T1 - 1, # From 1-5 to 0-4
Perceived_Stress_2_T1_scored = 5 - Perceived_Stress_2_T1, # Reverse score (5 - x)
Perceived_Stress_3_T1_scored = 5 - Perceived_Stress_3_T1, # Reverse score (5 - x)
Perceived_Stress_4_T1_scored = Perceived_Stress_4_T1 - 1) |> # From 1-5 to 0-4
mutate(
Perceived_Stress_1_T2_scored = Perceived_Stress_1_T2 - 1, # From 1-5 to 0-4
Perceived_Stress_2_T2_scored = 5 - Perceived_Stress_2_T2, # Reverse score (5 - x)
Perceived_Stress_3_T2_scored = 5 - Perceived_Stress_3_T2, # Reverse score (5 - x)
Perceived_Stress_4_T2_scored = Perceived_Stress_4_T2 - 1) |> # From 1-5 to 0-4
mutate(
Perceived_Stress_1_T3_scored = Perceived_Stress_1_T3 - 1, # From 1-5 to 0-4
Perceived_Stress_2_T3_scored = 5 - Perceived_Stress_2_T3, # Reverse score (5 - x)
Perceived_Stress_3_T3_scored = 5 - Perceived_Stress_3_T3, # Reverse score (5 - x)
Perceived_Stress_4_T3_scored = Perceived_Stress_4_T3 - 1) |> # From 1-5 to 0-4
mutate(
Perceived_Stress_1_T4_scored = Perceived_Stress_1_T4 - 1, # From 1-5 to 0-4
Perceived_Stress_2_T4_scored = 5 - Perceived_Stress_2_T4, # Reverse score (5 - x)
Perceived_Stress_3_T4_scored = 5 - Perceived_Stress_3_T4, # Reverse score (5 - x)
Perceived_Stress_4_T4_scored = Perceived_Stress_4_T4 - 1) # From 1-5 to 0-4
## stress T1 .74
stress_T1 <- stress |>
dplyr::select(Perceived_Stress_1_T1_scored, Perceived_Stress_2_T1_scored, Perceived_Stress_3_T1_scored, Perceived_Stress_4_T1_scored)
alpha_pos <- psych::alpha(stress_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = stress_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.73 0.74 0.69 0.41 2.8 0.019 1.7 0.74 0.39
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.73 0.77
## Duhachek 0.70 0.73 0.77
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Perceived_Stress_1_T1_scored 0.66 0.67 0.58 0.40 2.0 0.026
## Perceived_Stress_2_T1_scored 0.72 0.72 0.64 0.47 2.6 0.021
## Perceived_Stress_3_T1_scored 0.67 0.67 0.59 0.40 2.0 0.025
## Perceived_Stress_4_T1_scored 0.63 0.64 0.54 0.37 1.8 0.029
## var.r med.r
## Perceived_Stress_1_T1_scored 0.0039 0.39
## Perceived_Stress_2_T1_scored 0.0054 0.47
## Perceived_Stress_3_T1_scored 0.0148 0.34
## Perceived_Stress_4_T1_scored 0.0017 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Perceived_Stress_1_T1_scored 484 0.78 0.76 0.64 0.55 1.9 1.04
## Perceived_Stress_2_T1_scored 484 0.66 0.69 0.51 0.43 1.4 0.87
## Perceived_Stress_3_T1_scored 484 0.73 0.75 0.63 0.54 1.6 0.88
## Perceived_Stress_4_T1_scored 484 0.82 0.79 0.70 0.60 1.9 1.14
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## Perceived_Stress_1_T1_scored 0.10 0.27 0.38 0.19 0.06 0
## Perceived_Stress_2_T1_scored 0.13 0.44 0.32 0.10 0.01 0
## Perceived_Stress_3_T1_scored 0.09 0.36 0.40 0.13 0.02 0
## Perceived_Stress_4_T1_scored 0.10 0.32 0.28 0.21 0.09 0## stress T2 .66
stress_T2 <- stress |>
dplyr::select(Perceived_Stress_1_T2_scored, Perceived_Stress_2_T2_scored, Perceived_Stress_3_T2_scored, Perceived_Stress_4_T2_scored)
alpha_pos <- psych::alpha(stress_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = stress_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.66 0.66 0.64 0.33 1.9 0.026 1.7 0.69 0.25
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.60 0.66 0.70
## Duhachek 0.61 0.66 0.71
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Perceived_Stress_1_T2_scored 0.58 0.59 0.52 0.33 1.4 0.034
## Perceived_Stress_2_T2_scored 0.61 0.61 0.54 0.34 1.6 0.030
## Perceived_Stress_3_T2_scored 0.58 0.58 0.51 0.31 1.4 0.033
## Perceived_Stress_4_T2_scored 0.58 0.59 0.52 0.32 1.4 0.033
## var.r med.r
## Perceived_Stress_1_T2_scored 0.028 0.27
## Perceived_Stress_2_T2_scored 0.024 0.27
## Perceived_Stress_3_T2_scored 0.033 0.22
## Perceived_Stress_4_T2_scored 0.027 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Perceived_Stress_1_T2_scored 391 0.72 0.70 0.56 0.46 1.6 0.99
## Perceived_Stress_2_T2_scored 391 0.66 0.69 0.53 0.40 1.5 0.94
## Perceived_Stress_3_T2_scored 391 0.69 0.72 0.59 0.45 1.8 0.91
## Perceived_Stress_4_T2_scored 391 0.74 0.70 0.57 0.45 1.8 1.10
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## Perceived_Stress_1_T2_scored 0.14 0.35 0.35 0.13 0.03 0.2
## Perceived_Stress_2_T2_scored 0.12 0.43 0.31 0.11 0.03 0.2
## Perceived_Stress_3_T2_scored 0.06 0.34 0.42 0.15 0.04 0.2
## Perceived_Stress_4_T2_scored 0.11 0.27 0.38 0.15 0.09 0.2## stress T3 .72
stress_T3 <- stress |>
dplyr::select(Perceived_Stress_1_T3_scored, Perceived_Stress_2_T3_scored, Perceived_Stress_3_T3_scored, Perceived_Stress_4_T3_scored)
alpha_pos <- psych::alpha(stress_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = stress_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.72 0.69 0.4 2.6 0.021 1.6 0.71 0.34
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.72 0.76
## Duhachek 0.68 0.72 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Perceived_Stress_1_T3_scored 0.66 0.67 0.59 0.40 2.0 0.027
## Perceived_Stress_2_T3_scored 0.67 0.67 0.59 0.40 2.0 0.025
## Perceived_Stress_3_T3_scored 0.66 0.66 0.58 0.39 1.9 0.026
## Perceived_Stress_4_T3_scored 0.65 0.66 0.58 0.39 1.9 0.028
## var.r med.r
## Perceived_Stress_1_T3_scored 0.014 0.34
## Perceived_Stress_2_T3_scored 0.014 0.34
## Perceived_Stress_3_T3_scored 0.016 0.33
## Perceived_Stress_4_T3_scored 0.017 0.32
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Perceived_Stress_1_T3_scored 355 0.74 0.73 0.60 0.52 1.6 0.97
## Perceived_Stress_2_T3_scored 355 0.71 0.74 0.61 0.50 1.3 0.88
## Perceived_Stress_3_T3_scored 355 0.72 0.75 0.63 0.51 1.7 0.90
## Perceived_Stress_4_T3_scored 355 0.78 0.75 0.62 0.53 1.7 1.11
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## Perceived_Stress_1_T3_scored 0.13 0.35 0.39 0.09 0.04 0.27
## Perceived_Stress_2_T3_scored 0.16 0.44 0.30 0.09 0.01 0.27
## Perceived_Stress_3_T3_scored 0.09 0.34 0.42 0.13 0.02 0.27
## Perceived_Stress_4_T3_scored 0.13 0.34 0.31 0.14 0.08 0.27## stress T4 .73
stress_T4 <- stress |>
dplyr::select(Perceived_Stress_1_T4_scored, Perceived_Stress_2_T4_scored, Perceived_Stress_3_T4_scored, Perceived_Stress_4_T4_scored)
alpha_pos <- psych::alpha(stress_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = stress_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.73 0.7 0.4 2.6 0.02 1.7 0.71 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.72 0.76
## Duhachek 0.68 0.72 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## Perceived_Stress_1_T4_scored 0.62 0.65 0.57 0.38 1.8 0.030
## Perceived_Stress_2_T4_scored 0.72 0.72 0.65 0.46 2.5 0.021
## Perceived_Stress_3_T4_scored 0.66 0.65 0.60 0.38 1.9 0.026
## Perceived_Stress_4_T4_scored 0.63 0.64 0.56 0.37 1.8 0.029
## var.r med.r
## Perceived_Stress_1_T4_scored 0.012 0.39
## Perceived_Stress_2_T4_scored 0.019 0.39
## Perceived_Stress_3_T4_scored 0.041 0.27
## Perceived_Stress_4_T4_scored 0.012 0.36
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Perceived_Stress_1_T4_scored 349 0.78 0.76 0.66 0.57 1.7 0.97
## Perceived_Stress_2_T4_scored 349 0.64 0.68 0.51 0.40 1.4 0.87
## Perceived_Stress_3_T4_scored 349 0.72 0.76 0.63 0.53 1.7 0.83
## Perceived_Stress_4_T4_scored 349 0.81 0.77 0.68 0.57 1.9 1.15
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## Perceived_Stress_1_T4_scored 0.11 0.31 0.39 0.15 0.03 0.28
## Perceived_Stress_2_T4_scored 0.11 0.48 0.29 0.10 0.01 0.28
## Perceived_Stress_3_T4_scored 0.05 0.37 0.43 0.13 0.02 0.28
## Perceived_Stress_4_T4_scored 0.12 0.28 0.30 0.21 0.09 0.28## SAS - Calm T1 .81
SAS_Calm_T1 <- merged_data |>
dplyr::select(SAS_1_T1, SAS_2_T1, SAS_3_T1)
alpha_pos <- psych::alpha(SAS_Calm_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Calm_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.74 0.59 4.2 0.015 1.9 0.85 0.59
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.78 0.81 0.84
## Duhachek 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T1 0.77 0.77 0.62 0.62 3.3 0.021 NA 0.62
## SAS_2_T1 0.74 0.74 0.59 0.59 2.9 0.023 NA 0.59
## SAS_3_T1 0.71 0.71 0.55 0.55 2.4 0.027 NA 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T1 484 0.83 0.84 0.70 0.63 2.0 0.97
## SAS_2_T1 484 0.86 0.85 0.73 0.65 1.8 1.04
## SAS_3_T1 484 0.87 0.87 0.77 0.69 1.8 1.00
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T1 0.05 0.26 0.37 0.27 0.06 0
## SAS_2_T1 0.10 0.29 0.35 0.20 0.06 0
## SAS_3_T1 0.08 0.35 0.34 0.17 0.05 0## SAS - Calm T2 .85
SAS_Calm_T2 <- merged_data |>
dplyr::select(SAS_1_T2, SAS_2_T2, SAS_3_T2)
alpha_pos <- psych::alpha(SAS_Calm_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Calm_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.79 0.65 5.6 0.012 1.9 0.86 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.85 0.87
## Duhachek 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T2 0.79 0.79 0.65 0.65 3.8 0.019 NA 0.65
## SAS_2_T2 0.81 0.81 0.68 0.68 4.3 0.017 NA 0.68
## SAS_3_T2 0.76 0.76 0.62 0.62 3.2 0.021 NA 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T2 391 0.88 0.88 0.78 0.72 2.0 0.99
## SAS_2_T2 391 0.87 0.86 0.75 0.69 1.8 1.00
## SAS_3_T2 391 0.89 0.89 0.81 0.74 1.8 0.96
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T2 0.07 0.22 0.37 0.29 0.05 0.2
## SAS_2_T2 0.10 0.32 0.34 0.21 0.03 0.2
## SAS_3_T2 0.08 0.31 0.36 0.23 0.02 0.2## SAS - Calm T3 .85
SAS_Calm_T3 <- merged_data |>
dplyr::select(SAS_1_T3, SAS_2_T3, SAS_3_T3)
alpha_pos <- psych::alpha(SAS_Calm_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Calm_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.79 0.65 5.7 0.012 1.9 0.87 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.85 0.87
## Duhachek 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T3 0.78 0.78 0.64 0.64 3.6 0.020 NA 0.64
## SAS_2_T3 0.80 0.80 0.67 0.67 4.0 0.018 NA 0.67
## SAS_3_T3 0.79 0.79 0.65 0.65 3.7 0.019 NA 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T3 355 0.88 0.88 0.79 0.73 2.0 1.00
## SAS_2_T3 355 0.88 0.87 0.77 0.71 1.9 1.03
## SAS_3_T3 355 0.87 0.88 0.78 0.72 1.9 0.96
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T3 0.05 0.25 0.37 0.26 0.07 0.27
## SAS_2_T3 0.09 0.29 0.36 0.21 0.06 0.27
## SAS_3_T3 0.06 0.31 0.37 0.21 0.05 0.27## SAS - Calm T4 .82
SAS_Calm_T4 <- merged_data |>
dplyr::select(SAS_1_T4, SAS_2_T4, SAS_3_T4)
alpha_pos <- psych::alpha(SAS_Calm_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Calm_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.75 0.6 4.5 0.014 2 0.84 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.84
## Duhachek 0.79 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T4 0.73 0.73 0.58 0.58 2.8 0.024 NA 0.58
## SAS_2_T4 0.79 0.79 0.65 0.65 3.7 0.019 NA 0.65
## SAS_3_T4 0.72 0.72 0.56 0.56 2.6 0.025 NA 0.56
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T4 349 0.86 0.86 0.76 0.68 2.1 0.98
## SAS_2_T4 349 0.83 0.84 0.69 0.63 1.9 0.97
## SAS_3_T4 349 0.87 0.87 0.77 0.70 1.9 1.00
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T4 0.05 0.24 0.34 0.31 0.06 0.28
## SAS_2_T4 0.07 0.30 0.34 0.26 0.03 0.28
## SAS_3_T4 0.08 0.28 0.35 0.24 0.04 0.28## SAS - Wellbeing T1 .83
SAS_WellBeing_T1 <- merged_data |>
dplyr::select(SAS_4_T1, SAS_5_T1, SAS_6_T1)
alpha_pos <- psych::alpha(SAS_WellBeing_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_WellBeing_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.77 0.62 4.8 0.014 2.4 0.83 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.83 0.85
## Duhachek 0.8 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_4_T1 0.71 0.71 0.55 0.55 2.5 0.026 NA 0.55
## SAS_5_T1 0.77 0.77 0.62 0.62 3.3 0.021 NA 0.62
## SAS_6_T1 0.81 0.81 0.68 0.68 4.2 0.017 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_4_T1 484 0.88 0.89 0.81 0.74 2.6 0.90
## SAS_5_T1 484 0.86 0.86 0.76 0.68 2.3 0.99
## SAS_6_T1 484 0.84 0.84 0.70 0.64 2.3 0.99
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_4_T1 0.01 0.10 0.34 0.40 0.15 0
## SAS_5_T1 0.04 0.17 0.34 0.34 0.10 0
## SAS_6_T1 0.05 0.16 0.37 0.33 0.09 0## SAS - Wellbeing T2 .83
SAS_WellBeing_T2 <- merged_data |>
dplyr::select(SAS_4_T2, SAS_5_T2, SAS_6_T2)
alpha_pos <- psych::alpha(SAS_WellBeing_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_WellBeing_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.76 0.62 4.8 0.014 2.3 0.8 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.83 0.85
## Duhachek 0.8 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_4_T2 0.76 0.76 0.62 0.62 3.2 0.021 NA 0.62
## SAS_5_T2 0.77 0.77 0.62 0.62 3.3 0.021 NA 0.62
## SAS_6_T2 0.75 0.75 0.60 0.60 3.1 0.022 NA 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_4_T2 391 0.85 0.86 0.75 0.68 2.5 0.86
## SAS_5_T2 391 0.87 0.86 0.75 0.68 2.1 0.97
## SAS_6_T2 391 0.87 0.87 0.76 0.69 2.2 0.96
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_4_T2 0.01 0.11 0.37 0.40 0.11 0.2
## SAS_5_T2 0.04 0.25 0.34 0.32 0.06 0.2
## SAS_6_T2 0.04 0.20 0.38 0.31 0.07 0.2## SAS - Wellbeing T3 .86
SAS_WellBeing_T3 <- merged_data |>
dplyr::select(SAS_4_T3, SAS_5_T3, SAS_6_T3)
alpha_pos <- psych::alpha(SAS_WellBeing_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_WellBeing_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.8 0.67 6 0.011 2.3 0.87 0.66
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.88
## Duhachek 0.83 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_4_T3 0.77 0.77 0.63 0.63 3.4 0.020 NA 0.63
## SAS_5_T3 0.83 0.83 0.71 0.71 4.8 0.015 NA 0.71
## SAS_6_T3 0.79 0.80 0.66 0.66 3.9 0.019 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_4_T3 355 0.89 0.90 0.82 0.76 2.5 0.94
## SAS_5_T3 355 0.87 0.87 0.75 0.70 2.1 1.03
## SAS_6_T3 354 0.88 0.88 0.80 0.73 2.2 0.97
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_4_T3 0.02 0.12 0.33 0.39 0.14 0.27
## SAS_5_T3 0.06 0.22 0.34 0.30 0.08 0.27
## SAS_6_T3 0.03 0.21 0.36 0.31 0.09 0.27## SAS - Wellbeing T4 .84
SAS_WellBeing_T4 <- merged_data |>
dplyr::select(SAS_4_T4, SAS_5_T4, SAS_6_T4)
alpha_pos <- psych::alpha(SAS_WellBeing_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_WellBeing_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.78 0.64 5.3 0.013 2.3 0.83 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.81 0.84 0.86
## Duhachek 0.82 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_4_T4 0.75 0.75 0.60 0.60 3.1 0.022 NA 0.60
## SAS_5_T4 0.80 0.80 0.67 0.67 4.0 0.018 NA 0.67
## SAS_6_T4 0.78 0.79 0.65 0.65 3.7 0.019 NA 0.65
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_4_T4 349 0.88 0.89 0.80 0.73 2.5 0.90
## SAS_5_T4 349 0.87 0.86 0.75 0.69 2.1 1.00
## SAS_6_T4 349 0.87 0.87 0.76 0.70 2.2 0.94
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_4_T4 0.02 0.11 0.30 0.46 0.12 0.28
## SAS_5_T4 0.04 0.26 0.32 0.31 0.07 0.28
## SAS_6_T4 0.04 0.16 0.40 0.33 0.07 0.28## SAS - Vigour T1 .83
SAS_Vigour_T1 <- merged_data |>
dplyr::select(SAS_7_T1, SAS_8_T1, SAS_9_T1)
alpha_pos <- psych::alpha(SAS_Vigour_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Vigour_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.83 0.83 0.77 0.62 4.8 0.014 2 0.9 0.62
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.8 0.83 0.85
## Duhachek 0.8 0.83 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_7_T1 0.79 0.79 0.66 0.66 3.8 0.019 NA 0.66
## SAS_8_T1 0.76 0.77 0.62 0.62 3.3 0.021 NA 0.62
## SAS_9_T1 0.73 0.73 0.57 0.57 2.6 0.025 NA 0.57
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_7_T1 484 0.85 0.85 0.72 0.65 1.7 1.1
## SAS_8_T1 484 0.86 0.86 0.75 0.68 2.2 1.0
## SAS_9_T1 484 0.88 0.88 0.80 0.72 2.1 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_7_T1 0.15 0.31 0.33 0.15 0.06 0
## SAS_8_T1 0.05 0.20 0.33 0.31 0.11 0
## SAS_9_T1 0.05 0.26 0.37 0.24 0.09 0## SAS - Vigour T2 .86
SAS_Vigour_T2 <- merged_data |>
dplyr::select(SAS_7_T2, SAS_8_T2, SAS_9_T2)
alpha_pos <- psych::alpha(SAS_Vigour_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Vigour_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.81 0.68 6.4 0.011 1.9 0.89 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_7_T2 0.84 0.84 0.73 0.73 5.4 0.014 NA 0.73
## SAS_8_T2 0.77 0.77 0.63 0.63 3.4 0.021 NA 0.63
## SAS_9_T2 0.81 0.81 0.68 0.68 4.2 0.017 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_7_T2 391 0.87 0.87 0.75 0.70 1.5 1.05
## SAS_8_T2 391 0.90 0.91 0.84 0.78 2.1 0.98
## SAS_9_T2 391 0.88 0.89 0.80 0.74 1.9 0.98
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_7_T2 0.18 0.31 0.32 0.17 0.02 0.2
## SAS_8_T2 0.04 0.23 0.36 0.29 0.07 0.2
## SAS_9_T2 0.07 0.27 0.36 0.27 0.04 0.2## SAS - Vigour T3 .87
SAS_Vigour_T3 <- merged_data |>
dplyr::select(SAS_7_T3, SAS_8_T3, SAS_9_T3)
alpha_pos <- psych::alpha(SAS_Vigour_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Vigour_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.82 0.69 6.7 0.01 1.9 0.93 0.69
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.87 0.89
## Duhachek 0.85 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_7_T3 0.83 0.83 0.71 0.71 4.9 0.015 NA 0.71
## SAS_8_T3 0.81 0.81 0.68 0.68 4.2 0.017 NA 0.68
## SAS_9_T3 0.81 0.81 0.69 0.69 4.4 0.017 NA 0.69
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_7_T3 355 0.89 0.88 0.79 0.74 1.6 1.1
## SAS_8_T3 355 0.90 0.90 0.82 0.76 2.1 1.0
## SAS_9_T3 355 0.89 0.89 0.81 0.76 1.9 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_7_T3 0.16 0.32 0.30 0.19 0.04 0.27
## SAS_8_T3 0.05 0.27 0.29 0.31 0.08 0.27
## SAS_9_T3 0.07 0.28 0.34 0.26 0.05 0.27## SAS - Vigour T4 .87
SAS_Vigour_T4 <- merged_data |>
dplyr::select(SAS_7_T4, SAS_8_T4, SAS_9_T4)
alpha_pos <- psych::alpha(SAS_Vigour_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Vigour_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.82 0.69 6.7 0.01 1.9 0.92 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.87 0.89
## Duhachek 0.85 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_7_T4 0.84 0.84 0.73 0.73 5.4 0.014 NA 0.73
## SAS_8_T4 0.81 0.81 0.68 0.68 4.2 0.018 NA 0.68
## SAS_9_T4 0.80 0.80 0.66 0.66 3.9 0.018 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_7_T4 348 0.88 0.88 0.77 0.72 1.6 1.1
## SAS_8_T4 349 0.89 0.90 0.82 0.76 2.1 1.0
## SAS_9_T4 349 0.90 0.90 0.83 0.77 1.9 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_7_T4 0.16 0.33 0.29 0.19 0.03 0.28
## SAS_8_T4 0.06 0.22 0.35 0.31 0.06 0.28
## SAS_9_T4 0.08 0.30 0.32 0.24 0.05 0.28## SAS - Depression T1 .85
SAS_Depression_T1 <- merged_data |>
dplyr::select(SAS_10_T1, SAS_11_T1, SAS_12_T1)
alpha_pos <- psych::alpha(SAS_Depression_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Depression_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.79 0.65 5.6 0.012 1.4 1 0.65
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.85 0.87
## Duhachek 0.82 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T1 0.78 0.79 0.65 0.65 3.7 0.019 NA 0.65
## SAS_11_T1 0.80 0.81 0.68 0.68 4.2 0.018 NA 0.68
## SAS_12_T1 0.77 0.77 0.63 0.63 3.4 0.021 NA 0.63
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T1 484 0.87 0.88 0.78 0.72 1.6 1.1
## SAS_11_T1 484 0.86 0.87 0.76 0.70 1.4 1.1
## SAS_12_T1 484 0.89 0.88 0.80 0.73 1.2 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T1 0.17 0.36 0.25 0.16 0.06 0
## SAS_11_T1 0.23 0.37 0.22 0.14 0.04 0
## SAS_12_T1 0.37 0.28 0.18 0.11 0.06 0## SAS - Depression T2 .86
SAS_Depression_T2 <- merged_data |>
dplyr::select(SAS_10_T2, SAS_11_T2, SAS_12_T2)
alpha_pos <- psych::alpha(SAS_Depression_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Depression_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.81 0.68 6.3 0.011 1.3 0.95 0.68
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T2 0.82 0.82 0.70 0.70 4.6 0.016 NA 0.70
## SAS_11_T2 0.79 0.79 0.65 0.65 3.8 0.019 NA 0.65
## SAS_12_T2 0.81 0.81 0.68 0.68 4.2 0.017 NA 0.68
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T2 391 0.88 0.88 0.78 0.72 1.5 1.1
## SAS_11_T2 391 0.89 0.89 0.81 0.76 1.3 1.0
## SAS_12_T2 391 0.89 0.88 0.79 0.74 1.2 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T2 0.17 0.40 0.25 0.14 0.04 0.2
## SAS_11_T2 0.22 0.44 0.21 0.10 0.03 0.2
## SAS_12_T2 0.36 0.31 0.18 0.10 0.04 0.2## SAS - Depression T3 .86
SAS_Depression_T3 <- merged_data |>
dplyr::select(SAS_10_T3, SAS_11_T3, SAS_12_T3)
alpha_pos <- psych::alpha(SAS_Depression_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Depression_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.8 0.67 6.1 0.011 1.3 0.99 0.67
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T3 0.81 0.81 0.68 0.68 4.2 0.017 NA 0.68
## SAS_11_T3 0.80 0.80 0.67 0.67 4.0 0.018 NA 0.67
## SAS_12_T3 0.79 0.79 0.66 0.66 3.8 0.019 NA 0.66
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T3 355 0.88 0.88 0.78 0.72 1.5 1.1
## SAS_11_T3 355 0.88 0.88 0.79 0.73 1.3 1.1
## SAS_12_T3 355 0.89 0.89 0.80 0.74 1.1 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T3 0.21 0.35 0.25 0.13 0.05 0.27
## SAS_11_T3 0.27 0.38 0.21 0.10 0.04 0.27
## SAS_12_T3 0.39 0.29 0.15 0.11 0.05 0.27## SAS - Depression T4 .82
SAS_Depression_T4 <- merged_data |>
dplyr::select(SAS_10_T4, SAS_11_T4, SAS_12_T4)
alpha_pos <- psych::alpha(SAS_Depression_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Depression_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.82 0.82 0.76 0.61 4.7 0.014 1.3 0.94 0.61
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.79 0.82 0.85
## Duhachek 0.79 0.82 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T4 0.79 0.80 0.66 0.66 4.0 0.018 NA 0.66
## SAS_11_T4 0.71 0.71 0.55 0.55 2.5 0.026 NA 0.55
## SAS_12_T4 0.76 0.76 0.61 0.61 3.1 0.022 NA 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T4 349 0.83 0.84 0.70 0.63 1.5 1.1
## SAS_11_T4 348 0.87 0.88 0.80 0.72 1.3 1.0
## SAS_12_T4 349 0.87 0.86 0.75 0.68 1.1 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T4 0.19 0.33 0.30 0.13 0.04 0.28
## SAS_11_T4 0.23 0.37 0.26 0.12 0.02 0.28
## SAS_12_T4 0.38 0.30 0.15 0.11 0.05 0.28## SAS - Anxiety T1 .79
SAS_Anxiety_T1 <- merged_data |>
dplyr::select(SAS_13_T1, SAS_14_T1, SAS_15_T1)
alpha_pos <- psych::alpha(SAS_Anxiety_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anxiety_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.72 0.56 3.9 0.016 2.1 0.98 0.56
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.76 0.79 0.82
## Duhachek 0.76 0.79 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_13_T1 0.71 0.71 0.56 0.56 2.5 0.026 NA 0.56
## SAS_14_T1 0.69 0.69 0.53 0.53 2.3 0.028 NA 0.53
## SAS_15_T1 0.75 0.75 0.60 0.60 3.0 0.022 NA 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_13_T1 484 0.86 0.85 0.72 0.64 1.9 1.2
## SAS_14_T1 484 0.85 0.85 0.75 0.66 2.1 1.1
## SAS_15_T1 484 0.82 0.83 0.68 0.61 2.4 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_13_T1 0.17 0.23 0.23 0.28 0.09 0
## SAS_14_T1 0.10 0.23 0.27 0.31 0.10 0
## SAS_15_T1 0.05 0.18 0.29 0.32 0.16 0## SAS - Anxiety T2 .78
SAS_Anxiety_T2 <- merged_data |>
dplyr::select(SAS_13_T2, SAS_14_T2, SAS_15_T2)
alpha_pos <- psych::alpha(SAS_Anxiety_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anxiety_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.71 0.55 3.6 0.017 2 0.94 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.78 0.81
## Duhachek 0.75 0.78 0.82
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_13_T2 0.66 0.66 0.50 0.50 2.0 0.031 NA 0.50
## SAS_14_T2 0.70 0.70 0.54 0.54 2.3 0.027 NA 0.54
## SAS_15_T2 0.75 0.75 0.60 0.60 3.0 0.023 NA 0.60
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_13_T2 391 0.86 0.85 0.75 0.66 1.8 1.2
## SAS_14_T2 391 0.84 0.84 0.71 0.63 1.9 1.1
## SAS_15_T2 391 0.80 0.81 0.65 0.58 2.1 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_13_T2 0.15 0.28 0.25 0.24 0.08 0.2
## SAS_14_T2 0.11 0.26 0.30 0.25 0.08 0.2
## SAS_15_T2 0.05 0.25 0.32 0.28 0.10 0.2## SAS - Anxiety T3 .78
SAS_Anxiety_T3 <- merged_data |>
dplyr::select(SAS_13_T3, SAS_14_T3, SAS_15_T3)
alpha_pos <- psych::alpha(SAS_Anxiety_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anxiety_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.71 0.54 3.6 0.017 1.9 0.91 0.53
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.75 0.78 0.81
## Duhachek 0.75 0.78 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_13_T3 0.69 0.69 0.53 0.53 2.3 0.028 NA 0.53
## SAS_14_T3 0.66 0.66 0.49 0.49 1.9 0.031 NA 0.49
## SAS_15_T3 0.76 0.76 0.61 0.61 3.1 0.022 NA 0.61
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_13_T3 355 0.84 0.84 0.72 0.63 1.7 1.1
## SAS_14_T3 355 0.86 0.85 0.75 0.66 1.9 1.1
## SAS_15_T3 355 0.80 0.81 0.64 0.57 2.1 1.0
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_13_T3 0.18 0.27 0.29 0.22 0.04 0.27
## SAS_14_T3 0.11 0.24 0.33 0.25 0.07 0.27
## SAS_15_T3 0.07 0.24 0.34 0.28 0.08 0.27## SAS - Anxiety T4 .81
SAS_Anxiety_T4 <- merged_data |>
dplyr::select(SAS_13_T4, SAS_14_T4, SAS_15_T4)
alpha_pos <- psych::alpha(SAS_Anxiety_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anxiety_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.81 0.81 0.74 0.58 4.2 0.015 2 0.96 0.58
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.77 0.81 0.83
## Duhachek 0.78 0.81 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_13_T4 0.71 0.71 0.55 0.55 2.4 0.027 NA 0.55
## SAS_14_T4 0.73 0.73 0.58 0.58 2.7 0.024 NA 0.58
## SAS_15_T4 0.77 0.77 0.62 0.62 3.3 0.021 NA 0.62
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_13_T4 349 0.87 0.86 0.76 0.68 1.8 1.2
## SAS_14_T4 349 0.85 0.85 0.73 0.66 2.0 1.1
## SAS_15_T4 349 0.83 0.83 0.69 0.62 2.2 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_13_T4 0.16 0.27 0.26 0.24 0.07 0.28
## SAS_14_T4 0.09 0.27 0.29 0.27 0.09 0.28
## SAS_15_T4 0.07 0.20 0.28 0.35 0.11 0.28## SAS - Anger T1 .74
SAS_Anger_T1 <- merged_data |>
dplyr::select(SAS_16_T1, SAS_17_T1, SAS_18_T1)
alpha_pos <- psych::alpha(SAS_Anger_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anger_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.74 0.66 0.48 2.8 0.02 0.86 0.81 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.69 0.74 0.77
## Duhachek 0.70 0.74 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_16_T1 0.69 0.69 0.53 0.53 2.3 0.028 NA 0.53
## SAS_17_T1 0.59 0.60 0.43 0.43 1.5 0.036 NA 0.43
## SAS_18_T1 0.66 0.66 0.49 0.49 1.9 0.031 NA 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_16_T1 484 0.77 0.79 0.61 0.52 0.66 0.91
## SAS_17_T1 484 0.84 0.83 0.71 0.61 1.00 1.01
## SAS_18_T1 484 0.82 0.81 0.65 0.56 0.92 1.07
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_16_T1 0.58 0.24 0.14 0.03 0.01 0
## SAS_17_T1 0.38 0.35 0.18 0.08 0.02 0
## SAS_18_T1 0.47 0.27 0.15 0.09 0.02 0## SAS - Anger T2 .77
SAS_Anger_T2 <- merged_data |>
dplyr::select(SAS_16_T2, SAS_17_T2, SAS_18_T2)
alpha_pos <- psych::alpha(SAS_Anger_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anger_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.69 0.52 3.3 0.018 0.83 0.82 0.52
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.77 0.8
## Duhachek 0.73 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_16_T2 0.69 0.69 0.52 0.52 2.2 0.028 NA 0.52
## SAS_17_T2 0.68 0.68 0.52 0.52 2.2 0.029 NA 0.52
## SAS_18_T2 0.68 0.68 0.52 0.52 2.2 0.029 NA 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_16_T2 391 0.82 0.82 0.68 0.6 0.68 0.95
## SAS_17_T2 391 0.83 0.83 0.69 0.6 1.00 1.03
## SAS_18_T2 391 0.83 0.83 0.68 0.6 0.80 0.99
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_16_T2 0.58 0.23 0.13 0.04 0.01 0.2
## SAS_17_T2 0.39 0.35 0.16 0.09 0.02 0.2
## SAS_18_T2 0.51 0.28 0.14 0.06 0.02 0.2## SAS - Anger T3 .80
SAS_Anger_T3 <- merged_data |>
dplyr::select(SAS_16_T3, SAS_17_T3, SAS_18_T3)
alpha_pos <- psych::alpha(SAS_Anger_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anger_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.8 0.8 0.73 0.58 4.1 0.015 0.84 0.87 0.59
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.77 0.8 0.83
## Duhachek 0.77 0.8 0.83
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_16_T3 0.74 0.74 0.59 0.59 2.9 0.023 NA 0.59
## SAS_17_T3 0.74 0.75 0.60 0.60 3.0 0.023 NA 0.60
## SAS_18_T3 0.70 0.71 0.55 0.55 2.4 0.027 NA 0.55
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_16_T3 355 0.83 0.84 0.71 0.64 0.65 0.96
## SAS_17_T3 355 0.84 0.84 0.71 0.64 1.01 1.05
## SAS_18_T3 355 0.87 0.86 0.76 0.68 0.86 1.08
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_16_T3 0.61 0.22 0.12 0.05 0.01 0.27
## SAS_17_T3 0.38 0.34 0.18 0.06 0.03 0.27
## SAS_18_T3 0.52 0.22 0.16 0.08 0.02 0.27## SAS - Anger T4 .77
SAS_Anger_T4 <- merged_data |>
dplyr::select(SAS_16_T4, SAS_17_T4, SAS_18_T4)
alpha_pos <- psych::alpha(SAS_Anger_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Anger_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.77 0.77 0.69 0.52 3.3 0.018 0.87 0.86 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.73 0.77 0.8
## Duhachek 0.73 0.77 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_16_T4 0.74 0.74 0.59 0.59 2.9 0.023 NA 0.59
## SAS_17_T4 0.66 0.66 0.50 0.50 2.0 0.030 NA 0.50
## SAS_18_T4 0.65 0.65 0.48 0.48 1.9 0.032 NA 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_16_T4 349 0.78 0.80 0.62 0.55 0.68 0.97
## SAS_17_T4 349 0.84 0.84 0.71 0.62 1.00 1.04
## SAS_18_T4 349 0.86 0.84 0.73 0.63 0.93 1.12
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_16_T4 0.60 0.20 0.13 0.07 0.01 0.28
## SAS_17_T4 0.41 0.31 0.17 0.09 0.01 0.28
## SAS_18_T4 0.49 0.23 0.15 0.11 0.02 0.28## SAS - Positive T1 .90
SAS_Positive_T1 <- merged_data |>
dplyr::select(SAS_1_T1, SAS_2_T1, SAS_3_T1, SAS_4_T1, SAS_5_T1, SAS_6_T1, SAS_7_T1, SAS_8_T1, SAS_9_T1)
alpha_pos <- psych::alpha(SAS_Positive_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Positive_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.91 0.5 8.9 0.007 2.1 0.75 0.49
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.9 0.91
## Duhachek 0.89 0.9 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T1 0.90 0.90 0.90 0.52 8.8 0.0071 0.0079 0.53
## SAS_2_T1 0.89 0.89 0.89 0.50 8.0 0.0077 0.0126 0.52
## SAS_3_T1 0.89 0.89 0.89 0.50 8.1 0.0076 0.0114 0.52
## SAS_4_T1 0.88 0.88 0.89 0.49 7.6 0.0081 0.0109 0.48
## SAS_5_T1 0.88 0.89 0.89 0.49 7.7 0.0080 0.0098 0.48
## SAS_6_T1 0.88 0.88 0.89 0.49 7.7 0.0080 0.0127 0.48
## SAS_7_T1 0.89 0.89 0.90 0.50 8.1 0.0077 0.0111 0.52
## SAS_8_T1 0.88 0.89 0.89 0.49 7.7 0.0080 0.0096 0.48
## SAS_9_T1 0.88 0.89 0.89 0.49 7.7 0.0080 0.0104 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T1 484 0.63 0.64 0.58 0.54 2.0 0.97
## SAS_2_T1 484 0.73 0.73 0.69 0.65 1.8 1.04
## SAS_3_T1 484 0.72 0.72 0.69 0.64 1.8 1.00
## SAS_4_T1 484 0.78 0.79 0.77 0.72 2.6 0.90
## SAS_5_T1 484 0.77 0.77 0.75 0.70 2.3 0.99
## SAS_6_T1 484 0.78 0.78 0.74 0.71 2.3 0.99
## SAS_7_T1 484 0.73 0.72 0.67 0.64 1.7 1.08
## SAS_8_T1 484 0.77 0.77 0.74 0.70 2.2 1.05
## SAS_9_T1 484 0.78 0.77 0.74 0.70 2.1 1.02
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T1 0.05 0.26 0.37 0.27 0.06 0
## SAS_2_T1 0.10 0.29 0.35 0.20 0.06 0
## SAS_3_T1 0.08 0.35 0.34 0.17 0.05 0
## SAS_4_T1 0.01 0.10 0.34 0.40 0.15 0
## SAS_5_T1 0.04 0.17 0.34 0.34 0.10 0
## SAS_6_T1 0.05 0.16 0.37 0.33 0.09 0
## SAS_7_T1 0.15 0.31 0.33 0.15 0.06 0
## SAS_8_T1 0.05 0.20 0.33 0.31 0.11 0
## SAS_9_T1 0.05 0.26 0.37 0.24 0.09 0## SAS - Positive T2 .92
SAS_Positive_T2 <- merged_data |>
dplyr::select(SAS_1_T2, SAS_2_T2, SAS_3_T2, SAS_4_T2, SAS_5_T2, SAS_6_T2, SAS_7_T2, SAS_8_T2, SAS_9_T2)
alpha_pos <- psych::alpha(SAS_Positive_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Positive_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.92 0.55 11 0.0058 2 0.75 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.9 0.92 0.93
## Duhachek 0.9 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T2 0.91 0.91 0.91 0.56 10.2 0.0062 0.0072 0.55
## SAS_2_T2 0.91 0.91 0.91 0.55 10.0 0.0064 0.0083 0.54
## SAS_3_T2 0.91 0.91 0.91 0.56 10.0 0.0063 0.0075 0.55
## SAS_4_T2 0.91 0.91 0.91 0.55 9.7 0.0066 0.0092 0.54
## SAS_5_T2 0.90 0.90 0.91 0.54 9.5 0.0067 0.0087 0.53
## SAS_6_T2 0.90 0.91 0.91 0.55 9.6 0.0066 0.0094 0.53
## SAS_7_T2 0.91 0.91 0.91 0.56 10.2 0.0062 0.0061 0.54
## SAS_8_T2 0.90 0.90 0.90 0.53 9.0 0.0070 0.0069 0.51
## SAS_9_T2 0.91 0.91 0.91 0.55 9.6 0.0066 0.0073 0.53
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T2 391 0.73 0.73 0.69 0.65 2.0 0.99
## SAS_2_T2 391 0.75 0.75 0.72 0.68 1.8 1.00
## SAS_3_T2 391 0.75 0.75 0.72 0.68 1.8 0.96
## SAS_4_T2 391 0.78 0.78 0.75 0.72 2.5 0.86
## SAS_5_T2 391 0.80 0.80 0.77 0.74 2.1 0.97
## SAS_6_T2 391 0.79 0.79 0.75 0.72 2.2 0.96
## SAS_7_T2 391 0.74 0.73 0.69 0.65 1.5 1.05
## SAS_8_T2 391 0.85 0.85 0.84 0.80 2.1 0.98
## SAS_9_T2 391 0.79 0.78 0.76 0.72 1.9 0.98
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T2 0.07 0.22 0.37 0.29 0.05 0.2
## SAS_2_T2 0.10 0.32 0.34 0.21 0.03 0.2
## SAS_3_T2 0.08 0.31 0.36 0.23 0.02 0.2
## SAS_4_T2 0.01 0.11 0.37 0.40 0.11 0.2
## SAS_5_T2 0.04 0.25 0.34 0.32 0.06 0.2
## SAS_6_T2 0.04 0.20 0.38 0.31 0.07 0.2
## SAS_7_T2 0.18 0.31 0.32 0.17 0.02 0.2
## SAS_8_T2 0.04 0.23 0.36 0.29 0.07 0.2
## SAS_9_T2 0.07 0.27 0.36 0.27 0.04 0.2## SAS - Positive T3 .92
SAS_Positive_T3 <- merged_data |>
dplyr::select(SAS_1_T3, SAS_2_T3, SAS_3_T3, SAS_4_T3, SAS_5_T3, SAS_6_T3, SAS_7_T3, SAS_8_T3, SAS_9_T3)
alpha_pos <- psych::alpha(SAS_Positive_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Positive_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.92 0.92 0.93 0.56 11 0.0055 2 0.79 0.54
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.91 0.92 0.93
## Duhachek 0.91 0.92 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T3 0.91 0.92 0.92 0.57 10.8 0.0059 0.0075 0.57
## SAS_2_T3 0.91 0.91 0.92 0.56 10.2 0.0062 0.0097 0.54
## SAS_3_T3 0.91 0.91 0.92 0.57 10.7 0.0059 0.0079 0.57
## SAS_4_T3 0.91 0.91 0.91 0.56 10.0 0.0063 0.0095 0.54
## SAS_5_T3 0.91 0.91 0.91 0.55 9.7 0.0066 0.0091 0.53
## SAS_6_T3 0.91 0.91 0.91 0.55 9.9 0.0064 0.0096 0.54
## SAS_7_T3 0.91 0.91 0.92 0.57 10.5 0.0060 0.0072 0.55
## SAS_8_T3 0.91 0.91 0.91 0.55 9.9 0.0064 0.0076 0.54
## SAS_9_T3 0.91 0.91 0.91 0.56 10.2 0.0063 0.0081 0.54
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T3 355 0.73 0.73 0.69 0.65 2.0 1.00
## SAS_2_T3 355 0.78 0.78 0.75 0.71 1.9 1.03
## SAS_3_T3 355 0.73 0.73 0.70 0.66 1.9 0.96
## SAS_4_T3 355 0.80 0.80 0.77 0.74 2.5 0.94
## SAS_5_T3 355 0.83 0.83 0.81 0.78 2.1 1.03
## SAS_6_T3 354 0.80 0.81 0.78 0.74 2.2 0.97
## SAS_7_T3 355 0.76 0.75 0.71 0.68 1.6 1.08
## SAS_8_T3 355 0.81 0.81 0.79 0.75 2.1 1.04
## SAS_9_T3 355 0.79 0.78 0.76 0.72 1.9 1.00
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T3 0.05 0.25 0.37 0.26 0.07 0.27
## SAS_2_T3 0.09 0.29 0.36 0.21 0.06 0.27
## SAS_3_T3 0.06 0.31 0.37 0.21 0.05 0.27
## SAS_4_T3 0.02 0.12 0.33 0.39 0.14 0.27
## SAS_5_T3 0.06 0.22 0.34 0.30 0.08 0.27
## SAS_6_T3 0.03 0.21 0.36 0.31 0.09 0.27
## SAS_7_T3 0.16 0.32 0.30 0.19 0.04 0.27
## SAS_8_T3 0.05 0.27 0.29 0.31 0.08 0.27
## SAS_9_T3 0.07 0.28 0.34 0.26 0.05 0.27## SAS - Positive T4 .90
SAS_Positive_T4 <- merged_data |>
dplyr::select(SAS_1_T4, SAS_2_T4, SAS_3_T4, SAS_4_T4, SAS_5_T4, SAS_6_T4, SAS_7_T4, SAS_8_T4, SAS_9_T4)
alpha_pos <- psych::alpha(SAS_Positive_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Positive_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.91 0.51 9.5 0.0067 2 0.74 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.89 0.9 0.92
## Duhachek 0.89 0.9 0.92
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_1_T4 0.90 0.90 0.91 0.54 9.3 0.0068 0.013 0.54
## SAS_2_T4 0.90 0.90 0.91 0.53 8.9 0.0071 0.018 0.54
## SAS_3_T4 0.90 0.90 0.90 0.52 8.8 0.0070 0.016 0.54
## SAS_4_T4 0.89 0.89 0.90 0.50 8.1 0.0077 0.017 0.48
## SAS_5_T4 0.89 0.89 0.90 0.50 7.9 0.0079 0.015 0.49
## SAS_6_T4 0.89 0.89 0.90 0.50 8.1 0.0077 0.018 0.48
## SAS_7_T4 0.89 0.89 0.90 0.52 8.5 0.0074 0.014 0.50
## SAS_8_T4 0.89 0.89 0.90 0.50 8.0 0.0078 0.014 0.49
## SAS_9_T4 0.89 0.89 0.90 0.51 8.2 0.0077 0.013 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_1_T4 349 0.65 0.65 0.61 0.55 2.1 0.98
## SAS_2_T4 349 0.69 0.70 0.64 0.61 1.9 0.97
## SAS_3_T4 349 0.70 0.70 0.66 0.61 1.9 1.00
## SAS_4_T4 349 0.79 0.80 0.77 0.73 2.5 0.90
## SAS_5_T4 349 0.81 0.81 0.79 0.75 2.1 1.00
## SAS_6_T4 349 0.78 0.79 0.76 0.72 2.2 0.94
## SAS_7_T4 348 0.75 0.74 0.70 0.66 1.6 1.05
## SAS_8_T4 349 0.81 0.80 0.78 0.74 2.1 1.01
## SAS_9_T4 349 0.79 0.78 0.76 0.71 1.9 1.04
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_1_T4 0.05 0.24 0.34 0.31 0.06 0.28
## SAS_2_T4 0.07 0.30 0.34 0.26 0.03 0.28
## SAS_3_T4 0.08 0.28 0.35 0.24 0.04 0.28
## SAS_4_T4 0.02 0.11 0.30 0.46 0.12 0.28
## SAS_5_T4 0.04 0.26 0.32 0.31 0.07 0.28
## SAS_6_T4 0.04 0.16 0.40 0.33 0.07 0.28
## SAS_7_T4 0.16 0.33 0.29 0.19 0.03 0.28
## SAS_8_T4 0.06 0.22 0.35 0.31 0.06 0.28
## SAS_9_T4 0.08 0.30 0.32 0.24 0.05 0.28## SAS - Negative T1 .85
SAS_Negative_T1 <- merged_data |>
dplyr::select(SAS_10_T1, SAS_11_T1, SAS_12_T1, SAS_13_T1, SAS_14_T1, SAS_15_T1, SAS_16_T1, SAS_17_T1, SAS_18_T1)
alpha_pos <- psych::alpha(SAS_Negative_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Negative_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.87 0.39 5.7 0.0098 1.5 0.75 0.39
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.85 0.87
## Duhachek 0.84 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T1 0.83 0.83 0.84 0.38 4.9 0.0114 0.019 0.38
## SAS_11_T1 0.83 0.82 0.84 0.37 4.7 0.0117 0.021 0.37
## SAS_12_T1 0.83 0.83 0.84 0.37 4.8 0.0116 0.020 0.39
## SAS_13_T1 0.83 0.83 0.85 0.38 4.8 0.0114 0.024 0.38
## SAS_14_T1 0.84 0.83 0.85 0.38 5.0 0.0112 0.021 0.38
## SAS_15_T1 0.85 0.84 0.86 0.40 5.4 0.0105 0.018 0.40
## SAS_16_T1 0.86 0.85 0.87 0.42 5.9 0.0098 0.017 0.42
## SAS_17_T1 0.84 0.84 0.85 0.40 5.3 0.0104 0.023 0.42
## SAS_18_T1 0.84 0.84 0.86 0.40 5.2 0.0104 0.024 0.40
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T1 484 0.74 0.73 0.70 0.64 1.58 1.13
## SAS_11_T1 484 0.77 0.76 0.74 0.68 1.38 1.11
## SAS_12_T1 484 0.76 0.75 0.73 0.67 1.22 1.22
## SAS_13_T1 484 0.74 0.73 0.69 0.64 1.88 1.25
## SAS_14_T1 484 0.71 0.70 0.66 0.61 2.09 1.15
## SAS_15_T1 484 0.63 0.62 0.56 0.51 2.37 1.10
## SAS_16_T1 484 0.49 0.52 0.43 0.37 0.66 0.91
## SAS_17_T1 484 0.62 0.64 0.58 0.51 1.00 1.01
## SAS_18_T1 484 0.63 0.64 0.58 0.52 0.92 1.07
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T1 0.17 0.36 0.25 0.16 0.06 0
## SAS_11_T1 0.23 0.37 0.22 0.14 0.04 0
## SAS_12_T1 0.37 0.28 0.18 0.11 0.06 0
## SAS_13_T1 0.17 0.23 0.23 0.28 0.09 0
## SAS_14_T1 0.10 0.23 0.27 0.31 0.10 0
## SAS_15_T1 0.05 0.18 0.29 0.32 0.16 0
## SAS_16_T1 0.58 0.24 0.14 0.03 0.01 0
## SAS_17_T1 0.38 0.35 0.18 0.08 0.02 0
## SAS_18_T1 0.47 0.27 0.15 0.09 0.02 0## SAS - Negative T2 .86
SAS_Negative_T2 <- merged_data |>
dplyr::select(SAS_10_T2, SAS_11_T2, SAS_12_T2, SAS_13_T2, SAS_14_T2, SAS_15_T2, SAS_16_T2, SAS_17_T2, SAS_18_T2)
alpha_pos <- psych::alpha(SAS_Negative_T2)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Negative_T2)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.87 0.4 6.1 0.0095 1.4 0.73 0.38
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T2 0.84 0.84 0.85 0.39 5.2 0.0111 0.014 0.36
## SAS_11_T2 0.84 0.84 0.85 0.39 5.1 0.0112 0.013 0.37
## SAS_12_T2 0.84 0.84 0.85 0.39 5.2 0.0112 0.014 0.38
## SAS_13_T2 0.84 0.84 0.85 0.40 5.4 0.0108 0.017 0.38
## SAS_14_T2 0.84 0.84 0.86 0.40 5.3 0.0109 0.017 0.38
## SAS_15_T2 0.85 0.85 0.86 0.42 5.8 0.0102 0.016 0.39
## SAS_16_T2 0.86 0.86 0.86 0.43 5.9 0.0099 0.014 0.39
## SAS_17_T2 0.85 0.85 0.86 0.41 5.5 0.0104 0.018 0.38
## SAS_18_T2 0.85 0.85 0.86 0.41 5.6 0.0102 0.018 0.39
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T2 391 0.74 0.74 0.72 0.65 1.50 1.06
## SAS_11_T2 391 0.76 0.76 0.74 0.68 1.27 1.00
## SAS_12_T2 391 0.75 0.74 0.72 0.66 1.15 1.15
## SAS_13_T2 391 0.71 0.70 0.66 0.61 1.83 1.19
## SAS_14_T2 391 0.72 0.71 0.66 0.62 1.94 1.13
## SAS_15_T2 391 0.62 0.62 0.55 0.51 2.14 1.05
## SAS_16_T2 391 0.57 0.59 0.52 0.46 0.68 0.95
## SAS_17_T2 391 0.66 0.67 0.62 0.56 1.00 1.03
## SAS_18_T2 391 0.63 0.65 0.59 0.53 0.80 0.99
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T2 0.17 0.40 0.25 0.14 0.04 0.2
## SAS_11_T2 0.22 0.44 0.21 0.10 0.03 0.2
## SAS_12_T2 0.36 0.31 0.18 0.10 0.04 0.2
## SAS_13_T2 0.15 0.28 0.25 0.24 0.08 0.2
## SAS_14_T2 0.11 0.26 0.30 0.25 0.08 0.2
## SAS_15_T2 0.05 0.25 0.32 0.28 0.10 0.2
## SAS_16_T2 0.58 0.23 0.13 0.04 0.01 0.2
## SAS_17_T2 0.39 0.35 0.16 0.09 0.02 0.2
## SAS_18_T2 0.51 0.28 0.14 0.06 0.02 0.2## SAS - Negative T3 .88
SAS_Negative_T3 <- merged_data |>
dplyr::select(SAS_10_T3, SAS_11_T3, SAS_12_T3, SAS_13_T3, SAS_14_T3, SAS_15_T3, SAS_16_T3, SAS_17_T3, SAS_18_T3)
alpha_pos <- psych::alpha(SAS_Negative_T3)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Negative_T3)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.88 0.88 0.89 0.44 7.1 0.0084 1.3 0.77 0.44
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.88 0.89
## Duhachek 0.86 0.88 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T3 0.86 0.86 0.87 0.43 6.1 0.0097 0.013 0.44
## SAS_11_T3 0.86 0.86 0.87 0.43 5.9 0.0099 0.013 0.42
## SAS_12_T3 0.86 0.86 0.87 0.43 6.0 0.0099 0.012 0.42
## SAS_13_T3 0.87 0.87 0.87 0.45 6.4 0.0092 0.014 0.44
## SAS_14_T3 0.86 0.86 0.87 0.44 6.2 0.0094 0.015 0.43
## SAS_15_T3 0.87 0.87 0.88 0.46 6.9 0.0087 0.011 0.45
## SAS_16_T3 0.87 0.87 0.88 0.45 6.5 0.0091 0.013 0.44
## SAS_17_T3 0.87 0.87 0.88 0.45 6.5 0.0090 0.014 0.44
## SAS_18_T3 0.87 0.86 0.87 0.44 6.4 0.0092 0.014 0.44
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T3 355 0.76 0.75 0.72 0.67 1.45 1.12
## SAS_11_T3 355 0.78 0.78 0.76 0.71 1.25 1.07
## SAS_12_T3 355 0.78 0.78 0.75 0.70 1.12 1.19
## SAS_13_T3 355 0.69 0.69 0.64 0.60 1.66 1.12
## SAS_14_T3 355 0.73 0.73 0.69 0.64 1.94 1.10
## SAS_15_T3 355 0.61 0.61 0.54 0.50 2.05 1.05
## SAS_16_T3 355 0.66 0.68 0.63 0.57 0.65 0.96
## SAS_17_T3 355 0.67 0.68 0.62 0.57 1.01 1.05
## SAS_18_T3 355 0.70 0.70 0.66 0.60 0.86 1.08
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T3 0.21 0.35 0.25 0.13 0.05 0.27
## SAS_11_T3 0.27 0.38 0.21 0.10 0.04 0.27
## SAS_12_T3 0.39 0.29 0.15 0.11 0.05 0.27
## SAS_13_T3 0.18 0.27 0.29 0.22 0.04 0.27
## SAS_14_T3 0.11 0.24 0.33 0.25 0.07 0.27
## SAS_15_T3 0.07 0.24 0.34 0.28 0.08 0.27
## SAS_16_T3 0.61 0.22 0.12 0.05 0.01 0.27
## SAS_17_T3 0.38 0.34 0.18 0.06 0.03 0.27
## SAS_18_T3 0.52 0.22 0.16 0.08 0.02 0.27## SAS - Negative T4 .87
SAS_Negative_T4 <- merged_data |>
dplyr::select(SAS_10_T4, SAS_11_T4, SAS_12_T4, SAS_13_T4, SAS_14_T4, SAS_15_T4, SAS_16_T4, SAS_17_T4, SAS_18_T4)
alpha_pos <- psych::alpha(SAS_Negative_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = SAS_Negative_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.88 0.42 6.4 0.0092 1.4 0.76 0.43
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.85 0.87 0.88
## Duhachek 0.85 0.87 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## SAS_10_T4 0.85 0.85 0.86 0.41 5.5 0.0107 0.016 0.39
## SAS_11_T4 0.84 0.84 0.85 0.40 5.3 0.0109 0.014 0.40
## SAS_12_T4 0.85 0.85 0.86 0.41 5.5 0.0105 0.015 0.43
## SAS_13_T4 0.85 0.85 0.86 0.41 5.7 0.0103 0.014 0.42
## SAS_14_T4 0.85 0.85 0.86 0.42 5.7 0.0102 0.014 0.43
## SAS_15_T4 0.86 0.86 0.86 0.43 6.0 0.0099 0.013 0.43
## SAS_16_T4 0.86 0.86 0.87 0.44 6.4 0.0094 0.012 0.43
## SAS_17_T4 0.85 0.85 0.86 0.42 5.7 0.0101 0.016 0.43
## SAS_18_T4 0.85 0.85 0.86 0.42 5.8 0.0100 0.015 0.42
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## SAS_10_T4 349 0.74 0.75 0.71 0.66 1.50 1.08
## SAS_11_T4 348 0.78 0.78 0.76 0.71 1.32 1.02
## SAS_12_T4 349 0.74 0.73 0.69 0.64 1.15 1.19
## SAS_13_T4 349 0.72 0.71 0.67 0.62 1.78 1.19
## SAS_14_T4 349 0.70 0.69 0.65 0.60 2.00 1.12
## SAS_15_T4 349 0.65 0.64 0.58 0.54 2.23 1.09
## SAS_16_T4 349 0.56 0.58 0.50 0.45 0.68 0.97
## SAS_17_T4 349 0.69 0.69 0.65 0.59 1.00 1.04
## SAS_18_T4 349 0.68 0.68 0.63 0.57 0.93 1.12
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## SAS_10_T4 0.19 0.33 0.30 0.13 0.04 0.28
## SAS_11_T4 0.23 0.37 0.26 0.12 0.02 0.28
## SAS_12_T4 0.38 0.30 0.15 0.11 0.05 0.28
## SAS_13_T4 0.16 0.27 0.26 0.24 0.07 0.28
## SAS_14_T4 0.09 0.27 0.29 0.27 0.09 0.28
## SAS_15_T4 0.07 0.20 0.28 0.35 0.11 0.28
## SAS_16_T4 0.60 0.20 0.13 0.07 0.01 0.28
## SAS_17_T4 0.41 0.31 0.17 0.09 0.01 0.28
## SAS_18_T4 0.49 0.23 0.15 0.11 0.02 0.28## Flourishing T1 .87
Flourishing_T1 <- merged_data |>
dplyr::select(Flourish_1_T1:Flourish_8_T1)
alpha_pos <- psych::alpha(Flourishing_T1)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = Flourishing_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.87 0.87 0.87 0.46 6.9 0.0087 5.6 0.81 0.46
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.86 0.87 0.89
## Duhachek 0.86 0.87 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Flourish_1_T1 0.86 0.86 0.84 0.46 6.0 0.0100 0.0031 0.46
## Flourish_2_T1 0.86 0.86 0.85 0.48 6.4 0.0094 0.0043 0.48
## Flourish_3_T1 0.86 0.86 0.85 0.46 6.0 0.0099 0.0041 0.46
## Flourish_4_T1 0.86 0.86 0.85 0.47 6.3 0.0095 0.0046 0.48
## Flourish_5_T1 0.85 0.85 0.84 0.46 5.9 0.0100 0.0042 0.45
## Flourish_6_T1 0.85 0.85 0.84 0.45 5.7 0.0103 0.0035 0.45
## Flourish_7_T1 0.85 0.85 0.84 0.46 5.9 0.0101 0.0034 0.45
## Flourish_8_T1 0.86 0.86 0.85 0.48 6.3 0.0094 0.0034 0.46
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Flourish_1_T1 484 0.76 0.74 0.70 0.65 5.4 1.3
## Flourish_2_T1 484 0.68 0.68 0.61 0.57 5.7 1.1
## Flourish_3_T1 484 0.74 0.73 0.68 0.64 5.3 1.2
## Flourish_4_T1 484 0.68 0.69 0.62 0.58 5.7 1.0
## Flourish_5_T1 484 0.75 0.76 0.71 0.66 5.7 1.0
## Flourish_6_T1 484 0.78 0.78 0.75 0.70 5.8 1.1
## Flourish_7_T1 484 0.76 0.76 0.72 0.67 5.6 1.2
## Flourish_8_T1 484 0.68 0.69 0.63 0.58 5.5 1.1
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Flourish_1_T1 0.01 0.01 0.05 0.12 0.20 0.43 0.17 0
## Flourish_2_T1 0.00 0.02 0.03 0.07 0.19 0.46 0.23 0
## Flourish_3_T1 0.01 0.01 0.07 0.12 0.29 0.40 0.10 0
## Flourish_4_T1 0.00 0.01 0.03 0.07 0.24 0.46 0.19 0
## Flourish_5_T1 0.00 0.01 0.04 0.06 0.21 0.48 0.20 0
## Flourish_6_T1 0.01 0.01 0.02 0.08 0.17 0.44 0.28 0
## Flourish_7_T1 0.00 0.02 0.06 0.07 0.21 0.40 0.24 0
## Flourish_8_T1 0.00 0.01 0.04 0.13 0.21 0.48 0.13 0## Flourishing T4 .89
Flourishing_T4 <- merged_data |>
dplyr::select(Flourish_1_T4:Flourish_8_T4)
alpha_pos <- psych::alpha(Flourishing_T4)
print(alpha_pos)##
## Reliability analysis
## Call: psych::alpha(x = Flourishing_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.89 0.89 0.89 0.51 8.3 0.0074 5.6 0.83 0.51
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.88 0.89 0.91
## Duhachek 0.88 0.89 0.91
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## Flourish_1_T4 0.87 0.88 0.87 0.50 7.1 0.0087 0.0057 0.51
## Flourish_2_T4 0.89 0.89 0.88 0.53 8.0 0.0077 0.0027 0.53
## Flourish_3_T4 0.87 0.88 0.87 0.50 7.0 0.0087 0.0066 0.49
## Flourish_4_T4 0.88 0.88 0.87 0.52 7.4 0.0083 0.0061 0.52
## Flourish_5_T4 0.88 0.88 0.87 0.51 7.2 0.0086 0.0036 0.51
## Flourish_6_T4 0.87 0.87 0.86 0.49 6.7 0.0090 0.0051 0.50
## Flourish_7_T4 0.88 0.88 0.87 0.51 7.2 0.0085 0.0046 0.51
## Flourish_8_T4 0.88 0.88 0.87 0.51 7.4 0.0084 0.0066 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## Flourish_1_T4 349 0.78 0.78 0.74 0.70 5.6 1.1
## Flourish_2_T4 349 0.67 0.67 0.61 0.56 5.7 1.1
## Flourish_3_T4 348 0.78 0.78 0.74 0.70 5.3 1.2
## Flourish_4_T4 349 0.73 0.73 0.68 0.64 5.6 1.1
## Flourish_5_T4 349 0.76 0.77 0.73 0.69 5.6 1.0
## Flourish_6_T4 349 0.82 0.82 0.79 0.75 5.8 1.0
## Flourish_7_T4 349 0.77 0.76 0.72 0.67 5.5 1.3
## Flourish_8_T4 349 0.74 0.74 0.69 0.65 5.5 1.0
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## Flourish_1_T4 0.00 0.01 0.04 0.09 0.23 0.46 0.17 0.28
## Flourish_2_T4 0.01 0.02 0.03 0.05 0.22 0.47 0.21 0.28
## Flourish_3_T4 0.00 0.03 0.07 0.10 0.30 0.40 0.10 0.28
## Flourish_4_T4 0.00 0.02 0.02 0.07 0.25 0.47 0.16 0.28
## Flourish_5_T4 0.00 0.01 0.03 0.09 0.23 0.47 0.17 0.28
## Flourish_6_T4 0.00 0.01 0.02 0.07 0.19 0.46 0.25 0.28
## Flourish_7_T4 0.00 0.02 0.07 0.08 0.26 0.34 0.23 0.28
## Flourish_8_T4 0.00 0.01 0.03 0.10 0.26 0.46 0.13 0.28## Social Fit T1 r = .20
cor.test(merged_data$social_fit_1_T1, merged_data$social_fit_2_T1_rev, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$social_fit_1_T1 and merged_data$social_fit_2_T1_rev
## t = 4.5257, df = 482, p-value = 7.591e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1148327 0.2858809
## sample estimates:
## cor
## 0.2018958## Social Fit T4 r = .22
cor.test(merged_data$social_fit_1_T4, merged_data$social_fit_2_T4_rev, method = "pearson")##
## Pearson's product-moment correlation
##
## data: merged_data$social_fit_1_T4 and merged_data$social_fit_2_T4_rev
## t = 4.2924, df = 347, p-value = 2.296e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1224515 0.3219362
## sample estimates:
## cor
## 0.2245451## Mindfulness T1 .84
Mindfulness_T1 <- merged_data |>
dplyr::select(mindfulness_1_T1:mindfulness_5_T1)
alpha_pos <- psych::alpha(Mindfulness_T1)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = Mindfulness_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.83 0.52 5.4 0.011 2.9 1.2 0.47
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.82 0.84 0.86
## Duhachek 0.82 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## mindfulness_1_T1 0.82 0.82 0.79 0.54 4.7 0.013 0.0082
## mindfulness_2_T1 0.79 0.79 0.75 0.49 3.8 0.016 0.0057
## mindfulness_3_T1 0.84 0.84 0.81 0.57 5.2 0.012 0.0086
## mindfulness_4_T1 0.80 0.81 0.77 0.51 4.2 0.015 0.0080
## mindfulness_5_T1 0.80 0.80 0.77 0.51 4.1 0.015 0.0097
## med.r
## mindfulness_1_T1 0.54
## mindfulness_2_T1 0.46
## mindfulness_3_T1 0.61
## mindfulness_4_T1 0.47
## mindfulness_5_T1 0.45
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mindfulness_1_T1 484 0.76 0.76 0.68 0.61 2.8 1.5
## mindfulness_2_T1 484 0.84 0.84 0.81 0.73 2.8 1.5
## mindfulness_3_T1 484 0.72 0.72 0.59 0.55 3.6 1.6
## mindfulness_4_T1 484 0.81 0.80 0.74 0.68 2.6 1.6
## mindfulness_5_T1 484 0.81 0.81 0.75 0.69 2.8 1.5
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## mindfulness_1_T1 0.07 0.12 0.22 0.26 0.20 0.09 0.04 0
## mindfulness_2_T1 0.06 0.14 0.17 0.33 0.17 0.09 0.04 0
## mindfulness_3_T1 0.05 0.06 0.11 0.24 0.24 0.19 0.12 0
## mindfulness_4_T1 0.11 0.14 0.19 0.28 0.17 0.07 0.04 0
## mindfulness_5_T1 0.08 0.14 0.21 0.26 0.18 0.10 0.03 0## Mindfulness T4 .86
Mindfulness_T4 <- merged_data |>
dplyr::select(mindfulness_1_T4:mindfulness_5_T4)
alpha_pos <- psych::alpha(Mindfulness_T4)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = Mindfulness_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.84 0.55 6.1 0.01 3.1 1.2 0.53
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.84 0.86 0.88
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## mindfulness_1_T4 0.84 0.84 0.81 0.56 5.2 0.012 0.0122
## mindfulness_2_T4 0.81 0.82 0.78 0.52 4.4 0.014 0.0053
## mindfulness_3_T4 0.86 0.86 0.83 0.60 6.0 0.011 0.0059
## mindfulness_4_T4 0.82 0.82 0.78 0.53 4.5 0.014 0.0061
## mindfulness_5_T4 0.81 0.81 0.79 0.52 4.4 0.014 0.0089
## med.r
## mindfulness_1_T4 0.56
## mindfulness_2_T4 0.48
## mindfulness_3_T4 0.60
## mindfulness_4_T4 0.53
## mindfulness_5_T4 0.48
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## mindfulness_1_T4 349 0.78 0.78 0.69 0.64 3.1 1.5
## mindfulness_2_T4 349 0.83 0.83 0.79 0.73 3.0 1.5
## mindfulness_3_T4 349 0.72 0.72 0.60 0.56 3.7 1.5
## mindfulness_4_T4 349 0.83 0.83 0.79 0.72 2.8 1.6
## mindfulness_5_T4 349 0.83 0.84 0.79 0.73 2.9 1.5
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 6 miss
## mindfulness_1_T4 0.04 0.11 0.19 0.26 0.20 0.13 0.07 0.28
## mindfulness_2_T4 0.07 0.09 0.19 0.28 0.23 0.11 0.05 0.28
## mindfulness_3_T4 0.03 0.06 0.11 0.23 0.26 0.17 0.15 0.28
## mindfulness_4_T4 0.08 0.15 0.16 0.28 0.21 0.09 0.05 0.28
## mindfulness_5_T4 0.05 0.11 0.21 0.28 0.18 0.10 0.05 0.28## Emotional Resilience T1 .86
EmoRes_T1 <- merged_data |>
dplyr::select(emo_res_1_T1, emo_res_2_T1_rev, emo_res_3_T1, emo_res_4_T1_rev, emo_res_5_T1, emo_res_6_T1_rev)
alpha_pos <- psych::alpha(EmoRes_T1)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = EmoRes_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.86 0.86 0.84 0.5 5.9 0.01 3.1 0.76 0.5
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.86 0.87
## Duhachek 0.84 0.86 0.88
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## emo_res_1_T1 0.82 0.82 0.79 0.48 4.6 0.013 0.0053
## emo_res_2_T1_rev 0.83 0.83 0.81 0.49 4.9 0.012 0.0063
## emo_res_3_T1 0.83 0.83 0.81 0.50 4.9 0.012 0.0064
## emo_res_4_T1_rev 0.82 0.82 0.79 0.48 4.6 0.013 0.0034
## emo_res_5_T1 0.85 0.85 0.82 0.53 5.6 0.011 0.0027
## emo_res_6_T1_rev 0.83 0.83 0.81 0.50 5.0 0.012 0.0044
## med.r
## emo_res_1_T1 0.47
## emo_res_2_T1_rev 0.49
## emo_res_3_T1 0.50
## emo_res_4_T1_rev 0.48
## emo_res_5_T1 0.52
## emo_res_6_T1_rev 0.49
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## emo_res_1_T1 483 0.80 0.80 0.76 0.70 3.4 0.97
## emo_res_2_T1_rev 483 0.77 0.77 0.70 0.65 3.0 1.04
## emo_res_3_T1 483 0.76 0.76 0.69 0.64 3.1 0.97
## emo_res_4_T1_rev 483 0.80 0.80 0.76 0.70 3.2 1.02
## emo_res_5_T1 483 0.68 0.69 0.59 0.54 2.9 0.93
## emo_res_6_T1_rev 483 0.76 0.76 0.69 0.64 3.2 1.03
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## emo_res_1_T1 0.02 0.16 0.27 0.43 0.11 0.01
## emo_res_2_T1_rev 0.05 0.31 0.24 0.35 0.05 0.01
## emo_res_3_T1 0.03 0.27 0.27 0.38 0.04 0.01
## emo_res_4_T1_rev 0.05 0.24 0.26 0.38 0.06 0.01
## emo_res_5_T1 0.04 0.34 0.31 0.29 0.03 0.01
## emo_res_6_T1_rev 0.06 0.22 0.28 0.37 0.06 0.01## Emotional Resilience T4 .85
EmoRes_T4 <- merged_data |>
dplyr::select(emo_res_1_T4, emo_res_2_T4_rev, emo_res_3_T4, emo_res_4_T4_rev, emo_res_5_T4, emo_res_6_T4_rev)
alpha_pos <- psych::alpha(EmoRes_T4)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = EmoRes_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.83 0.49 5.7 0.01 3.2 0.73 0.52
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.83 0.85 0.87
## Duhachek 0.83 0.85 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## emo_res_1_T4 0.82 0.82 0.79 0.47 4.5 0.013 0.00841
## emo_res_2_T4_rev 0.82 0.82 0.80 0.48 4.7 0.013 0.00745
## emo_res_3_T4 0.82 0.82 0.79 0.48 4.6 0.013 0.01001
## emo_res_4_T4_rev 0.82 0.82 0.79 0.47 4.5 0.013 0.00695
## emo_res_5_T4 0.85 0.85 0.83 0.54 5.9 0.011 0.00094
## emo_res_6_T4_rev 0.82 0.82 0.79 0.47 4.5 0.013 0.00617
## med.r
## emo_res_1_T4 0.51
## emo_res_2_T4_rev 0.51
## emo_res_3_T4 0.52
## emo_res_4_T4_rev 0.51
## emo_res_5_T4 0.53
## emo_res_6_T4_rev 0.51
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## emo_res_1_T4 349 0.78 0.79 0.74 0.68 3.5 0.91
## emo_res_2_T4_rev 349 0.77 0.76 0.70 0.64 3.1 0.98
## emo_res_3_T4 349 0.78 0.78 0.72 0.66 3.2 1.01
## emo_res_4_T4_rev 349 0.79 0.79 0.73 0.67 3.2 0.98
## emo_res_5_T4 349 0.64 0.64 0.52 0.48 2.9 0.93
## emo_res_6_T4_rev 349 0.78 0.78 0.74 0.67 3.2 0.94
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## emo_res_1_T4 0.02 0.13 0.27 0.49 0.09 0.28
## emo_res_2_T4_rev 0.05 0.25 0.31 0.35 0.05 0.28
## emo_res_3_T4 0.03 0.28 0.24 0.38 0.07 0.28
## emo_res_4_T4_rev 0.03 0.26 0.26 0.39 0.05 0.28
## emo_res_5_T4 0.03 0.34 0.33 0.27 0.03 0.28
## emo_res_6_T4_rev 0.03 0.22 0.32 0.38 0.05 0.28## Academic Self-Efficacy T1 .75
Acad_Selfefficacy_T1 <- merged_data |>
dplyr::select(acad_selfefficacy_1_T1:acad_selfefficacy_5_T1)
alpha_pos <- psych::alpha(Acad_Selfefficacy_T1)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = Acad_Selfefficacy_T1)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.74 0.75 0.71 0.37 2.9 0.018 4.8 0.84 0.37
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.70 0.74 0.78
## Duhachek 0.71 0.74 0.78
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## acad_selfefficacy_1_T1 0.71 0.71 0.66 0.38 2.5 0.021
## acad_selfefficacy_2_T1 0.70 0.71 0.65 0.37 2.4 0.021
## acad_selfefficacy_3_T1 0.68 0.69 0.63 0.35 2.2 0.023
## acad_selfefficacy_4_T1 0.69 0.70 0.64 0.36 2.3 0.023
## acad_selfefficacy_5_T1 0.70 0.71 0.65 0.38 2.4 0.022
## var.r med.r
## acad_selfefficacy_1_T1 0.0021 0.41
## acad_selfefficacy_2_T1 0.0028 0.38
## acad_selfefficacy_3_T1 0.0023 0.34
## acad_selfefficacy_4_T1 0.0026 0.37
## acad_selfefficacy_5_T1 0.0014 0.37
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## acad_selfefficacy_1_T1 483 0.65 0.68 0.55 0.47 5.1 1.0
## acad_selfefficacy_2_T1 482 0.67 0.70 0.58 0.49 5.1 1.0
## acad_selfefficacy_3_T1 483 0.76 0.73 0.64 0.55 4.1 1.4
## acad_selfefficacy_4_T1 483 0.71 0.72 0.61 0.53 5.0 1.1
## acad_selfefficacy_5_T1 483 0.73 0.70 0.58 0.50 4.5 1.4
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## acad_selfefficacy_1_T1 0.00 0.02 0.03 0.28 0.17 0.50 0.01
## acad_selfefficacy_2_T1 0.00 0.01 0.05 0.28 0.20 0.46 0.01
## acad_selfefficacy_3_T1 0.02 0.11 0.18 0.39 0.04 0.26 0.01
## acad_selfefficacy_4_T1 0.01 0.03 0.06 0.21 0.23 0.46 0.01
## acad_selfefficacy_5_T1 0.03 0.07 0.10 0.31 0.16 0.33 0.01## Academic Self-Efficacy T4 .73
Acad_Selfefficacy_T4 <- merged_data |>
dplyr::select(acad_selfefficacy_1_T4:acad_selfefficacy_5_T4)
alpha_pos <- psych::alpha(Acad_Selfefficacy_T4)
print(alpha_pos) ##
## Reliability analysis
## Call: psych::alpha(x = Acad_Selfefficacy_T4)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.72 0.73 0.69 0.35 2.7 0.02 4.9 0.78 0.33
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.68 0.72 0.76
## Duhachek 0.68 0.72 0.76
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se
## acad_selfefficacy_1_T4 0.68 0.68 0.62 0.35 2.2 0.023
## acad_selfefficacy_2_T4 0.66 0.66 0.60 0.33 2.0 0.025
## acad_selfefficacy_3_T4 0.67 0.68 0.63 0.35 2.1 0.025
## acad_selfefficacy_4_T4 0.67 0.68 0.63 0.35 2.1 0.024
## acad_selfefficacy_5_T4 0.69 0.70 0.64 0.36 2.3 0.023
## var.r med.r
## acad_selfefficacy_1_T4 0.0016 0.35
## acad_selfefficacy_2_T4 0.0034 0.32
## acad_selfefficacy_3_T4 0.0064 0.33
## acad_selfefficacy_4_T4 0.0076 0.32
## acad_selfefficacy_5_T4 0.0058 0.35
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## acad_selfefficacy_1_T4 349 0.65 0.68 0.57 0.46 5.1 1.02
## acad_selfefficacy_2_T4 349 0.69 0.72 0.63 0.52 5.1 0.97
## acad_selfefficacy_3_T4 349 0.72 0.69 0.57 0.49 4.4 1.28
## acad_selfefficacy_4_T4 349 0.68 0.70 0.57 0.49 5.0 1.05
## acad_selfefficacy_5_T4 349 0.70 0.66 0.53 0.45 4.6 1.29
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 miss
## acad_selfefficacy_1_T4 0.00 0.01 0.04 0.26 0.20 0.48 0.28
## acad_selfefficacy_2_T4 0.00 0.01 0.04 0.25 0.21 0.49 0.28
## acad_selfefficacy_3_T4 0.01 0.07 0.13 0.37 0.11 0.31 0.28
## acad_selfefficacy_4_T4 0.00 0.02 0.05 0.23 0.27 0.42 0.28
## acad_selfefficacy_5_T4 0.02 0.04 0.12 0.27 0.20 0.35 0.28Prep data
Make data long
merged_data <- merged_data %>%
rename_with(~ gsub("_(T1|T2|T3|T4)$", "_\\1", .x)) # treat as suffix
merged_data_long <- merged_data %>%
pivot_longer(cols = matches("_T[1234]$"), # Matches columns ending with _T1, _T2, _T3, or _T4
names_to = c(".value", "time"), # Split the variable name and time
names_pattern = "(.*)_T(1|2|3|4)") %>% # Use a regex to split correctly
mutate(time = as.numeric(time)) # Convert the time column to numeric (1 for T1, 2 for T2, etc.)
# fill in the demographic values for rest of timepoints
merged_data_long <- merged_data_long %>%
group_by(unique_ID) %>%
fill(cond, Gender, Sex, Age, Education, starts_with("Ethnicity"), int_student, int_student_country, starts_with("SES"), .direction = "downup") %>%
ungroup()Set contrasts
# condition
contrasts(merged_data_long$cond) <- cbind(flourish_vs_control=c(-1,1))
# time
merged_data_long <- merged_data_long |>
dplyr::mutate(time_f = factor(time),
treatment_vs_baseline = ifelse(time > 1, 0.33, -1))
contrasts(merged_data_long$time_f) <- cbind(linear=c(-1.5, -0.5, 0.5, 1.5))Exclusions
3 levels: 1. Intention to treat (no exclusions) 2. Preregistered: “Participants who fail to complete a minimum of 2 timepoints will be excluded from the final analysis. Additionally, students in the treatment condition who do not use the Flourish app, as well as those in the control condition who gain access to the app will also be excluded.” 3. In addition to preregistered exclusions, also exclude students who report unreasonable engagement numbers
Intent to treat
No exclusions
# rename
data_ITT <- merged_data_long
# count number of timepoints participants completed
data_ITT %>%
dplyr::group_by(unique_ID) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## # A tibble: 4 × 2
## num_timepoints_completed n
## <int> <int>
## 1 1 76
## 2 2 51
## 3 3 40
## 4 4 319# write.csv(data_ITT, "merged_no_exclusions.csv")
# retention rates per condition
data_ITT %>%
dplyr::group_by(unique_ID, cond) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::group_by(cond) %>%
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## `summarise()` has grouped output by 'unique_ID'. You can override using the
## `.groups` argument.## # A tibble: 8 × 3
## # Groups: cond [2]
## cond num_timepoints_completed n
## <fct> <int> <int>
## 1 control 1 44
## 2 control 2 26
## 3 control 3 15
## 4 control 4 162
## 5 flourish 1 32
## 6 flourish 2 25
## 7 flourish 3 25
## 8 flourish 4 157Preregistered exclusions
“Participants who fail to complete a minimum of 2 timepoints will be excluded from the final analysis. Additionally, students in the treatment condition who do not use the Flourish app, as well as those in the control condition who gain access to the app will also be excluded.”
# exclude those in the Flourish condition who did not use Flourish app
data_excluded <- merged_data_long %>%
dplyr::filter(!(cond == "flourish" & time %in% c(2, 3, 4) & (is.na(Engagement_1) | Engagement_1 == 0)))
# remove people in control condition who said they used Flourish
ids_to_exclude <- data_excluded %>%
dplyr::filter(cond == "control" & time == 4 & contamination == 1) %>%
pull(unique_ID)
data_excluded <- data_excluded %>%
filter(!unique_ID %in% ids_to_exclude)
# exclude those with less than minimum of 2 timepoints
data_excluded <- data_excluded %>%
group_by(unique_ID) %>%
filter(sum(Finished == 1, na.rm = TRUE) >= 2) %>% # Check if they have completed 2 or more timepoints
ungroup()
# count number of timepoints participants completed
data_excluded %>%
dplyr::group_by(unique_ID) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## # A tibble: 3 × 2
## num_timepoints_completed n
## <int> <int>
## 1 2 51
## 2 3 46
## 3 4 292# write.csv(data_excluded, "merged_excluded_prereg.csv")
# retention rates per condition
data_excluded %>%
dplyr::group_by(unique_ID, cond) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::group_by(cond) %>%
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## `summarise()` has grouped output by 'unique_ID'. You can override using the
## `.groups` argument.## # A tibble: 6 × 3
## # Groups: cond [2]
## cond num_timepoints_completed n
## <fct> <int> <int>
## 1 control 2 25
## 2 control 3 15
## 3 control 4 147
## 4 flourish 2 26
## 5 flourish 3 31
## 6 flourish 4 145Additionally exclude unreasonable numbers
In addition to preregistered exclusions, also exclude students who report unreasonable engagement numbers
# exclude those who copied the example engagement numbers (76, 7, 31)
data_excluded_unreasonable <- data_excluded |>
dplyr::filter(!unique_ID %in% c("Foothill_1801J", "Foothill_997G", "UW_940E", "UW_1263Y", "Chapman_1026S"))
# exclude those with time 3 numbers that are too high
# (can safely exclude those who reported > 16 days at time 3 because there were max 16 days between time 2 and 3)
# data_excluded_unreasonable %>%
# dplyr::select(unique_ID, time, Engagement_1) |>
# pivot_wider(names_from = "time", values_from = "Engagement_1") |>
# dplyr::mutate(diff_1 = `3` - `2`) |>
# dplyr::filter(diff_1 > 16)
data_excluded_unreasonable <- data_excluded_unreasonable |>
dplyr::filter(!unique_ID %in% c("Foothill_350P", "UW_480K", "UW_866L", "UW_1097M", "Chapman_1032C", "UW_932Y", "Chapman_763N", "Chapman_1153M", "Chapman_1009B"))
# exclude those with time 4 numbers that are too high
# (can safely exclude those who reported > 18 days at time 4 because there were max 18 days between time 3 and 4)
# data_excluded_unreasonable %>%
# dplyr::select(unique_ID, time, Engagement_1) |>
# pivot_wider(names_from = "time", values_from = "Engagement_1") |>
# dplyr::mutate(diff_2 = `4` - `3`) |>
# dplyr::filter(diff_2 > 18)
data_excluded_unreasonable <- data_excluded_unreasonable |>
dplyr::filter(!unique_ID %in% c("UW_2412G", "UW_369S", "UW_198E", "UW_699E", "UW_1264V", "UW_184B", "UW_851K", "UW_951M", "Chapman_610M", "Chapman_524Y", "Chapman_504U", "Chapman_991I", "Chapman_135D", "Chapman_734K", "Chapman_875S"))
# remove those with differences between timepoints that are negative (which is impossible)
# data_excluded_unreasonable %>%
# dplyr::select(unique_ID, time, Engagement_1) |>
# pivot_wider(names_from = "time", values_from = "Engagement_1") |>
# dplyr::mutate(diff_1 = `3` - `2`,
# diff_2 = `4` - `3`) |>
# dplyr::filter(diff_1 < 0 | diff_2 < 0)
data_excluded_unreasonable <- data_excluded_unreasonable |>
dplyr::filter(!unique_ID %in% c("Chapman_706R", "Chapman_875M", "Chapman_554S"))
# count number of timepoints participants completed
data_excluded_unreasonable %>%
dplyr::group_by(unique_ID) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## # A tibble: 3 × 2
## num_timepoints_completed n
## <int> <int>
## 1 2 50
## 2 3 38
## 3 4 269# write.csv(data_excluded_unreasonable, "merged_excluded_unreasonable.csv")
# retention rates per condition
data_excluded_unreasonable %>%
dplyr::group_by(unique_ID, cond) %>%
dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>% # Count only rows where Finished is 1
dplyr::group_by(cond) %>%
dplyr::count(num_timepoints_completed) # Count participants by number of completed timepoints## `summarise()` has grouped output by 'unique_ID'. You can override using the
## `.groups` argument.## # A tibble: 6 × 3
## # Groups: cond [2]
## cond num_timepoints_completed n
## <fct> <int> <int>
## 1 control 2 25
## 2 control 3 15
## 3 control 4 147
## 4 flourish 2 25
## 5 flourish 3 23
## 6 flourish 4 122Demographics
data_ITT_demog <- data_ITT |>
dplyr::select(unique_ID, univ, Age, Sex, Gender, contains("Ethnicity"), int_student, int_student_country, SES, SES_num, Education, cond) |>
distinct()
data_excluded_demog <- data_excluded |>
dplyr::select(unique_ID, univ, Age, Sex, Gender, contains("Ethnicity"), int_student, int_student_country, SES, SES_num, Education, cond) |>
distinct()
data_excluded_unreasonable_demog <- data_excluded_unreasonable |>
dplyr::select(unique_ID, univ, Age, Sex, Gender, contains("Ethnicity"), int_student, int_student_country, SES, SES_num, Education, cond) |>
distinct() Age
Intention to Treat
data_ITT_demog %>%
dplyr::summarise(mean_age = mean(Age, na.rm = TRUE),
sd_age = sd(Age, na.rm = TRUE)) |>
kable(digits = 2)| mean_age | sd_age |
|---|---|
| 20.34 | 4.03 |
# by condition
data_ITT_demog |>
dplyr::group_by(cond) |>
dplyr::summarise(mean_age = mean(Age, na.rm=T),
sd_age = sd(Age, na.rm=T))|>
kable(digits = 2)| cond | mean_age | sd_age |
|---|---|---|
| control | 20.21 | 3.28 |
| flourish | 20.48 | 4.69 |
lm(data=data_ITT_demog, Age ~ cond) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_ITT_demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.483 -1.483 -1.211 -0.211 32.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3472 0.1837 110.79 <2e-16 ***
## condflourish_vs_control 0.1358 0.1837 0.74 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.031 on 480 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.001138, Adjusted R-squared: -0.0009426
## F-statistic: 0.5471 on 1 and 480 DF, p-value: 0.4599Excluded Preregistered
data_excluded_demog %>%
dplyr::summarise(mean_age = mean(Age, na.rm = TRUE),
sd_age = sd(Age, na.rm = TRUE)) |>
kable(digits = 2)| mean_age | sd_age |
|---|---|
| 20.32 | 4.21 |
# by condition
data_excluded_demog |>
dplyr::group_by(cond) |>
dplyr::summarise(mean_age = mean(Age, na.rm=T),
sd_age = sd(Age, na.rm=T))|>
kable(digits = 2)| cond | mean_age | sd_age |
|---|---|---|
| control | 20.04 | 3.11 |
| flourish | 20.57 | 5.01 |
lm(data=data_excluded_demog, Age ~ cond) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_excluded_demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.570 -1.570 -1.043 -0.043 32.430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3064 0.2137 95.008 <2e-16 ***
## condflourish_vs_control 0.2636 0.2137 1.233 0.218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.202 on 385 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.003936, Adjusted R-squared: 0.001348
## F-statistic: 1.521 on 1 and 385 DF, p-value: 0.2182Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
dplyr::summarise(mean_age = mean(Age, na.rm = TRUE),
sd_age = sd(Age, na.rm = TRUE)) |>
kable(digits = 2)| mean_age | sd_age |
|---|---|
| 20.35 | 4.28 |
# by condition
data_excluded_unreasonable_demog |>
dplyr::group_by(cond) |>
dplyr::summarise(mean_age = mean(Age, na.rm=T),
sd_age = sd(Age, na.rm=T))|>
kable(digits = 2)| cond | mean_age | sd_age |
|---|---|---|
| control | 20.04 | 3.11 |
| flourish | 20.69 | 5.27 |
lm(data=data_excluded_unreasonable_demog, Age ~ cond) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_excluded_unreasonable_demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.686 -1.686 -1.043 -0.043 32.314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3646 0.2269 89.748 <2e-16 ***
## condflourish_vs_control 0.3218 0.2269 1.418 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.276 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.00565, Adjusted R-squared: 0.002841
## F-statistic: 2.011 on 1 and 354 DF, p-value: 0.157Sex
Intention to Treat
data_ITT_demog %>%
group_by(Sex) %>%
summarise(count = n()) |>
kable(digits = 2)| Sex | count |
|---|---|
| Man | 100 |
| Woman | 383 |
| NA | 3 |
# by condition
data_ITT_demog |>
group_by(Sex, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
## argument.| Sex | cond | count |
|---|---|---|
| Man | control | 46 |
| Man | flourish | 54 |
| Woman | control | 200 |
| Woman | flourish | 183 |
| NA | control | 1 |
| NA | flourish | 2 |
sex_table <- table(data_ITT_demog$Sex, data_ITT_demog$cond)
formattable(sex_table)##
## control flourish
## Man 46 54
## Woman 200 183
## Intersex 0 0sex_table <- sex_table[rowSums(sex_table) != 0, ] # remove rows with 0 for chi square
chisq.test(sex_table)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: sex_table
## X-squared = 0.99105, df = 1, p-value = 0.3195Excluded Preregistered
data_excluded_demog %>%
group_by(Sex) %>%
summarise(count = n()) |>
kable(digits = 2)| Sex | count |
|---|---|
| Man | 69 |
| Woman | 318 |
| NA | 2 |
# by condition
data_excluded_demog |>
group_by(Sex, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
## argument.| Sex | cond | count |
|---|---|---|
| Man | control | 29 |
| Man | flourish | 40 |
| Woman | control | 158 |
| Woman | flourish | 160 |
| NA | flourish | 2 |
sex_table <- table(data_excluded_demog$Sex, data_excluded_demog$cond)
formattable(sex_table)##
## control flourish
## Man 29 40
## Woman 158 160
## Intersex 0 0sex_table <- sex_table[rowSums(sex_table) != 0, ] # remove rows with 0 for chi square
chisq.test(sex_table)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: sex_table
## X-squared = 1.0421, df = 1, p-value = 0.3073Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(Sex) %>%
summarise(count = n()) |>
kable(digits = 2)| Sex | count |
|---|---|
| Man | 63 |
| Woman | 293 |
| NA | 1 |
# by condition
data_excluded_unreasonable_demog |>
group_by(Sex, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
## argument.| Sex | cond | count |
|---|---|---|
| Man | control | 29 |
| Man | flourish | 34 |
| Woman | control | 158 |
| Woman | flourish | 135 |
| NA | flourish | 1 |
sex_table <- table(data_excluded_unreasonable_demog$Sex, data_excluded_unreasonable_demog$cond)
formattable(sex_table)##
## control flourish
## Man 29 34
## Woman 158 135
## Intersex 0 0sex_table <- sex_table[rowSums(sex_table) != 0, ] # remove rows with 0 for chi square
chisq.test(sex_table)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: sex_table
## X-squared = 0.99829, df = 1, p-value = 0.3177Gender
Intention to Treat
data_ITT_demog %>%
group_by(Gender) %>%
summarise(count = n()) |>
kable(digits = 2)| Gender | count |
|---|---|
| Female | 368 |
| Male | 101 |
| Genderqueer/Gender non-conforming | 4 |
| Gender non-binary | 6 |
| NA | 7 |
# by condition
data_ITT_demog |>
group_by(Gender, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Gender'. You can override using the
## `.groups` argument.| Gender | cond | count |
|---|---|---|
| Female | control | 191 |
| Female | flourish | 177 |
| Male | control | 47 |
| Male | flourish | 54 |
| Genderqueer/Gender non-conforming | control | 3 |
| Genderqueer/Gender non-conforming | flourish | 1 |
| Gender non-binary | control | 2 |
| Gender non-binary | flourish | 4 |
| NA | control | 4 |
| NA | flourish | 3 |
gender_table <- table(data_ITT_demog$Gender, data_ITT_demog$cond)
formattable(gender_table)##
## control flourish
## Female 191 177
## Male 47 54
## Trans male/Trans man 0 0
## Trans female/Trans woman 0 0
## Genderqueer/Gender non-conforming 3 1
## Self-identify 0 0
## Gender non-binary 2 4gender_table <- gender_table[rowSums(gender_table) != 0, ] # remove rows with 0 for chi square
chisq.test(gender_table)## Warning in chisq.test(gender_table): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: gender_table
## X-squared = 2.5827, df = 3, p-value = 0.4605Excluded Preregistered
data_excluded_demog %>%
group_by(Gender) %>%
summarise(count = n()) |>
kable(digits = 2)| Gender | count |
|---|---|
| Female | 305 |
| Male | 70 |
| Genderqueer/Gender non-conforming | 4 |
| Gender non-binary | 4 |
| NA | 6 |
# by condition
data_excluded_demog |>
group_by(Gender, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Gender'. You can override using the
## `.groups` argument.| Gender | cond | count |
|---|---|---|
| Female | control | 150 |
| Female | flourish | 155 |
| Male | control | 30 |
| Male | flourish | 40 |
| Genderqueer/Gender non-conforming | control | 3 |
| Genderqueer/Gender non-conforming | flourish | 1 |
| Gender non-binary | control | 1 |
| Gender non-binary | flourish | 3 |
| NA | control | 3 |
| NA | flourish | 3 |
gender_table <- table(data_excluded_demog$Gender, data_excluded_demog$cond)
formattable(gender_table)##
## control flourish
## Female 150 155
## Male 30 40
## Trans male/Trans man 0 0
## Trans female/Trans woman 0 0
## Genderqueer/Gender non-conforming 3 1
## Self-identify 0 0
## Gender non-binary 1 3gender_table <- gender_table[rowSums(gender_table) != 0, ] # remove rows with 0 for chi square
chisq.test(gender_table)## Warning in chisq.test(gender_table): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: gender_table
## X-squared = 2.9276, df = 3, p-value = 0.4029Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(Gender) %>%
summarise(count = n()) |>
kable(digits = 2)| Gender | count |
|---|---|
| Female | 280 |
| Male | 64 |
| Genderqueer/Gender non-conforming | 4 |
| Gender non-binary | 4 |
| NA | 5 |
# by condition
data_excluded_unreasonable_demog |>
group_by(Gender, cond) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'Gender'. You can override using the
## `.groups` argument.| Gender | cond | count |
|---|---|---|
| Female | control | 150 |
| Female | flourish | 130 |
| Male | control | 30 |
| Male | flourish | 34 |
| Genderqueer/Gender non-conforming | control | 3 |
| Genderqueer/Gender non-conforming | flourish | 1 |
| Gender non-binary | control | 1 |
| Gender non-binary | flourish | 3 |
| NA | control | 3 |
| NA | flourish | 2 |
gender_table <- table(data_excluded_unreasonable_demog$Gender, data_excluded_unreasonable_demog$cond)
formattable(gender_table)##
## control flourish
## Female 150 130
## Male 30 34
## Trans male/Trans man 0 0
## Trans female/Trans woman 0 0
## Genderqueer/Gender non-conforming 3 1
## Self-identify 0 0
## Gender non-binary 1 3gender_table <- gender_table[rowSums(gender_table) != 0, ] # remove rows with 0 for chi square
chisq.test(gender_table)## Warning in chisq.test(gender_table): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: gender_table
## X-squared = 2.9574, df = 3, p-value = 0.3982Ethnicity
Intention to Treat
data_ITT_demog %>%
summarise(
across(
c(Ethnicity_White, Ethnicity_Hispanic, Ethnicity_Black, Ethnicity_East_Asian,
Ethnicity_South_Asian, Ethnicity_Native_Hawaiian_Pacific_Islander,
Ethnicity_Middle_Eastern, Ethnicity_American_Indian, Ethnicity_Mixed, Ethnicity_Self_Identify),
list(Count = ~sum(. == 1, na.rm = TRUE),
Percent = ~mean(. == 1, na.rm = TRUE)*100)
)
) %>%
pivot_longer(
cols = starts_with("Ethnicity_"),
names_to = c("Ethnicity", ".value"),
names_pattern = "Ethnicity_(.*)_(Count|Percent)$"
) %>%
kable(digits = 2)| Ethnicity | Count | Percent |
|---|---|---|
| White | 214 | 44.03 |
| Hispanic | 48 | 9.88 |
| Black | 21 | 4.32 |
| East_Asian | 61 | 12.55 |
| South_Asian | 26 | 5.35 |
| Native_Hawaiian_Pacific_Islander | 2 | 0.41 |
| Middle_Eastern | 10 | 2.06 |
| American_Indian | 2 | 0.41 |
| Mixed | 86 | 17.70 |
| Self_Identify | 13 | 2.67 |
# by condition
data_ITT_demog |>
group_by(cond) |>
summarise(
across(
c(Ethnicity_White, Ethnicity_Hispanic, Ethnicity_Black, Ethnicity_East_Asian,
Ethnicity_South_Asian, Ethnicity_Native_Hawaiian_Pacific_Islander,
Ethnicity_Middle_Eastern, Ethnicity_American_Indian, Ethnicity_Mixed, Ethnicity_Self_Identify),
list(Count = ~sum(. == 1, na.rm = TRUE),
Percent = ~mean(. == 1, na.rm = TRUE)*100)
)
) %>%
pivot_longer(
cols = starts_with("Ethnicity_"),
names_to = c("Ethnicity", ".value"),
names_pattern = "Ethnicity_(.*)_(Count|Percent)$"
) %>%
kable(digits = 2)| cond | Ethnicity | Count | Percent |
|---|---|---|---|
| control | White | 119 | 48.18 |
| control | Hispanic | 19 | 7.69 |
| control | Black | 10 | 4.05 |
| control | East_Asian | 26 | 10.53 |
| control | South_Asian | 14 | 5.67 |
| control | Native_Hawaiian_Pacific_Islander | 0 | 0.00 |
| control | Middle_Eastern | 4 | 1.62 |
| control | American_Indian | 1 | 0.40 |
| control | Mixed | 47 | 19.03 |
| control | Self_Identify | 6 | 2.43 |
| flourish | White | 95 | 39.75 |
| flourish | Hispanic | 29 | 12.13 |
| flourish | Black | 11 | 4.60 |
| flourish | East_Asian | 35 | 14.64 |
| flourish | South_Asian | 12 | 5.02 |
| flourish | Native_Hawaiian_Pacific_Islander | 2 | 0.84 |
| flourish | Middle_Eastern | 6 | 2.51 |
| flourish | American_Indian | 1 | 0.42 |
| flourish | Mixed | 39 | 16.32 |
| flourish | Self_Identify | 7 | 2.93 |
Excluded Preregistered
data_excluded_demog %>%
summarise(
across(
c(Ethnicity_White, Ethnicity_Hispanic, Ethnicity_Black, Ethnicity_East_Asian,
Ethnicity_South_Asian, Ethnicity_Native_Hawaiian_Pacific_Islander,
Ethnicity_Middle_Eastern, Ethnicity_American_Indian, Ethnicity_Mixed, Ethnicity_Self_Identify),
list(Count = ~sum(. == 1, na.rm = TRUE),
Percent = ~mean(. == 1, na.rm = TRUE)*100)
)
) %>%
pivot_longer(
cols = starts_with("Ethnicity_"),
names_to = c("Ethnicity", ".value"),
names_pattern = "Ethnicity_(.*)_(Count|Percent)$"
) %>%
kable(digits = 2)| Ethnicity | Count | Percent |
|---|---|---|
| White | 179 | 46.02 |
| Hispanic | 32 | 8.23 |
| Black | 16 | 4.11 |
| East_Asian | 52 | 13.37 |
| South_Asian | 22 | 5.66 |
| Native_Hawaiian_Pacific_Islander | 2 | 0.51 |
| Middle_Eastern | 9 | 2.31 |
| American_Indian | 2 | 0.51 |
| Mixed | 62 | 15.94 |
| Self_Identify | 11 | 2.83 |
Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
summarise(
across(
c(Ethnicity_White, Ethnicity_Hispanic, Ethnicity_Black, Ethnicity_East_Asian,
Ethnicity_South_Asian, Ethnicity_Native_Hawaiian_Pacific_Islander,
Ethnicity_Middle_Eastern, Ethnicity_American_Indian, Ethnicity_Mixed, Ethnicity_Self_Identify),
list(Count = ~sum(. == 1, na.rm = TRUE),
Percent = ~mean(. == 1, na.rm = TRUE)*100)
)
) %>%
pivot_longer(
cols = starts_with("Ethnicity_"),
names_to = c("Ethnicity", ".value"),
names_pattern = "Ethnicity_(.*)_(Count|Percent)$"
) %>%
kable(digits = 2)| Ethnicity | Count | Percent |
|---|---|---|
| White | 167 | 46.78 |
| Hispanic | 31 | 8.68 |
| Black | 15 | 4.20 |
| East_Asian | 48 | 13.45 |
| South_Asian | 18 | 5.04 |
| Native_Hawaiian_Pacific_Islander | 2 | 0.56 |
| Middle_Eastern | 5 | 1.40 |
| American_Indian | 2 | 0.56 |
| Mixed | 57 | 15.97 |
| Self_Identify | 11 | 3.08 |
International student
Intention to Treat
data_ITT_demog %>%
group_by(int_student) %>%
summarise(count = n()) |>
kable(digits = 2)| int_student | count |
|---|---|
| Yes | 33 |
| No | 450 |
| NA | 3 |
data_ITT_demog |>
dplyr::filter(int_student == "Yes") |>
group_by(int_student_country) |>
summarise(count = n()) |>
kable(digits = 2)| int_student_country | count |
|---|---|
| Brazil | 2 |
| China | 10 |
| Colombia | 1 |
| Cote d’Ivoire | 1 |
| England | 1 |
| Hong Kong | 1 |
| Hong Kong(China) | 1 |
| India | 4 |
| Indonesia | 1 |
| Japan | 1 |
| Philippines | 1 |
| South Africa | 1 |
| Sweden | 1 |
| Taiwan | 1 |
| Thailand | 1 |
| Turkey | 1 |
| Vietnam | 1 |
| china | 1 |
| mexico | 1 |
| NA | 1 |
Excluded Preregistered
data_excluded_demog %>%
group_by(int_student) %>%
summarise(count = n()) |>
kable(digits = 2)| int_student | count |
|---|---|
| Yes | 28 |
| No | 359 |
| NA | 2 |
data_excluded_demog |>
dplyr::filter(int_student == "Yes") |>
group_by(int_student_country) |>
summarise(count = n()) |>
kable(digits = 2)| int_student_country | count |
|---|---|
| Brazil | 2 |
| China | 10 |
| Colombia | 1 |
| Cote d’Ivoire | 1 |
| England | 1 |
| Hong Kong | 1 |
| India | 3 |
| Indonesia | 1 |
| Japan | 1 |
| Philippines | 1 |
| South Africa | 1 |
| Sweden | 1 |
| Taiwan | 1 |
| Thailand | 1 |
| Vietnam | 1 |
| china | 1 |
Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(int_student) %>%
summarise(count = n()) |>
kable(digits = 2)| int_student | count |
|---|---|
| Yes | 23 |
| No | 333 |
| NA | 1 |
data_excluded_unreasonable_demog |>
dplyr::filter(int_student == "Yes") |>
group_by(int_student_country) |>
summarise(count = n()) |>
kable(digits = 2)| int_student_country | count |
|---|---|
| Brazil | 2 |
| China | 9 |
| Colombia | 1 |
| Cote d’Ivoire | 1 |
| England | 1 |
| India | 1 |
| Indonesia | 1 |
| Japan | 1 |
| Philippines | 1 |
| South Africa | 1 |
| Sweden | 1 |
| Taiwan | 1 |
| Thailand | 1 |
| Vietnam | 1 |
SES
Intention to Treat
data_ITT_demog %>%
group_by(SES) %>%
summarise(count = n()) |>
kable(digits = 2)| SES | count |
|---|---|
| 1 | 37 |
| 2 | 78 |
| 3 | 148 |
| 4 | 142 |
| 5 | 78 |
| NA | 3 |
data_ITT_demog |>
dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
sd_SES = sd(SES_num, na.rm = TRUE)) |>
kable(digits = 2)| mean_SES | sd_SES |
|---|---|
| 3.3 | 1.15 |
Excluded Preregistered
data_excluded_demog %>%
group_by(SES) %>%
summarise(count = n()) |>
kable(digits = 2)| SES | count |
|---|---|
| 1 | 29 |
| 2 | 61 |
| 3 | 121 |
| 4 | 112 |
| 5 | 64 |
| NA | 2 |
data_excluded_demog |>
dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
sd_SES = sd(SES_num, na.rm = TRUE)) |>
kable(digits = 2)| mean_SES | sd_SES |
|---|---|
| 3.31 | 1.15 |
Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(SES) %>%
summarise(count = n()) |>
kable(digits = 2)| SES | count |
|---|---|
| 1 | 28 |
| 2 | 56 |
| 3 | 115 |
| 4 | 102 |
| 5 | 55 |
| NA | 1 |
data_excluded_unreasonable_demog |>
dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
sd_SES = sd(SES_num, na.rm = TRUE)) |>
kable(digits = 2)| mean_SES | sd_SES |
|---|---|
| 3.28 | 1.14 |
Current degree program
Intention to Treat
data_ITT_demog %>%
group_by(univ,Education) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'univ'. You can override using the
## `.groups` argument.| univ | Education | count |
|---|---|---|
| Chapman | Associates | 8 |
| Chapman | Bachelors | 224 |
| Chapman | Masters | 1 |
| Chapman | PhD | 2 |
| Chapman | Other | 1 |
| Chapman | Non-degree student | 1 |
| Chapman | NA | 8 |
| Foothill | Associates | 44 |
| Foothill | Bachelors | 7 |
| Foothill | Masters | 1 |
| Foothill | Non-degree student | 8 |
| Foothill | NA | 6 |
| UW | Associates | 3 |
| UW | Bachelors | 158 |
| UW | Masters | 2 |
| UW | Other | 2 |
| UW | Non-degree student | 4 |
| UW | NA | 6 |
Excluded Preregistered
data_excluded_demog %>%
group_by(univ,Education) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'univ'. You can override using the
## `.groups` argument.| univ | Education | count |
|---|---|---|
| Chapman | Associates | 5 |
| Chapman | Bachelors | 167 |
| Chapman | Masters | 1 |
| Chapman | PhD | 2 |
| Chapman | Other | 1 |
| Chapman | NA | 6 |
| Foothill | Associates | 31 |
| Foothill | Bachelors | 5 |
| Foothill | Masters | 1 |
| Foothill | Non-degree student | 7 |
| Foothill | NA | 5 |
| UW | Associates | 3 |
| UW | Bachelors | 143 |
| UW | Masters | 1 |
| UW | Other | 2 |
| UW | Non-degree student | 4 |
| UW | NA | 5 |
Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(univ,Education) %>%
summarise(count = n()) |>
kable(digits = 2)## `summarise()` has grouped output by 'univ'. You can override using the
## `.groups` argument.| univ | Education | count |
|---|---|---|
| Chapman | Associates | 5 |
| Chapman | Bachelors | 152 |
| Chapman | Masters | 1 |
| Chapman | PhD | 2 |
| Chapman | Other | 1 |
| Chapman | NA | 6 |
| Foothill | Associates | 29 |
| Foothill | Bachelors | 4 |
| Foothill | Masters | 1 |
| Foothill | Non-degree student | 7 |
| Foothill | NA | 5 |
| UW | Associates | 2 |
| UW | Bachelors | 131 |
| UW | Masters | 1 |
| UW | Other | 2 |
| UW | Non-degree student | 4 |
| UW | NA | 4 |
Condition count
Intention to Treat
data_ITT_demog %>%
group_by(cond) %>%
summarise(count = n()) |>
kable(digits = 2)| cond | count |
|---|---|
| control | 247 |
| flourish | 239 |
Excluded Preregistered
data_excluded_demog %>%
group_by(cond) %>%
summarise(count = n()) |>
kable(digits = 2)| cond | count |
|---|---|
| control | 187 |
| flourish | 202 |
Excluded Unreasonable Numbers
data_excluded_unreasonable_demog %>%
group_by(cond) %>%
summarise(count = n()) |>
kable(digits = 2)| cond | count |
|---|---|
| control | 187 |
| flourish | 170 |
Condition randomization check
Age
Intention to Treat
lm(Age ~ cond, data = data_ITT_demog) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_ITT_demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.483 -1.483 -1.211 -0.211 32.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3472 0.1837 110.79 <2e-16 ***
## condflourish_vs_control 0.1358 0.1837 0.74 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.031 on 480 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.001138, Adjusted R-squared: -0.0009426
## F-statistic: 0.5471 on 1 and 480 DF, p-value: 0.4599Excluded Preregistered
lm(Age ~ cond, data = data_excluded) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_excluded)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.669 -1.669 -1.043 -0.043 32.331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3561 0.1115 182.612 < 2e-16 ***
## condflourish_vs_control 0.3133 0.1115 2.811 0.00501 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.27 on 1466 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.005361, Adjusted R-squared: 0.004682
## F-statistic: 7.901 on 1 and 1466 DF, p-value: 0.005007Excluded Unreasonable Numbers
lm(Age ~ cond, data = data_excluded_unreasonable_demog) |> summary()##
## Call:
## lm(formula = Age ~ cond, data = data_excluded_unreasonable_demog)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.686 -1.686 -1.043 -0.043 32.314
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.3646 0.2269 89.748 <2e-16 ***
## condflourish_vs_control 0.3218 0.2269 1.418 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.276 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.00565, Adjusted R-squared: 0.002841
## F-statistic: 2.011 on 1 and 354 DF, p-value: 0.157Gender
Intention to Treat
contingency_table <- table(data_ITT_demog$Sex, data_ITT_demog$cond)
chisq.test(contingency_table)## Warning in chisq.test(contingency_table): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: contingency_table
## X-squared = NaN, df = 2, p-value = NAExcluded Preregistered
contingency_table <- table(data_excluded$Sex, data_excluded$cond)
chisq.test(contingency_table)## Warning in chisq.test(contingency_table): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: contingency_table
## X-squared = NaN, df = 2, p-value = NAExcluded Unreasonable Numbers
contingency_table <- table(data_excluded_unreasonable_demog$Sex, data_excluded_unreasonable_demog$cond)
chisq.test(contingency_table)## Warning in chisq.test(contingency_table): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: contingency_table
## X-squared = NaN, df = 2, p-value = NAEngagement
Time 2
Intention to Treat
data_ITT %>%
dplyr::filter(time == 2) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 7.08 | 9.43 | 8.07 | 8.48 |
Excluded Preregistered
data_excluded |>
dplyr::filter(time == 2) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 7.35 | 9.51 | 8.37 | 8.5 |
Excluded Unreasonable Numbers
data_excluded_unreasonable |>
dplyr::filter(time == 2) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 5.42 | 3.97 | 7.92 | 8.24 |
Time 3
Intention to Treat
data_ITT %>%
dplyr::filter(time == 3) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 13.65 | 15.88 | 14.69 | 11.91 |
Excluded Preregistered
data_excluded |>
dplyr::filter(time == 3) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 13.81 | 15.9 | 14.86 | 11.88 |
Excluded Unreasonable Numbers
data_excluded_unreasonable |>
dplyr::filter(time == 3) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 9.84 | 7.95 | 14.21 | 11.62 |
Time 4
Intention to Treat
data_ITT %>%
dplyr::filter(time == 4) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 22.56 | 20.8 | 21.82 | 15.11 |
Excluded Preregistered
data_excluded |>
dplyr::filter(time == 4) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 22.96 | 20.77 | 22.21 | 14.95 |
Excluded Unreasonable Numbers
data_excluded_unreasonable |>
dplyr::filter(time == 4) |>
dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
days_sd = sd(Engagement_1, na.rm = TRUE),
activities_mean = mean(Engagement_3, na.rm = TRUE),
activities_sd = sd(Engagement_3, na.rm = TRUE)) |>
kable(digits = 2)| days_mean | days_sd | activities_mean | activities_sd |
|---|---|---|---|
| 16.48 | 11.5 | 21.04 | 14.76 |
Histograms
Depression
Intention to Treat
ggplot(subset(data_ITT, time == 1), aes(x = depression)) +
geom_density(fill = "blue", alpha = 0.5) +
theme_minimal()## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).
data_ITT %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 81.4 18.6Excluded Preregistered
ggplot(subset(data_excluded, time == 1), aes(x = depression)) +
geom_density(fill = "blue", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 82.7 17.3Excluded Unreasonable Numbers
ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = depression)) +
geom_density(fill = "blue", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded_unreasonable %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 82.6 17.4Anxiety
Intention to Treat
ggplot(subset(data_ITT, time == 1), aes(x = anxiety)) +
geom_density(fill = "red", alpha = 0.5) +
theme_minimal()## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).
data_ITT %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 58.9 41.1Excluded Preregistered
ggplot(subset(data_excluded, time == 1), aes(x = anxiety)) +
geom_density(fill = "red", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 59.5 40.5Excluded Unreasonable Numbers
ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = anxiety)) +
geom_density(fill = "red", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded_unreasonable %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 57.9 42.1Loneliness
Intention to Treat
ggplot(subset(data_ITT, time == 1), aes(x = loneliness)) +
geom_density(fill = "green", alpha = 0.5) +
theme_minimal()## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).
data_ITT %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(loneliness < 3, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(loneliness >= 3, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 0 100Excluded Preregistered
ggplot(subset(data_excluded, time == 1), aes(x = loneliness)) +
geom_density(fill = "green", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(loneliness < 5, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(loneliness >= 5, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 28.1 71.9Excluded Unreasonable Numbers
ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = loneliness)) +
geom_density(fill = "green", alpha = 0.5) +
theme_minimal()## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).
data_excluded_unreasonable %>%
dplyr::filter(time == 1) |>
dplyr::summarise(under_threshold = round(mean(loneliness < 5, na.rm = TRUE) * 100, 2),
above_threshold = round(mean(loneliness >= 5, na.rm = TRUE) * 100, 2))## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 26.7 73.3ggplot(merged_data, aes(x = loneliness_T1)) +
geom_density(fill = "green", alpha = 0.5) +
theme_minimal()
merged_data %>%
dplyr::summarise(under_threshold = mean(loneliness_T1 < 5, na.rm = TRUE) * 100,
above_threshold = mean(loneliness_T1 >= 5, na.rm = TRUE) * 100)## # A tibble: 1 × 2
## under_threshold above_threshold
## <dbl> <dbl>
## 1 27.1 72.9Analyses
Linear time (-1.5,-0.5,0.5,1.5)
Depression
Intention to Treat
m0 <- lmer(depression ~ cond * time + (1 | unique_ID) + (1 | univ), data = data_ITT)
m1 <- lmer(depression ~ cond * time + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(depression ~ cond * time + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| depression | depression | depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.47 | 1.25 – 1.70 | <0.001 | 2.93 | 1.94 – 3.92 | <0.001 | 2.48 | 1.47 – 3.49 | <0.001 |
| condflourish vs control | 0.04 | -0.11 – 0.19 | 0.604 | 0.05 | -0.09 – 0.20 | 0.487 | 0.04 | -0.10 – 0.19 | 0.564 |
| time | 0.02 | -0.03 – 0.06 | 0.449 | 0.01 | -0.03 – 0.06 | 0.506 | 0.01 | -0.03 – 0.06 | 0.500 |
|
condflourish vs control × time |
-0.03 | -0.07 – 0.01 | 0.114 | -0.03 | -0.08 – 0.01 | 0.102 | -0.03 | -0.08 – 0.01 | 0.098 |
| Sex [Woman] | 0.10 | -0.19 – 0.38 | 0.499 | 0.11 | -0.17 – 0.40 | 0.447 | |||
| Age | -0.03 | -0.06 – -0.00 | 0.028 | -0.03 | -0.06 – -0.00 | 0.045 | |||
| int student [No] | -0.01 | -0.46 – 0.44 | 0.967 | 0.26 | -0.23 – 0.74 | 0.296 | |||
| SES num | -0.24 | -0.34 – -0.14 | <0.001 | -0.23 | -0.33 – -0.13 | <0.001 | |||
| Ethnicity White | -0.10 | -0.40 – 0.21 | 0.533 | ||||||
| Ethnicity Hispanic | 0.02 | -0.42 – 0.46 | 0.936 | ||||||
| Ethnicity Black | 0.66 | 0.07 – 1.26 | 0.029 | ||||||
| Ethnicity East Asian | 0.19 | -0.22 – 0.61 | 0.353 | ||||||
| Ethnicity South Asian | 0.87 | 0.31 – 1.43 | 0.002 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.41 | -1.31 – 2.13 | 0.638 | ||||||
| Ethnicity Middle Eastern | 0.51 | -0.30 – 1.32 | 0.219 | ||||||
| Ethnicity American Indian | 0.79 | -0.90 – 2.47 | 0.362 | ||||||
| Random Effects | |||||||||
| σ2 | 0.82 | 0.82 | 0.82 | ||||||
| τ00 | 1.35 unique_ID | 1.27 unique_ID | 1.24 unique_ID | ||||||
| 0.02 univ | 0.05 univ | 0.03 univ | |||||||
| ICC | 0.63 | 0.62 | 0.61 | ||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.001 / 0.628 | 0.044 / 0.635 | 0.075 / 0.638 | ||||||
Excluded Preregistered
m0 <- lmer(depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| depression | depression | depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.45 | 1.23 – 1.68 | <0.001 | 3.02 | 1.98 – 4.07 | <0.001 | 2.65 | 1.56 – 3.73 | <0.001 |
| condflourish vs control | -0.07 | -0.19 – 0.06 | 0.280 | -0.05 | -0.17 – 0.07 | 0.448 | -0.05 | -0.17 – 0.07 | 0.430 |
| time - 2 5 | 0.01 | -0.03 – 0.06 | 0.562 | 0.01 | -0.03 – 0.06 | 0.590 | 0.01 | -0.03 – 0.06 | 0.597 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.07 – 0.02 | 0.246 | -0.03 | -0.07 – 0.02 | 0.244 | -0.03 | -0.07 – 0.02 | 0.239 |
| Sex [Woman] | 0.08 | -0.24 – 0.40 | 0.618 | 0.09 | -0.23 – 0.41 | 0.568 | |||
| Age | -0.04 | -0.07 – -0.01 | 0.022 | -0.03 | -0.06 – -0.00 | 0.031 | |||
| int student [No] | 0.03 | -0.44 – 0.50 | 0.888 | 0.23 | -0.28 – 0.73 | 0.378 | |||
| SES num | -0.27 | -0.38 – -0.17 | <0.001 | -0.27 | -0.38 – -0.17 | <0.001 | |||
| Ethnicity White | 0.04 | -0.29 – 0.38 | 0.798 | ||||||
| Ethnicity Hispanic | -0.02 | -0.52 – 0.48 | 0.944 | ||||||
| Ethnicity Black | 0.54 | -0.12 – 1.19 | 0.108 | ||||||
| Ethnicity East Asian | 0.27 | -0.16 – 0.71 | 0.217 | ||||||
| Ethnicity South Asian | 0.74 | 0.15 – 1.33 | 0.013 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.48 | -1.21 – 2.16 | 0.579 | ||||||
| Ethnicity Middle Eastern | 0.49 | -0.34 – 1.32 | 0.250 | ||||||
| Ethnicity American Indian | 0.95 | -0.72 – 2.63 | 0.264 | ||||||
| Random Effects | |||||||||
| σ2 | 0.82 | 0.82 | 0.83 | ||||||
| τ00 | 1.28 unique_ID | 1.18 unique_ID | 1.16 unique_ID | ||||||
| 0.03 univ | 0.06 univ | 0.04 univ | |||||||
| ICC | 0.61 | 0.60 | 0.59 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.614 | 0.056 / 0.623 | 0.075 / 0.625 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| depression | depression | depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.45 | 1.21 – 1.69 | <0.001 | 3.05 | 1.96 – 4.14 | <0.001 | 2.73 | 1.61 – 3.85 | <0.001 |
| condflourish vs control | -0.07 | -0.20 – 0.05 | 0.250 | -0.06 | -0.19 – 0.06 | 0.318 | -0.06 | -0.18 – 0.07 | 0.358 |
| time - 2 5 | 0.00 | -0.04 – 0.05 | 0.909 | 0.00 | -0.04 – 0.05 | 0.914 | 0.00 | -0.04 – 0.05 | 0.915 |
|
condflourish vs control × time - 2 5 |
-0.04 | -0.08 – 0.01 | 0.121 | -0.04 | -0.08 – 0.01 | 0.128 | -0.04 | -0.08 – 0.01 | 0.127 |
| Sex [Woman] | 0.07 | -0.26 – 0.40 | 0.679 | 0.09 | -0.23 – 0.42 | 0.573 | |||
| Age | -0.04 | -0.07 – -0.00 | 0.024 | -0.03 | -0.07 – -0.00 | 0.032 | |||
| int student [No] | 0.07 | -0.45 – 0.58 | 0.793 | 0.26 | -0.28 – 0.79 | 0.349 | |||
| SES num | -0.29 | -0.40 – -0.18 | <0.001 | -0.29 | -0.40 – -0.17 | <0.001 | |||
| Ethnicity White | -0.02 | -0.36 – 0.32 | 0.904 | ||||||
| Ethnicity Hispanic | -0.17 | -0.68 – 0.33 | 0.496 | ||||||
| Ethnicity Black | 0.49 | -0.18 – 1.17 | 0.149 | ||||||
| Ethnicity East Asian | 0.22 | -0.23 – 0.66 | 0.334 | ||||||
| Ethnicity South Asian | 0.84 | 0.22 – 1.46 | 0.008 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.40 | -1.27 – 2.07 | 0.638 | ||||||
| Ethnicity Middle Eastern | -0.09 | -1.15 – 0.98 | 0.875 | ||||||
| Ethnicity American Indian | 0.91 | -0.74 – 2.56 | 0.282 | ||||||
| Random Effects | |||||||||
| σ2 | 0.81 | 0.81 | 0.82 | ||||||
| τ00 | 1.26 unique_ID | 1.15 unique_ID | 1.13 unique_ID | ||||||
| 0.03 univ | 0.07 univ | 0.06 univ | |||||||
| ICC | 0.61 | 0.60 | 0.59 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.003 / 0.615 | 0.062 / 0.625 | 0.086 / 0.628 | ||||||
Anxiety
Intention to Treat
m0 <- lmer(anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m1 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m2 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| anxiety | anxiety | anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.34 | 2.21 – 2.48 | <0.001 | 3.37 | 2.32 – 4.41 | <0.001 | 3.39 | 2.28 – 4.50 | <0.001 |
| condflourish vs control | -0.12 | -0.25 – 0.01 | 0.076 | -0.10 | -0.22 – 0.03 | 0.136 | -0.10 | -0.23 – 0.02 | 0.112 |
| time - 2 5 | -0.05 | -0.10 – -0.00 | 0.050 | -0.05 | -0.10 – -0.00 | 0.035 | -0.05 | -0.10 – -0.00 | 0.035 |
|
condflourish vs control × time - 2 5 |
-0.02 | -0.07 – 0.03 | 0.499 | -0.02 | -0.07 – 0.03 | 0.428 | -0.02 | -0.07 – 0.03 | 0.435 |
| Sex [Woman] | 0.51 | 0.19 – 0.84 | 0.002 | 0.51 | 0.19 – 0.84 | 0.002 | |||
| Age | -0.03 | -0.07 – -0.00 | 0.034 | -0.04 | -0.07 – -0.00 | 0.029 | |||
| int student [No] | 0.35 | -0.16 – 0.85 | 0.178 | 0.34 | -0.21 – 0.89 | 0.224 | |||
| SES num | -0.32 | -0.43 – -0.21 | <0.001 | -0.30 | -0.41 – -0.18 | <0.001 | |||
| Ethnicity White | -0.14 | -0.49 – 0.20 | 0.420 | ||||||
| Ethnicity Hispanic | 0.10 | -0.40 – 0.60 | 0.699 | ||||||
| Ethnicity Black | 0.40 | -0.27 – 1.08 | 0.243 | ||||||
| Ethnicity East Asian | -0.32 | -0.79 – 0.14 | 0.176 | ||||||
| Ethnicity South Asian | 0.16 | -0.47 – 0.80 | 0.616 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.61 | -1.35 – 2.56 | 0.542 | ||||||
| Ethnicity Middle Eastern | 0.72 | -0.20 – 1.64 | 0.124 | ||||||
| Ethnicity American Indian | 0.49 | -1.44 – 2.41 | 0.620 | ||||||
| Random Effects | |||||||||
| σ2 | 1.14 | 1.14 | 1.14 | ||||||
| τ00 | 1.79 unique_ID | 1.59 unique_ID | 1.59 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.58 | ||||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.015 / NA | 0.078 / 0.616 | 0.196 / NA | ||||||
Excluded Preregistered
m0 <- lmer(anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m1 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m2 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| anxiety | anxiety | anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.33 | 2.19 – 2.47 | <0.001 | 3.47 | 2.36 – 4.58 | <0.001 | 3.65 | 2.46 – 4.83 | <0.001 |
| condflourish vs control | -0.12 | -0.26 – 0.02 | 0.100 | -0.09 | -0.22 – 0.05 | 0.201 | -0.09 | -0.23 – 0.04 | 0.184 |
| time - 2 5 | -0.06 | -0.11 – -0.01 | 0.018 | -0.06 | -0.12 – -0.01 | 0.015 | -0.06 | -0.12 – -0.01 | 0.016 |
|
condflourish vs control × time - 2 5 |
-0.01 | -0.06 – 0.04 | 0.626 | -0.01 | -0.07 – 0.04 | 0.579 | -0.01 | -0.07 – 0.04 | 0.571 |
| Sex [Woman] | 0.52 | 0.16 – 0.88 | 0.005 | 0.51 | 0.15 – 0.88 | 0.006 | |||
| Age | -0.04 | -0.07 – -0.01 | 0.022 | -0.04 | -0.08 – -0.01 | 0.014 | |||
| int student [No] | 0.41 | -0.11 – 0.94 | 0.123 | 0.32 | -0.25 – 0.89 | 0.276 | |||
| SES num | -0.35 | -0.47 – -0.23 | <0.001 | -0.34 | -0.47 – -0.22 | <0.001 | |||
| Ethnicity White | -0.09 | -0.47 – 0.29 | 0.634 | ||||||
| Ethnicity Hispanic | 0.12 | -0.45 – 0.68 | 0.687 | ||||||
| Ethnicity Black | 0.40 | -0.35 – 1.14 | 0.295 | ||||||
| Ethnicity East Asian | -0.27 | -0.77 – 0.22 | 0.282 | ||||||
| Ethnicity South Asian | -0.11 | -0.77 – 0.56 | 0.756 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.56 | -1.36 – 2.49 | 0.565 | ||||||
| Ethnicity Middle Eastern | 0.67 | -0.28 – 1.61 | 0.165 | ||||||
| Ethnicity American Indian | 0.53 | -1.38 – 2.44 | 0.587 | ||||||
| Random Effects | |||||||||
| σ2 | 1.16 | 1.16 | 1.16 | ||||||
| τ00 | 1.74 unique_ID | 1.50 unique_ID | 1.50 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.016 / NA | 0.194 / NA | 0.213 / NA | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m1 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m2 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| anxiety | anxiety | anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.33 | 2.18 – 2.48 | <0.001 | 3.55 | 2.38 – 4.72 | <0.001 | 3.66 | 2.42 – 4.90 | <0.001 |
| condflourish vs control | -0.12 | -0.27 – 0.03 | 0.122 | -0.10 | -0.24 – 0.04 | 0.166 | -0.10 | -0.25 – 0.04 | 0.176 |
| time - 2 5 | -0.09 | -0.14 – -0.03 | 0.002 | -0.08 | -0.14 – -0.03 | 0.002 | -0.08 | -0.14 – -0.03 | 0.002 |
|
condflourish vs control × time - 2 5 |
-0.04 | -0.09 – 0.02 | 0.193 | -0.04 | -0.09 – 0.02 | 0.194 | -0.04 | -0.09 – 0.02 | 0.193 |
| Sex [Woman] | 0.50 | 0.13 – 0.88 | 0.009 | 0.51 | 0.13 – 0.89 | 0.009 | |||
| Age | -0.04 | -0.07 – -0.00 | 0.025 | -0.04 | -0.08 – -0.01 | 0.019 | |||
| int student [No] | 0.38 | -0.20 – 0.96 | 0.199 | 0.33 | -0.29 – 0.95 | 0.292 | |||
| SES num | -0.37 | -0.49 – -0.24 | <0.001 | -0.35 | -0.48 – -0.22 | <0.001 | |||
| Ethnicity White | -0.11 | -0.51 – 0.28 | 0.572 | ||||||
| Ethnicity Hispanic | 0.00 | -0.58 – 0.58 | 0.989 | ||||||
| Ethnicity Black | 0.41 | -0.37 – 1.18 | 0.306 | ||||||
| Ethnicity East Asian | -0.32 | -0.83 – 0.20 | 0.229 | ||||||
| Ethnicity South Asian | -0.01 | -0.73 – 0.71 | 0.977 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.54 | -1.40 – 2.47 | 0.586 | ||||||
| Ethnicity Middle Eastern | -0.04 | -1.28 – 1.21 | 0.954 | ||||||
| Ethnicity American Indian | 0.50 | -1.43 – 2.43 | 0.611 | ||||||
| Random Effects | |||||||||
| σ2 | 1.15 | 1.15 | 1.15 | ||||||
| τ00 | 1.74 unique_ID | 1.51 unique_ID | 1.52 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.020 / NA | 0.198 / NA | 0.211 / NA | ||||||
Loneliness
Intention to Treat
m0 <- lmer(loneliness ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')#standardize_parameters(m0)
m1 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| loneliness | loneliness | loneliness | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.29 | 5.16 – 5.42 | <0.001 | 6.02 | 4.93 – 7.11 | <0.001 | 5.66 | 4.52 – 6.79 | <0.001 |
| condflourish vs control | -0.09 | -0.22 – 0.04 | 0.187 | -0.08 | -0.21 – 0.05 | 0.250 | -0.09 | -0.22 – 0.05 | 0.199 |
| time - 2 5 | -0.12 | -0.16 – -0.07 | <0.001 | -0.12 | -0.16 – -0.07 | <0.001 | -0.12 | -0.16 – -0.07 | <0.001 |
|
condflourish vs control × time - 2 5 |
-0.05 | -0.09 – -0.00 | 0.038 | -0.05 | -0.09 – -0.00 | 0.038 | -0.05 | -0.09 – -0.00 | 0.038 |
| Sex [Woman] | 0.16 | -0.17 – 0.48 | 0.349 | 0.16 | -0.17 – 0.49 | 0.341 | |||
| Age | -0.03 | -0.06 – 0.01 | 0.139 | -0.02 | -0.05 – 0.01 | 0.237 | |||
| int student [No] | 0.45 | -0.07 – 0.97 | 0.090 | 0.65 | 0.09 – 1.21 | 0.023 | |||
| SES num | -0.22 | -0.33 – -0.10 | <0.001 | -0.20 | -0.31 – -0.08 | 0.001 | |||
| Ethnicity White | -0.17 | -0.52 – 0.19 | 0.355 | ||||||
| Ethnicity Hispanic | 0.14 | -0.37 – 0.65 | 0.590 | ||||||
| Ethnicity Black | 0.11 | -0.58 – 0.80 | 0.750 | ||||||
| Ethnicity East Asian | 0.03 | -0.45 – 0.50 | 0.903 | ||||||
| Ethnicity South Asian | 0.59 | -0.06 – 1.24 | 0.074 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.11 | -1.88 – 2.10 | 0.914 | ||||||
| Ethnicity Middle Eastern | 0.25 | -0.69 – 1.18 | 0.606 | ||||||
| Ethnicity American Indian | 1.49 | -0.47 – 3.46 | 0.136 | ||||||
| Random Effects | |||||||||
| σ2 | 0.97 | 0.97 | 0.97 | ||||||
| τ00 | 1.79 unique_ID | 1.71 unique_ID | 1.71 unique_ID | ||||||
| 0.00 univ | 0.02 univ | 0.01 univ | |||||||
| ICC | 0.64 | 0.64 | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.028 / NA | 0.043 / 0.656 | 0.057 / 0.660 | ||||||
Excluded Preregistered
m0 <- lmer(loneliness ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m1 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| loneliness | loneliness | loneliness | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.27 | 5.12 – 5.41 | <0.001 | 6.06 | 4.87 – 7.26 | <0.001 | 5.83 | 4.58 – 7.09 | <0.001 |
| condflourish vs control | -0.10 | -0.24 – 0.05 | 0.194 | -0.08 | -0.22 – 0.06 | 0.271 | -0.09 | -0.23 – 0.05 | 0.216 |
| time - 2 5 | -0.12 | -0.17 – -0.07 | <0.001 | -0.12 | -0.16 – -0.07 | <0.001 | -0.12 | -0.16 – -0.07 | <0.001 |
|
condflourish vs control × time - 2 5 |
-0.05 | -0.10 – -0.00 | 0.039 | -0.05 | -0.10 – -0.00 | 0.046 | -0.05 | -0.10 – -0.00 | 0.045 |
| Sex [Woman] | 0.05 | -0.33 – 0.43 | 0.799 | 0.05 | -0.34 – 0.43 | 0.816 | |||
| Age | -0.03 | -0.06 – 0.01 | 0.126 | -0.03 | -0.06 – 0.01 | 0.154 | |||
| int student [No] | 0.56 | 0.00 – 1.12 | 0.049 | 0.71 | 0.11 – 1.32 | 0.020 | |||
| SES num | -0.23 | -0.36 – -0.11 | <0.001 | -0.21 | -0.34 – -0.08 | 0.001 | |||
| Ethnicity White | -0.20 | -0.60 – 0.20 | 0.323 | ||||||
| Ethnicity Hispanic | 0.07 | -0.53 – 0.66 | 0.818 | ||||||
| Ethnicity Black | 0.33 | -0.45 – 1.12 | 0.408 | ||||||
| Ethnicity East Asian | 0.09 | -0.43 – 0.61 | 0.742 | ||||||
| Ethnicity South Asian | 0.36 | -0.35 – 1.06 | 0.321 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.07 | -1.95 – 2.09 | 0.947 | ||||||
| Ethnicity Middle Eastern | 0.23 | -0.77 – 1.22 | 0.652 | ||||||
| Ethnicity American Indian | 1.73 | -0.28 – 3.74 | 0.092 | ||||||
| Random Effects | |||||||||
| σ2 | 0.97 | 0.97 | 0.97 | ||||||
| τ00 | 1.83 unique_ID | 1.75 unique_ID | 1.75 unique_ID | ||||||
| 0.00 univ | 0.02 univ | 0.01 univ | |||||||
| ICC | 0.65 | 0.64 | 0.64 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.010 / 0.657 | 0.049 / 0.662 | 0.061 / 0.665 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(loneliness ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m1 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -8.2e+01m2 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0168869 (tol = 0.002, component 1)tab_model(m0, m1, m2)| loneliness | loneliness | loneliness | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.27 | 5.12 – 5.42 | <0.001 | 6.02 | 4.79 – 7.25 | <0.001 | 5.82 | 4.53 – 7.11 | <0.001 |
| condflourish vs control | -0.09 | -0.24 – 0.06 | 0.245 | -0.09 | -0.24 – 0.06 | 0.240 | -0.09 | -0.25 – 0.06 | 0.221 |
| time - 2 5 | -0.13 | -0.18 – -0.08 | <0.001 | -0.13 | -0.18 – -0.08 | <0.001 | -0.13 | -0.18 – -0.08 | <0.001 |
|
condflourish vs control × time - 2 5 |
-0.06 | -0.11 – -0.01 | 0.011 | -0.06 | -0.11 – -0.02 | 0.010 | -0.06 | -0.11 – -0.02 | 0.010 |
| Sex [Woman] | 0.00 | -0.39 – 0.40 | 0.983 | 0.01 | -0.39 – 0.41 | 0.957 | |||
| Age | -0.02 | -0.06 – 0.01 | 0.204 | -0.02 | -0.06 – 0.01 | 0.237 | |||
| int student [No] | 0.47 | -0.14 – 1.08 | 0.130 | 0.67 | 0.02 – 1.31 | 0.043 | |||
| SES num | -0.22 | -0.35 – -0.09 | 0.001 | -0.20 | -0.33 – -0.06 | 0.004 | |||
| Ethnicity White | -0.28 | -0.69 – 0.14 | 0.188 | ||||||
| Ethnicity Hispanic | -0.07 | -0.67 – 0.54 | 0.831 | ||||||
| Ethnicity Black | 0.25 | -0.55 – 1.06 | 0.538 | ||||||
| Ethnicity East Asian | -0.02 | -0.55 – 0.51 | 0.939 | ||||||
| Ethnicity South Asian | 0.44 | -0.31 – 1.19 | 0.247 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.03 | -1.99 – 2.04 | 0.980 | ||||||
| Ethnicity Middle Eastern | 0.05 | -1.24 – 1.34 | 0.938 | ||||||
| Ethnicity American Indian | 1.67 | -0.34 – 3.67 | 0.103 | ||||||
| Random Effects | |||||||||
| σ2 | 0.97 | 0.97 | 0.97 | ||||||
| τ00 | 1.80 unique_ID | 1.74 unique_ID | 1.73 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.64 | 0.64 | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.033 / NA | 0.043 / 0.657 | 0.061 / 0.662 | ||||||
Perceived Stress
Intention to Treat
m0 <- lmer(perceived_stress ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')#standardize_parameters(m0)
m1 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m2 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| perceived stress | perceived stress | perceived stress | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.63 | 6.41 – 6.85 | <0.001 | 8.90 | 7.16 – 10.65 | <0.001 | 8.64 | 6.80 – 10.48 | <0.001 |
| condflourish vs control | -0.08 | -0.30 – 0.14 | 0.482 | -0.06 | -0.28 – 0.15 | 0.552 | -0.08 | -0.29 – 0.13 | 0.465 |
| time - 2 5 | -0.08 | -0.17 – 0.00 | 0.061 | -0.08 | -0.16 – 0.01 | 0.067 | -0.08 | -0.16 – 0.01 | 0.069 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.12 – 0.05 | 0.461 | -0.03 | -0.11 – 0.06 | 0.509 | -0.03 | -0.11 – 0.06 | 0.504 |
| Sex [Woman] | 0.72 | 0.18 – 1.26 | 0.009 | 0.71 | 0.17 – 1.25 | 0.010 | |||
| Age | -0.05 | -0.10 – 0.01 | 0.086 | -0.04 | -0.10 – 0.01 | 0.105 | |||
| int student [No] | 0.07 | -0.77 – 0.91 | 0.874 | 0.42 | -0.49 – 1.32 | 0.368 | |||
| SES num | -0.60 | -0.78 – -0.41 | <0.001 | -0.55 | -0.74 – -0.36 | <0.001 | |||
| Ethnicity White | -0.61 | -1.18 – -0.04 | 0.036 | ||||||
| Ethnicity Hispanic | -0.14 | -0.97 – 0.68 | 0.733 | ||||||
| Ethnicity Black | 0.53 | -0.59 – 1.65 | 0.354 | ||||||
| Ethnicity East Asian | -0.49 | -1.26 – 0.28 | 0.214 | ||||||
| Ethnicity South Asian | 0.86 | -0.19 – 1.91 | 0.109 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.56 | -2.67 – 3.79 | 0.735 | ||||||
| Ethnicity Middle Eastern | 0.65 | -0.87 – 2.17 | 0.401 | ||||||
| Ethnicity American Indian | 0.59 | -2.58 – 3.77 | 0.713 | ||||||
| Random Effects | |||||||||
| σ2 | 3.54 | 3.52 | 3.52 | ||||||
| τ00 | 4.83 unique_ID | 4.29 unique_ID | 4.22 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.58 | 0.55 | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.578 | 0.074 / 0.583 | 0.191 / NA | ||||||
Excluded Preregistered
m0 <- lmer(perceived_stress ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m1 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m2 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| perceived stress | perceived stress | perceived stress | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.57 | 6.33 – 6.81 | <0.001 | 8.95 | 7.13 – 10.78 | <0.001 | 8.85 | 6.91 – 10.78 | <0.001 |
| condflourish vs control | -0.09 | -0.33 – 0.14 | 0.436 | -0.06 | -0.28 – 0.17 | 0.626 | -0.07 | -0.29 – 0.16 | 0.551 |
| time - 2 5 | -0.10 | -0.19 – -0.01 | 0.025 | -0.10 | -0.19 – -0.01 | 0.033 | -0.10 | -0.19 – -0.01 | 0.033 |
|
condflourish vs control × time - 2 5 |
-0.02 | -0.11 – 0.06 | 0.582 | -0.02 | -0.11 – 0.07 | 0.662 | -0.02 | -0.11 – 0.07 | 0.651 |
| Sex [Woman] | 0.66 | 0.07 – 1.26 | 0.028 | 0.64 | 0.04 – 1.24 | 0.035 | |||
| Age | -0.04 | -0.10 – 0.01 | 0.103 | -0.05 | -0.10 – 0.01 | 0.088 | |||
| int student [No] | 0.17 | -0.69 – 1.04 | 0.695 | 0.36 | -0.57 – 1.30 | 0.446 | |||
| SES num | -0.66 | -0.85 – -0.46 | <0.001 | -0.62 | -0.83 – -0.42 | <0.001 | |||
| Ethnicity White | -0.37 | -0.99 – 0.26 | 0.252 | ||||||
| Ethnicity Hispanic | 0.05 | -0.88 – 0.97 | 0.919 | ||||||
| Ethnicity Black | 0.79 | -0.43 – 2.01 | 0.203 | ||||||
| Ethnicity East Asian | -0.27 | -1.08 – 0.54 | 0.519 | ||||||
| Ethnicity South Asian | 0.55 | -0.54 – 1.64 | 0.321 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.64 | -2.51 – 3.79 | 0.690 | ||||||
| Ethnicity Middle Eastern | 0.76 | -0.79 – 2.30 | 0.339 | ||||||
| Ethnicity American Indian | 0.76 | -2.37 – 3.90 | 0.633 | ||||||
| Random Effects | |||||||||
| σ2 | 3.48 | 3.47 | 3.47 | ||||||
| τ00 | 4.59 unique_ID | 3.96 unique_ID | 3.93 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.57 | 0.53 | |||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.003 / 0.570 | 0.173 / NA | 0.102 / 0.579 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(perceived_stress ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m1 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m2 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| perceived stress | perceived stress | perceived stress | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.54 | 6.30 – 6.78 | <0.001 | 9.14 | 7.25 – 11.02 | <0.001 | 8.97 | 6.98 – 10.95 | <0.001 |
| condflourish vs control | -0.12 | -0.37 – 0.12 | 0.324 | -0.10 | -0.33 – 0.13 | 0.411 | -0.11 | -0.34 – 0.13 | 0.365 |
| time - 2 5 | -0.11 | -0.20 – -0.01 | 0.024 | -0.11 | -0.20 – -0.01 | 0.028 | -0.11 | -0.20 – -0.01 | 0.027 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.13 – 0.06 | 0.510 | -0.03 | -0.12 – 0.07 | 0.550 | -0.03 | -0.12 – 0.07 | 0.549 |
| Sex [Woman] | 0.66 | 0.06 – 1.27 | 0.032 | 0.68 | 0.07 – 1.29 | 0.029 | |||
| Age | -0.05 | -0.10 – 0.01 | 0.090 | -0.05 | -0.10 – 0.01 | 0.083 | |||
| int student [No] | 0.11 | -0.83 – 1.04 | 0.824 | 0.34 | -0.65 – 1.33 | 0.498 | |||
| SES num | -0.69 | -0.90 – -0.49 | <0.001 | -0.65 | -0.86 – -0.45 | <0.001 | |||
| Ethnicity White | -0.39 | -1.02 – 0.25 | 0.232 | ||||||
| Ethnicity Hispanic | -0.10 | -1.03 – 0.83 | 0.831 | ||||||
| Ethnicity Black | 0.82 | -0.43 – 2.07 | 0.197 | ||||||
| Ethnicity East Asian | -0.23 | -1.05 – 0.60 | 0.590 | ||||||
| Ethnicity South Asian | 0.64 | -0.51 – 1.79 | 0.273 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.64 | -2.46 – 3.74 | 0.684 | ||||||
| Ethnicity Middle Eastern | -0.93 | -2.92 – 1.06 | 0.360 | ||||||
| Ethnicity American Indian | 0.77 | -2.32 – 3.85 | 0.626 | ||||||
| Random Effects | |||||||||
| σ2 | 3.53 | 3.52 | 3.52 | ||||||
| τ00 | 4.45 unique_ID | 3.76 unique_ID | 3.74 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.52 | 0.52 | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.008 / NA | 0.097 / 0.563 | 0.110 / 0.568 | ||||||
SAS: Calm
Intention to Treat
m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT) ## boundary (singular) fit: see help('isSingular')#standardize_parameters(m0)
m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS calm | SAS calm | SAS calm | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.74 | 5.55 – 5.94 | <0.001 | 4.92 | 3.37 – 6.46 | <0.001 | 4.97 | 3.32 – 6.62 | <0.001 |
| condflourish vs control | 0.22 | 0.03 – 0.41 | 0.026 | 0.19 | 0.01 – 0.38 | 0.042 | 0.20 | 0.01 – 0.39 | 0.037 |
| time - 2 5 | 0.11 | 0.03 – 0.19 | 0.006 | 0.11 | 0.03 – 0.19 | 0.006 | 0.11 | 0.03 – 0.19 | 0.006 |
|
condflourish vs control × time - 2 5 |
0.10 | 0.02 – 0.18 | 0.015 | 0.10 | 0.02 – 0.18 | 0.014 | 0.10 | 0.02 – 0.18 | 0.014 |
| Sex [Woman] | -0.67 | -1.15 – -0.20 | 0.005 | -0.66 | -1.14 – -0.18 | 0.007 | |||
| Age | 0.02 | -0.03 – 0.07 | 0.387 | 0.02 | -0.03 – 0.07 | 0.389 | |||
| int student [No] | -0.61 | -1.35 – 0.13 | 0.107 | -0.66 | -1.47 – 0.15 | 0.110 | |||
| SES num | 0.45 | 0.29 – 0.61 | <0.001 | 0.43 | 0.26 – 0.60 | <0.001 | |||
| Ethnicity White | 0.14 | -0.37 – 0.65 | 0.585 | ||||||
| Ethnicity Hispanic | -0.25 | -0.99 – 0.49 | 0.502 | ||||||
| Ethnicity Black | -0.11 | -1.11 – 0.89 | 0.830 | ||||||
| Ethnicity East Asian | 0.13 | -0.56 – 0.82 | 0.709 | ||||||
| Ethnicity South Asian | -0.14 | -1.07 – 0.80 | 0.775 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.24 | -2.64 – 3.11 | 0.872 | ||||||
| Ethnicity Middle Eastern | -0.27 | -1.63 – 1.09 | 0.696 | ||||||
| Ethnicity American Indian | -0.44 | -3.26 – 2.38 | 0.759 | ||||||
| Random Effects | |||||||||
| σ2 | 3.06 | 3.05 | 3.05 | ||||||
| τ00 | 3.58 unique_ID | 3.22 unique_ID | 3.27 unique_ID | ||||||
| 0.00 univ | 0.01 univ | 0.01 univ | |||||||
| ICC | 0.51 | 0.52 | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.023 / NA | 0.068 / 0.547 | 0.070 / 0.552 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00220683 (tol = 0.002, component 1)m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00208136 (tol = 0.002, component 1)m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| SAS calm | SAS calm | SAS calm | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.73 | 5.53 – 5.94 | <0.001 | 5.15 | 3.48 – 6.82 | <0.001 | 5.13 | 3.34 – 6.92 | <0.001 |
| condflourish vs control | 0.27 | 0.07 – 0.48 | 0.010 | 0.25 | 0.05 – 0.45 | 0.016 | 0.26 | 0.05 – 0.46 | 0.014 |
| time - 2 5 | 0.15 | 0.07 – 0.23 | <0.001 | 0.15 | 0.07 – 0.23 | <0.001 | 0.15 | 0.07 – 0.23 | <0.001 |
|
condflourish vs control × time - 2 5 |
0.09 | 0.01 – 0.17 | 0.036 | 0.09 | 0.00 – 0.17 | 0.041 | 0.09 | 0.00 – 0.17 | 0.041 |
| Sex [Woman] | -0.61 | -1.15 – -0.08 | 0.024 | -0.59 | -1.13 – -0.04 | 0.034 | |||
| Age | 0.01 | -0.04 – 0.06 | 0.695 | 0.01 | -0.04 – 0.06 | 0.695 | |||
| int student [No] | -0.72 | -1.50 – 0.07 | 0.073 | -0.68 | -1.53 – 0.17 | 0.117 | |||
| SES num | 0.46 | 0.29 – 0.64 | <0.001 | 0.45 | 0.26 – 0.63 | <0.001 | |||
| Ethnicity White | 0.08 | -0.49 – 0.65 | 0.776 | ||||||
| Ethnicity Hispanic | -0.48 | -1.33 – 0.36 | 0.265 | ||||||
| Ethnicity Black | 0.01 | -1.10 – 1.13 | 0.980 | ||||||
| Ethnicity East Asian | 0.14 | -0.60 – 0.88 | 0.712 | ||||||
| Ethnicity South Asian | 0.12 | -0.88 – 1.12 | 0.808 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.15 | -2.72 – 3.02 | 0.919 | ||||||
| Ethnicity Middle Eastern | -0.22 | -1.63 – 1.19 | 0.758 | ||||||
| Ethnicity American Indian | -0.33 | -3.18 – 2.52 | 0.819 | ||||||
| Random Effects | |||||||||
| σ2 | 3.03 | 3.03 | 3.03 | ||||||
| τ00 | 3.50 unique_ID | 3.15 unique_ID | 3.21 unique_ID | ||||||
| 0.00 univ | 0.03 univ | 0.04 univ | |||||||
| ICC | 0.54 | 0.51 | 0.52 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.017 / 0.544 | 0.075 / 0.548 | 0.078 / 0.554 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS calm | SAS calm | SAS calm | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.72 | 5.47 – 5.96 | <0.001 | 5.24 | 3.50 – 6.99 | <0.001 | 5.38 | 3.54 – 7.23 | <0.001 |
| condflourish vs control | 0.26 | 0.04 – 0.47 | 0.018 | 0.26 | 0.05 – 0.47 | 0.015 | 0.27 | 0.06 – 0.48 | 0.013 |
| time - 2 5 | 0.14 | 0.06 – 0.23 | 0.001 | 0.14 | 0.06 – 0.23 | 0.001 | 0.14 | 0.06 – 0.23 | 0.001 |
|
condflourish vs control × time - 2 5 |
0.08 | -0.00 – 0.17 | 0.064 | 0.08 | -0.01 – 0.17 | 0.074 | 0.08 | -0.01 – 0.16 | 0.074 |
| Sex [Woman] | -0.58 | -1.13 – -0.03 | 0.038 | -0.57 | -1.13 – -0.01 | 0.045 | |||
| Age | 0.00 | -0.05 – 0.05 | 0.883 | 0.00 | -0.05 – 0.05 | 0.919 | |||
| int student [No] | -0.60 | -1.45 – 0.26 | 0.171 | -0.62 | -1.53 – 0.29 | 0.180 | |||
| SES num | 0.43 | 0.25 – 0.62 | <0.001 | 0.42 | 0.23 – 0.61 | <0.001 | |||
| Ethnicity White | 0.00 | -0.58 – 0.58 | 0.995 | ||||||
| Ethnicity Hispanic | -0.50 | -1.36 – 0.36 | 0.252 | ||||||
| Ethnicity Black | -0.10 | -1.25 – 1.04 | 0.860 | ||||||
| Ethnicity East Asian | 0.02 | -0.73 – 0.78 | 0.952 | ||||||
| Ethnicity South Asian | -0.15 | -1.21 – 0.90 | 0.776 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.01 | -2.83 – 2.86 | 0.993 | ||||||
| Ethnicity Middle Eastern | 0.48 | -1.35 – 2.30 | 0.609 | ||||||
| Ethnicity American Indian | -0.42 | -3.25 – 2.40 | 0.769 | ||||||
| Random Effects | |||||||||
| σ2 | 3.00 | 2.99 | 2.99 | ||||||
| τ00 | 3.34 unique_ID | 3.06 unique_ID | 3.13 unique_ID | ||||||
| 0.01 univ | 0.04 univ | 0.03 univ | |||||||
| ICC | 0.53 | 0.51 | 0.51 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.015 / 0.535 | 0.065 / 0.540 | 0.068 / 0.547 | ||||||
SAS: Well-Being
Intention to Treat
m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
#standardize_parameters(m0)
m1 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS well being | SAS well being | SAS well being | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.86 | 6.46 – 7.27 | <0.001 | 4.56 | 2.90 – 6.22 | <0.001 | 5.02 | 3.31 – 6.73 | <0.001 |
| condflourish vs control | 0.19 | -0.01 – 0.38 | 0.057 | 0.17 | -0.01 – 0.36 | 0.067 | 0.19 | 0.01 – 0.38 | 0.042 |
| time - 2 5 | -0.06 | -0.13 – 0.01 | 0.112 | -0.06 | -0.14 – 0.01 | 0.090 | -0.06 | -0.14 – 0.01 | 0.086 |
|
condflourish vs control × time - 2 5 |
0.08 | 0.01 – 0.16 | 0.022 | 0.08 | 0.01 – 0.16 | 0.025 | 0.08 | 0.01 – 0.16 | 0.023 |
| Sex [Woman] | 0.21 | -0.27 – 0.68 | 0.387 | 0.18 | -0.29 – 0.66 | 0.450 | |||
| Age | 0.05 | -0.00 – 0.10 | 0.058 | 0.04 | -0.01 – 0.09 | 0.082 | |||
| int student [No] | -0.44 | -1.19 – 0.31 | 0.251 | -0.74 | -1.54 – 0.06 | 0.072 | |||
| SES num | 0.45 | 0.29 – 0.61 | <0.001 | 0.43 | 0.26 – 0.60 | <0.001 | |||
| Ethnicity White | 0.29 | -0.21 – 0.80 | 0.257 | ||||||
| Ethnicity Hispanic | 0.15 | -0.59 – 0.88 | 0.694 | ||||||
| Ethnicity Black | -0.82 | -1.81 – 0.18 | 0.107 | ||||||
| Ethnicity East Asian | -0.23 | -0.92 – 0.45 | 0.508 | ||||||
| Ethnicity South Asian | -0.74 | -1.68 – 0.19 | 0.119 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.53 | -4.39 – 1.33 | 0.293 | ||||||
| Ethnicity Middle Eastern | -0.43 | -1.77 – 0.92 | 0.535 | ||||||
| Ethnicity American Indian | -1.43 | -4.23 – 1.38 | 0.318 | ||||||
| Random Effects | |||||||||
| σ2 | 2.57 | 2.57 | 2.56 | ||||||
| τ00 | 3.66 unique_ID | 3.37 unique_ID | 3.33 unique_ID | ||||||
| 0.09 univ | 0.21 univ | 0.14 univ | |||||||
| ICC | 0.59 | 0.58 | 0.58 | ||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1578 | 1569 | 1569 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / 0.596 | 0.054 / 0.604 | 0.072 / 0.606 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0146806 (tol = 0.002, component 1)tab_model(m0, m1, m2)| SAS well being | SAS well being | SAS well being | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.89 | 6.47 – 7.30 | <0.001 | 4.53 | 2.78 – 6.28 | <0.001 | 5.01 | 3.20 – 6.83 | <0.001 |
| condflourish vs control | 0.21 | 0.01 – 0.42 | 0.044 | 0.19 | -0.01 – 0.39 | 0.068 | 0.21 | 0.01 – 0.41 | 0.041 |
| time - 2 5 | -0.06 | -0.13 – 0.02 | 0.154 | -0.06 | -0.13 – 0.02 | 0.132 | -0.06 | -0.13 – 0.02 | 0.131 |
|
condflourish vs control × time - 2 5 |
0.07 | -0.01 – 0.14 | 0.092 | 0.06 | -0.02 – 0.14 | 0.119 | 0.06 | -0.01 – 0.14 | 0.115 |
| Sex [Woman] | 0.24 | -0.29 – 0.77 | 0.370 | 0.23 | -0.31 – 0.76 | 0.401 | |||
| Age | 0.05 | -0.00 – 0.10 | 0.075 | 0.04 | -0.01 – 0.09 | 0.108 | |||
| int student [No] | -0.48 | -1.26 – 0.31 | 0.235 | -0.73 | -1.57 – 0.11 | 0.089 | |||
| SES num | 0.48 | 0.31 – 0.66 | <0.001 | 0.44 | 0.26 – 0.62 | <0.001 | |||
| Ethnicity White | 0.27 | -0.29 – 0.83 | 0.347 | ||||||
| Ethnicity Hispanic | -0.09 | -0.93 – 0.75 | 0.834 | ||||||
| Ethnicity Black | -0.54 | -1.64 – 0.56 | 0.337 | ||||||
| Ethnicity East Asian | -0.27 | -1.01 – 0.46 | 0.465 | ||||||
| Ethnicity South Asian | -0.50 | -1.49 – 0.49 | 0.319 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.59 | -4.42 – 1.24 | 0.271 | ||||||
| Ethnicity Middle Eastern | 0.05 | -1.35 – 1.44 | 0.945 | ||||||
| Ethnicity American Indian | -1.43 | -4.24 – 1.38 | 0.319 | ||||||
| Random Effects | |||||||||
| σ2 | 2.54 | 2.54 | 2.54 | ||||||
| τ00 | 3.53 unique_ID | 3.22 unique_ID | 3.23 unique_ID | ||||||
| 0.09 univ | 0.18 univ | 0.11 univ | |||||||
| ICC | 0.59 | 0.57 | 0.57 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1411 | 1405 | 1405 | ||||||
| Marginal R2 / Conditional R2 | 0.008 / 0.591 | 0.062 / 0.600 | 0.074 / 0.600 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS well being | SAS well being | SAS well being | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.86 | 6.45 – 7.27 | <0.001 | 4.56 | 2.74 – 6.38 | <0.001 | 5.14 | 3.29 – 7.00 | <0.001 |
| condflourish vs control | 0.18 | -0.03 – 0.40 | 0.095 | 0.18 | -0.03 – 0.39 | 0.095 | 0.20 | -0.01 – 0.41 | 0.058 |
| time - 2 5 | -0.06 | -0.14 – 0.02 | 0.164 | -0.06 | -0.14 – 0.02 | 0.147 | -0.06 | -0.14 – 0.02 | 0.146 |
|
condflourish vs control × time - 2 5 |
0.06 | -0.01 – 0.14 | 0.112 | 0.06 | -0.02 – 0.14 | 0.135 | 0.06 | -0.02 – 0.14 | 0.134 |
| Sex [Woman] | 0.28 | -0.27 – 0.83 | 0.324 | 0.25 | -0.31 – 0.80 | 0.380 | |||
| Age | 0.04 | -0.01 – 0.09 | 0.131 | 0.03 | -0.02 – 0.09 | 0.217 | |||
| int student [No] | -0.37 | -1.23 – 0.50 | 0.408 | -0.67 | -1.57 – 0.23 | 0.144 | |||
| SES num | 0.47 | 0.29 – 0.66 | <0.001 | 0.44 | 0.25 – 0.62 | <0.001 | |||
| Ethnicity White | 0.29 | -0.29 – 0.86 | 0.331 | ||||||
| Ethnicity Hispanic | -0.00 | -0.85 – 0.85 | 1.000 | ||||||
| Ethnicity Black | -0.53 | -1.66 – 0.61 | 0.361 | ||||||
| Ethnicity East Asian | -0.23 | -0.98 – 0.52 | 0.541 | ||||||
| Ethnicity South Asian | -0.94 | -1.98 – 0.11 | 0.079 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.60 | -4.41 – 1.22 | 0.266 | ||||||
| Ethnicity Middle Eastern | 1.10 | -0.71 – 2.91 | 0.233 | ||||||
| Ethnicity American Indian | -1.40 | -4.20 – 1.39 | 0.324 | ||||||
| Random Effects | |||||||||
| σ2 | 2.51 | 2.51 | 2.51 | ||||||
| τ00 | 3.49 unique_ID | 3.21 unique_ID | 3.18 unique_ID | ||||||
| 0.09 univ | 0.16 univ | 0.08 univ | |||||||
| ICC | 0.59 | 0.57 | 0.57 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / 0.591 | 0.057 / 0.598 | 0.077 / 0.599 | ||||||
SAS: Vigour
Intention to Treat
m0 <- lmer(SAS_vigour ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
#standardize_parameters(m0)
m1 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS vigour | SAS vigour | SAS vigour | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.72 | 5.33 – 6.11 | <0.001 | 3.89 | 2.09 – 5.70 | <0.001 | 4.22 | 2.35 – 6.08 | <0.001 |
| condflourish vs control | 0.14 | -0.07 – 0.36 | 0.188 | 0.13 | -0.08 – 0.34 | 0.235 | 0.13 | -0.08 – 0.34 | 0.222 |
| time - 2 5 | -0.07 | -0.15 – 0.00 | 0.057 | -0.08 | -0.15 – 0.00 | 0.055 | -0.08 | -0.16 – -0.00 | 0.047 |
|
condflourish vs control × time - 2 5 |
0.05 | -0.03 – 0.13 | 0.205 | 0.05 | -0.03 – 0.13 | 0.199 | 0.05 | -0.03 – 0.13 | 0.189 |
| Sex [Woman] | -0.04 | -0.58 – 0.49 | 0.869 | -0.06 | -0.60 – 0.47 | 0.813 | |||
| Age | 0.05 | -0.01 – 0.10 | 0.082 | 0.05 | -0.01 – 0.10 | 0.088 | |||
| int student [No] | -0.49 | -1.33 – 0.35 | 0.255 | -0.82 | -1.73 – 0.08 | 0.074 | |||
| SES num | 0.38 | 0.20 – 0.56 | <0.001 | 0.36 | 0.17 – 0.54 | <0.001 | |||
| Ethnicity White | 0.39 | -0.17 – 0.96 | 0.174 | ||||||
| Ethnicity Hispanic | 0.42 | -0.40 – 1.24 | 0.313 | ||||||
| Ethnicity Black | -0.86 | -1.98 – 0.25 | 0.128 | ||||||
| Ethnicity East Asian | -0.24 | -1.01 – 0.52 | 0.535 | ||||||
| Ethnicity South Asian | -0.62 | -1.67 – 0.42 | 0.242 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.06 | -3.15 – 3.27 | 0.970 | ||||||
| Ethnicity Middle Eastern | 1.10 | -0.42 – 2.61 | 0.155 | ||||||
| Ethnicity American Indian | -0.91 | -4.06 – 2.25 | 0.573 | ||||||
| Random Effects | |||||||||
| σ2 | 2.87 | 2.87 | 2.87 | ||||||
| τ00 | 4.58 unique_ID | 4.35 unique_ID | 4.32 unique_ID | ||||||
| 0.08 univ | 0.13 univ | 0.06 univ | |||||||
| ICC | 0.62 | 0.61 | 0.60 | ||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1578 | 1569 | 1569 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.621 | 0.036 / 0.624 | 0.054 / 0.626 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_vigour ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| SAS vigour | SAS vigour | SAS vigour | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.69 | 5.31 – 6.06 | <0.001 | 3.79 | 1.90 – 5.68 | <0.001 | 4.03 | 2.07 – 5.99 | <0.001 |
| condflourish vs control | 0.25 | 0.03 – 0.48 | 0.027 | 0.23 | 0.01 – 0.46 | 0.038 | 0.25 | 0.03 – 0.47 | 0.027 |
| time - 2 5 | -0.07 | -0.15 – 0.01 | 0.100 | -0.07 | -0.15 – 0.01 | 0.094 | -0.07 | -0.15 – 0.01 | 0.088 |
|
condflourish vs control × time - 2 5 |
0.04 | -0.04 – 0.12 | 0.307 | 0.04 | -0.04 – 0.12 | 0.336 | 0.04 | -0.04 – 0.12 | 0.333 |
| Sex [Woman] | 0.21 | -0.38 – 0.80 | 0.484 | 0.21 | -0.38 – 0.80 | 0.484 | |||
| Age | 0.05 | -0.01 – 0.10 | 0.097 | 0.05 | -0.01 – 0.10 | 0.105 | |||
| int student [No] | -0.68 | -1.55 – 0.19 | 0.123 | -1.00 | -1.93 – -0.07 | 0.034 | |||
| SES num | 0.41 | 0.21 – 0.60 | <0.001 | 0.35 | 0.15 – 0.56 | 0.001 | |||
| Ethnicity White | 0.55 | -0.07 – 1.17 | 0.083 | ||||||
| Ethnicity Hispanic | 0.35 | -0.57 – 1.27 | 0.454 | ||||||
| Ethnicity Black | -0.32 | -1.53 – 0.90 | 0.611 | ||||||
| Ethnicity East Asian | -0.13 | -0.94 – 0.67 | 0.743 | ||||||
| Ethnicity South Asian | -0.23 | -1.32 – 0.86 | 0.675 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.21 | -2.92 – 3.33 | 0.896 | ||||||
| Ethnicity Middle Eastern | 1.76 | 0.22 – 3.29 | 0.025 | ||||||
| Ethnicity American Indian | -0.45 | -3.55 – 2.66 | 0.777 | ||||||
| Random Effects | |||||||||
| σ2 | 2.87 | 2.87 | 2.87 | ||||||
| τ00 | 4.27 unique_ID | 4.04 unique_ID | 4.01 unique_ID | ||||||
| 0.06 univ | 0.11 univ | 0.05 univ | |||||||
| ICC | 0.60 | 0.59 | 0.59 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1411 | 1405 | 1405 | ||||||
| Marginal R2 / Conditional R2 | 0.010 / 0.606 | 0.049 / 0.611 | 0.066 / 0.613 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_vigour ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00303228 (tol = 0.002, component 1)m2 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS vigour | SAS vigour | SAS vigour | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.60 | 5.24 – 5.97 | <0.001 | 3.98 | 2.00 – 5.96 | <0.001 | 4.22 | 2.20 – 6.25 | <0.001 |
| condflourish vs control | 0.17 | -0.06 – 0.41 | 0.153 | 0.18 | -0.06 – 0.41 | 0.138 | 0.20 | -0.03 – 0.44 | 0.087 |
| time - 2 5 | -0.09 | -0.17 – -0.00 | 0.040 | -0.09 | -0.18 – -0.01 | 0.035 | -0.09 | -0.18 – -0.01 | 0.033 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.06 – 0.11 | 0.616 | 0.02 | -0.07 – 0.10 | 0.671 | 0.02 | -0.07 – 0.10 | 0.667 |
| Sex [Woman] | 0.28 | -0.34 – 0.89 | 0.374 | 0.28 | -0.33 – 0.90 | 0.367 | |||
| Age | 0.04 | -0.02 – 0.10 | 0.164 | 0.04 | -0.02 – 0.09 | 0.209 | |||
| int student [No] | -0.66 | -1.63 – 0.30 | 0.176 | -1.03 | -2.04 – -0.03 | 0.044 | |||
| SES num | 0.35 | 0.15 – 0.56 | 0.001 | 0.31 | 0.11 – 0.52 | 0.003 | |||
| Ethnicity White | 0.65 | 0.01 – 1.29 | 0.048 | ||||||
| Ethnicity Hispanic | 0.49 | -0.45 – 1.43 | 0.308 | ||||||
| Ethnicity Black | -0.30 | -1.56 – 0.96 | 0.639 | ||||||
| Ethnicity East Asian | -0.06 | -0.89 – 0.78 | 0.894 | ||||||
| Ethnicity South Asian | -0.56 | -1.73 – 0.60 | 0.346 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.33 | -2.81 – 3.46 | 0.838 | ||||||
| Ethnicity Middle Eastern | 2.22 | 0.21 – 4.23 | 0.031 | ||||||
| Ethnicity American Indian | -0.33 | -3.45 – 2.78 | 0.834 | ||||||
| Random Effects | |||||||||
| σ2 | 2.85 | 2.85 | 2.85 | ||||||
| τ00 | 4.24 unique_ID | 4.09 unique_ID | 4.03 unique_ID | ||||||
| 0.05 univ | 0.10 univ | 0.03 univ | |||||||
| ICC | 0.60 | 0.60 | 0.59 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1292 | 1289 | 1289 | ||||||
| Marginal R2 / Conditional R2 | 0.005 / 0.604 | 0.036 / 0.610 | 0.059 / 0.612 | ||||||
SAS: Depression
Intention to Treat
m0 <- lmer(SAS_depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS depression | SAS depression | SAS depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.06 | 3.83 – 4.28 | <0.001 | 5.58 | 3.67 – 7.48 | <0.001 | 4.76 | 2.77 – 6.75 | <0.001 |
| condflourish vs control | -0.14 | -0.37 – 0.08 | 0.216 | -0.12 | -0.35 – 0.10 | 0.283 | -0.14 | -0.36 – 0.09 | 0.233 |
| time - 2 5 | -0.05 | -0.13 – 0.04 | 0.279 | -0.05 | -0.13 – 0.04 | 0.263 | -0.05 | -0.14 – 0.04 | 0.249 |
|
condflourish vs control × time - 2 5 |
0.03 | -0.06 – 0.11 | 0.535 | 0.03 | -0.06 – 0.11 | 0.511 | 0.03 | -0.06 – 0.11 | 0.518 |
| Sex [Woman] | 0.44 | -0.13 – 1.02 | 0.130 | 0.47 | -0.10 – 1.04 | 0.109 | |||
| Age | -0.06 | -0.12 – 0.00 | 0.053 | -0.05 | -0.11 – 0.01 | 0.113 | |||
| int student [No] | 0.69 | -0.21 – 1.60 | 0.133 | 1.07 | 0.10 – 2.04 | 0.030 | |||
| SES num | -0.39 | -0.58 – -0.19 | <0.001 | -0.38 | -0.58 – -0.18 | <0.001 | |||
| Ethnicity White | -0.01 | -0.62 – 0.60 | 0.970 | ||||||
| Ethnicity Hispanic | 0.13 | -0.75 – 1.01 | 0.770 | ||||||
| Ethnicity Black | 0.45 | -0.75 – 1.65 | 0.460 | ||||||
| Ethnicity East Asian | 0.05 | -0.77 – 0.87 | 0.903 | ||||||
| Ethnicity South Asian | 1.60 | 0.47 – 2.72 | 0.005 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.60 | -1.85 – 5.04 | 0.364 | ||||||
| Ethnicity Middle Eastern | 1.66 | 0.04 – 3.29 | 0.045 | ||||||
| Ethnicity American Indian | 2.17 | -1.22 – 5.56 | 0.209 | ||||||
| Random Effects | |||||||||
| σ2 | 3.54 | 3.54 | 3.53 | ||||||
| τ00 | 5.24 unique_ID | 5.00 unique_ID | 4.92 unique_ID | ||||||
| 0.00 univ | 0.07 univ | 0.04 univ | |||||||
| ICC | 0.59 | 0.58 | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1578 | 1569 | 1569 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / NA | 0.040 / 0.605 | 0.063 / 0.610 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| SAS depression | SAS depression | SAS depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.92 | 3.63 – 4.21 | <0.001 | 5.36 | 3.35 – 7.37 | <0.001 | 4.74 | 2.62 – 6.85 | <0.001 |
| condflourish vs control | -0.18 | -0.42 – 0.07 | 0.157 | -0.14 | -0.38 – 0.09 | 0.235 | -0.14 | -0.38 – 0.10 | 0.239 |
| time - 2 5 | -0.04 | -0.13 – 0.05 | 0.337 | -0.04 | -0.13 – 0.05 | 0.358 | -0.04 | -0.13 – 0.05 | 0.343 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.07 – 0.11 | 0.679 | 0.02 | -0.07 – 0.11 | 0.634 | 0.02 | -0.07 – 0.11 | 0.636 |
| Sex [Woman] | 0.45 | -0.18 – 1.08 | 0.160 | 0.49 | -0.15 – 1.12 | 0.134 | |||
| Age | -0.05 | -0.11 – 0.01 | 0.085 | -0.04 | -0.10 – 0.02 | 0.142 | |||
| int student [No] | 0.79 | -0.14 – 1.72 | 0.097 | 0.99 | -0.01 – 1.99 | 0.053 | |||
| SES num | -0.43 | -0.64 – -0.22 | <0.001 | -0.44 | -0.66 – -0.23 | <0.001 | |||
| Ethnicity White | 0.24 | -0.42 – 0.91 | 0.471 | ||||||
| Ethnicity Hispanic | 0.13 | -0.87 – 1.12 | 0.804 | ||||||
| Ethnicity Black | 0.36 | -0.94 – 1.67 | 0.586 | ||||||
| Ethnicity East Asian | 0.15 | -0.71 – 1.02 | 0.728 | ||||||
| Ethnicity South Asian | 1.31 | 0.14 – 2.48 | 0.028 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.83 | -1.53 – 5.19 | 0.285 | ||||||
| Ethnicity Middle Eastern | 1.48 | -0.17 – 3.14 | 0.079 | ||||||
| Ethnicity American Indian | 2.48 | -0.86 – 5.81 | 0.145 | ||||||
| Random Effects | |||||||||
| σ2 | 3.42 | 3.42 | 3.42 | ||||||
| τ00 | 4.96 unique_ID | 4.63 unique_ID | 4.60 unique_ID | ||||||
| 0.02 univ | 0.08 univ | 0.07 univ | |||||||
| ICC | 0.59 | 0.58 | 0.58 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1411 | 1405 | 1405 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.594 | 0.050 / 0.601 | 0.066 / 0.605 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS depression | SAS depression | SAS depression | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.91 | 3.63 – 4.20 | <0.001 | 5.32 | 3.21 – 7.43 | <0.001 | 4.71 | 2.51 – 6.91 | <0.001 |
| condflourish vs control | -0.18 | -0.43 – 0.07 | 0.162 | -0.16 | -0.41 – 0.09 | 0.205 | -0.14 | -0.39 – 0.11 | 0.274 |
| time - 2 5 | -0.06 | -0.15 – 0.03 | 0.215 | -0.06 | -0.15 – 0.03 | 0.213 | -0.06 | -0.15 – 0.03 | 0.205 |
|
condflourish vs control × time - 2 5 |
0.00 | -0.09 – 0.10 | 0.947 | 0.00 | -0.09 – 0.10 | 0.930 | 0.00 | -0.09 – 0.10 | 0.923 |
| Sex [Woman] | 0.52 | -0.13 – 1.18 | 0.117 | 0.58 | -0.08 – 1.24 | 0.086 | |||
| Age | -0.05 | -0.11 – 0.01 | 0.120 | -0.04 | -0.10 – 0.02 | 0.207 | |||
| int student [No] | 0.74 | -0.29 – 1.76 | 0.160 | 0.90 | -0.17 – 1.98 | 0.100 | |||
| SES num | -0.45 | -0.67 – -0.23 | <0.001 | -0.44 | -0.66 – -0.21 | <0.001 | |||
| Ethnicity White | 0.17 | -0.51 – 0.86 | 0.621 | ||||||
| Ethnicity Hispanic | -0.09 | -1.10 – 0.92 | 0.864 | ||||||
| Ethnicity Black | 0.18 | -1.17 – 1.53 | 0.795 | ||||||
| Ethnicity East Asian | -0.04 | -0.93 – 0.86 | 0.936 | ||||||
| Ethnicity South Asian | 1.57 | 0.32 – 2.82 | 0.014 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.77 | -1.59 – 5.14 | 0.301 | ||||||
| Ethnicity Middle Eastern | 0.56 | -1.60 – 2.72 | 0.612 | ||||||
| Ethnicity American Indian | 2.44 | -0.90 – 5.77 | 0.152 | ||||||
| Random Effects | |||||||||
| σ2 | 3.44 | 3.44 | 3.44 | ||||||
| τ00 | 4.96 unique_ID | 4.61 unique_ID | 4.58 unique_ID | ||||||
| 0.01 univ | 0.09 univ | 0.08 univ | |||||||
| ICC | 0.59 | 0.58 | 0.57 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.593 | 0.051 / 0.599 | 0.069 / 0.604 | ||||||
SAS: Anxiety
Intention to Treat
m0 <- lmer(SAS_anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS anxiety | SAS anxiety | SAS anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.02 | 5.80 – 6.24 | <0.001 | 6.31 | 4.62 – 8.01 | <0.001 | 6.13 | 4.33 – 7.94 | <0.001 |
| condflourish vs control | -0.17 | -0.39 – 0.05 | 0.126 | -0.15 | -0.35 – 0.06 | 0.169 | -0.16 | -0.36 – 0.05 | 0.146 |
| time - 2 5 | -0.12 | -0.21 – -0.03 | 0.006 | -0.12 | -0.21 – -0.04 | 0.006 | -0.12 | -0.21 – -0.04 | 0.006 |
|
condflourish vs control × time - 2 5 |
-0.02 | -0.11 – 0.06 | 0.580 | -0.03 | -0.11 – 0.06 | 0.575 | -0.02 | -0.11 – 0.06 | 0.588 |
| Sex [Woman] | 1.12 | 0.59 – 1.65 | <0.001 | 1.13 | 0.60 – 1.66 | <0.001 | |||
| Age | -0.03 | -0.08 – 0.02 | 0.284 | -0.03 | -0.08 – 0.02 | 0.258 | |||
| int student [No] | 0.98 | 0.16 – 1.80 | 0.019 | 1.05 | 0.16 – 1.94 | 0.021 | |||
| SES num | -0.46 | -0.65 – -0.28 | <0.001 | -0.44 | -0.62 – -0.25 | <0.001 | |||
| Ethnicity White | -0.08 | -0.64 – 0.49 | 0.792 | ||||||
| Ethnicity Hispanic | 0.40 | -0.41 – 1.22 | 0.331 | ||||||
| Ethnicity Black | 0.67 | -0.43 – 1.77 | 0.231 | ||||||
| Ethnicity East Asian | -0.14 | -0.90 – 0.62 | 0.715 | ||||||
| Ethnicity South Asian | 0.40 | -0.64 – 1.43 | 0.452 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.18 | -2.99 – 3.35 | 0.911 | ||||||
| Ethnicity Middle Eastern | 0.47 | -1.02 – 1.96 | 0.535 | ||||||
| Ethnicity American Indian | 2.28 | -0.83 – 5.39 | 0.151 | ||||||
| Random Effects | |||||||||
| σ2 | 3.79 | 3.76 | 3.76 | ||||||
| τ00 | 4.47 unique_ID | 3.92 unique_ID | 3.95 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.013 / NA | 0.146 / NA | 0.160 / NA | ||||||
Excluded Preregistered
m0 <- lmer(SAS_anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS anxiety | SAS anxiety | SAS anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.97 | 5.73 – 6.20 | <0.001 | 5.91 | 4.11 – 7.72 | <0.001 | 5.99 | 4.06 – 7.92 | <0.001 |
| condflourish vs control | -0.19 | -0.42 – 0.05 | 0.118 | -0.15 | -0.37 – 0.07 | 0.184 | -0.15 | -0.38 – 0.07 | 0.181 |
| time - 2 5 | -0.14 | -0.23 – -0.05 | 0.003 | -0.14 | -0.23 – -0.04 | 0.004 | -0.14 | -0.23 – -0.04 | 0.004 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.13 – 0.06 | 0.491 | -0.03 | -0.13 – 0.06 | 0.492 | -0.03 | -0.13 – 0.06 | 0.490 |
| Sex [Woman] | 1.15 | 0.56 – 1.74 | <0.001 | 1.17 | 0.57 – 1.76 | <0.001 | |||
| Age | -0.02 | -0.07 – 0.03 | 0.485 | -0.02 | -0.08 – 0.03 | 0.403 | |||
| int student [No] | 1.26 | 0.41 – 2.12 | 0.004 | 1.18 | 0.25 – 2.12 | 0.013 | |||
| SES num | -0.51 | -0.70 – -0.31 | <0.001 | -0.49 | -0.69 – -0.28 | <0.001 | |||
| Ethnicity White | -0.06 | -0.69 – 0.56 | 0.843 | ||||||
| Ethnicity Hispanic | 0.30 | -0.62 – 1.23 | 0.520 | ||||||
| Ethnicity Black | 0.59 | -0.63 – 1.81 | 0.341 | ||||||
| Ethnicity East Asian | -0.20 | -1.01 – 0.61 | 0.626 | ||||||
| Ethnicity South Asian | -0.07 | -1.16 – 1.02 | 0.899 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.13 | -3.01 – 3.28 | 0.933 | ||||||
| Ethnicity Middle Eastern | 0.32 | -1.22 – 1.86 | 0.685 | ||||||
| Ethnicity American Indian | 2.56 | -0.57 – 5.69 | 0.109 | ||||||
| Random Effects | |||||||||
| σ2 | 3.78 | 3.77 | 3.77 | ||||||
| τ00 | 4.42 unique_ID | 3.79 unique_ID | 3.83 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.016 / NA | 0.166 / NA | 0.177 / NA | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_anxiety ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS anxiety | SAS anxiety | SAS anxiety | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 6.01 | 5.76 – 6.25 | <0.001 | 5.89 | 3.97 – 7.82 | <0.001 | 5.86 | 3.84 – 7.89 | <0.001 |
| condflourish vs control | -0.15 | -0.39 – 0.10 | 0.237 | -0.13 | -0.37 – 0.10 | 0.268 | -0.14 | -0.37 – 0.10 | 0.256 |
| time - 2 5 | -0.15 | -0.25 – -0.05 | 0.002 | -0.15 | -0.25 – -0.06 | 0.002 | -0.15 | -0.25 – -0.06 | 0.002 |
|
condflourish vs control × time - 2 5 |
-0.05 | -0.14 – 0.05 | 0.358 | -0.05 | -0.15 – 0.05 | 0.326 | -0.05 | -0.15 – 0.05 | 0.329 |
| Sex [Woman] | 1.17 | 0.55 – 1.78 | <0.001 | 1.22 | 0.59 – 1.84 | <0.001 | |||
| Age | -0.02 | -0.07 – 0.04 | 0.575 | -0.02 | -0.08 – 0.04 | 0.512 | |||
| int student [No] | 1.17 | 0.21 – 2.12 | 0.016 | 1.14 | 0.13 – 2.16 | 0.027 | |||
| SES num | -0.49 | -0.70 – -0.29 | <0.001 | -0.47 | -0.68 – -0.26 | <0.001 | |||
| Ethnicity White | -0.04 | -0.68 – 0.61 | 0.915 | ||||||
| Ethnicity Hispanic | 0.23 | -0.72 – 1.18 | 0.640 | ||||||
| Ethnicity Black | 0.66 | -0.62 – 1.93 | 0.311 | ||||||
| Ethnicity East Asian | -0.23 | -1.07 – 0.61 | 0.591 | ||||||
| Ethnicity South Asian | 0.16 | -1.01 – 1.33 | 0.786 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.14 | -3.03 – 3.31 | 0.931 | ||||||
| Ethnicity Middle Eastern | -0.98 | -3.01 – 1.06 | 0.346 | ||||||
| Ethnicity American Indian | 2.56 | -0.59 – 5.71 | 0.111 | ||||||
| Random Effects | |||||||||
| σ2 | 3.77 | 3.77 | 3.77 | ||||||
| τ00 | 4.43 unique_ID | 3.85 unique_ID | 3.88 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.54 | ||||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.006 / 0.543 | 0.155 / NA | 0.169 / NA | ||||||
SAS: Anger
Intention to Treat
m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS anger | SAS anger | SAS anger | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.73 | 2.18 – 3.28 | <0.001 | 3.20 | 1.51 – 4.89 | <0.001 | 2.43 | 0.68 – 4.18 | 0.007 |
| condflourish vs control | 0.07 | -0.12 – 0.26 | 0.475 | 0.07 | -0.12 – 0.26 | 0.488 | 0.05 | -0.14 – 0.24 | 0.608 |
| time - 2 5 | 0.05 | -0.03 – 0.12 | 0.198 | 0.05 | -0.02 – 0.13 | 0.169 | 0.05 | -0.02 – 0.13 | 0.179 |
|
condflourish vs control × time - 2 5 |
0.00 | -0.07 – 0.07 | 0.999 | 0.00 | -0.07 – 0.08 | 0.912 | 0.00 | -0.07 – 0.08 | 0.900 |
| Sex [Woman] | 0.14 | -0.35 – 0.62 | 0.583 | 0.14 | -0.35 – 0.62 | 0.576 | |||
| Age | -0.02 | -0.07 – 0.03 | 0.468 | -0.01 | -0.06 – 0.04 | 0.686 | |||
| int student [No] | 0.48 | -0.28 – 1.25 | 0.218 | 0.78 | -0.04 – 1.59 | 0.062 | |||
| SES num | -0.20 | -0.37 – -0.03 | 0.018 | -0.19 | -0.36 – -0.02 | 0.032 | |||
| Ethnicity White | 0.12 | -0.39 – 0.63 | 0.648 | ||||||
| Ethnicity Hispanic | 0.65 | -0.10 – 1.39 | 0.090 | ||||||
| Ethnicity Black | 0.30 | -0.71 – 1.31 | 0.558 | ||||||
| Ethnicity East Asian | 0.18 | -0.52 – 0.88 | 0.611 | ||||||
| Ethnicity South Asian | 1.32 | 0.37 – 2.27 | 0.006 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.75 | -2.15 – 3.66 | 0.611 | ||||||
| Ethnicity Middle Eastern | 1.42 | 0.05 – 2.78 | 0.043 | ||||||
| Ethnicity American Indian | 1.32 | -1.53 – 4.16 | 0.363 | ||||||
| Random Effects | |||||||||
| σ2 | 2.73 | 2.71 | 2.71 | ||||||
| τ00 | 3.59 unique_ID | 3.47 unique_ID | 3.42 unique_ID | ||||||
| 0.20 univ | 0.22 univ | 0.17 univ | |||||||
| ICC | 0.58 | 0.58 | 0.57 | ||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1579 | 1570 | 1570 | ||||||
| Marginal R2 / Conditional R2 | 0.001 / 0.582 | 0.013 / 0.582 | 0.033 / 0.584 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| SAS anger | SAS anger | SAS anger | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.65 | 2.16 – 3.13 | <0.001 | 3.37 | 1.55 – 5.19 | <0.001 | 2.45 | 0.58 – 4.33 | 0.010 |
| condflourish vs control | 0.12 | -0.09 – 0.32 | 0.270 | 0.12 | -0.08 – 0.33 | 0.242 | 0.11 | -0.09 – 0.32 | 0.288 |
| time - 2 5 | 0.05 | -0.03 – 0.12 | 0.262 | 0.05 | -0.03 – 0.13 | 0.245 | 0.04 | -0.03 – 0.12 | 0.270 |
|
condflourish vs control × time - 2 5 |
-0.01 | -0.09 – 0.06 | 0.725 | -0.01 | -0.09 – 0.07 | 0.763 | -0.01 | -0.09 – 0.07 | 0.761 |
| Sex [Woman] | 0.10 | -0.45 – 0.65 | 0.728 | 0.08 | -0.47 – 0.63 | 0.771 | |||
| Age | -0.02 | -0.07 – 0.04 | 0.569 | -0.01 | -0.06 – 0.05 | 0.848 | |||
| int student [No] | 0.37 | -0.44 – 1.19 | 0.369 | 0.61 | -0.25 – 1.48 | 0.166 | |||
| SES num | -0.24 | -0.43 – -0.06 | 0.009 | -0.26 | -0.44 – -0.07 | 0.007 | |||
| Ethnicity White | 0.42 | -0.16 – 1.00 | 0.153 | ||||||
| Ethnicity Hispanic | 1.04 | 0.18 – 1.90 | 0.018 | ||||||
| Ethnicity Black | 0.43 | -0.70 – 1.57 | 0.454 | ||||||
| Ethnicity East Asian | 0.53 | -0.22 – 1.29 | 0.165 | ||||||
| Ethnicity South Asian | 1.47 | 0.45 – 2.49 | 0.005 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.91 | -2.01 – 3.82 | 0.542 | ||||||
| Ethnicity Middle Eastern | 1.86 | 0.42 – 3.29 | 0.011 | ||||||
| Ethnicity American Indian | 1.66 | -1.23 – 4.55 | 0.261 | ||||||
| Random Effects | |||||||||
| σ2 | 2.74 | 2.74 | 2.74 | ||||||
| τ00 | 3.55 unique_ID | 3.49 unique_ID | 3.40 unique_ID | ||||||
| 0.14 univ | 0.20 univ | 0.14 univ | |||||||
| ICC | 0.57 | 0.57 | 0.56 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1412 | 1406 | 1406 | ||||||
| Marginal R2 / Conditional R2 | 0.003 / 0.575 | 0.017 / 0.581 | 0.043 / 0.583 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS anger | SAS anger | SAS anger | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 2.61 | 2.04 – 3.17 | <0.001 | 3.06 | 1.16 – 4.96 | 0.002 | 2.23 | 0.26 – 4.19 | 0.026 |
| condflourish vs control | 0.06 | -0.16 – 0.27 | 0.601 | 0.06 | -0.16 – 0.27 | 0.594 | 0.05 | -0.16 – 0.27 | 0.628 |
| time - 2 5 | 0.03 | -0.05 – 0.11 | 0.474 | 0.03 | -0.05 – 0.11 | 0.482 | 0.03 | -0.05 – 0.11 | 0.513 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.11 – 0.05 | 0.455 | -0.03 | -0.11 – 0.05 | 0.454 | -0.03 | -0.11 – 0.05 | 0.459 |
| Sex [Woman] | 0.21 | -0.35 – 0.78 | 0.461 | 0.21 | -0.36 – 0.79 | 0.463 | |||
| Age | -0.01 | -0.06 – 0.05 | 0.753 | 0.00 | -0.05 – 0.06 | 0.944 | |||
| int student [No] | 0.59 | -0.30 – 1.48 | 0.196 | 0.78 | -0.15 – 1.71 | 0.102 | |||
| SES num | -0.30 | -0.49 – -0.11 | 0.002 | -0.29 | -0.48 – -0.09 | 0.004 | |||
| Ethnicity White | 0.32 | -0.28 – 0.91 | 0.292 | ||||||
| Ethnicity Hispanic | 0.90 | 0.02 – 1.78 | 0.045 | ||||||
| Ethnicity Black | 0.12 | -1.05 – 1.29 | 0.840 | ||||||
| Ethnicity East Asian | 0.42 | -0.36 – 1.20 | 0.289 | ||||||
| Ethnicity South Asian | 1.37 | 0.29 – 2.45 | 0.013 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.84 | -2.07 – 3.75 | 0.571 | ||||||
| Ethnicity Middle Eastern | 0.57 | -1.29 – 2.44 | 0.547 | ||||||
| Ethnicity American Indian | 1.64 | -1.25 – 4.52 | 0.265 | ||||||
| Random Effects | |||||||||
| σ2 | 2.61 | 2.61 | 2.62 | ||||||
| τ00 | 3.52 unique_ID | 3.42 unique_ID | 3.40 unique_ID | ||||||
| 0.20 univ | 0.25 univ | 0.20 univ | |||||||
| ICC | 0.59 | 0.58 | 0.58 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.001 / 0.588 | 0.024 / 0.594 | 0.041 / 0.597 | ||||||
SAS: Positive
Intention to Treat
m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
#standardize_parameters(m0)
m1 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)| SAS positive | SAS positive | SAS positive | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.36 | 17.49 – 19.22 | <0.001 | 13.42 | 8.95 – 17.89 | <0.001 | 14.29 | 9.63 – 18.95 | <0.001 |
| condflourish vs control | 0.53 | -0.00 – 1.07 | 0.051 | 0.48 | -0.04 – 1.00 | 0.069 | 0.51 | -0.01 – 1.03 | 0.056 |
| time - 2 5 | -0.02 | -0.21 – 0.16 | 0.796 | -0.03 | -0.22 – 0.16 | 0.764 | -0.03 | -0.22 – 0.16 | 0.736 |
|
condflourish vs control × time - 2 5 |
0.22 | 0.04 – 0.41 | 0.020 | 0.22 | 0.03 – 0.41 | 0.020 | 0.22 | 0.04 – 0.41 | 0.019 |
| Sex [Woman] | -0.49 | -1.81 – 0.82 | 0.463 | -0.53 | -1.85 – 0.80 | 0.436 | |||
| Age | 0.12 | -0.02 – 0.25 | 0.096 | 0.11 | -0.03 – 0.25 | 0.118 | |||
| int student [No] | -1.57 | -3.66 – 0.52 | 0.140 | -2.23 | -4.48 – 0.02 | 0.052 | |||
| SES num | 1.29 | 0.83 – 1.74 | <0.001 | 1.22 | 0.75 – 1.69 | <0.001 | |||
| Ethnicity White | 0.81 | -0.60 – 2.22 | 0.260 | ||||||
| Ethnicity Hispanic | 0.31 | -1.73 – 2.35 | 0.767 | ||||||
| Ethnicity Black | -1.77 | -4.54 – 1.00 | 0.209 | ||||||
| Ethnicity East Asian | -0.31 | -2.22 – 1.60 | 0.750 | ||||||
| Ethnicity South Asian | -1.52 | -4.12 – 1.09 | 0.254 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.28 | -9.28 – 6.71 | 0.753 | ||||||
| Ethnicity Middle Eastern | 0.31 | -3.45 – 4.07 | 0.871 | ||||||
| Ethnicity American Indian | -2.75 | -10.61 – 5.11 | 0.492 | ||||||
| Random Effects | |||||||||
| σ2 | 17.05 | 17.01 | 17.00 | ||||||
| τ00 | 29.49 unique_ID | 26.89 unique_ID | 27.03 unique_ID | ||||||
| 0.32 univ | 0.77 univ | 0.45 univ | |||||||
| ICC | 0.64 | 0.62 | 0.62 | ||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1577 | 1568 | 1568 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / 0.639 | 0.061 / 0.643 | 0.072 / 0.645 | ||||||
Excluded Preregistered
m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)| SAS positive | SAS positive | SAS positive | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.31 | 17.37 – 19.26 | <0.001 | 13.54 | 8.78 – 18.29 | <0.001 | 14.22 | 9.23 – 19.22 | <0.001 |
| condflourish vs control | 0.73 | 0.16 – 1.30 | 0.012 | 0.66 | 0.11 – 1.21 | 0.019 | 0.71 | 0.15 – 1.27 | 0.013 |
| time - 2 5 | 0.02 | -0.17 – 0.22 | 0.806 | 0.02 | -0.18 – 0.21 | 0.858 | 0.02 | -0.18 – 0.21 | 0.864 |
|
condflourish vs control × time - 2 5 |
0.19 | -0.01 – 0.39 | 0.059 | 0.18 | -0.02 – 0.38 | 0.073 | 0.18 | -0.02 – 0.38 | 0.071 |
| Sex [Woman] | -0.14 | -1.61 – 1.33 | 0.856 | -0.12 | -1.61 – 1.37 | 0.874 | |||
| Age | 0.10 | -0.04 – 0.24 | 0.156 | 0.10 | -0.05 – 0.24 | 0.185 | |||
| int student [No] | -1.91 | -4.09 – 0.26 | 0.085 | -2.42 | -4.77 – -0.08 | 0.043 | |||
| SES num | 1.35 | 0.86 – 1.83 | <0.001 | 1.24 | 0.74 – 1.75 | <0.001 | |||
| Ethnicity White | 0.86 | -0.70 – 2.42 | 0.278 | ||||||
| Ethnicity Hispanic | -0.26 | -2.58 – 2.07 | 0.829 | ||||||
| Ethnicity Black | -0.81 | -3.88 – 2.25 | 0.604 | ||||||
| Ethnicity East Asian | -0.25 | -2.29 – 1.79 | 0.812 | ||||||
| Ethnicity South Asian | -0.60 | -3.35 – 2.15 | 0.668 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.26 | -9.14 – 6.61 | 0.753 | ||||||
| Ethnicity Middle Eastern | 1.54 | -2.34 – 5.43 | 0.435 | ||||||
| Ethnicity American Indian | -2.19 | -10.01 – 5.62 | 0.582 | ||||||
| Random Effects | |||||||||
| σ2 | 16.93 | 16.94 | 16.95 | ||||||
| τ00 | 27.99 unique_ID | 25.48 unique_ID | 25.79 unique_ID | ||||||
| 0.39 univ | 0.84 univ | 0.53 univ | |||||||
| ICC | 0.63 | 0.61 | 0.61 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1410 | 1404 | 1404 | ||||||
| Marginal R2 / Conditional R2 | 0.012 / 0.631 | 0.072 / 0.637 | 0.078 / 0.639 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)| SAS positive | SAS positive | SAS positive | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.19 | 17.22 – 19.16 | <0.001 | 13.84 | 8.89 – 18.80 | <0.001 | 14.80 | 9.68 – 19.93 | <0.001 |
| condflourish vs control | 0.60 | 0.02 – 1.19 | 0.044 | 0.61 | 0.03 – 1.18 | 0.039 | 0.67 | 0.09 – 1.26 | 0.024 |
| time - 2 5 | -0.00 | -0.21 – 0.20 | 0.975 | -0.01 | -0.21 – 0.19 | 0.919 | -0.01 | -0.22 – 0.19 | 0.914 |
|
condflourish vs control × time - 2 5 |
0.16 | -0.04 – 0.37 | 0.122 | 0.15 | -0.05 – 0.36 | 0.146 | 0.15 | -0.05 – 0.36 | 0.145 |
| Sex [Woman] | -0.01 | -1.53 – 1.51 | 0.988 | -0.03 | -1.57 – 1.51 | 0.973 | |||
| Age | 0.08 | -0.06 – 0.23 | 0.259 | 0.07 | -0.07 – 0.21 | 0.342 | |||
| int student [No] | -1.66 | -4.05 – 0.73 | 0.173 | -2.34 | -4.85 – 0.17 | 0.068 | |||
| SES num | 1.26 | 0.75 – 1.76 | <0.001 | 1.17 | 0.65 – 1.69 | <0.001 | |||
| Ethnicity White | 0.90 | -0.70 – 2.50 | 0.270 | ||||||
| Ethnicity Hispanic | -0.04 | -2.40 – 2.32 | 0.973 | ||||||
| Ethnicity Black | -0.90 | -4.05 – 2.25 | 0.575 | ||||||
| Ethnicity East Asian | -0.25 | -2.34 – 1.83 | 0.813 | ||||||
| Ethnicity South Asian | -1.65 | -4.56 – 1.26 | 0.267 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.28 | -9.11 – 6.54 | 0.748 | ||||||
| Ethnicity Middle Eastern | 3.75 | -1.28 – 8.77 | 0.144 | ||||||
| Ethnicity American Indian | -2.16 | -9.92 – 5.61 | 0.586 | ||||||
| Random Effects | |||||||||
| σ2 | 16.66 | 16.64 | 16.65 | ||||||
| τ00 | 27.22 unique_ID | 25.29 unique_ID | 25.39 unique_ID | ||||||
| 0.42 univ | 0.80 univ | 0.42 univ | |||||||
| ICC | 0.62 | 0.61 | 0.61 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1292 | 1289 | 1289 | ||||||
| Marginal R2 / Conditional R2 | 0.009 / 0.627 | 0.059 / 0.634 | 0.072 / 0.636 | ||||||
SAS: Negative
Intention to Treat
options(scipen = 99)
m0 <- lmer(SAS_negative ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS negative | SAS negative | SAS negative | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 12.69 | 12.16 – 13.23 | <0.001 | 14.41 | 10.12 – 18.70 | <0.001 | 12.76 | 8.23 – 17.29 | <0.001 |
| condflourish vs control | -0.25 | -0.78 – 0.29 | 0.366 | -0.21 | -0.73 – 0.31 | 0.431 | -0.26 | -0.78 – 0.27 | 0.335 |
| time - 2 5 | -0.12 | -0.31 – 0.07 | 0.223 | -0.12 | -0.31 – 0.07 | 0.228 | -0.12 | -0.31 – 0.07 | 0.216 |
|
condflourish vs control × time - 2 5 |
0.00 | -0.19 – 0.19 | 0.987 | 0.01 | -0.19 – 0.20 | 0.956 | 0.01 | -0.18 – 0.20 | 0.947 |
| Sex [Woman] | 1.70 | 0.38 – 3.03 | 0.012 | 1.75 | 0.42 – 3.07 | 0.010 | |||
| Age | -0.07 | -0.20 – 0.06 | 0.269 | -0.06 | -0.20 – 0.07 | 0.338 | |||
| int student [No] | 2.03 | -0.05 – 4.10 | 0.056 | 2.81 | 0.57 – 5.05 | 0.014 | |||
| SES num | -1.05 | -1.51 – -0.60 | <0.001 | -0.99 | -1.46 – -0.52 | <0.001 | |||
| Ethnicity White | -0.02 | -1.43 – 1.38 | 0.976 | ||||||
| Ethnicity Hispanic | 1.32 | -0.71 – 3.36 | 0.202 | ||||||
| Ethnicity Black | 1.49 | -1.27 – 4.25 | 0.290 | ||||||
| Ethnicity East Asian | 0.03 | -1.87 – 1.93 | 0.973 | ||||||
| Ethnicity South Asian | 3.30 | 0.70 – 5.89 | 0.013 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
2.88 | -5.11 – 10.86 | 0.480 | ||||||
| Ethnicity Middle Eastern | 3.63 | -0.12 – 7.38 | 0.058 | ||||||
| Ethnicity American Indian | 5.66 | -2.19 – 13.52 | 0.158 | ||||||
| Random Effects | |||||||||
| σ2 | 17.75 | 17.59 | 17.58 | ||||||
| τ00 | 29.49 unique_ID | 27.29 unique_ID | 26.88 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 486 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1578 | 1569 | 1569 | ||||||
| Marginal R2 / Conditional R2 | 0.005 / NA | 0.124 / NA | 0.171 / NA | ||||||
Excluded Preregistered
m0 <- lmer(SAS_negative ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS negative | SAS negative | SAS negative | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 12.43 | 11.86 – 13.01 | <0.001 | 14.04 | 9.48 – 18.60 | <0.001 | 12.67 | 7.83 – 17.51 | <0.001 |
| condflourish vs control | -0.25 | -0.82 – 0.33 | 0.402 | -0.18 | -0.74 – 0.38 | 0.530 | -0.20 | -0.76 – 0.36 | 0.492 |
| time - 2 5 | -0.14 | -0.34 – 0.06 | 0.180 | -0.13 | -0.33 – 0.07 | 0.196 | -0.14 | -0.34 – 0.06 | 0.184 |
|
condflourish vs control × time - 2 5 |
-0.03 | -0.23 – 0.17 | 0.772 | -0.03 | -0.23 – 0.17 | 0.803 | -0.03 | -0.23 – 0.17 | 0.800 |
| Sex [Woman] | 1.70 | 0.22 – 3.18 | 0.025 | 1.73 | 0.24 – 3.22 | 0.023 | |||
| Age | -0.06 | -0.20 – 0.07 | 0.378 | -0.05 | -0.19 – 0.08 | 0.445 | |||
| int student [No] | 2.32 | 0.16 – 4.48 | 0.036 | 2.70 | 0.36 – 5.04 | 0.024 | |||
| SES num | -1.18 | -1.67 – -0.69 | <0.001 | -1.18 | -1.68 – -0.67 | <0.001 | |||
| Ethnicity White | 0.58 | -0.98 – 2.14 | 0.463 | ||||||
| Ethnicity Hispanic | 1.62 | -0.69 – 3.93 | 0.169 | ||||||
| Ethnicity Black | 1.46 | -1.59 – 4.51 | 0.348 | ||||||
| Ethnicity East Asian | 0.48 | -1.54 – 2.50 | 0.640 | ||||||
| Ethnicity South Asian | 2.71 | -0.02 – 5.44 | 0.051 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
3.18 | -4.68 – 11.03 | 0.428 | ||||||
| Ethnicity Middle Eastern | 3.72 | -0.15 – 7.58 | 0.060 | ||||||
| Ethnicity American Indian | 6.61 | -1.21 – 14.43 | 0.098 | ||||||
| Random Effects | |||||||||
| σ2 | 17.52 | 17.45 | 17.45 | ||||||
| τ00 | 28.46 unique_ID | 25.90 unique_ID | 25.68 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1411 | 1405 | 1405 | ||||||
| Marginal R2 / Conditional R2 | 0.005 / NA | 0.145 / NA | 0.178 / NA | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(SAS_negative ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m1 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')m2 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| SAS negative | SAS negative | SAS negative | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 12.41 | 11.81 – 13.01 | <0.001 | 13.63 | 8.84 – 18.43 | <0.001 | 12.20 | 7.16 – 17.24 | <0.001 |
| condflourish vs control | -0.27 | -0.87 – 0.33 | 0.382 | -0.24 | -0.82 – 0.34 | 0.420 | -0.23 | -0.82 – 0.36 | 0.435 |
| time - 2 5 | -0.18 | -0.39 – 0.03 | 0.088 | -0.18 | -0.39 – 0.02 | 0.085 | -0.19 | -0.39 – 0.02 | 0.079 |
|
condflourish vs control × time - 2 5 |
-0.07 | -0.28 – 0.13 | 0.488 | -0.08 | -0.28 – 0.13 | 0.473 | -0.07 | -0.28 – 0.13 | 0.478 |
| Sex [Woman] | 1.88 | 0.34 – 3.41 | 0.017 | 1.98 | 0.43 – 3.54 | 0.012 | |||
| Age | -0.04 | -0.18 – 0.09 | 0.535 | -0.03 | -0.17 – 0.11 | 0.659 | |||
| int student [No] | 2.35 | -0.03 – 4.73 | 0.053 | 2.73 | 0.21 – 5.25 | 0.034 | |||
| SES num | -1.24 | -1.75 – -0.73 | <0.001 | -1.19 | -1.71 – -0.66 | <0.001 | |||
| Ethnicity White | 0.44 | -1.17 – 2.06 | 0.588 | ||||||
| Ethnicity Hispanic | 1.23 | -1.13 – 3.59 | 0.306 | ||||||
| Ethnicity Black | 1.02 | -2.14 – 4.18 | 0.526 | ||||||
| Ethnicity East Asian | 0.13 | -1.96 – 2.22 | 0.903 | ||||||
| Ethnicity South Asian | 3.09 | 0.17 – 6.01 | 0.038 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
3.13 | -4.74 – 11.00 | 0.435 | ||||||
| Ethnicity Middle Eastern | 0.25 | -4.80 – 5.30 | 0.923 | ||||||
| Ethnicity American Indian | 6.53 | -1.30 – 14.36 | 0.102 | ||||||
| Random Effects | |||||||||
| σ2 | 17.07 | 17.08 | 17.08 | ||||||
| τ00 | 28.49 unique_ID | 25.76 unique_ID | 25.75 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 1293 | 1290 | 1290 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / NA | 0.156 / NA | 0.183 / NA | ||||||
# set up data for pre vs. post analyses
# intention to treat
data_ITT_factor <- data_ITT |>
dplyr::filter(time == 1 | time == 4) |>
dplyr::mutate(time_factor = as.factor(time)) |>
dplyr::mutate(cond_factor = as.factor(cond))
contrasts(data_ITT_factor$time_factor) <- c(-1,1)
# excluded data
data_excluded_factor <- data_excluded |>
dplyr::filter(time == 1 | time == 4) |>
dplyr::mutate(time_factor = as.factor(time)) |>
dplyr::mutate(cond_factor = as.factor(cond))
contrasts(data_excluded_factor$time_factor) <- c(-1,1)
# excluded unreasonable
data_excluded_unreasonable_factor <- data_excluded_unreasonable |>
dplyr::filter(time == 1 | time == 4) |>
dplyr::mutate(time_factor = as.factor(time)) |>
dplyr::mutate(cond_factor = as.factor(cond))
contrasts(data_excluded_unreasonable_factor$time_factor) <- c(-1,1)Flourishing Score
Intention to Treat
m0 <- lmer(flourishing ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
#standardize_parameters(m0)
m1 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| flourishing | flourishing | flourishing | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 44.44 | 43.01 – 45.88 | <0.001 | 38.14 | 33.33 – 42.95 | <0.001 | 39.93 | 34.99 – 44.87 | <0.001 |
| condflourish vs control | 0.13 | -0.42 – 0.68 | 0.648 | 0.08 | -0.46 – 0.62 | 0.763 | 0.14 | -0.40 – 0.68 | 0.606 |
| time - 2 5 | -0.03 | -0.21 – 0.14 | 0.716 | -0.03 | -0.21 – 0.14 | 0.720 | -0.03 | -0.21 – 0.14 | 0.705 |
|
condflourish vs control × time - 2 5 |
0.19 | 0.02 – 0.36 | 0.032 | 0.19 | 0.02 – 0.36 | 0.031 | 0.19 | 0.02 – 0.37 | 0.028 |
| Sex [Woman] | 0.40 | -0.96 – 1.75 | 0.564 | 0.33 | -1.03 – 1.68 | 0.637 | |||
| Age | 0.11 | -0.03 – 0.25 | 0.129 | 0.09 | -0.05 – 0.23 | 0.209 | |||
| int student [No] | -0.81 | -2.98 – 1.36 | 0.464 | -2.03 | -4.35 – 0.29 | 0.086 | |||
| SES num | 1.32 | 0.85 – 1.79 | <0.001 | 1.26 | 0.78 – 1.74 | <0.001 | |||
| Ethnicity White | 0.88 | -0.56 – 2.33 | 0.232 | ||||||
| Ethnicity Hispanic | 0.45 | -1.63 – 2.54 | 0.670 | ||||||
| Ethnicity Black | -1.28 | -4.12 – 1.57 | 0.378 | ||||||
| Ethnicity East Asian | -1.03 | -3.00 – 0.94 | 0.306 | ||||||
| Ethnicity South Asian | -2.90 | -5.58 – -0.22 | 0.034 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-4.92 | -13.14 – 3.29 | 0.240 | ||||||
| Ethnicity Middle Eastern | -0.62 | -4.54 – 3.30 | 0.757 | ||||||
| Ethnicity American Indian | -3.24 | -11.40 – 4.91 | 0.435 | ||||||
| Random Effects | |||||||||
| σ2 | 12.77 | 12.77 | 12.78 | ||||||
| τ00 | 29.89 unique_ID | 27.66 unique_ID | 27.24 unique_ID | ||||||
| 1.30 univ | 1.89 univ | 1.31 univ | |||||||
| ICC | 0.71 | 0.70 | 0.69 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 832 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.710 | 0.055 / 0.715 | 0.080 / 0.716 | ||||||
Excluded Preregistered
m0 <- lmer(flourishing ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)| flourishing | flourishing | flourishing | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 44.61 | 43.13 – 46.08 | <0.001 | 38.17 | 32.96 – 43.38 | <0.001 | 40.05 | 34.66 – 45.44 | <0.001 |
| condflourish vs control | 0.15 | -0.45 – 0.76 | 0.621 | 0.10 | -0.49 – 0.69 | 0.743 | 0.18 | -0.42 – 0.78 | 0.555 |
| time - 2 5 | -0.05 | -0.23 – 0.14 | 0.612 | -0.05 | -0.23 – 0.14 | 0.607 | -0.05 | -0.23 – 0.14 | 0.609 |
|
condflourish vs control × time - 2 5 |
0.18 | -0.01 – 0.36 | 0.060 | 0.17 | -0.01 – 0.36 | 0.068 | 0.18 | -0.01 – 0.36 | 0.063 |
| Sex [Woman] | 0.97 | -0.60 – 2.55 | 0.225 | 0.92 | -0.66 – 2.51 | 0.253 | |||
| Age | 0.11 | -0.04 – 0.26 | 0.158 | 0.09 | -0.06 – 0.24 | 0.229 | |||
| int student [No] | -0.74 | -3.07 – 1.58 | 0.531 | -1.82 | -4.30 – 0.67 | 0.152 | |||
| SES num | 1.21 | 0.69 – 1.73 | <0.001 | 1.13 | 0.60 – 1.67 | <0.001 | |||
| Ethnicity White | 0.60 | -1.05 – 2.26 | 0.475 | ||||||
| Ethnicity Hispanic | -0.07 | -2.53 – 2.40 | 0.957 | ||||||
| Ethnicity Black | -1.41 | -4.66 – 1.85 | 0.397 | ||||||
| Ethnicity East Asian | -1.57 | -3.74 – 0.60 | 0.157 | ||||||
| Ethnicity South Asian | -2.34 | -5.25 – 0.58 | 0.116 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-5.45 | -13.71 – 2.80 | 0.195 | ||||||
| Ethnicity Middle Eastern | -0.86 | -5.02 – 3.30 | 0.686 | ||||||
| Ethnicity American Indian | -3.38 | -11.56 – 4.81 | 0.418 | ||||||
| Random Effects | |||||||||
| σ2 | 13.12 | 13.17 | 13.21 | ||||||
| τ00 | 29.04 unique_ID | 27.22 unique_ID | 26.97 unique_ID | ||||||
| 1.34 univ | 1.73 univ | 1.10 univ | |||||||
| ICC | 0.70 | 0.69 | 0.68 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 711 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.699 | 0.049 / 0.703 | 0.071 / 0.703 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(flourishing ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)| flourishing | flourishing | flourishing | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 44.61 | 43.25 – 45.97 | <0.001 | 38.23 | 32.94 – 43.51 | <0.001 | 40.00 | 34.59 – 45.41 | <0.001 |
| condflourish vs control | 0.15 | -0.47 – 0.76 | 0.641 | 0.13 | -0.47 – 0.74 | 0.666 | 0.21 | -0.40 – 0.82 | 0.500 |
| time - 2 5 | 0.02 | -0.17 – 0.21 | 0.811 | 0.02 | -0.17 – 0.21 | 0.816 | 0.02 | -0.17 – 0.21 | 0.817 |
|
condflourish vs control × time - 2 5 |
0.25 | 0.06 – 0.44 | 0.011 | 0.24 | 0.05 – 0.44 | 0.013 | 0.24 | 0.05 – 0.44 | 0.012 |
| Sex [Woman] | 0.83 | -0.78 – 2.43 | 0.311 | 0.73 | -0.88 – 2.34 | 0.373 | |||
| Age | 0.09 | -0.06 – 0.24 | 0.243 | 0.07 | -0.08 – 0.22 | 0.369 | |||
| int student [No] | -0.20 | -2.72 – 2.31 | 0.875 | -1.26 | -3.88 – 1.36 | 0.346 | |||
| SES num | 1.22 | 0.68 – 1.75 | <0.001 | 1.13 | 0.59 – 1.67 | <0.001 | |||
| Ethnicity White | 0.83 | -0.84 – 2.51 | 0.329 | ||||||
| Ethnicity Hispanic | 0.54 | -1.93 – 3.01 | 0.667 | ||||||
| Ethnicity Black | -0.97 | -4.27 – 2.33 | 0.563 | ||||||
| Ethnicity East Asian | -1.30 | -3.49 – 0.88 | 0.242 | ||||||
| Ethnicity South Asian | -2.64 | -5.68 – 0.40 | 0.089 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-5.37 | -13.45 – 2.71 | 0.192 | ||||||
| Ethnicity Middle Eastern | 2.25 | -3.12 – 7.61 | 0.412 | ||||||
| Ethnicity American Indian | -3.28 | -11.28 – 4.73 | 0.422 | ||||||
| Random Effects | |||||||||
| σ2 | 12.93 | 12.97 | 13.01 | ||||||
| τ00 | 27.58 unique_ID | 25.88 unique_ID | 25.51 unique_ID | ||||||
| 1.08 univ | 1.36 univ | 0.74 univ | |||||||
| ICC | 0.69 | 0.68 | 0.67 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.690 | 0.050 / 0.693 | 0.077 / 0.694 | ||||||
Cohesion
Intention to Treat
m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
#standardize_parameters(m0)
m1 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| cohesion | cohesion | cohesion | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.68 | 5.33 – 6.04 | <0.001 | 4.37 | 2.84 – 5.90 | <0.001 | 4.32 | 2.73 – 5.92 | <0.001 |
| condflourish vs control | 0.11 | -0.08 – 0.29 | 0.252 | 0.12 | -0.06 – 0.30 | 0.186 | 0.16 | -0.02 – 0.34 | 0.083 |
| time - 2 5 | 0.08 | 0.03 – 0.14 | 0.001 | 0.08 | 0.03 – 0.14 | 0.002 | 0.08 | 0.03 – 0.13 | 0.002 |
|
condflourish vs control × time - 2 5 |
0.06 | 0.01 – 0.11 | 0.029 | 0.06 | 0.01 – 0.11 | 0.027 | 0.06 | 0.01 – 0.11 | 0.026 |
| Sex [Woman] | 0.78 | 0.33 – 1.23 | 0.001 | 0.75 | 0.30 – 1.20 | 0.001 | |||
| Age | -0.02 | -0.07 – 0.03 | 0.440 | -0.01 | -0.06 – 0.03 | 0.553 | |||
| int student [No] | 0.29 | -0.44 – 1.01 | 0.439 | 0.35 | -0.43 – 1.13 | 0.378 | |||
| SES num | 0.25 | 0.09 – 0.40 | 0.002 | 0.22 | 0.06 – 0.38 | 0.008 | |||
| Ethnicity White | 0.28 | -0.20 – 0.77 | 0.251 | ||||||
| Ethnicity Hispanic | -0.22 | -0.91 – 0.48 | 0.538 | ||||||
| Ethnicity Black | -0.82 | -1.77 – 0.13 | 0.090 | ||||||
| Ethnicity East Asian | -0.17 | -0.83 – 0.49 | 0.609 | ||||||
| Ethnicity South Asian | 0.47 | -0.43 – 1.36 | 0.308 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.45 | -5.20 – 0.31 | 0.082 | ||||||
| Ethnicity Middle Eastern | -0.86 | -2.17 – 0.45 | 0.196 | ||||||
| Ethnicity American Indian | -1.68 | -4.42 – 1.06 | 0.228 | ||||||
| Random Effects | |||||||||
| σ2 | 1.13 | 1.14 | 1.14 | ||||||
| τ00 | 3.46 unique_ID | 3.29 unique_ID | 3.23 unique_ID | ||||||
| 0.07 univ | 0.05 univ | 0.04 univ | |||||||
| ICC | 0.76 | 0.75 | 0.74 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 833 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / 0.758 | 0.046 / 0.758 | 0.075 / 0.762 | ||||||
Excluded Preregistered
m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)| cohesion | cohesion | cohesion | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.60 | 5.14 – 6.07 | <0.001 | 4.00 | 2.29 – 5.71 | <0.001 | 3.90 | 2.12 – 5.67 | <0.001 |
| condflourish vs control | 0.17 | -0.03 – 0.38 | 0.093 | 0.19 | -0.01 – 0.40 | 0.059 | 0.24 | 0.03 – 0.44 | 0.023 |
| time - 2 5 | 0.08 | 0.02 – 0.13 | 0.007 | 0.08 | 0.02 – 0.13 | 0.007 | 0.08 | 0.02 – 0.13 | 0.007 |
|
condflourish vs control × time - 2 5 |
0.05 | -0.00 – 0.11 | 0.066 | 0.05 | -0.00 – 0.11 | 0.073 | 0.05 | -0.00 – 0.11 | 0.066 |
| Sex [Woman] | 0.97 | 0.43 – 1.51 | <0.001 | 0.97 | 0.43 – 1.50 | <0.001 | |||
| Age | -0.01 | -0.06 – 0.04 | 0.747 | -0.01 | -0.06 – 0.04 | 0.802 | |||
| int student [No] | 0.31 | -0.48 – 1.10 | 0.440 | 0.40 | -0.45 – 1.24 | 0.353 | |||
| SES num | 0.22 | 0.04 – 0.40 | 0.014 | 0.18 | -0.00 – 0.36 | 0.052 | |||
| Ethnicity White | 0.41 | -0.16 – 0.97 | 0.156 | ||||||
| Ethnicity Hispanic | -0.31 | -1.14 – 0.53 | 0.472 | ||||||
| Ethnicity Black | -0.42 | -1.52 – 0.68 | 0.458 | ||||||
| Ethnicity East Asian | -0.01 | -0.74 – 0.73 | 0.985 | ||||||
| Ethnicity South Asian | 0.81 | -0.18 – 1.80 | 0.109 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.29 | -5.09 – 0.52 | 0.110 | ||||||
| Ethnicity Middle Eastern | -0.72 | -2.13 – 0.69 | 0.314 | ||||||
| Ethnicity American Indian | -1.48 | -4.27 – 1.30 | 0.296 | ||||||
| Random Effects | |||||||||
| σ2 | 1.15 | 1.16 | 1.16 | ||||||
| τ00 | 3.50 unique_ID | 3.38 unique_ID | 3.32 unique_ID | ||||||
| 0.13 univ | 0.06 univ | 0.04 univ | |||||||
| ICC | 0.76 | 0.75 | 0.74 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 712 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.010 / 0.761 | 0.050 / 0.761 | 0.083 / 0.764 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)| cohesion | cohesion | cohesion | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 5.57 | 5.06 – 6.09 | <0.001 | 3.63 | 1.79 – 5.46 | <0.001 | 3.67 | 1.78 – 5.57 | <0.001 |
| condflourish vs control | 0.16 | -0.06 – 0.37 | 0.158 | 0.17 | -0.04 – 0.39 | 0.118 | 0.21 | -0.01 – 0.43 | 0.057 |
| time - 2 5 | 0.08 | 0.03 – 0.14 | 0.003 | 0.08 | 0.03 – 0.14 | 0.003 | 0.08 | 0.03 – 0.14 | 0.003 |
|
condflourish vs control × time - 2 5 |
0.06 | 0.00 – 0.12 | 0.036 | 0.06 | 0.00 – 0.12 | 0.039 | 0.06 | 0.00 – 0.12 | 0.036 |
| Sex [Woman] | 0.96 | 0.39 – 1.53 | 0.001 | 0.96 | 0.38 – 1.53 | 0.001 | |||
| Age | -0.00 | -0.06 – 0.05 | 0.913 | -0.00 | -0.06 – 0.05 | 0.893 | |||
| int student [No] | 0.58 | -0.32 – 1.47 | 0.205 | 0.59 | -0.34 – 1.52 | 0.215 | |||
| SES num | 0.22 | 0.03 – 0.41 | 0.022 | 0.17 | -0.02 – 0.36 | 0.084 | |||
| Ethnicity White | 0.41 | -0.19 – 1.00 | 0.178 | ||||||
| Ethnicity Hispanic | -0.28 | -1.16 – 0.59 | 0.529 | ||||||
| Ethnicity Black | -0.41 | -1.58 – 0.76 | 0.488 | ||||||
| Ethnicity East Asian | 0.06 | -0.72 – 0.84 | 0.878 | ||||||
| Ethnicity South Asian | 0.66 | -0.42 – 1.74 | 0.232 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.26 | -5.14 – 0.62 | 0.124 | ||||||
| Ethnicity Middle Eastern | -0.40 | -2.30 – 1.50 | 0.680 | ||||||
| Ethnicity American Indian | -1.47 | -4.33 – 1.39 | 0.312 | ||||||
| Random Effects | |||||||||
| σ2 | 1.10 | 1.10 | 1.10 | ||||||
| τ00 | 3.69 unique_ID | 3.57 unique_ID | 3.54 unique_ID | ||||||
| 0.16 univ | 0.08 univ | 0.04 univ | |||||||
| ICC | 0.78 | 0.77 | 0.77 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.009 / 0.780 | 0.049 / 0.779 | 0.076 / 0.783 | ||||||
Mindfulness
Intention to Treat
m0 <- lmer(mindfulness_rev ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')#standardize_parameters(m0)
m1 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| mindfulness rev | mindfulness rev | mindfulness rev | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 14.81 | 14.31 – 15.30 | <0.001 | 11.33 | 7.42 – 15.24 | <0.001 | 11.69 | 7.52 – 15.85 | <0.001 |
| condflourish vs control | 0.20 | -0.30 – 0.70 | 0.430 | 0.11 | -0.37 – 0.59 | 0.655 | 0.13 | -0.36 – 0.61 | 0.607 |
| time - 2 5 | -0.34 | -0.53 – -0.16 | <0.001 | -0.34 | -0.52 – -0.16 | <0.001 | -0.34 | -0.52 – -0.15 | <0.001 |
|
condflourish vs control × time - 2 5 |
0.19 | 0.00 – 0.37 | 0.044 | 0.19 | 0.01 – 0.38 | 0.039 | 0.19 | 0.01 – 0.38 | 0.041 |
| Sex [Woman] | -1.79 | -2.99 – -0.58 | 0.004 | -1.81 | -3.02 – -0.59 | 0.004 | |||
| Age | 0.22 | 0.10 – 0.34 | <0.001 | 0.22 | 0.10 – 0.34 | <0.001 | |||
| int student [No] | -2.45 | -4.34 – -0.56 | 0.011 | -2.48 | -4.53 – -0.42 | 0.018 | |||
| SES num | 0.81 | 0.40 – 1.23 | <0.001 | 0.76 | 0.33 – 1.19 | 0.001 | |||
| Ethnicity White | 0.03 | -1.26 – 1.32 | 0.963 | ||||||
| Ethnicity Hispanic | -1.07 | -2.93 – 0.79 | 0.259 | ||||||
| Ethnicity Black | -0.45 | -2.98 – 2.08 | 0.726 | ||||||
| Ethnicity East Asian | 0.35 | -1.40 – 2.10 | 0.698 | ||||||
| Ethnicity South Asian | -0.61 | -2.98 – 1.77 | 0.616 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.85 | -6.42 – 8.12 | 0.818 | ||||||
| Ethnicity Middle Eastern | -1.13 | -4.62 – 2.37 | 0.528 | ||||||
| Ethnicity American Indian | -6.39 | -13.63 – 0.84 | 0.083 | ||||||
| Random Effects | |||||||||
| σ2 | 14.63 | 14.59 | 14.57 | ||||||
| τ00 | 21.51 unique_ID | 19.05 unique_ID | 19.20 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 833 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.024 / NA | 0.182 / NA | 0.199 / NA | ||||||
Excluded Preregistered
m0 <- lmer(mindfulness_rev ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| mindfulness rev | mindfulness rev | mindfulness rev | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 14.94 | 14.40 – 15.48 | <0.001 | 11.42 | 7.25 – 15.59 | <0.001 | 11.09 | 6.63 – 15.54 | <0.001 |
| condflourish vs control | 0.30 | -0.23 – 0.84 | 0.268 | 0.19 | -0.32 – 0.71 | 0.459 | 0.19 | -0.32 – 0.71 | 0.464 |
| time - 2 5 | -0.37 | -0.56 – -0.17 | <0.001 | -0.37 | -0.56 – -0.17 | <0.001 | -0.37 | -0.56 – -0.17 | <0.001 |
|
condflourish vs control × time - 2 5 |
0.23 | 0.03 – 0.42 | 0.022 | 0.23 | 0.04 – 0.42 | 0.019 | 0.23 | 0.04 – 0.42 | 0.018 |
| Sex [Woman] | -1.54 | -2.89 – -0.18 | 0.026 | -1.56 | -2.93 – -0.18 | 0.026 | |||
| Age | 0.21 | 0.09 – 0.33 | 0.001 | 0.22 | 0.09 – 0.34 | 0.001 | |||
| int student [No] | -2.80 | -4.78 – -0.83 | 0.005 | -2.43 | -4.57 – -0.28 | 0.027 | |||
| SES num | 0.94 | 0.49 – 1.39 | <0.001 | 0.94 | 0.48 – 1.41 | <0.001 | |||
| Ethnicity White | -0.15 | -1.59 – 1.29 | 0.841 | ||||||
| Ethnicity Hispanic | -0.79 | -2.91 – 1.33 | 0.465 | ||||||
| Ethnicity Black | -0.89 | -3.70 – 1.92 | 0.533 | ||||||
| Ethnicity East Asian | 0.33 | -1.54 – 2.20 | 0.729 | ||||||
| Ethnicity South Asian | 0.39 | -2.11 – 2.89 | 0.757 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.91 | -6.20 – 8.02 | 0.802 | ||||||
| Ethnicity Middle Eastern | -2.15 | -5.75 – 1.46 | 0.243 | ||||||
| Ethnicity American Indian | -6.45 | -13.52 – 0.62 | 0.074 | ||||||
| Random Effects | |||||||||
| σ2 | 14.51 | 14.48 | 14.48 | ||||||
| τ00 | 20.50 unique_ID | 17.72 unique_ID | 17.83 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.59 | ||||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 712 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.013 / 0.591 | 0.200 / NA | 0.218 / NA | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(mindfulness_rev ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(mindfulness_rev ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| mindfulness rev | mindfulness rev | mindfulness rev | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 14.90 | 14.35 – 15.45 | <0.001 | 10.74 | 6.40 – 15.09 | <0.001 | 10.51 | 5.94 – 15.09 | <0.001 |
| condflourish vs control | 0.26 | -0.29 – 0.81 | 0.354 | 0.20 | -0.33 – 0.73 | 0.449 | 0.21 | -0.32 – 0.75 | 0.435 |
| time - 2 5 | -0.37 | -0.57 – -0.17 | <0.001 | -0.38 | -0.58 – -0.18 | <0.001 | -0.38 | -0.58 – -0.18 | <0.001 |
|
condflourish vs control × time - 2 5 |
0.22 | 0.02 – 0.42 | 0.032 | 0.22 | 0.02 – 0.42 | 0.033 | 0.21 | 0.01 – 0.41 | 0.036 |
| Sex [Woman] | -1.39 | -2.78 – 0.01 | 0.051 | -1.48 | -2.89 – -0.07 | 0.039 | |||
| Age | 0.20 | 0.07 – 0.32 | 0.002 | 0.20 | 0.07 – 0.32 | 0.002 | |||
| int student [No] | -2.32 | -4.47 – -0.17 | 0.035 | -2.14 | -4.42 – 0.14 | 0.065 | |||
| SES num | 1.06 | 0.59 – 1.52 | <0.001 | 1.03 | 0.55 – 1.50 | <0.001 | |||
| Ethnicity White | 0.17 | -1.30 – 1.64 | 0.818 | ||||||
| Ethnicity Hispanic | -0.19 | -2.33 – 1.95 | 0.860 | ||||||
| Ethnicity Black | -0.25 | -3.13 – 2.63 | 0.867 | ||||||
| Ethnicity East Asian | 0.92 | -0.99 – 2.82 | 0.345 | ||||||
| Ethnicity South Asian | 0.19 | -2.45 – 2.83 | 0.889 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.36 | -5.66 – 8.38 | 0.704 | ||||||
| Ethnicity Middle Eastern | 3.86 | -0.86 – 8.58 | 0.109 | ||||||
| Ethnicity American Indian | -6.05 | -13.04 – 0.93 | 0.089 | ||||||
| Random Effects | |||||||||
| σ2 | 14.21 | 14.23 | 14.23 | ||||||
| τ00 | 19.85 unique_ID | 17.17 unique_ID | 17.23 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.033 / NA | 0.196 / NA | 0.219 / NA | ||||||
Emotional Resilience
Intention to Treat
m0 <- lmer(emo_res ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
#standardize_parameters(m0)
m1 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| emo res | emo res | emo res | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.99 | 18.61 – 19.37 | <0.001 | 18.46 | 15.42 – 21.50 | <0.001 | 19.01 | 15.79 – 22.23 | <0.001 |
| condflourish vs control | -0.02 | -0.40 – 0.36 | 0.906 | -0.06 | -0.43 – 0.31 | 0.732 | -0.04 | -0.41 – 0.34 | 0.848 |
| time - 2 5 | 0.09 | -0.02 – 0.20 | 0.094 | 0.10 | -0.01 – 0.21 | 0.078 | 0.10 | -0.01 – 0.21 | 0.080 |
|
condflourish vs control × time - 2 5 |
0.11 | 0.00 – 0.22 | 0.047 | 0.11 | 0.00 – 0.22 | 0.041 | 0.11 | 0.00 – 0.22 | 0.041 |
| Sex [Woman] | -2.23 | -3.16 – -1.30 | <0.001 | -2.19 | -3.12 – -1.25 | <0.001 | |||
| Age | 0.06 | -0.03 – 0.15 | 0.205 | 0.06 | -0.04 – 0.15 | 0.229 | |||
| int student [No] | -0.72 | -2.19 – 0.76 | 0.342 | -1.02 | -2.62 – 0.58 | 0.210 | |||
| SES num | 0.53 | 0.21 – 0.86 | 0.001 | 0.47 | 0.14 – 0.81 | 0.006 | |||
| Ethnicity White | 0.20 | -0.80 – 1.20 | 0.696 | ||||||
| Ethnicity Hispanic | -0.94 | -2.37 – 0.49 | 0.198 | ||||||
| Ethnicity Black | -0.37 | -2.33 – 1.58 | 0.707 | ||||||
| Ethnicity East Asian | 0.42 | -0.93 – 1.77 | 0.546 | ||||||
| Ethnicity South Asian | -1.28 | -3.13 – 0.57 | 0.174 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.40 | -7.09 – 4.28 | 0.628 | ||||||
| Ethnicity Middle Eastern | -0.98 | -3.68 – 1.72 | 0.476 | ||||||
| Ethnicity American Indian | 3.31 | -2.34 – 8.97 | 0.251 | ||||||
| Random Effects | |||||||||
| σ2 | 5.09 | 5.08 | 5.08 | ||||||
| τ00 | 14.78 unique_ID | 13.66 unique_ID | 13.66 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.74 | 0.73 | 0.73 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 832 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.744 | 0.070 / 0.748 | 0.084 / 0.752 | ||||||
Excluded Preregistered
m0 <- lmer(emo_res ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| emo res | emo res | emo res | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.94 | 18.53 – 19.36 | <0.001 | 17.86 | 14.57 – 21.16 | <0.001 | 17.94 | 14.43 – 21.46 | <0.001 |
| condflourish vs control | 0.18 | -0.24 – 0.60 | 0.395 | 0.12 | -0.29 – 0.52 | 0.565 | 0.14 | -0.27 – 0.55 | 0.494 |
| time - 2 5 | 0.12 | 0.00 – 0.24 | 0.043 | 0.12 | 0.00 – 0.23 | 0.045 | 0.12 | 0.00 – 0.24 | 0.044 |
|
condflourish vs control × time - 2 5 |
0.09 | -0.02 – 0.21 | 0.113 | 0.10 | -0.02 – 0.21 | 0.105 | 0.10 | -0.02 – 0.21 | 0.104 |
| Sex [Woman] | -1.99 | -3.06 – -0.92 | <0.001 | -1.87 | -2.95 – -0.78 | 0.001 | |||
| Age | 0.08 | -0.02 – 0.18 | 0.106 | 0.08 | -0.02 – 0.18 | 0.115 | |||
| int student [No] | -1.08 | -2.64 – 0.48 | 0.176 | -1.15 | -2.85 – 0.55 | 0.185 | |||
| SES num | 0.63 | 0.27 – 0.98 | 0.001 | 0.57 | 0.21 – 0.94 | 0.002 | |||
| Ethnicity White | 0.25 | -0.88 – 1.39 | 0.658 | ||||||
| Ethnicity Hispanic | -1.00 | -2.67 – 0.67 | 0.240 | ||||||
| Ethnicity Black | -0.17 | -2.39 – 2.04 | 0.877 | ||||||
| Ethnicity East Asian | 0.69 | -0.78 – 2.16 | 0.355 | ||||||
| Ethnicity South Asian | -0.69 | -2.67 – 1.29 | 0.494 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.17 | -6.81 – 4.47 | 0.684 | ||||||
| Ethnicity Middle Eastern | -0.58 | -3.41 – 2.26 | 0.690 | ||||||
| Ethnicity American Indian | 3.61 | -2.00 – 9.22 | 0.207 | ||||||
| Random Effects | |||||||||
| σ2 | 5.18 | 5.18 | 5.18 | ||||||
| τ00 | 14.35 unique_ID | 13.14 unique_ID | 13.20 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.72 | ||||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 711 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.016 / NA | 0.228 / NA | 0.090 / 0.744 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(emo_res ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(emo_res ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| emo res | emo res | emo res | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 18.91 | 18.47 – 19.35 | <0.001 | 17.45 | 13.95 – 20.95 | <0.001 | 17.45 | 13.76 – 21.13 | <0.001 |
| condflourish vs control | 0.15 | -0.29 – 0.59 | 0.495 | 0.11 | -0.32 – 0.53 | 0.614 | 0.14 | -0.29 – 0.57 | 0.517 |
| time - 2 5 | 0.15 | 0.03 – 0.27 | 0.015 | 0.15 | 0.03 – 0.27 | 0.015 | 0.15 | 0.03 – 0.27 | 0.014 |
|
condflourish vs control × time - 2 5 |
0.12 | 0.00 – 0.25 | 0.044 | 0.13 | 0.01 – 0.25 | 0.040 | 0.13 | 0.01 – 0.25 | 0.041 |
| Sex [Woman] | -2.05 | -3.17 – -0.93 | <0.001 | -1.94 | -3.08 – -0.81 | 0.001 | |||
| Age | 0.07 | -0.03 – 0.17 | 0.159 | 0.07 | -0.03 – 0.17 | 0.190 | |||
| int student [No] | -0.65 | -2.39 – 1.09 | 0.465 | -0.79 | -2.63 – 1.05 | 0.398 | |||
| SES num | 0.70 | 0.32 – 1.07 | <0.001 | 0.62 | 0.24 – 1.00 | 0.002 | |||
| Ethnicity White | 0.60 | -0.58 – 1.78 | 0.316 | ||||||
| Ethnicity Hispanic | -0.71 | -2.43 – 1.02 | 0.421 | ||||||
| Ethnicity Black | 0.15 | -2.16 – 2.45 | 0.902 | ||||||
| Ethnicity East Asian | 1.07 | -0.46 – 2.59 | 0.171 | ||||||
| Ethnicity South Asian | -0.60 | -2.73 – 1.53 | 0.578 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.87 | -6.55 – 4.81 | 0.764 | ||||||
| Ethnicity Middle Eastern | 1.91 | -1.84 – 5.66 | 0.318 | ||||||
| Ethnicity American Indian | 3.87 | -1.78 – 9.52 | 0.179 | ||||||
| Random Effects | |||||||||
| σ2 | 5.12 | 5.12 | 5.11 | ||||||
| τ00 | 14.69 unique_ID | 13.40 unique_ID | 13.41 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.74 | ||||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.005 / 0.743 | 0.238 / NA | 0.279 / NA | ||||||
School Satisfaction
Intention to Treat
m0 <- lmer(school_satis ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m1 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| school satis | school satis | school satis | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.54 | 4.35 – 4.72 | <0.001 | 3.31 | 2.67 – 3.94 | <0.001 | 3.57 | 2.94 – 4.21 | <0.001 |
| condflourish vs control | 0.03 | -0.04 – 0.11 | 0.387 | 0.03 | -0.04 – 0.10 | 0.393 | 0.04 | -0.03 – 0.11 | 0.244 |
| time - 2 5 | 0.03 | 0.01 – 0.05 | 0.018 | 0.03 | 0.00 – 0.05 | 0.026 | 0.03 | 0.00 – 0.05 | 0.028 |
|
condflourish vs control × time - 2 5 |
0.01 | -0.02 – 0.03 | 0.612 | 0.00 | -0.02 – 0.03 | 0.702 | 0.01 | -0.02 – 0.03 | 0.669 |
| Sex [Woman] | 0.20 | 0.02 – 0.38 | 0.032 | 0.17 | -0.00 – 0.35 | 0.056 | |||
| Age | 0.02 | 0.00 – 0.04 | 0.045 | 0.02 | 0.00 – 0.04 | 0.044 | |||
| int student [No] | 0.07 | -0.22 – 0.37 | 0.618 | -0.07 | -0.38 – 0.23 | 0.639 | |||
| SES num | 0.18 | 0.12 – 0.24 | <0.001 | 0.16 | 0.10 – 0.23 | <0.001 | |||
| Ethnicity White | 0.10 | -0.09 – 0.29 | 0.290 | ||||||
| Ethnicity Hispanic | 0.04 | -0.23 – 0.32 | 0.756 | ||||||
| Ethnicity Black | -0.74 | -1.11 – -0.37 | <0.001 | ||||||
| Ethnicity East Asian | -0.15 | -0.41 – 0.11 | 0.246 | ||||||
| Ethnicity South Asian | -0.44 | -0.79 – -0.08 | 0.015 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.36 | -2.43 – -0.28 | 0.014 | ||||||
| Ethnicity Middle Eastern | 0.09 | -0.43 – 0.60 | 0.740 | ||||||
| Ethnicity American Indian | -0.47 | -1.54 – 0.60 | 0.390 | ||||||
| Random Effects | |||||||||
| σ2 | 0.26 | 0.26 | 0.26 | ||||||
| τ00 | 0.52 unique_ID | 0.48 unique_ID | 0.45 unique_ID | ||||||
| 0.02 univ | 0.03 univ | 0.01 univ | |||||||
| ICC | 0.68 | 0.66 | 0.64 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 833 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.679 | 0.062 / 0.683 | 0.123 / 0.684 | ||||||
Excluded Preregistered
m0 <- lmer(school_satis ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)| school satis | school satis | school satis | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.56 | 4.36 – 4.76 | <0.001 | 3.35 | 2.66 – 4.04 | <0.001 | 3.60 | 2.90 – 4.30 | <0.001 |
| condflourish vs control | 0.06 | -0.02 – 0.14 | 0.152 | 0.05 | -0.03 – 0.13 | 0.221 | 0.06 | -0.02 – 0.14 | 0.128 |
| time - 2 5 | 0.02 | -0.01 – 0.05 | 0.126 | 0.02 | -0.01 – 0.04 | 0.146 | 0.02 | -0.01 – 0.04 | 0.150 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.01 – 0.04 | 0.251 | 0.01 | -0.01 – 0.04 | 0.308 | 0.01 | -0.01 – 0.04 | 0.288 |
| Sex [Woman] | 0.19 | -0.02 – 0.40 | 0.070 | 0.17 | -0.04 – 0.38 | 0.111 | |||
| Age | 0.02 | -0.00 – 0.04 | 0.063 | 0.02 | -0.00 – 0.04 | 0.051 | |||
| int student [No] | 0.07 | -0.24 – 0.38 | 0.650 | -0.04 | -0.37 – 0.28 | 0.790 | |||
| SES num | 0.18 | 0.11 – 0.24 | <0.001 | 0.15 | 0.08 – 0.23 | <0.001 | |||
| Ethnicity White | 0.07 | -0.15 – 0.28 | 0.541 | ||||||
| Ethnicity Hispanic | -0.00 | -0.32 – 0.32 | 0.991 | ||||||
| Ethnicity Black | -0.70 | -1.13 – -0.28 | 0.001 | ||||||
| Ethnicity East Asian | -0.20 | -0.48 – 0.09 | 0.176 | ||||||
| Ethnicity South Asian | -0.32 | -0.70 – 0.07 | 0.104 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.42 | -2.50 – -0.34 | 0.010 | ||||||
| Ethnicity Middle Eastern | 0.12 | -0.42 – 0.67 | 0.662 | ||||||
| Ethnicity American Indian | -0.50 | -1.57 – 0.57 | 0.360 | ||||||
| Random Effects | |||||||||
| σ2 | 0.26 | 0.26 | 0.26 | ||||||
| τ00 | 0.51 unique_ID | 0.47 unique_ID | 0.45 unique_ID | ||||||
| 0.02 univ | 0.03 univ | 0.01 univ | |||||||
| ICC | 0.67 | 0.66 | 0.64 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 712 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.006 / 0.677 | 0.060 / 0.682 | 0.112 / 0.682 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(school_satis ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)| school satis | school satis | school satis | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.54 | 4.34 – 4.74 | <0.001 | 3.37 | 2.65 – 4.09 | <0.001 | 3.59 | 2.86 – 4.31 | <0.001 |
| condflourish vs control | 0.05 | -0.04 – 0.13 | 0.279 | 0.04 | -0.04 – 0.12 | 0.315 | 0.05 | -0.03 – 0.13 | 0.227 |
| time - 2 5 | 0.03 | -0.00 – 0.05 | 0.057 | 0.03 | -0.00 – 0.05 | 0.057 | 0.03 | -0.00 – 0.05 | 0.059 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.01 – 0.05 | 0.124 | 0.02 | -0.01 – 0.05 | 0.136 | 0.02 | -0.01 – 0.05 | 0.129 |
| Sex [Woman] | 0.19 | -0.03 – 0.40 | 0.094 | 0.15 | -0.06 – 0.37 | 0.165 | |||
| Age | 0.02 | -0.00 – 0.04 | 0.106 | 0.02 | -0.00 – 0.04 | 0.098 | |||
| int student [No] | 0.14 | -0.20 – 0.49 | 0.409 | 0.04 | -0.31 – 0.39 | 0.817 | |||
| SES num | 0.16 | 0.09 – 0.23 | <0.001 | 0.14 | 0.07 – 0.22 | <0.001 | |||
| Ethnicity White | 0.08 | -0.15 – 0.30 | 0.493 | ||||||
| Ethnicity Hispanic | 0.11 | -0.22 – 0.44 | 0.513 | ||||||
| Ethnicity Black | -0.66 | -1.10 – -0.22 | 0.004 | ||||||
| Ethnicity East Asian | -0.13 | -0.43 – 0.16 | 0.366 | ||||||
| Ethnicity South Asian | -0.40 | -0.81 – 0.00 | 0.050 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.40 | -2.48 – -0.32 | 0.011 | ||||||
| Ethnicity Middle Eastern | 0.23 | -0.48 – 0.95 | 0.524 | ||||||
| Ethnicity American Indian | -0.49 | -1.56 – 0.58 | 0.368 | ||||||
| Random Effects | |||||||||
| σ2 | 0.25 | 0.25 | 0.25 | ||||||
| τ00 | 0.50 unique_ID | 0.47 unique_ID | 0.44 unique_ID | ||||||
| 0.02 univ | 0.03 univ | 0.01 univ | |||||||
| ICC | 0.67 | 0.66 | 0.64 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.005 / 0.676 | 0.053 / 0.681 | 0.109 / 0.682 | ||||||
School Prioritizes Well-Being
“At my school, I feel that students’ mental and emotional well-being is a priority.”
Intention to Treat
m0 <- lmer(wellbeing_priority ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m1 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| wellbeing priority | wellbeing priority | wellbeing priority | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.57 | 4.17 – 4.97 | <0.001 | 4.06 | 3.17 – 4.95 | <0.001 | 4.03 | 3.07 – 4.98 | <0.001 |
| condflourish vs control | 0.03 | -0.07 – 0.13 | 0.499 | 0.03 | -0.07 – 0.13 | 0.559 | 0.04 | -0.07 – 0.14 | 0.491 |
| time - 2 5 | 0.03 | -0.01 – 0.07 | 0.142 | 0.03 | -0.01 – 0.07 | 0.165 | 0.03 | -0.01 – 0.07 | 0.166 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.02 – 0.06 | 0.407 | 0.02 | -0.03 – 0.06 | 0.426 | 0.02 | -0.03 – 0.06 | 0.431 |
| Sex [Woman] | 0.03 | -0.22 – 0.28 | 0.805 | 0.03 | -0.23 – 0.28 | 0.837 | |||
| Age | 0.02 | -0.01 – 0.05 | 0.121 | 0.02 | -0.01 – 0.05 | 0.135 | |||
| int student [No] | -0.41 | -0.81 – -0.02 | 0.040 | -0.35 | -0.78 – 0.08 | 0.109 | |||
| SES num | 0.13 | 0.04 – 0.21 | 0.004 | 0.11 | 0.02 – 0.20 | 0.015 | |||
| Ethnicity White | 0.10 | -0.17 – 0.37 | 0.463 | ||||||
| Ethnicity Hispanic | -0.08 | -0.47 – 0.31 | 0.677 | ||||||
| Ethnicity Black | -0.16 | -0.69 – 0.37 | 0.550 | ||||||
| Ethnicity East Asian | 0.18 | -0.19 – 0.54 | 0.343 | ||||||
| Ethnicity South Asian | 0.16 | -0.33 – 0.66 | 0.519 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.45 | -1.96 – 1.05 | 0.553 | ||||||
| Ethnicity Middle Eastern | -0.02 | -0.75 – 0.71 | 0.953 | ||||||
| Ethnicity American Indian | -0.75 | -2.24 – 0.74 | 0.322 | ||||||
| Random Effects | |||||||||
| σ2 | 0.78 | 0.79 | 0.79 | ||||||
| τ00 | 0.75 unique_ID | 0.72 unique_ID | 0.73 unique_ID | ||||||
| 0.12 univ | 0.08 univ | 0.10 univ | |||||||
| ICC | 0.52 | 0.50 | 0.51 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 833 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.526 | 0.027 / 0.517 | 0.033 / 0.529 | ||||||
Excluded Preregistered
m0 <- lmer(wellbeing_priority ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)| wellbeing priority | wellbeing priority | wellbeing priority | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.53 | 4.22 – 4.84 | <0.001 | 4.08 | 3.14 – 5.02 | <0.001 | 3.91 | 2.90 – 4.91 | <0.001 |
| condflourish vs control | 0.04 | -0.07 – 0.15 | 0.451 | 0.03 | -0.08 – 0.14 | 0.551 | 0.04 | -0.07 – 0.15 | 0.511 |
| time - 2 5 | 0.03 | -0.02 – 0.07 | 0.246 | 0.02 | -0.02 – 0.07 | 0.270 | 0.02 | -0.02 – 0.07 | 0.281 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.03 – 0.06 | 0.494 | 0.01 | -0.03 – 0.06 | 0.514 | 0.01 | -0.03 – 0.06 | 0.504 |
| Sex [Woman] | -0.03 | -0.33 – 0.26 | 0.815 | -0.05 | -0.34 – 0.25 | 0.748 | |||
| Age | 0.02 | -0.00 – 0.05 | 0.091 | 0.03 | -0.00 – 0.05 | 0.068 | |||
| int student [No] | -0.55 | -0.98 – -0.12 | 0.011 | -0.47 | -0.94 – -0.01 | 0.044 | |||
| SES num | 0.14 | 0.04 – 0.24 | 0.004 | 0.13 | 0.03 – 0.23 | 0.013 | |||
| Ethnicity White | 0.15 | -0.16 – 0.46 | 0.331 | ||||||
| Ethnicity Hispanic | 0.12 | -0.34 – 0.58 | 0.599 | ||||||
| Ethnicity Black | -0.22 | -0.82 – 0.39 | 0.484 | ||||||
| Ethnicity East Asian | 0.15 | -0.25 – 0.56 | 0.454 | ||||||
| Ethnicity South Asian | 0.38 | -0.16 – 0.92 | 0.163 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.29 | -1.82 – 1.23 | 0.708 | ||||||
| Ethnicity Middle Eastern | 0.07 | -0.70 – 0.85 | 0.854 | ||||||
| Ethnicity American Indian | -0.72 | -2.24 – 0.79 | 0.348 | ||||||
| Random Effects | |||||||||
| σ2 | 0.74 | 0.74 | 0.74 | ||||||
| τ00 | 0.80 unique_ID | 0.77 unique_ID | 0.77 unique_ID | ||||||
| 0.06 univ | 0.03 univ | 0.04 univ | |||||||
| ICC | 0.54 | 0.52 | 0.52 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 712 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.540 | 0.040 / 0.537 | 0.049 / 0.546 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(wellbeing_priority ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)| wellbeing priority | wellbeing priority | wellbeing priority | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 4.50 | 4.21 – 4.79 | <0.001 | 4.09 | 3.11 – 5.06 | <0.001 | 3.87 | 2.85 – 4.90 | <0.001 |
| condflourish vs control | 0.02 | -0.09 – 0.14 | 0.678 | 0.02 | -0.09 – 0.14 | 0.703 | 0.02 | -0.09 – 0.14 | 0.703 |
| time - 2 5 | 0.03 | -0.02 – 0.07 | 0.252 | 0.02 | -0.02 – 0.07 | 0.275 | 0.02 | -0.02 – 0.07 | 0.295 |
|
condflourish vs control × time - 2 5 |
0.02 | -0.03 – 0.06 | 0.493 | 0.02 | -0.03 – 0.06 | 0.507 | 0.02 | -0.03 – 0.06 | 0.497 |
| Sex [Woman] | -0.10 | -0.40 – 0.20 | 0.523 | -0.12 | -0.42 – 0.19 | 0.460 | |||
| Age | 0.02 | -0.00 – 0.05 | 0.083 | 0.03 | -0.00 – 0.06 | 0.058 | |||
| int student [No] | -0.48 | -0.95 – -0.01 | 0.046 | -0.42 | -0.92 – 0.08 | 0.097 | |||
| SES num | 0.12 | 0.02 – 0.22 | 0.019 | 0.11 | 0.01 – 0.21 | 0.038 | |||
| Ethnicity White | 0.18 | -0.14 – 0.50 | 0.276 | ||||||
| Ethnicity Hispanic | 0.29 | -0.18 – 0.76 | 0.223 | ||||||
| Ethnicity Black | -0.19 | -0.82 – 0.44 | 0.550 | ||||||
| Ethnicity East Asian | 0.22 | -0.20 – 0.64 | 0.304 | ||||||
| Ethnicity South Asian | 0.38 | -0.19 – 0.96 | 0.194 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.24 | -1.77 – 1.29 | 0.758 | ||||||
| Ethnicity Middle Eastern | 0.00 | -1.03 – 1.03 | 0.996 | ||||||
| Ethnicity American Indian | -0.72 | -2.24 – 0.79 | 0.350 | ||||||
| Random Effects | |||||||||
| σ2 | 0.72 | 0.72 | 0.72 | ||||||
| τ00 | 0.81 unique_ID | 0.78 unique_ID | 0.79 unique_ID | ||||||
| 0.05 univ | 0.03 univ | 0.03 univ | |||||||
| ICC | 0.54 | 0.53 | 0.53 | ||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.002 / 0.545 | 0.034 / 0.544 | 0.046 / 0.552 | ||||||
Academic Self-Efficacy
Intention to Treat
m0 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')#standardize_parameters(m0)
m1 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| acad selfefficacy | acad selfefficacy | acad selfefficacy | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 24.00 | 23.66 – 24.33 | <0.001 | 21.18 | 18.45 – 23.92 | <0.001 | 22.17 | 19.28 – 25.07 | <0.001 |
| condflourish vs control | -0.00 | -0.34 – 0.33 | 0.995 | -0.02 | -0.36 – 0.31 | 0.905 | 0.04 | -0.30 – 0.37 | 0.830 |
| time - 2 5 | 0.15 | 0.02 – 0.28 | 0.021 | 0.15 | 0.02 – 0.28 | 0.023 | 0.15 | 0.02 – 0.28 | 0.023 |
|
condflourish vs control × time - 2 5 |
0.05 | -0.08 – 0.18 | 0.436 | 0.05 | -0.08 – 0.18 | 0.474 | 0.05 | -0.08 – 0.18 | 0.456 |
| Sex [Woman] | -0.31 | -1.15 – 0.54 | 0.475 | -0.31 | -1.16 – 0.53 | 0.466 | |||
| Age | 0.07 | -0.01 – 0.15 | 0.103 | 0.07 | -0.02 – 0.15 | 0.124 | |||
| int student [No] | 0.15 | -1.18 – 1.47 | 0.827 | -0.64 | -2.07 – 0.80 | 0.384 | |||
| SES num | 0.46 | 0.17 – 0.75 | 0.002 | 0.42 | 0.12 – 0.72 | 0.006 | |||
| Ethnicity White | 0.48 | -0.41 – 1.38 | 0.291 | ||||||
| Ethnicity Hispanic | -0.33 | -1.62 – 0.96 | 0.619 | ||||||
| Ethnicity Black | -0.72 | -2.47 – 1.04 | 0.423 | ||||||
| Ethnicity East Asian | -0.84 | -2.05 – 0.38 | 0.176 | ||||||
| Ethnicity South Asian | -1.49 | -3.14 – 0.16 | 0.077 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-3.01 | -8.06 – 2.04 | 0.242 | ||||||
| Ethnicity Middle Eastern | -0.84 | -3.27 – 1.59 | 0.497 | ||||||
| Ethnicity American Indian | 1.58 | -3.45 – 6.60 | 0.538 | ||||||
| Random Effects | |||||||||
| σ2 | 7.13 | 7.13 | 7.12 | ||||||
| τ00 | 9.52 unique_ID | 9.33 unique_ID | 9.19 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 831 | 828 | 828 | ||||||
| Marginal R2 / Conditional R2 | 0.008 / NA | 0.056 / NA | 0.101 / NA | ||||||
Excluded Preregistered
m0 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| acad selfefficacy | acad selfefficacy | acad selfefficacy | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 24.07 | 23.71 – 24.44 | <0.001 | 20.56 | 17.56 – 23.56 | <0.001 | 21.52 | 18.33 – 24.70 | <0.001 |
| condflourish vs control | -0.00 | -0.37 – 0.36 | 0.981 | -0.05 | -0.42 – 0.32 | 0.797 | 0.00 | -0.37 – 0.37 | 0.994 |
| time - 2 5 | 0.15 | 0.01 – 0.29 | 0.031 | 0.15 | 0.01 – 0.28 | 0.033 | 0.15 | 0.01 – 0.29 | 0.031 |
|
condflourish vs control × time - 2 5 |
0.07 | -0.06 – 0.21 | 0.298 | 0.07 | -0.07 – 0.21 | 0.323 | 0.07 | -0.06 – 0.21 | 0.301 |
| Sex [Woman] | 0.01 | -0.96 – 0.99 | 0.982 | 0.02 | -0.96 – 1.00 | 0.972 | |||
| Age | 0.06 | -0.02 – 0.15 | 0.152 | 0.06 | -0.03 – 0.15 | 0.169 | |||
| int student [No] | 0.55 | -0.87 – 1.96 | 0.450 | -0.17 | -1.70 – 1.37 | 0.829 | |||
| SES num | 0.51 | 0.18 – 0.83 | 0.002 | 0.47 | 0.13 – 0.80 | 0.006 | |||
| Ethnicity White | 0.34 | -0.69 – 1.37 | 0.516 | ||||||
| Ethnicity Hispanic | -0.25 | -1.77 – 1.27 | 0.744 | ||||||
| Ethnicity Black | -1.06 | -3.07 – 0.95 | 0.299 | ||||||
| Ethnicity East Asian | -0.89 | -2.23 – 0.44 | 0.190 | ||||||
| Ethnicity South Asian | -1.39 | -3.17 – 0.40 | 0.129 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.99 | -8.08 – 2.09 | 0.248 | ||||||
| Ethnicity Middle Eastern | -1.14 | -3.71 – 1.44 | 0.387 | ||||||
| Ethnicity American Indian | 1.51 | -3.55 – 6.57 | 0.559 | ||||||
| Random Effects | |||||||||
| σ2 | 7.24 | 7.24 | 7.25 | ||||||
| τ00 | 9.52 unique_ID | 9.30 unique_ID | 9.21 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 711 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.009 / NA | 0.059 / NA | 0.101 / NA | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m1 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(acad_selfefficacy ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')tab_model(m0, m1, m2)| acad selfefficacy | acad selfefficacy | acad selfefficacy | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 24.12 | 23.74 – 24.49 | <0.001 | 20.44 | 17.34 – 23.54 | <0.001 | 21.34 | 18.08 – 24.60 | <0.001 |
| condflourish vs control | 0.04 | -0.34 – 0.41 | 0.851 | -0.01 | -0.38 – 0.37 | 0.978 | 0.03 | -0.36 – 0.41 | 0.888 |
| time - 2 5 | 0.15 | 0.01 – 0.29 | 0.033 | 0.15 | 0.01 – 0.29 | 0.033 | 0.15 | 0.01 – 0.29 | 0.031 |
|
condflourish vs control × time - 2 5 |
0.07 | -0.07 – 0.21 | 0.298 | 0.07 | -0.07 – 0.21 | 0.308 | 0.07 | -0.07 – 0.21 | 0.297 |
| Sex [Woman] | -0.29 | -1.29 – 0.70 | 0.563 | -0.29 | -1.29 – 0.72 | 0.575 | |||
| Age | 0.07 | -0.02 – 0.16 | 0.143 | 0.06 | -0.03 – 0.15 | 0.192 | |||
| int student [No] | 0.89 | -0.64 – 2.43 | 0.253 | 0.28 | -1.35 – 1.90 | 0.738 | |||
| SES num | 0.53 | 0.19 – 0.86 | 0.002 | 0.48 | 0.13 – 0.82 | 0.006 | |||
| Ethnicity White | 0.37 | -0.67 – 1.42 | 0.485 | ||||||
| Ethnicity Hispanic | -0.16 | -1.69 – 1.36 | 0.836 | ||||||
| Ethnicity Black | -0.55 | -2.61 – 1.50 | 0.597 | ||||||
| Ethnicity East Asian | -0.57 | -1.93 – 0.79 | 0.411 | ||||||
| Ethnicity South Asian | -1.55 | -3.43 – 0.33 | 0.105 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-3.10 | -8.11 – 1.91 | 0.224 | ||||||
| Ethnicity Middle Eastern | -0.53 | -3.89 – 2.83 | 0.758 | ||||||
| Ethnicity American Indian | 1.42 | -3.56 – 6.40 | 0.575 | ||||||
| Random Effects | |||||||||
| σ2 | 6.99 | 6.99 | 6.99 | ||||||
| τ00 | 9.18 unique_ID | 8.89 unique_ID | 8.88 unique_ID | ||||||
| 0.00 univ | 0.00 univ | 0.00 univ | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.009 / NA | 0.071 / NA | 0.107 / NA | ||||||
Closeness to School (IOS)
Intention to Treat
m0 <- lmer(ios ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
#standardize_parameters(m0)
m1 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)| ios | ios | ios | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.29 | 3.13 – 3.46 | <0.001 | 2.82 | 1.86 – 3.77 | <0.001 | 3.07 | 2.07 – 4.08 | <0.001 |
| condflourish vs control | 0.07 | -0.05 – 0.18 | 0.253 | 0.08 | -0.04 – 0.20 | 0.182 | 0.10 | -0.01 – 0.22 | 0.081 |
| time - 2 5 | 0.05 | 0.00 – 0.09 | 0.034 | 0.05 | 0.00 – 0.09 | 0.039 | 0.05 | 0.00 – 0.09 | 0.037 |
|
condflourish vs control × time - 2 5 |
0.04 | -0.00 – 0.08 | 0.069 | 0.04 | -0.00 – 0.08 | 0.063 | 0.04 | -0.00 – 0.08 | 0.061 |
| Sex [Woman] | 0.47 | 0.18 – 0.77 | 0.002 | 0.45 | 0.15 – 0.74 | 0.003 | |||
| Age | -0.02 | -0.05 – 0.01 | 0.275 | -0.02 | -0.05 – 0.01 | 0.270 | |||
| int student [No] | 0.13 | -0.33 – 0.59 | 0.591 | 0.08 | -0.41 – 0.58 | 0.745 | |||
| SES num | 0.10 | -0.00 – 0.20 | 0.057 | 0.08 | -0.02 – 0.19 | 0.110 | |||
| Ethnicity White | -0.05 | -0.36 – 0.26 | 0.750 | ||||||
| Ethnicity Hispanic | -0.45 | -0.89 – 0.00 | 0.050 | ||||||
| Ethnicity Black | -0.06 | -0.67 – 0.55 | 0.853 | ||||||
| Ethnicity East Asian | -0.40 | -0.82 – 0.03 | 0.066 | ||||||
| Ethnicity South Asian | -0.00 | -0.58 – 0.57 | 0.987 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.85 | -3.60 – -0.10 | 0.038 | ||||||
| Ethnicity Middle Eastern | -0.28 | -1.12 – 0.57 | 0.520 | ||||||
| Ethnicity American Indian | -1.81 | -3.55 – -0.07 | 0.042 | ||||||
| Random Effects | |||||||||
| σ2 | 0.80 | 0.80 | 0.80 | ||||||
| τ00 | 1.18 unique_ID | 1.16 unique_ID | 1.13 unique_ID | ||||||
| 0.01 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.60 | 0.59 | 0.59 | ||||||
| N | 485 unique_ID | 482 unique_ID | 482 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 833 | 829 | 829 | ||||||
| Marginal R2 / Conditional R2 | 0.006 / 0.602 | 0.033 / 0.606 | 0.058 / 0.612 | ||||||
Excluded Preregistered
m0 <- lmer(ios ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)| ios | ios | ios | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.34 | 3.16 – 3.52 | <0.001 | 3.03 | 1.97 – 4.10 | <0.001 | 3.45 | 2.33 – 4.57 | <0.001 |
| condflourish vs control | 0.09 | -0.03 – 0.22 | 0.150 | 0.10 | -0.02 – 0.23 | 0.113 | 0.13 | -0.00 – 0.26 | 0.055 |
| time - 2 5 | 0.04 | -0.01 – 0.08 | 0.116 | 0.04 | -0.01 – 0.08 | 0.107 | 0.04 | -0.01 – 0.08 | 0.095 |
|
condflourish vs control × time - 2 5 |
0.04 | -0.00 – 0.09 | 0.066 | 0.04 | -0.00 – 0.09 | 0.063 | 0.04 | -0.00 – 0.09 | 0.059 |
| Sex [Woman] | 0.42 | 0.08 – 0.77 | 0.016 | 0.38 | 0.03 – 0.72 | 0.032 | |||
| Age | -0.02 | -0.05 – 0.02 | 0.334 | -0.02 | -0.05 – 0.01 | 0.235 | |||
| int student [No] | 0.14 | -0.36 – 0.64 | 0.574 | 0.08 | -0.45 – 0.62 | 0.763 | |||
| SES num | 0.05 | -0.07 – 0.16 | 0.435 | 0.04 | -0.08 – 0.16 | 0.492 | |||
| Ethnicity White | -0.19 | -0.55 – 0.17 | 0.312 | ||||||
| Ethnicity Hispanic | -0.51 | -1.04 – 0.02 | 0.059 | ||||||
| Ethnicity Black | 0.08 | -0.63 – 0.78 | 0.832 | ||||||
| Ethnicity East Asian | -0.53 | -0.99 – -0.06 | 0.028 | ||||||
| Ethnicity South Asian | -0.09 | -0.71 – 0.54 | 0.788 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.08 | -3.86 – -0.30 | 0.022 | ||||||
| Ethnicity Middle Eastern | -0.38 | -1.28 – 0.52 | 0.404 | ||||||
| Ethnicity American Indian | -1.98 | -3.75 – -0.20 | 0.029 | ||||||
| Random Effects | |||||||||
| σ2 | 0.80 | 0.81 | 0.80 | ||||||
| τ00 | 1.21 unique_ID | 1.20 unique_ID | 1.17 unique_ID | ||||||
| 0.01 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.60 | 0.60 | 0.59 | ||||||
| N | 389 unique_ID | 387 unique_ID | 387 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 712 | 709 | 709 | ||||||
| Marginal R2 / Conditional R2 | 0.007 / 0.606 | 0.025 / 0.610 | 0.058 / 0.617 | ||||||
Excluded Unreasonable Numbers
m0 <- lmer(ios ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00662956 (tol = 0.002, component 1)m1 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)## boundary (singular) fit: see help('isSingular')m2 <- lmer(ios ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)| ios | ios | ios | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 3.31 | 3.15 – 3.48 | <0.001 | 2.99 | 1.90 – 4.09 | <0.001 | 3.41 | 2.27 – 4.55 | <0.001 |
| condflourish vs control | 0.06 | -0.07 – 0.19 | 0.357 | 0.07 | -0.06 – 0.20 | 0.293 | 0.09 | -0.04 – 0.23 | 0.166 |
| time - 2 5 | 0.03 | -0.02 – 0.08 | 0.227 | 0.03 | -0.02 – 0.08 | 0.230 | 0.03 | -0.02 – 0.08 | 0.212 |
|
condflourish vs control × time - 2 5 |
0.04 | -0.01 – 0.08 | 0.138 | 0.03 | -0.01 – 0.08 | 0.146 | 0.04 | -0.01 – 0.08 | 0.135 |
| Sex [Woman] | 0.39 | 0.04 – 0.74 | 0.029 | 0.35 | 0.00 – 0.71 | 0.049 | |||
| Age | -0.02 | -0.05 – 0.02 | 0.316 | -0.02 | -0.05 – 0.01 | 0.206 | |||
| int student [No] | 0.22 | -0.32 – 0.76 | 0.431 | 0.13 | -0.44 – 0.70 | 0.651 | |||
| SES num | 0.04 | -0.08 – 0.16 | 0.497 | 0.03 | -0.09 – 0.15 | 0.648 | |||
| Ethnicity White | -0.10 | -0.46 – 0.27 | 0.602 | ||||||
| Ethnicity Hispanic | -0.41 | -0.94 – 0.13 | 0.136 | ||||||
| Ethnicity Black | 0.18 | -0.54 – 0.90 | 0.627 | ||||||
| Ethnicity East Asian | -0.43 | -0.90 – 0.05 | 0.076 | ||||||
| Ethnicity South Asian | -0.12 | -0.78 – 0.54 | 0.728 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.98 | -3.73 – -0.22 | 0.027 | ||||||
| Ethnicity Middle Eastern | -0.61 | -1.78 – 0.57 | 0.311 | ||||||
| Ethnicity American Indian | -1.90 | -3.65 – -0.15 | 0.033 | ||||||
| Random Effects | |||||||||
| σ2 | 0.77 | 0.77 | 0.77 | ||||||
| τ00 | 1.17 unique_ID | 1.16 unique_ID | 1.14 unique_ID | ||||||
| 0.01 univ | 0.00 univ | 0.00 univ | |||||||
| ICC | 0.60 | 0.60 | |||||||
| N | 357 unique_ID | 356 unique_ID | 356 unique_ID | ||||||
| 3 univ | 3 univ | 3 univ | |||||||
| Observations | 652 | 651 | 651 | ||||||
| Marginal R2 / Conditional R2 | 0.004 / 0.606 | 0.052 / NA | 0.054 / 0.617 | ||||||
Probe significant interactions
Loneliness
Intention to Treat
# Time 1
lm(loneliness ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5976 -1.5798 0.4024 1.4024 3.4202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.588696 0.074864 74.651 <0.0000000000000002 ***
## condflourish_vs_control -0.008865 0.074864 -0.118 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.647 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 2.909e-05, Adjusted R-squared: -0.002046
## F-statistic: 0.01402 on 1 and 482 DF, p-value: 0.9058# Time 2
lm(loneliness ~ cond, data = subset(data_ITT, time == 2)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time ==
## 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2727 -1.2727 -0.0777 0.9223 3.9223
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.17522 0.08065 64.170 <0.0000000000000002 ***
## condflourish_vs_control -0.09750 0.08065 -1.209 0.227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.595 on 389 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.003743, Adjusted R-squared: 0.001182
## F-statistic: 1.462 on 1 and 389 DF, p-value: 0.2274# Time 3
lm(loneliness ~ cond, data = subset(data_ITT, time == 3)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time ==
## 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1582 -1.1582 -0.1582 1.0225 4.0225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.06786 0.08931 56.746 <0.0000000000000002 ***
## condflourish_vs_control -0.09033 0.08931 -1.011 0.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.683 on 353 degrees of freedom
## (131 observations deleted due to missingness)
## Multiple R-squared: 0.00289, Adjusted R-squared: 6.519e-05
## F-statistic: 1.023 on 1 and 353 DF, p-value: 0.3125cohens_d(loneliness ~ cond, data = subset(data_ITT, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## 0.11 | [-0.10, 0.32]
##
## - Estimated using pooled SD.# Time 4
lm(loneliness ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3918 -1.3918 -0.0955 0.9045 3.9045
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.24366 0.09062 57.863 <0.0000000000000002 ***
## condflourish_vs_control -0.14815 0.09062 -1.635 0.103
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.693 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.007644, Adjusted R-squared: 0.004784
## F-statistic: 2.673 on 1 and 347 DF, p-value: 0.103cohens_d(loneliness ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## 0.18 | [-0.04, 0.39]
##
## - Estimated using pooled SD.# Flourish cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 2657.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.07365 -0.52507 -0.01295 0.47543 3.02493
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.7635 1.328
## Residual 0.9565 0.978
## Number of obs: 787, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.20327 0.09425 238.11892 55.207 < 0.0000000000000002 ***
## I(time - 2.5) -0.16696 0.03192 577.31120 -5.231 0.000000237 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.083standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.28 | [-0.44, -0.12]
## time - 2 5 | -0.11 | [-0.16, -0.07]# Control cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 2700.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0586 -0.5271 -0.0342 0.5360 3.2079
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.8152 1.3473
## Residual 0.9839 0.9919
## Number of obs: 792, groups: unique_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.37957 0.09475 246.16356 56.775 <0.0000000000000002 ***
## I(time - 2.5) -0.07197 0.03273 581.21600 -2.199 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.106standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.11 | [-0.27, 0.05]
## time - 2 5 | -0.05 | [-0.09, -0.01]Excluded Preregistered
# Time 1
lm(loneliness ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5775 -1.5721 0.4225 1.4225 3.4279
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.57484 0.08469 65.825 <0.0000000000000002 ***
## condflourish_vs_control -0.00270 0.08469 -0.032 0.975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.667 on 386 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 2.634e-06, Adjusted R-squared: -0.002588
## F-statistic: 0.001017 on 1 and 386 DF, p-value: 0.9746# Time 2
lm(loneliness ~ cond, data = subset(data_excluded, time == 2)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded,
## time == 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2732 -1.2732 -0.1087 0.8913 3.8913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.19096 0.08324 62.361 <0.0000000000000002 ***
## condflourish_vs_control -0.08226 0.08324 -0.988 0.324
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.595 on 365 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.002669, Adjusted R-squared: -6.372e-05
## F-statistic: 0.9767 on 1 and 365 DF, p-value: 0.3237# Time 3
lm(loneliness ~ cond, data = subset(data_excluded, time == 3)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded,
## time == 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1677 -1.1677 -0.1677 1.0291 4.0291
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.06932 0.09266 54.709 <0.0000000000000002 ***
## condflourish_vs_control -0.09839 0.09266 -1.062 0.289
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.69 on 331 degrees of freedom
## (26 observations deleted due to missingness)
## Multiple R-squared: 0.003395, Adjusted R-squared: 0.0003836
## F-statistic: 1.127 on 1 and 331 DF, p-value: 0.2891# Time 4
lm(loneliness ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3613 -1.3613 -0.1124 0.8876 3.8876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.23686 0.09494 55.159 <0.0000000000000002 ***
## condflourish_vs_control -0.12443 0.09494 -1.311 0.191
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.707 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.005306, Adjusted R-squared: 0.002217
## F-statistic: 1.718 on 1 and 322 DF, p-value: 0.1909# Flourish cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 2445.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.06036 -0.54573 -0.00642 0.50502 3.02017
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.7842 1.3357
## Residual 0.9618 0.9807
## Number of obs: 726, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.1713 0.1012 200.5307 51.116 < 0.0000000000000002 ***
## I(time - 2.5) -0.1677 0.0331 533.0931 -5.066 0.000000559 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.037standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.30 | [-0.46, -0.13]
## time - 2 5 | -0.11 | [-0.16, -0.07]# Control cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 2326.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0572 -0.5323 -0.0300 0.5393 3.2051
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.8881 1.374
## Residual 0.9821 0.991
## Number of obs: 686, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.36317 0.10778 184.93939 49.761 <0.0000000000000002 ***
## I(time - 2.5) -0.06868 0.03474 506.52395 -1.977 0.0486 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.043standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.10 | [-0.27, 0.07]
## time - 2 5 | -0.05 | [-0.09, 0.00]Excluded Unreasonable Numbers
# Time 1
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6272 -1.5775 0.3728 1.3728 3.4225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.60238 0.08772 63.870 <0.0000000000000002 ***
## condflourish_vs_control 0.02484 0.08772 0.283 0.777
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.653 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0002265, Adjusted R-squared: -0.002598
## F-statistic: 0.08019 on 1 and 354 DF, p-value: 0.7772# Time 2
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 2)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable,
## time == 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2732 -1.2732 -0.1474 0.8526 3.8526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.21033 0.08734 59.66 <0.0000000000000002 ***
## condflourish_vs_control -0.06289 0.08734 -0.72 0.472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.603 on 337 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.001536, Adjusted R-squared: -0.001426
## F-statistic: 0.5186 on 1 and 337 DF, p-value: 0.472# Time 3
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 3)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable,
## time == 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1677 -1.1677 -0.1677 1.0142 4.0142
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.07676 0.09687 52.408 <0.0000000000000002 ***
## condflourish_vs_control -0.09094 0.09687 -0.939 0.349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.68 on 300 degrees of freedom
## (26 observations deleted due to missingness)
## Multiple R-squared: 0.002929, Adjusted R-squared: -0.0003943
## F-statistic: 0.8814 on 1 and 300 DF, p-value: 0.3486# Time 4
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3613 -1.3613 -0.0993 0.9007 3.9007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.23029 0.09767 53.548 <0.0000000000000002 ***
## condflourish_vs_control -0.13100 0.09767 -1.341 0.181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.679 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.006081, Adjusted R-squared: 0.0027
## F-statistic: 1.799 on 1 and 294 DF, p-value: 0.1809# Flourish cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 2035.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8654 -0.5787 0.0022 0.5025 2.8698
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.6937 1.3014
## Residual 0.9554 0.9775
## Number of obs: 607, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.18538 0.10788 168.92218 48.064 < 0.0000000000000002 ***
## I(time - 2.5) -0.19706 0.03615 446.18570 -5.451 0.0000000831 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.042standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.36 | [-0.54, -0.18]
## time - 2 5 | -0.14 | [-0.18, -0.09]# Control cond: over time
model <- lmer(loneliness ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 2326.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0572 -0.5323 -0.0300 0.5393 3.2051
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.8881 1.374
## Residual 0.9821 0.991
## Number of obs: 686, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.36317 0.10778 184.93939 49.761 <0.0000000000000002 ***
## I(time - 2.5) -0.06868 0.03474 506.52395 -1.977 0.0486 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.043standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.10 | [-0.27, 0.07]
## time - 2 5 | -0.05 | [-0.09, 0.00]SAS: Calm
Intention to Treat
# Time 1
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7185 -1.7185 0.2815 1.4553 6.4553
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.63160 0.11664 48.281 <0.0000000000000002 ***
## condflourish_vs_control 0.08689 0.11664 0.745 0.457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.566 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00115, Adjusted R-squared: -0.0009225
## F-statistic: 0.5549 on 1 and 482 DF, p-value: 0.4567# Time 2
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 2)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time ==
## 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7876 -1.7876 0.2124 2.2124 6.6364
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5756 0.1306 42.706 <0.0000000000000002 ***
## condflourish_vs_control 0.2120 0.1306 1.624 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.581 on 389 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.00673, Adjusted R-squared: 0.004177
## F-statistic: 2.636 on 1 and 389 DF, p-value: 0.1053# Time 3
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 3)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time ==
## 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0506 -2.0506 -0.0506 1.9494 6.5085
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.7710 0.1387 41.608 <0.0000000000000002 ***
## condflourish_vs_control 0.2795 0.1387 2.015 0.0446 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.613 on 353 degrees of freedom
## (131 observations deleted due to missingness)
## Multiple R-squared: 0.01137, Adjusted R-squared: 0.008574
## F-statistic: 4.061 on 1 and 353 DF, p-value: 0.04464cohens_d(SAS_calm ~ cond, data = subset(data_ITT, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.21 | [-0.42, -0.01]
##
## - Estimated using pooled SD.# Time 4
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2753 -1.4503 -0.2753 1.7247 6.5497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8628 0.1337 43.851 <0.0000000000000002 ***
## condflourish_vs_control 0.4125 0.1337 3.085 0.0022 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.497 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.0267, Adjusted R-squared: 0.02389
## F-statistic: 9.519 on 1 and 347 DF, p-value: 0.002197cohens_d(SAS_calm ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.33 | [-0.54, -0.12]
##
## - Estimated using pooled SD.# Flourish cond: over time
model <- lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 3512.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3044 -0.6100 0.0433 0.5270 3.3115
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.722 1.929
## Residual 3.176 1.782
## Number of obs: 787, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.96423 0.14293 229.59720 41.727 < 0.0000000000000002 ***
## I(time - 2.5) 0.20984 0.05786 580.02738 3.627 0.000312 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.093standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.23 | [0.06, 0.40]
## time - 2 5 | 0.09 | [0.04, 0.14]# Control cond: over time
lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 3477.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6351 -0.5626 0.0430 0.6064 3.3250
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.445 1.856
## Residual 2.947 1.717
## Number of obs: 792, groups: unique_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.52396 0.13660 242.93591 40.439 <0.0000000000000002 ***
## I(time - 2.5) 0.01313 0.05628 591.80043 0.233 0.816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.118Excluded Preregistered
# Time 1
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7562 -1.7562 0.2438 1.6043 6.6043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5760 0.1286 43.358 <0.0000000000000002 ***
## condflourish_vs_control 0.1802 0.1286 1.402 0.162
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.532 on 386 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.005063, Adjusted R-squared: 0.002486
## F-statistic: 1.964 on 1 and 386 DF, p-value: 0.1618# Time 2
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 2)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time ==
## 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.7228 -1.7228 0.2772 1.9637 6.6503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5363 0.1341 41.271 <0.0000000000000002 ***
## condflourish_vs_control 0.1865 0.1341 1.391 0.165
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.57 on 365 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.005271, Adjusted R-squared: 0.002545
## F-statistic: 1.934 on 1 and 365 DF, p-value: 0.1652# Time 3
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 3)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time ==
## 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.093 -2.093 -0.093 1.907 6.472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8105 0.1438 40.408 <0.0000000000000002 ***
## condflourish_vs_control 0.2825 0.1438 1.965 0.0503 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.623 on 331 degrees of freedom
## (26 observations deleted due to missingness)
## Multiple R-squared: 0.01153, Adjusted R-squared: 0.008543
## F-statistic: 3.861 on 1 and 331 DF, p-value: 0.05027cohens_d(SAS_calm ~ cond, data = subset(data_excluded, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## -0.22 | [-0.43, 0.00]
##
## - Estimated using pooled SD.# Time 4
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3609 -1.4774 -0.3609 1.6391 6.5226
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9192 0.1386 42.715 < 0.0000000000000002 ***
## condflourish_vs_control 0.4418 0.1386 3.188 0.00157 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.492 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.0306, Adjusted R-squared: 0.02759
## F-statistic: 10.16 on 1 and 322 DF, p-value: 0.001574cohens_d(SAS_calm ~ cond, data = subset(data_excluded, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.35 | [-0.57, -0.13]
##
## - Estimated using pooled SD.# Flourish cond: over time
model <- lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 3227.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3070 -0.6101 0.0399 0.5344 3.3053
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.629 1.905
## Residual 3.163 1.779
## Number of obs: 726, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.00844 0.15018 197.22369 40.009 < 0.0000000000000002 ***
## I(time - 2.5) 0.23938 0.05989 534.46481 3.997 0.0000732 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.044standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.27 | [0.10, 0.44]
## time - 2 5 | 0.10 | [0.05, 0.15]# Control cond: over time
lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 2986.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6976 -0.5777 0.0299 0.6084 3.3787
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.367 1.835
## Residual 2.890 1.700
## Number of obs: 686, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.45986 0.14983 189.33385 36.440 <0.0000000000000002 ***
## I(time - 2.5) 0.06216 0.05946 515.03617 1.045 0.296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.051SAS: Well-Being
Intention to Treat
# Time 1
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2395 -2.0285 -0.0285 1.7605 4.9715
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.1340 0.1134 62.920 <0.0000000000000002 ***
## condflourish_vs_control 0.1055 0.1134 0.931 0.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.494 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.001794, Adjusted R-squared: -0.0002772
## F-statistic: 0.8661 on 1 and 482 DF, p-value: 0.3525# Time 2
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 2)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time ==
## 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7071 -1.7071 0.1503 2.1503 5.2929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.77841 0.12200 55.558 <0.0000000000000002 ***
## condflourish_vs_control 0.07134 0.12200 0.585 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.412 on 389 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.0008781, Adjusted R-squared: -0.00169
## F-statistic: 0.3419 on 1 and 389 DF, p-value: 0.5591# Time 3
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 3)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time ==
## 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1638 -1.5819 -0.1638 1.8362 5.4181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.8729 0.1374 50.033 <0.0000000000000002 ***
## condflourish_vs_control 0.2910 0.1374 2.118 0.0349 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.585 on 352 degrees of freedom
## (132 observations deleted due to missingness)
## Multiple R-squared: 0.01259, Adjusted R-squared: 0.00978
## F-statistic: 4.486 on 1 and 352 DF, p-value: 0.03486cohens_d(SAS_well_being ~ cond, data = subset(data_ITT, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.23 | [-0.43, -0.02]
##
## - Estimated using pooled SD.# Time 4
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2472 -1.4971 -0.2472 1.7528 5.5029
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.8721 0.1313 52.327 < 0.0000000000000002 ***
## condflourish_vs_control 0.3751 0.1313 2.856 0.00455 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.453 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.02296, Adjusted R-squared: 0.02015
## F-statistic: 8.156 on 1 and 347 DF, p-value: 0.004551cohens_d(SAS_well_being ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.31 | [-0.52, -0.09]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(SAS_well_being ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 3384
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2113 -0.5509 0.0342 0.5666 3.5552
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.356 1.832
## Residual 2.663 1.632
## Number of obs: 786, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.12678 0.13469 228.83359 52.913 <0.0000000000000002 ***
## I(time - 2.5) 0.02255 0.05304 576.41091 0.425 0.671
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.091# Control cond: over time
model <- lmer(SAS_well_being ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 3405.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1816 -0.5645 0.0224 0.5232 3.3843
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 4.056 2.014
## Residual 2.479 1.574
## Number of obs: 792, groups: unique_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.76501 0.14314 243.78085 47.262 < 0.0000000000000002 ***
## I(time - 2.5) -0.14733 0.05186 582.40708 -2.841 0.00466 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.110standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.14 | [-0.30, 0.02]
## time - 2 5 | -0.07 | [-0.11, -0.02]Excluded Preregistered
# Time 1
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3134 -1.4824 0.0107 1.6866 5.0107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.1514 0.1248 57.307 <0.0000000000000002 ***
## condflourish_vs_control 0.1621 0.1248 1.299 0.195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.456 on 386 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.00435, Adjusted R-squared: 0.001771
## F-statistic: 1.687 on 1 and 386 DF, p-value: 0.1948# Time 2
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 2)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded,
## time == 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6885 -1.6885 0.1793 2.1793 5.3115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.75459 0.12546 53.840 <0.0000000000000002 ***
## condflourish_vs_control 0.06606 0.12546 0.527 0.599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.403 on 365 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.0007591, Adjusted R-squared: -0.001979
## F-statistic: 0.2773 on 1 and 365 DF, p-value: 0.5988# Time 3
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 3)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded,
## time == 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1871 -1.6335 -0.1871 1.8129 5.3665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.9103 0.1400 49.360 <0.0000000000000002 ***
## condflourish_vs_control 0.2768 0.1400 1.977 0.0489 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.55 on 330 degrees of freedom
## (27 observations deleted due to missingness)
## Multiple R-squared: 0.01171, Adjusted R-squared: 0.008712
## F-statistic: 3.909 on 1 and 330 DF, p-value: 0.04886cohens_d(SAS_well_being ~ cond, data = subset(data_excluded, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.22 | [-0.43, 0.00]
##
## - Estimated using pooled SD.# Time 4
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.1893 -1.5355 0.4645 1.8107 5.4645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.8624 0.1368 50.17 <0.0000000000000002 ***
## condflourish_vs_control 0.3269 0.1368 2.39 0.0174 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.46 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.01743, Adjusted R-squared: 0.01438
## F-statistic: 5.712 on 1 and 322 DF, p-value: 0.01742cohens_d(SAS_well_being ~ cond, data = subset(data_excluded, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.27 | [-0.48, -0.05]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(SAS_well_being ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 3098.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2377 -0.5391 0.0348 0.5702 3.5855
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.248 1.802
## Residual 2.618 1.618
## Number of obs: 725, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.160433 0.141011 198.475903 50.779 <0.0000000000000002 ***
## I(time - 2.5) 0.007556 0.054520 533.830855 0.139 0.89
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.043# Control cond: over time
model <- lmer(SAS_well_being ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 2924
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9241 -0.6007 0.0398 0.5342 3.4260
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.936 1.984
## Residual 2.453 1.566
## Number of obs: 686, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.73698 0.15760 186.99796 42.747 <0.0000000000000002 ***
## I(time - 2.5) -0.12206 0.05487 509.95645 -2.225 0.0265 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.046standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.12 | [-0.30, 0.05]
## time - 2 5 | -0.05 | [-0.10, -0.01]SAS: Positive
Intention to Treat
# Time 1
lm(SAS_positive ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = SAS_positive ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.4797 -4.4797 0.0714 4.0714 17.5203
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.7041 0.3063 61.070 <0.0000000000000002 ***
## condflourish_vs_control 0.2244 0.3063 0.733 0.464
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.737 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.001113, Adjusted R-squared: -0.0009594
## F-statistic: 0.5371 on 1 and 482 DF, p-value: 0.464# Time 2
lm(SAS_positive ~ cond, data = subset(data_ITT, time == 2)) |> summary()##
## Call:
## lm(formula = SAS_positive ~ cond, data = subset(data_ITT, time ==
## 2))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.513 -5.374 0.487 4.626 18.626
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.9433 0.3416 52.523 <0.0000000000000002 ***
## condflourish_vs_control 0.5696 0.3416 1.667 0.0963 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.755 on 389 degrees of freedom
## (95 observations deleted due to missingness)
## Multiple R-squared: 0.007096, Adjusted R-squared: 0.004543
## F-statistic: 2.78 on 1 and 389 DF, p-value: 0.09625cohens_d(SAS_positive ~ cond, data = subset(data_ITT, time == 2))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## -0.17 | [-0.37, 0.03]
##
## - Estimated using pooled SD.# Time 3
lm(SAS_positive ~ cond, data = subset(data_ITT, time == 3)) |> summary()##
## Call:
## lm(formula = SAS_positive ~ cond, data = subset(data_ITT, time ==
## 3))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.4633 -4.9506 -0.4633 4.8870 17.5367
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.2881 0.3741 48.882 <0.0000000000000002 ***
## condflourish_vs_control 0.8249 0.3741 2.205 0.0281 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.039 on 352 degrees of freedom
## (132 observations deleted due to missingness)
## Multiple R-squared: 0.01362, Adjusted R-squared: 0.01082
## F-statistic: 4.861 on 1 and 352 DF, p-value: 0.02812cohens_d(SAS_positive ~ cond, data = subset(data_ITT, time == 3))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.23 | [-0.44, -0.03]
##
## - Estimated using pooled SD.# Time 4
lm(SAS_positive ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = SAS_positive ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.2047 -4.3785 -0.2047 4.7953 17.7953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.2916 0.3542 51.649 < 0.0000000000000002 ***
## condflourish_vs_control 1.0869 0.3542 3.069 0.00232 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.606 on 346 degrees of freedom
## (138 observations deleted due to missingness)
## Multiple R-squared: 0.0265, Adjusted R-squared: 0.02369
## F-statistic: 9.419 on 1 and 346 DF, p-value: 0.002317cohens_d(SAS_positive ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.33 | [-0.54, -0.12]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(SAS_positive ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_positive ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 4912
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6582 -0.5314 0.0175 0.5229 4.1828
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 27.22 5.217
## Residual 18.04 4.248
## Number of obs: 785, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.9590 0.3768 231.4955 50.311 <0.0000000000000002 ***
## I(time - 2.5) 0.1921 0.1386 573.6030 1.386 0.166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.089# Control cond: over time
model <- lmer(SAS_positive ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_positive ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 4923
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9763 -0.5577 -0.0234 0.5574 4.3450
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 32.17 5.671
## Residual 16.06 4.007
## Number of obs: 792, groups: unique_ID, 247
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 17.9208 0.3962 243.3704 45.23 <0.0000000000000002 ***
## I(time - 2.5) -0.2514 0.1324 576.4465 -1.90 0.058 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.104standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -0.07 | [-0.22, 0.09]
## time - 2 5 | -0.04 | [-0.08, 0.00]Flourishing Scale
Intention to Treat
# Time 1
lm(flourishing ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.968 -3.967 1.032 4.412 11.412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.7779 0.2963 151.15 <0.0000000000000002 ***
## condflourish_vs_control -0.1896 0.2963 -0.64 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.517 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0008492, Adjusted R-squared: -0.001224
## F-statistic: 0.4097 on 1 and 482 DF, p-value: 0.5224# Time 4
lm(flourishing ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.345 -3.921 1.079 4.079 11.655
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.6330 0.3545 125.894 <0.0000000000000002 ***
## condflourish_vs_control 0.2879 0.3545 0.812 0.417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.613 on 346 degrees of freedom
## (138 observations deleted due to missingness)
## Multiple R-squared: 0.001903, Adjusted R-squared: -0.0009819
## F-statistic: 0.6596 on 1 and 346 DF, p-value: 0.4173# Flourish cond: over time
lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 2592.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.58345 -0.38205 0.07545 0.42343 1.85076
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 31.89 5.647
## Residual 10.80 3.286
## Number of obs: 415, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.8152 0.4037 239.3478 111.004 <0.0000000000000002 ***
## I(time - 2.5) 0.1430 0.1148 185.9160 1.246 0.214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.098# Control cond: over time
model <- lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 2665.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5743 -0.4046 0.0814 0.4465 2.7338
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 29.70 5.450
## Residual 14.81 3.848
## Number of obs: 417, groups: unique_ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.6044 0.4023 243.8148 110.877 <0.0000000000000002 ***
## I(time - 2.5) -0.2420 0.1352 182.5680 -1.791 0.075 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.135standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | -0.14 | [-0.34, 0.05]
## time - 2 5 | -0.05 | [-0.11, 0.01]Excluded Unreasonable Numbers
# Time 1
lm(flourishing ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.503 -3.616 1.043 4.043 11.497
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.7301 0.3355 133.338 <0.0000000000000002 ***
## condflourish_vs_control -0.2271 0.3355 -0.677 0.499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.321 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.001293, Adjusted R-squared: -0.001528
## F-statistic: 0.4584 on 1 and 354 DF, p-value: 0.4988# Time 4
lm(flourishing ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.3742 -4.0851 0.9149 3.6981 11.6258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.7296 0.3806 117.536 <0.0000000000000002 ***
## condflourish_vs_control 0.3555 0.3806 0.934 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.54 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.002959, Adjusted R-squared: -0.0004327
## F-statistic: 0.8724 on 1 and 294 DF, p-value: 0.3511# Flourish cond: over time
model <- lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 1903.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.62936 -0.37597 0.05827 0.41930 1.87235
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 28.88 5.374
## Residual 10.09 3.176
## Number of obs: 310, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.9202 0.4527 169.1751 99.223 <0.0000000000000002 ***
## I(time - 2.5) 0.2674 0.1250 145.1821 2.139 0.0341 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.061standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.18 | [-0.03, 0.39]
## time - 2 5 | 0.06 | [ 0.01, 0.12]# Control cond: over time
lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 2177.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5327 -0.4104 0.0600 0.4705 2.6399
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 27.98 5.290
## Residual 15.49 3.936
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 44.6042 0.4451 185.7692 100.222 <0.0000000000000002 ***
## I(time - 2.5) -0.2354 0.1467 162.7959 -1.604 0.111
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.072Cohesion
Intention to Treat
# Time 1
lm(cohesion ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6387 -1.6057 0.3613 1.3943 4.3943
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.62217 0.09764 57.578 <0.0000000000000002 ***
## condflourish_vs_control 0.01648 0.09764 0.169 0.866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.148 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 5.911e-05, Adjusted R-squared: -0.002015
## F-statistic: 0.02849 on 1 and 482 DF, p-value: 0.866# Time 4
lm(cohesion ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1067 -1.1067 0.3275 1.3275 4.3275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8896 0.1157 50.902 <0.0000000000000002 ***
## condflourish_vs_control 0.2171 0.1157 1.876 0.0614 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.161 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.01005, Adjusted R-squared: 0.007192
## F-statistic: 3.521 on 1 and 347 DF, p-value: 0.06143cohens_d(cohesion ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## -0.20 | [-0.41, 0.01]
##
## - Estimated using pooled SD.# Flourish cond: over time
model <- lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 1696.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.24918 -0.43582 -0.03829 0.47162 1.89841
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.624 1.904
## Residual 1.221 1.105
## Number of obs: 416, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.84424 0.13596 238.17755 42.985 < 0.0000000000000002 ***
## I(time - 2.5) 0.13926 0.03849 185.66322 3.618 0.000382 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.096standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.22 | [0.04, 0.40]
## time - 2 5 | 0.09 | [0.04, 0.15]# Control cond: over time
lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 1659.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4673 -0.3786 0.0809 0.5052 2.5259
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.391 1.841
## Residual 1.043 1.021
## Number of obs: 417, groups: unique_ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.64038 0.12933 251.67813 43.61 <0.0000000000000002 ***
## I(time - 2.5) 0.02313 0.03615 185.08337 0.64 0.523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.117Excluded Unreasonable Numbers
# Time 1
lm(cohesion ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6331 -1.4973 0.3669 1.5027 4.5027
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.5652 0.1168 47.632 <0.0000000000000002 ***
## condflourish_vs_control 0.0679 0.1168 0.581 0.561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.202 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0009533, Adjusted R-squared: -0.001869
## F-statistic: 0.3378 on 1 and 354 DF, p-value: 0.5615# Time 4
lm(cohesion ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0496 -1.6194 0.3806 1.3806 4.3806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8345 0.1290 45.224 <0.0000000000000002 ***
## condflourish_vs_control 0.2151 0.1290 1.668 0.0965 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.217 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.009371, Adjusted R-squared: 0.006001
## F-statistic: 2.781 on 1 and 294 DF, p-value: 0.09645# Flourish cond: over time
model <- lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 1258.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.18777 -0.41409 0.01838 0.46319 1.99315
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 4.014 2.004
## Residual 1.121 1.059
## Number of obs: 310, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.84112 0.16591 168.50397 35.206 < 0.0000000000000002 ***
## I(time - 2.5) 0.14190 0.04176 143.45334 3.398 0.000878 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.056standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.23 | [0.03, 0.43]
## time - 2 5 | 0.09 | [0.04, 0.15]# Control cond: over time
lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 1361.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.40666 -0.38214 0.02661 0.52749 2.49832
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.560 1.887
## Residual 1.078 1.038
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.53131 0.14981 188.08233 36.923 <0.0000000000000002 ***
## I(time - 2.5) 0.02266 0.03891 161.57126 0.582 0.561
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.060Mindfulness
Intention to Treat
# Time 1
lm(mindfulness_rev ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_ITT,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.4065 -4.2311 -0.4065 3.5935 14.7689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.31880 0.27130 56.465 <0.0000000000000002 ***
## condflourish_vs_control -0.08771 0.27130 -0.323 0.747
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.968 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0002168, Adjusted R-squared: -0.001857
## F-statistic: 0.1045 on 1 and 482 DF, p-value: 0.7466# Time 4
lm(mindfulness_rev ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_ITT,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.9944 -3.9944 0.0056 4.0056 15.0056
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.3656 0.3243 44.296 <0.0000000000000002 ***
## condflourish_vs_control 0.6288 0.3243 1.939 0.0533 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.057 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.01072, Adjusted R-squared: 0.007866
## F-statistic: 3.759 on 1 and 347 DF, p-value: 0.05334cohens_d(mindfulness_rev ~ cond, data = subset(data_ITT, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## -0.21 | [-0.42, 0.00]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 2628.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.45922 -0.52118 0.00727 0.50981 2.26629
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 20.51 4.529
## Residual 17.21 4.148
## Number of obs: 416, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.0148 0.3625 233.9249 41.420 <0.0000000000000002 ***
## I(time - 2.5) -0.1491 0.1425 192.8186 -1.046 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.121# Control cond: over time
model <- lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 2566.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35469 -0.42423 -0.02245 0.48029 2.21617
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 22.40 4.733
## Residual 12.07 3.474
## Number of obs: 417, groups: unique_ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6087 0.3528 252.1561 41.410 < 0.0000000000000002 ***
## I(time - 2.5) -0.5318 0.1218 192.2772 -4.365 0.0000208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.138standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.33 | [-0.52, -0.13]
## time - 2 5 | -0.13 | [-0.19, -0.07]Excluded Preregistered
# Time 1
lm(mindfulness_rev ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_excluded,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.5241 -3.5241 -0.5241 3.5572 14.5572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.48343 0.29737 52.069 <0.0000000000000002 ***
## condflourish_vs_control -0.04064 0.29737 -0.137 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.854 on 386 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 4.838e-05, Adjusted R-squared: -0.002542
## F-statistic: 0.01868 on 1 and 386 DF, p-value: 0.8914# Time 4
lm(mindfulness_rev ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_excluded,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0947 -3.7226 -0.0947 4.2774 14.9053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.4086 0.3343 43.101 <0.0000000000000002 ***
## condflourish_vs_control 0.6860 0.3343 2.052 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.012 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.01291, Adjusted R-squared: 0.009845
## F-statistic: 4.211 on 1 and 322 DF, p-value: 0.04096cohens_d(mindfulness_rev ~ cond, data = subset(data_excluded, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## --------------------------
## -0.23 | [-0.45, -0.01]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 2326.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4968 -0.5073 -0.0235 0.5175 2.2521
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 19.19 4.380
## Residual 17.32 4.162
## Number of obs: 370, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.2448 0.3800 200.8598 40.120 <0.0000000000000002 ***
## I(time - 2.5) -0.1371 0.1483 180.7662 -0.925 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.074# Control cond: over time
lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 2083.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38364 -0.42930 -0.02933 0.50623 2.18221
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 21.87 4.677
## Residual 11.49 3.390
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6386 0.3911 187.8765 37.433 < 0.0000000000000002 ***
## I(time - 2.5) -0.5903 0.1264 164.5608 -4.669 0.00000626 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.071Excluded Unreasonable Numbers
# Time 1
lm(mindfulness_rev ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.5241 -3.5241 -0.5241 3.4759 14.6213
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.45138 0.30501 50.658 <0.0000000000000002 ***
## condflourish_vs_control -0.07268 0.30501 -0.238 0.812
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.748 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.0001604, Adjusted R-squared: -0.002664
## F-statistic: 0.05678 on 1 and 354 DF, p-value: 0.8118# Time 4
lm(mindfulness_rev ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = mindfulness_rev ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.0142 -3.7226 -0.0142 4.0587 14.2774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14.3684 0.3467 41.439 <0.0000000000000002 ***
## condflourish_vs_control 0.6458 0.3467 1.863 0.0635 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.959 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.01166, Adjusted R-squared: 0.0083
## F-statistic: 3.469 on 1 and 294 DF, p-value: 0.06353cohens_d(mindfulness_rev ~ cond, data = subset(data_excluded_unreasonable, time == 4))## Warning: Missing values detected. NAs dropped.## Cohen's d | 95% CI
## -------------------------
## -0.22 | [-0.45, 0.01]
##
## - Estimated using pooled SD.# Flourish cond: over time
lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 1938.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.52078 -0.52291 -0.04009 0.49768 2.28421
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 17.53 4.187
## Residual 17.26 4.155
## Number of obs: 310, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.1630 0.4024 168.4224 37.682 <0.0000000000000002 ***
## I(time - 2.5) -0.1499 0.1618 151.2869 -0.926 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.078# Control cond: over time
lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control")) |> summary()## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 2083.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38364 -0.42930 -0.02933 0.50623 2.18221
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 21.87 4.677
## Residual 11.49 3.390
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6386 0.3911 187.8765 37.433 < 0.0000000000000002 ***
## I(time - 2.5) -0.5903 0.1264 164.5608 -4.669 0.00000626 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.071Emotional Resilience
Intention to Treat
# Time 1
lm(emo_res ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_ITT, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.0366 -3.0366 0.3333 3.3333 10.9634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.852 0.207 91.071 <0.0000000000000002 ***
## condflourish_vs_control -0.185 0.207 -0.894 0.372
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.548 on 481 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.001657, Adjusted R-squared: -0.0004185
## F-statistic: 0.7984 on 1 and 481 DF, p-value: 0.372# Time 4
lm(emo_res ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_ITT, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.3146 -2.7953 0.2047 3.2047 11.2047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.0550 0.2333 81.667 <0.0000000000000002 ***
## condflourish_vs_control 0.2596 0.2333 1.113 0.267
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.358 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.003556, Adjusted R-squared: 0.0006844
## F-statistic: 1.238 on 1 and 347 DF, p-value: 0.2666# Flourish cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 2275.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.88014 -0.44224 0.03937 0.47525 2.29807
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 13.770 3.711
## Residual 5.349 2.313
## Number of obs: 415, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.96500 0.26861 240.42626 70.605 <0.0000000000000002 ***
## I(time - 2.5) 0.20607 0.08059 188.80147 2.557 0.0113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.099standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.17 | [-0.01, 0.35]
## time - 2 5 | 0.07 | [ 0.02, 0.12]# Control cond: over time
model <- lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 2566.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35469 -0.42423 -0.02245 0.48029 2.21617
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 22.40 4.733
## Residual 12.07 3.474
## Number of obs: 417, groups: unique_ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6087 0.3528 252.1561 41.410 < 0.0000000000000002 ***
## I(time - 2.5) -0.5318 0.1218 192.2772 -4.365 0.0000208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.138standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.33 | [-0.52, -0.13]
## time - 2 5 | -0.13 | [-0.19, -0.07]Excluded Preregistered
# Time 1
lm(emo_res ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.722 -2.815 0.185 3.278 11.278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.76846 0.23115 81.197 <0.0000000000000002 ***
## condflourish_vs_control 0.04654 0.23115 0.201 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.545 on 385 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0001053, Adjusted R-squared: -0.002492
## F-statistic: 0.04053 on 1 and 385 DF, p-value: 0.8405# Time 4
lm(emo_res ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4260 -2.7806 0.2194 3.2194 11.2194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.1033 0.2402 79.532 <0.0000000000000002 ***
## condflourish_vs_control 0.3227 0.2402 1.343 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.319 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.005574, Adjusted R-squared: 0.002486
## F-statistic: 1.805 on 1 and 322 DF, p-value: 0.1801# Flourish cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 2019.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8624 -0.4298 0.0326 0.4597 2.2635
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 13.676 3.698
## Residual 5.482 2.341
## Number of obs: 369, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.1211 0.2892 202.6132 66.12 <0.0000000000000002 ***
## I(time - 2.5) 0.2133 0.0843 175.7398 2.53 0.0123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.059standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.18 | [-0.01, 0.37]
## time - 2 5 | 0.07 | [ 0.02, 0.13]# Control cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 1864.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36123 -0.45615 0.02325 0.44945 2.13938
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 15.07 3.882
## Residual 4.86 2.205
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.76079 0.30972 188.64406 60.573 <0.0000000000000002 ***
## I(time - 2.5) 0.02591 0.08260 162.45211 0.314 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.061standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.02 | [-0.17, 0.22]
## time - 2 5 | 8.63e-03 | [-0.05, 0.06]Excluded Unreasonable Numbers
# Time 1
lm(emo_res ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7219 -2.9586 0.3047 3.3314 11.2781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.69528 0.24212 77.22 <0.0000000000000002 ***
## condflourish_vs_control -0.02664 0.24212 -0.11 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.562 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 3.42e-05, Adjusted R-squared: -0.002791
## F-statistic: 0.01211 on 1 and 354 DF, p-value: 0.9124# Time 4
lm(emo_res ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4397 -2.7806 0.2194 3.2194 11.2194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.1102 0.2543 75.147 <0.0000000000000002 ***
## condflourish_vs_control 0.3295 0.2543 1.296 0.196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.37 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.005679, Adjusted R-squared: 0.002297
## F-statistic: 1.679 on 1 and 294 DF, p-value: 0.196# Flourish cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 1700.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.81861 -0.41970 0.02446 0.47115 2.08063
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 14.26 3.776
## Residual 5.41 2.326
## Number of obs: 310, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.06483 0.32043 170.52436 59.498 < 0.0000000000000002 ***
## I(time - 2.5) 0.27406 0.09151 146.97210 2.995 0.00322 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.062standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.23 | [0.02, 0.44]
## time - 2 5 | 0.09 | [0.03, 0.15]# Control cond: over time
model <- lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 2083.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38364 -0.42930 -0.02933 0.50623 2.18221
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 21.87 4.677
## Residual 11.49 3.390
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6386 0.3911 187.8765 37.433 < 0.0000000000000002 ***
## I(time - 2.5) -0.5903 0.1264 164.5608 -4.669 0.00000626 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.071standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.37 | [-0.58, -0.17]
## time - 2 5 | -0.15 | [-0.21, -0.09]Closeness to School (IOS)
Intention to Treat
# Time 1
lm(ios ~ cond, data = subset(data_ITT, time == 1)) |> summary()##
## Call:
## lm(formula = ios ~ cond, data = subset(data_ITT, time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2479 -1.2358 -0.2358 0.7642 3.7642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.241836 0.063577 50.991 <0.0000000000000002 ***
## condflourish_vs_control 0.006063 0.063577 0.095 0.924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 482 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 1.887e-05, Adjusted R-squared: -0.002056
## F-statistic: 0.009096 on 1 and 482 DF, p-value: 0.9241# Time 4
lm(ios ~ cond, data = subset(data_ITT, time == 4)) |> summary()##
## Call:
## lm(formula = ios ~ cond, data = subset(data_ITT, time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5225 -1.3099 -0.3099 0.6901 3.6901
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.41621 0.07664 44.576 <0.0000000000000002 ***
## condflourish_vs_control 0.10627 0.07664 1.387 0.166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.431 on 347 degrees of freedom
## (137 observations deleted due to missingness)
## Multiple R-squared: 0.00551, Adjusted R-squared: 0.002644
## F-statistic: 1.923 on 1 and 347 DF, p-value: 0.1665# Flourish cond: over time
model <- lmer(ios ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ios ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "flourish")
##
## REML criterion at convergence: 1398.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0094 -0.5395 -0.0959 0.4484 3.3619
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.1868 1.0894
## Residual 0.8128 0.9015
## Number of obs: 416, groups: unique_ID, 239
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.37714 0.08446 240.29666 39.984 < 0.0000000000000002 ***
## I(time - 2.5) 0.08534 0.03108 196.22672 2.746 0.00659 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.117standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.22 | [0.02, 0.42]
## time - 2 5 | 0.09 | [0.03, 0.15]# Control cond: over time
model <- lmer(ios ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ios ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_ITT, cond == "control")
##
## REML criterion at convergence: 1395.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7664 -0.5029 -0.0765 0.4815 3.1886
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.1931 1.0923
## Residual 0.7807 0.8836
## Number of obs: 417, groups: unique_ID, 246
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.243371 0.083638 251.823880 38.779 <0.0000000000000002 ***
## I(time - 2.5) 0.005066 0.030877 195.019692 0.164 0.87
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.144Excluded Preregistered
# Time 1
lm(emo_res ~ cond, data = subset(data_excluded, time == 1)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded, time ==
## 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.722 -2.815 0.185 3.278 11.278
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.76846 0.23115 81.197 <0.0000000000000002 ***
## condflourish_vs_control 0.04654 0.23115 0.201 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.545 on 385 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0001053, Adjusted R-squared: -0.002492
## F-statistic: 0.04053 on 1 and 385 DF, p-value: 0.8405# Time 4
lm(emo_res ~ cond, data = subset(data_excluded, time == 4)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded, time ==
## 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4260 -2.7806 0.2194 3.2194 11.2194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.1033 0.2402 79.532 <0.0000000000000002 ***
## condflourish_vs_control 0.3227 0.2402 1.343 0.18
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.319 on 322 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.005574, Adjusted R-squared: 0.002486
## F-statistic: 1.805 on 1 and 322 DF, p-value: 0.1801# Flourish cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "flourish")
##
## REML criterion at convergence: 2019.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8624 -0.4298 0.0326 0.4597 2.2635
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 13.676 3.698
## Residual 5.482 2.341
## Number of obs: 369, groups: unique_ID, 202
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.1211 0.2892 202.6132 66.12 <0.0000000000000002 ***
## I(time - 2.5) 0.2133 0.0843 175.7398 2.53 0.0123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.059standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.18 | [-0.01, 0.37]
## time - 2 5 | 0.07 | [ 0.02, 0.13]# Control cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded, cond == "control")
##
## REML criterion at convergence: 1864.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.36123 -0.45615 0.02325 0.44945 2.13938
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 15.07 3.882
## Residual 4.86 2.205
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.76079 0.30972 188.64406 60.573 <0.0000000000000002 ***
## I(time - 2.5) 0.02591 0.08260 162.45211 0.314 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.061standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ----------------------------------------
## (Intercept) | 0.02 | [-0.17, 0.22]
## time - 2 5 | 8.63e-03 | [-0.05, 0.06]Excluded Unreasonable Numbers
# Time 1
lm(emo_res ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded_unreasonable,
## time == 1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.7219 -2.9586 0.3047 3.3314 11.2781
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.69528 0.24212 77.22 <0.0000000000000002 ***
## condflourish_vs_control -0.02664 0.24212 -0.11 0.912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.562 on 354 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 3.42e-05, Adjusted R-squared: -0.002791
## F-statistic: 0.01211 on 1 and 354 DF, p-value: 0.9124# Time 4
lm(emo_res ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()##
## Call:
## lm(formula = emo_res ~ cond, data = subset(data_excluded_unreasonable,
## time == 4))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.4397 -2.7806 0.2194 3.2194 11.2194
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.1102 0.2543 75.147 <0.0000000000000002 ***
## condflourish_vs_control 0.3295 0.2543 1.296 0.196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.37 on 294 degrees of freedom
## (32 observations deleted due to missingness)
## Multiple R-squared: 0.005679, Adjusted R-squared: 0.002297
## F-statistic: 1.679 on 1 and 294 DF, p-value: 0.196# Flourish cond: over time
model <- lmer(emo_res ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "flourish"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "flourish")
##
## REML criterion at convergence: 1700.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.81861 -0.41970 0.02446 0.47115 2.08063
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 14.26 3.776
## Residual 5.41 2.326
## Number of obs: 310, groups: unique_ID, 170
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 19.06483 0.32043 170.52436 59.498 < 0.0000000000000002 ***
## I(time - 2.5) 0.27406 0.09151 146.97210 2.995 0.00322 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.062standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## ---------------------------------------
## (Intercept) | 0.23 | [0.02, 0.44]
## time - 2 5 | 0.09 | [0.03, 0.15]# Control cond: over time
model <- lmer(mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control"))
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
## Data: subset(data_excluded_unreasonable, cond == "control")
##
## REML criterion at convergence: 2083.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.38364 -0.42930 -0.02933 0.50623 2.18221
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 21.87 4.677
## Residual 11.49 3.390
## Number of obs: 342, groups: unique_ID, 187
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 14.6386 0.3911 187.8765 37.433 < 0.0000000000000002 ***
## I(time - 2.5) -0.5903 0.1264 164.5608 -4.669 0.00000626 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## I(time-2.5) 0.071standardize_parameters(model)## # Standardization method: refit
##
## Parameter | Std. Coef. | 95% CI
## -----------------------------------------
## (Intercept) | -0.37 | [-0.58, -0.17]
## time - 2 5 | -0.15 | [-0.21, -0.09]Who gets the most out of Flourish?
Difference scores within Flourish condition
# make wide again - ITT
merged_data_wide_ITT <- data_ITT %>%
pivot_wider(
id_cols = c(unique_ID, univ, cond, Gender, Sex, Age, int_student, SES, SES_num, starts_with("Ethnicity")),
names_from = time,
names_prefix = "",
names_sep = "_",
values_from = c(depression, anxiety, loneliness, perceived_stress,
SAS_calm, SAS_well_being, SAS_vigour, SAS_depression, SAS_anxiety,
SAS_anger, SAS_positive, SAS_negative, flourishing, social_fit,
cohesion, mindfulness_rev, emo_res, school_satis, wellbeing_priority,
acad_selfefficacy, ios, Engagement_1, Engagement_2, Engagement_3)) |>
dplyr::select(-c(Engagement_1_2, Engagement_1_3, Engagement_2_2, Engagement_2_3, Engagement_3_2, Engagement_3_3)) |>
dplyr::rename(ActiveDays = Engagement_1_4,
Reports = Engagement_2_4,
Activities = Engagement_3_4) |>
dplyr::select(where(~ !all(is.na(.))))
diff_flourish_ITT <- merged_data_wide_ITT |>
dplyr::filter(cond == "flourish") |>
dplyr::mutate(depression_diff = depression_4 - depression_1,
anxiety_diff = anxiety_4 - anxiety_1,
loneliness_diff = loneliness_4 - loneliness_1,
perceived_stress_diff = perceived_stress_4 - perceived_stress_1,
SAS_calm_diff = SAS_calm_4 - SAS_calm_1,
SAS_well_being_diff = SAS_well_being_4 - SAS_well_being_1,
SAS_vigour_diff = SAS_vigour_4 - SAS_vigour_1,
SAS_depression_diff = SAS_depression_4 - SAS_depression_1,
SAS_anxiety_diff = SAS_anxiety_4 - SAS_anxiety_1,
SAS_anger_diff = SAS_anger_4 - SAS_anger_1,
SAS_positive_diff = SAS_positive_4 - SAS_positive_1,
SAS_negative_diff = SAS_negative_4 - SAS_negative_1,
flourishing_diff = flourishing_4 - flourishing_1,
social_fit_diff = social_fit_4 - social_fit_1,
cohesion_diff = cohesion_4 - cohesion_1,
mindfulness_diff = mindfulness_rev_4 - mindfulness_rev_1,
emo_res_diff = emo_res_4 - emo_res_1,
school_satis_diff = school_satis_4 - school_satis_1,
wellbeing_priority_diff = wellbeing_priority_4 - wellbeing_priority_1,
acad_selfefficacy_diff = acad_selfefficacy_4 - acad_selfefficacy_1,
ios_diff = ios_4 - ios_1)
# make wide again - data_excluded
merged_data_wide_excluded <- data_excluded %>%
pivot_wider(
id_cols = c(unique_ID, univ, cond, Gender, Sex, Age, int_student, SES, SES_num, starts_with("Ethnicity")),
names_from = time,
names_prefix = "",
names_sep = "_",
values_from = c(depression, anxiety, loneliness, perceived_stress,
SAS_calm, SAS_well_being, SAS_vigour, SAS_depression, SAS_anxiety,
SAS_anger, SAS_positive, SAS_negative, flourishing, social_fit,
cohesion, mindfulness_rev, emo_res, school_satis, wellbeing_priority,
acad_selfefficacy, ios, Engagement_1, Engagement_2, Engagement_3)) |>
dplyr::select(-c(Engagement_1_2, Engagement_1_3, Engagement_2_2, Engagement_2_3, Engagement_3_2, Engagement_3_3)) |>
dplyr::rename(ActiveDays = Engagement_1_4,
Reports = Engagement_2_4,
Activities = Engagement_3_4) |>
dplyr::select(where(~ !all(is.na(.))))
diff_flourish_excluded <- merged_data_wide_excluded |>
dplyr::filter(cond == "flourish") |>
dplyr::mutate(depression_diff = depression_4 - depression_1,
anxiety_diff = anxiety_4 - anxiety_1,
loneliness_diff = loneliness_4 - loneliness_1,
perceived_stress_diff = perceived_stress_4 - perceived_stress_1,
SAS_calm_diff = SAS_calm_4 - SAS_calm_1,
SAS_well_being_diff = SAS_well_being_4 - SAS_well_being_1,
SAS_vigour_diff = SAS_vigour_4 - SAS_vigour_1,
SAS_depression_diff = SAS_depression_4 - SAS_depression_1,
SAS_anxiety_diff = SAS_anxiety_4 - SAS_anxiety_1,
SAS_anger_diff = SAS_anger_4 - SAS_anger_1,
SAS_positive_diff = SAS_positive_4 - SAS_positive_1,
SAS_negative_diff = SAS_negative_4 - SAS_negative_1,
flourishing_diff = flourishing_4 - flourishing_1,
social_fit_diff = social_fit_4 - social_fit_1,
cohesion_diff = cohesion_4 - cohesion_1,
mindfulness_diff = mindfulness_rev_4 - mindfulness_rev_1,
emo_res_diff = emo_res_4 - emo_res_1,
school_satis_diff = school_satis_4 - school_satis_1,
wellbeing_priority_diff = wellbeing_priority_4 - wellbeing_priority_1,
acad_selfefficacy_diff = acad_selfefficacy_4 - acad_selfefficacy_1,
ios_diff = ios_4 - ios_1)
# make wide again - data_excluded_unreasonable
merged_data_wide_excluded_unreasonable <- data_excluded_unreasonable %>%
pivot_wider(
id_cols = c(unique_ID, univ, cond, Gender, Sex, Age, int_student, SES, SES_num, starts_with("Ethnicity")),
names_from = time,
names_prefix = "",
names_sep = "_",
values_from = c(depression, anxiety, loneliness, perceived_stress,
SAS_calm, SAS_well_being, SAS_vigour, SAS_depression, SAS_anxiety,
SAS_anger, SAS_positive, SAS_negative, flourishing, social_fit,
cohesion, mindfulness_rev, emo_res, school_satis, wellbeing_priority,
acad_selfefficacy, ios, Engagement_1, Engagement_2, Engagement_3)) |>
dplyr::select(-c(Engagement_1_2, Engagement_1_3, Engagement_2_2, Engagement_2_3, Engagement_3_2, Engagement_3_3)) |>
dplyr::rename(ActiveDays = Engagement_1_4,
Reports = Engagement_2_4,
Activities = Engagement_3_4) |>
dplyr::select(where(~ !all(is.na(.))))
diff_flourish_excluded_unreasonable <- merged_data_wide_excluded_unreasonable |>
dplyr::filter(cond == "flourish") |>
dplyr::mutate(depression_diff = depression_4 - depression_1,
anxiety_diff = anxiety_4 - anxiety_1,
loneliness_diff = loneliness_4 - loneliness_1,
perceived_stress_diff = perceived_stress_4 - perceived_stress_1,
SAS_calm_diff = SAS_calm_4 - SAS_calm_1,
SAS_well_being_diff = SAS_well_being_4 - SAS_well_being_1,
SAS_vigour_diff = SAS_vigour_4 - SAS_vigour_1,
SAS_depression_diff = SAS_depression_4 - SAS_depression_1,
SAS_anxiety_diff = SAS_anxiety_4 - SAS_anxiety_1,
SAS_anger_diff = SAS_anger_4 - SAS_anger_1,
SAS_positive_diff = SAS_positive_4 - SAS_positive_1,
SAS_negative_diff = SAS_negative_4 - SAS_negative_1,
flourishing_diff = flourishing_4 - flourishing_1,
social_fit_diff = social_fit_4 - social_fit_1,
cohesion_diff = cohesion_4 - cohesion_1,
mindfulness_diff = mindfulness_rev_4 - mindfulness_rev_1,
emo_res_diff = emo_res_4 - emo_res_1,
school_satis_diff = school_satis_4 - school_satis_1,
wellbeing_priority_diff = wellbeing_priority_4 - wellbeing_priority_1,
acad_selfefficacy_diff = acad_selfefficacy_4 - acad_selfefficacy_1,
ios_diff = ios_4 - ios_1)Depression
Intention to Treat
m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)| depression diff | depression diff | depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.15 | -0.54 – 0.23 | 0.434 | -0.94 | -2.58 – 0.70 | 0.259 | -0.69 | -2.54 – 1.15 | 0.459 |
| ActiveDays | -0.00 | -0.01 – 0.01 | 0.918 | 0.00 | -0.01 – 0.01 | 0.867 | 0.00 | -0.01 – 0.01 | 0.992 |
| Reports | -0.00 | -0.04 – 0.03 | 0.889 | -0.00 | -0.04 – 0.04 | 0.966 | -0.00 | -0.05 – 0.04 | 0.894 |
| Activities | 0.01 | -0.01 – 0.02 | 0.341 | 0.01 | -0.01 – 0.03 | 0.316 | 0.01 | -0.01 – 0.03 | 0.275 |
| univ [Foothill] | 0.68 | -0.06 – 1.43 | 0.073 | 0.69 | -0.10 – 1.47 | 0.085 | |||
| univ [UW] | 0.22 | -0.26 – 0.70 | 0.370 | 0.21 | -0.32 – 0.74 | 0.432 | |||
| Sex [Woman] | 0.15 | -0.40 – 0.70 | 0.600 | 0.11 | -0.45 – 0.68 | 0.693 | |||
| Age | -0.01 | -0.05 – 0.04 | 0.739 | -0.01 | -0.06 – 0.04 | 0.726 | |||
| int student [No] | 0.53 | -0.30 – 1.37 | 0.209 | 0.56 | -0.40 – 1.52 | 0.251 | |||
| SES num | 0.02 | -0.17 – 0.21 | 0.838 | -0.01 | -0.22 – 0.20 | 0.928 | |||
| Ethnicity White | -0.20 | -0.84 – 0.43 | 0.529 | ||||||
| Ethnicity Hispanic | -0.21 | -1.07 – 0.65 | 0.628 | ||||||
| Ethnicity Black | -0.34 | -1.59 – 0.91 | 0.591 | ||||||
| Ethnicity East Asian | 0.07 | -0.74 – 0.89 | 0.856 | ||||||
| Ethnicity South Asian | -0.28 | -1.37 – 0.82 | 0.620 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.73 | -2.84 – 1.38 | 0.496 | ||||||
| Ethnicity Middle Eastern | 0.71 | -0.76 – 2.17 | 0.343 | ||||||
| Ethnicity American Indian | -0.21 | -3.49 – 3.08 | 0.902 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.006 / -0.012 | 0.032 / -0.023 | 0.051 / -0.055 | ||||||
Excluded Preregistered
m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)| depression diff | depression diff | depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.12 | -0.52 – 0.28 | 0.551 | -0.81 | -2.56 – 0.95 | 0.365 | -0.53 | -2.48 – 1.42 | 0.591 |
| ActiveDays | -0.00 | -0.01 – 0.01 | 0.902 | 0.00 | -0.01 – 0.01 | 0.906 | -0.00 | -0.01 – 0.01 | 0.958 |
| Reports | -0.00 | -0.04 – 0.03 | 0.870 | -0.00 | -0.04 – 0.04 | 0.942 | -0.00 | -0.05 – 0.04 | 0.856 |
| Activities | 0.01 | -0.01 – 0.02 | 0.397 | 0.01 | -0.01 – 0.03 | 0.350 | 0.01 | -0.01 – 0.03 | 0.303 |
| univ [Foothill] | 0.78 | 0.01 – 1.55 | 0.047 | 0.79 | -0.02 – 1.60 | 0.055 | |||
| univ [UW] | 0.21 | -0.28 – 0.70 | 0.390 | 0.20 | -0.34 – 0.73 | 0.471 | |||
| Sex [Woman] | 0.13 | -0.44 – 0.69 | 0.662 | 0.09 | -0.49 – 0.68 | 0.753 | |||
| Age | -0.01 | -0.06 – 0.04 | 0.649 | -0.01 | -0.06 – 0.04 | 0.633 | |||
| int student [No] | 0.48 | -0.39 – 1.36 | 0.277 | 0.52 | -0.47 – 1.51 | 0.297 | |||
| SES num | 0.02 | -0.17 – 0.22 | 0.812 | -0.00 | -0.21 – 0.21 | 0.988 | |||
| Ethnicity White | -0.25 | -0.90 – 0.39 | 0.438 | ||||||
| Ethnicity Hispanic | -0.29 | -1.17 – 0.59 | 0.515 | ||||||
| Ethnicity Black | -0.39 | -1.64 – 0.87 | 0.545 | ||||||
| Ethnicity East Asian | 0.02 | -0.81 – 0.84 | 0.969 | ||||||
| Ethnicity South Asian | -0.27 | -1.39 – 0.86 | 0.642 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.84 | -2.97 – 1.29 | 0.438 | ||||||
| Ethnicity Middle Eastern | 0.65 | -0.83 – 2.14 | 0.388 | ||||||
| Ethnicity American Indian | -0.16 | -3.48 – 3.16 | 0.924 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.005 / -0.013 | 0.033 / -0.022 | 0.054 / -0.054 | ||||||
Excluded Unreasonable Numbers
m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)| depression diff | depression diff | depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.12 | -0.34 – 0.58 | 0.606 | -1.05 | -2.97 – 0.87 | 0.281 | -0.89 | -3.00 – 1.23 | 0.409 |
| ActiveDays | -0.01 | -0.04 – 0.02 | 0.446 | -0.00 | -0.03 – 0.02 | 0.763 | -0.01 | -0.04 – 0.02 | 0.633 |
| Reports | -0.01 | -0.06 – 0.04 | 0.681 | -0.01 | -0.06 – 0.04 | 0.632 | -0.02 | -0.08 – 0.04 | 0.451 |
| Activities | 0.00 | -0.02 – 0.02 | 0.744 | 0.00 | -0.02 – 0.02 | 0.992 | 0.00 | -0.02 – 0.02 | 0.838 |
| univ [Foothill] | 0.88 | 0.03 – 1.73 | 0.044 | 0.93 | 0.03 – 1.83 | 0.043 | |||
| univ [UW] | 0.36 | -0.19 – 0.90 | 0.195 | 0.34 | -0.26 – 0.93 | 0.268 | |||
| Sex [Woman] | 0.15 | -0.47 – 0.77 | 0.628 | 0.05 | -0.60 – 0.69 | 0.890 | |||
| Age | -0.00 | -0.05 – 0.04 | 0.851 | -0.00 | -0.05 – 0.05 | 0.971 | |||
| int student [No] | 1.17 | 0.08 – 2.25 | 0.035 | 1.28 | 0.11 – 2.44 | 0.032 | |||
| SES num | -0.08 | -0.30 – 0.13 | 0.444 | -0.06 | -0.29 – 0.16 | 0.583 | |||
| Ethnicity White | -0.33 | -1.02 – 0.37 | 0.352 | ||||||
| Ethnicity Hispanic | -0.24 | -1.17 – 0.69 | 0.612 | ||||||
| Ethnicity Black | -1.03 | -2.41 – 0.35 | 0.141 | ||||||
| Ethnicity East Asian | -0.18 | -1.04 – 0.69 | 0.687 | ||||||
| Ethnicity South Asian | -0.38 | -1.69 – 0.93 | 0.562 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.14 | -3.28 – 1.01 | 0.297 | ||||||
| Ethnicity Middle Eastern | -0.21 | -3.14 – 2.71 | 0.886 | ||||||
| Ethnicity American Indian | 1.10 | -2.52 – 4.72 | 0.547 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.011 / -0.011 | 0.070 / 0.005 | 0.098 / -0.028 | ||||||
Anxiety
Intention to Treat
m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)| anxiety diff | anxiety diff | anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.17 | -0.60 – 0.26 | 0.440 | 0.47 | -1.33 – 2.26 | 0.608 | 0.34 | -1.65 – 2.34 | 0.734 |
| ActiveDays | 0.01 | -0.00 – 0.02 | 0.196 | 0.01 | -0.00 – 0.03 | 0.185 | 0.01 | -0.00 – 0.03 | 0.146 |
| Reports | -0.01 | -0.05 – 0.03 | 0.759 | -0.01 | -0.05 – 0.04 | 0.761 | -0.00 | -0.05 – 0.04 | 0.900 |
| Activities | -0.01 | -0.02 – 0.01 | 0.469 | -0.00 | -0.02 – 0.02 | 0.724 | -0.00 | -0.02 – 0.02 | 0.664 |
| univ [Foothill] | 1.11 | 0.29 – 1.93 | 0.008 | 1.01 | 0.16 – 1.86 | 0.020 | |||
| univ [UW] | 0.26 | -0.27 – 0.78 | 0.339 | 0.04 | -0.53 – 0.61 | 0.884 | |||
| Sex [Woman] | -0.16 | -0.76 – 0.45 | 0.606 | -0.11 | -0.72 – 0.51 | 0.733 | |||
| Age | -0.04 | -0.09 – 0.01 | 0.133 | -0.04 | -0.09 – 0.01 | 0.111 | |||
| int student [No] | 0.05 | -0.86 – 0.96 | 0.910 | 0.39 | -0.65 – 1.43 | 0.463 | |||
| SES num | -0.04 | -0.25 – 0.17 | 0.699 | -0.01 | -0.24 – 0.21 | 0.903 | |||
| Ethnicity White | -0.43 | -1.12 – 0.26 | 0.217 | ||||||
| Ethnicity Hispanic | -0.35 | -1.28 – 0.59 | 0.462 | ||||||
| Ethnicity Black | 0.37 | -0.98 – 1.72 | 0.586 | ||||||
| Ethnicity East Asian | 0.30 | -0.58 – 1.18 | 0.502 | ||||||
| Ethnicity South Asian | 0.17 | -1.01 – 1.35 | 0.777 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.59 | -1.70 – 2.87 | 0.612 | ||||||
| Ethnicity Middle Eastern | -0.95 | -2.54 – 0.64 | 0.238 | ||||||
| Ethnicity American Indian | -0.17 | -3.74 – 3.39 | 0.924 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.011 / -0.007 | 0.059 / 0.006 | 0.097 / -0.004 | ||||||
Excluded Preregistered
m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)| anxiety diff | anxiety diff | anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.26 | -0.70 – 0.18 | 0.244 | 0.17 | -1.75 – 2.09 | 0.861 | -0.00 | -2.11 – 2.10 | 0.999 |
| ActiveDays | 0.01 | -0.00 – 0.02 | 0.178 | 0.01 | -0.00 – 0.03 | 0.168 | 0.01 | -0.00 – 0.03 | 0.123 |
| Reports | -0.00 | -0.05 – 0.04 | 0.811 | -0.01 | -0.05 – 0.04 | 0.799 | -0.00 | -0.05 – 0.05 | 0.983 |
| Activities | -0.00 | -0.02 – 0.01 | 0.631 | -0.00 | -0.02 – 0.02 | 0.834 | -0.00 | -0.02 – 0.02 | 0.789 |
| univ [Foothill] | 1.04 | 0.20 – 1.88 | 0.015 | 0.95 | 0.07 – 1.82 | 0.034 | |||
| univ [UW] | 0.25 | -0.28 – 0.78 | 0.358 | 0.03 | -0.55 – 0.61 | 0.919 | |||
| Sex [Woman] | -0.11 | -0.73 – 0.51 | 0.724 | -0.05 | -0.68 – 0.58 | 0.875 | |||
| Age | -0.03 | -0.08 – 0.02 | 0.177 | -0.04 | -0.09 – 0.01 | 0.148 | |||
| int student [No] | 0.16 | -0.80 – 1.12 | 0.744 | 0.51 | -0.56 – 1.57 | 0.352 | |||
| SES num | -0.03 | -0.24 – 0.19 | 0.789 | 0.01 | -0.22 – 0.23 | 0.964 | |||
| Ethnicity White | -0.46 | -1.16 – 0.24 | 0.196 | ||||||
| Ethnicity Hispanic | -0.32 | -1.27 – 0.63 | 0.504 | ||||||
| Ethnicity Black | 0.36 | -1.00 – 1.72 | 0.601 | ||||||
| Ethnicity East Asian | 0.33 | -0.56 – 1.22 | 0.462 | ||||||
| Ethnicity South Asian | 0.06 | -1.16 – 1.28 | 0.926 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.68 | -1.62 – 2.99 | 0.559 | ||||||
| Ethnicity Middle Eastern | -1.02 | -2.63 – 0.58 | 0.210 | ||||||
| Ethnicity American Indian | -0.36 | -3.94 – 3.23 | 0.843 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.012 / -0.006 | 0.051 / -0.003 | 0.093 / -0.010 | ||||||
Excluded Unreasonable Numbers
m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)| anxiety diff | anxiety diff | anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.02 | -0.48 – 0.52 | 0.935 | -0.45 | -2.53 – 1.63 | 0.669 | -0.20 | -2.47 – 2.08 | 0.862 |
| ActiveDays | -0.01 | -0.04 – 0.02 | 0.633 | -0.00 | -0.04 – 0.03 | 0.815 | -0.01 | -0.04 – 0.03 | 0.724 |
| Reports | 0.00 | -0.05 – 0.05 | 0.994 | -0.00 | -0.05 – 0.05 | 0.986 | -0.01 | -0.07 – 0.05 | 0.744 |
| Activities | -0.01 | -0.03 – 0.01 | 0.430 | -0.01 | -0.03 – 0.01 | 0.425 | -0.01 | -0.03 – 0.01 | 0.486 |
| univ [Foothill] | 1.27 | 0.35 – 2.19 | 0.007 | 1.19 | 0.22 – 2.15 | 0.016 | |||
| univ [UW] | 0.28 | -0.31 – 0.87 | 0.349 | 0.12 | -0.53 – 0.76 | 0.719 | |||
| Sex [Woman] | -0.27 | -0.94 – 0.40 | 0.428 | -0.28 | -0.97 – 0.41 | 0.420 | |||
| Age | -0.03 | -0.08 – 0.03 | 0.328 | -0.03 | -0.08 – 0.03 | 0.325 | |||
| int student [No] | 1.14 | -0.04 – 2.31 | 0.057 | 1.34 | 0.09 – 2.59 | 0.036 | |||
| SES num | -0.06 | -0.29 – 0.17 | 0.626 | -0.03 | -0.27 – 0.21 | 0.801 | |||
| Ethnicity White | -0.59 | -1.34 – 0.16 | 0.121 | ||||||
| Ethnicity Hispanic | -0.48 | -1.48 – 0.52 | 0.346 | ||||||
| Ethnicity Black | -0.33 | -1.81 – 1.16 | 0.662 | ||||||
| Ethnicity East Asian | 0.07 | -0.86 – 1.01 | 0.875 | ||||||
| Ethnicity South Asian | -0.74 | -2.15 – 0.67 | 0.300 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.20 | -2.11 – 2.50 | 0.867 | ||||||
| Ethnicity Middle Eastern | -1.11 | -4.25 – 2.03 | 0.485 | ||||||
| Ethnicity American Indian | 1.05 | -2.84 – 4.94 | 0.594 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.013 / -0.008 | 0.094 / 0.032 | 0.134 / 0.013 | ||||||
Loneliness
Intention to Treat
m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)| loneliness diff | loneliness diff | loneliness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.34 | -0.72 – 0.05 | 0.086 | -1.53 | -3.17 – 0.10 | 0.066 | -1.89 | -3.72 – -0.07 | 0.042 |
| ActiveDays | 0.01 | -0.00 – 0.03 | 0.060 | 0.02 | 0.00 – 0.03 | 0.030 | 0.01 | 0.00 – 0.03 | 0.038 |
| Reports | -0.02 | -0.05 – 0.02 | 0.411 | -0.01 | -0.05 – 0.03 | 0.581 | 0.01 | -0.04 – 0.05 | 0.760 |
| Activities | -0.02 | -0.03 – 0.00 | 0.066 | -0.02 | -0.04 – -0.00 | 0.039 | -0.02 | -0.03 – 0.00 | 0.077 |
| univ [Foothill] | 0.13 | -0.61 – 0.87 | 0.734 | 0.12 | -0.66 – 0.90 | 0.758 | |||
| univ [UW] | 0.22 | -0.26 – 0.70 | 0.369 | 0.16 | -0.36 – 0.68 | 0.554 | |||
| Sex [Woman] | 0.07 | -0.48 – 0.62 | 0.810 | 0.08 | -0.48 – 0.65 | 0.765 | |||
| Age | 0.02 | -0.03 – 0.06 | 0.390 | 0.02 | -0.02 – 0.07 | 0.320 | |||
| int student [No] | 0.58 | -0.25 – 1.41 | 0.167 | 0.83 | -0.11 – 1.78 | 0.084 | |||
| SES num | 0.02 | -0.17 – 0.21 | 0.826 | 0.02 | -0.19 – 0.22 | 0.875 | |||
| Ethnicity White | -0.16 | -0.78 – 0.47 | 0.621 | ||||||
| Ethnicity Hispanic | 0.07 | -0.79 – 0.92 | 0.878 | ||||||
| Ethnicity Black | -0.50 | -1.73 – 0.74 | 0.428 | ||||||
| Ethnicity East Asian | 0.28 | -0.52 – 1.08 | 0.494 | ||||||
| Ethnicity South Asian | -0.04 | -1.12 – 1.04 | 0.940 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.05 | -2.04 – 2.13 | 0.966 | ||||||
| Ethnicity Middle Eastern | -0.39 | -1.84 – 1.06 | 0.596 | ||||||
| Ethnicity American Indian | -2.97 | -6.23 – 0.28 | 0.073 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.029 / 0.012 | 0.048 / -0.005 | 0.082 / -0.021 | ||||||
Excluded Preregistered
m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)| loneliness diff | loneliness diff | loneliness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.34 | -0.73 – 0.06 | 0.096 | -2.01 | -3.74 – -0.28 | 0.023 | -2.41 | -4.31 – -0.50 | 0.013 |
| ActiveDays | 0.01 | -0.00 – 0.03 | 0.060 | 0.02 | 0.00 – 0.03 | 0.023 | 0.02 | 0.00 – 0.03 | 0.024 |
| Reports | -0.02 | -0.05 – 0.02 | 0.412 | -0.01 | -0.05 – 0.03 | 0.609 | 0.01 | -0.03 – 0.05 | 0.660 |
| Activities | -0.02 | -0.03 – 0.00 | 0.069 | -0.02 | -0.04 – -0.00 | 0.041 | -0.02 | -0.03 – 0.00 | 0.084 |
| univ [Foothill] | 0.15 | -0.61 – 0.91 | 0.697 | 0.13 | -0.66 – 0.92 | 0.748 | |||
| univ [UW] | 0.24 | -0.24 – 0.73 | 0.318 | 0.19 | -0.33 – 0.71 | 0.471 | |||
| Sex [Woman] | 0.18 | -0.38 – 0.74 | 0.535 | 0.22 | -0.35 – 0.79 | 0.453 | |||
| Age | 0.02 | -0.02 – 0.07 | 0.307 | 0.03 | -0.02 – 0.08 | 0.238 | |||
| int student [No] | 0.80 | -0.06 – 1.67 | 0.069 | 1.06 | 0.09 – 2.02 | 0.032 | |||
| SES num | 0.04 | -0.15 – 0.24 | 0.670 | 0.05 | -0.16 – 0.25 | 0.656 | |||
| Ethnicity White | -0.24 | -0.87 – 0.39 | 0.458 | ||||||
| Ethnicity Hispanic | 0.01 | -0.84 – 0.87 | 0.972 | ||||||
| Ethnicity Black | -0.59 | -1.81 – 0.64 | 0.348 | ||||||
| Ethnicity East Asian | 0.25 | -0.56 – 1.05 | 0.545 | ||||||
| Ethnicity South Asian | -0.32 | -1.42 – 0.79 | 0.572 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.10 | -1.98 – 2.18 | 0.928 | ||||||
| Ethnicity Middle Eastern | -0.54 | -1.99 – 0.91 | 0.459 | ||||||
| Ethnicity American Indian | -3.20 | -6.44 – 0.04 | 0.053 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.030 / 0.012 | 0.059 / 0.005 | 0.100 / -0.003 | ||||||
Excluded Unreasonable Numbers
m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)| loneliness diff | loneliness diff | loneliness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.26 | -0.71 – 0.20 | 0.267 | -1.70 | -3.65 – 0.25 | 0.086 | -1.95 | -4.03 – 0.14 | 0.068 |
| ActiveDays | -0.00 | -0.03 – 0.02 | 0.782 | -0.00 | -0.03 – 0.03 | 0.905 | -0.00 | -0.03 – 0.03 | 0.872 |
| Reports | 0.01 | -0.04 – 0.06 | 0.737 | 0.01 | -0.04 – 0.06 | 0.690 | 0.04 | -0.02 – 0.09 | 0.237 |
| Activities | -0.01 | -0.03 – 0.00 | 0.151 | -0.02 | -0.04 – 0.00 | 0.098 | -0.01 | -0.03 – 0.01 | 0.216 |
| univ [Foothill] | 0.03 | -0.83 – 0.90 | 0.942 | -0.03 | -0.91 – 0.86 | 0.949 | |||
| univ [UW] | 0.12 | -0.43 – 0.67 | 0.673 | 0.01 | -0.58 – 0.60 | 0.980 | |||
| Sex [Woman] | 0.13 | -0.50 – 0.76 | 0.686 | 0.12 | -0.51 – 0.76 | 0.704 | |||
| Age | 0.03 | -0.02 – 0.08 | 0.271 | 0.04 | -0.01 – 0.09 | 0.158 | |||
| int student [No] | 0.73 | -0.37 – 1.83 | 0.190 | 0.97 | -0.18 – 2.12 | 0.099 | |||
| SES num | 0.01 | -0.20 – 0.23 | 0.905 | 0.03 | -0.19 – 0.26 | 0.769 | |||
| Ethnicity White | -0.48 | -1.17 – 0.20 | 0.167 | ||||||
| Ethnicity Hispanic | -0.30 | -1.22 – 0.62 | 0.524 | ||||||
| Ethnicity Black | -1.24 | -2.60 – 0.12 | 0.074 | ||||||
| Ethnicity East Asian | -0.09 | -0.94 – 0.77 | 0.841 | ||||||
| Ethnicity South Asian | -0.23 | -1.53 – 1.06 | 0.720 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.20 | -2.32 – 1.92 | 0.853 | ||||||
| Ethnicity Middle Eastern | -2.77 | -5.65 – 0.12 | 0.060 | ||||||
| Ethnicity American Indian | -3.66 | -7.23 – -0.09 | 0.045 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.024 / 0.002 | 0.043 / -0.023 | 0.124 / 0.002 | ||||||
Perceived Stress
Intention to Treat
m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)| perceived stress diff | perceived stress diff | perceived stress diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.15 | -0.91 – 0.62 | 0.703 | -1.87 | -5.03 – 1.28 | 0.242 | -1.16 | -4.69 – 2.37 | 0.517 |
| ActiveDays | 0.01 | -0.01 – 0.04 | 0.321 | 0.02 | -0.01 – 0.04 | 0.166 | 0.02 | -0.01 – 0.05 | 0.161 |
| Reports | 0.02 | -0.05 – 0.10 | 0.538 | 0.04 | -0.03 – 0.11 | 0.292 | 0.04 | -0.04 – 0.13 | 0.320 |
| Activities | -0.02 | -0.06 – 0.01 | 0.135 | -0.03 | -0.07 – -0.00 | 0.046 | -0.04 | -0.07 – -0.00 | 0.030 |
| univ [Foothill] | 1.08 | -0.35 – 2.52 | 0.137 | 1.02 | -0.48 – 2.52 | 0.179 | |||
| univ [UW] | 0.64 | -0.28 – 1.57 | 0.173 | 0.55 | -0.46 – 1.55 | 0.285 | |||
| Sex [Woman] | -0.81 | -1.87 – 0.25 | 0.131 | -0.69 | -1.77 – 0.39 | 0.211 | |||
| Age | 0.07 | -0.01 – 0.16 | 0.089 | 0.06 | -0.03 – 0.15 | 0.172 | |||
| int student [No] | 0.44 | -1.16 – 2.04 | 0.589 | 0.21 | -1.62 – 2.04 | 0.823 | |||
| SES num | -0.00 | -0.38 – 0.37 | 0.986 | 0.04 | -0.36 – 0.43 | 0.861 | |||
| Ethnicity White | -0.45 | -1.66 – 0.76 | 0.462 | ||||||
| Ethnicity Hispanic | -1.09 | -2.73 – 0.56 | 0.195 | ||||||
| Ethnicity Black | 1.10 | -1.29 – 3.48 | 0.365 | ||||||
| Ethnicity East Asian | -0.51 | -2.06 – 1.04 | 0.518 | ||||||
| Ethnicity South Asian | -0.55 | -2.64 – 1.54 | 0.604 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.79 | -2.24 – 5.82 | 0.382 | ||||||
| Ethnicity Middle Eastern | -0.68 | -3.48 – 2.13 | 0.635 | ||||||
| Ethnicity American Indian | 0.18 | -6.10 – 6.47 | 0.955 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.018 / 0.001 | 0.091 / 0.040 | 0.119 / 0.021 | ||||||
Excluded Preregistered
m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)| perceived stress diff | perceived stress diff | perceived stress diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.24 | -1.02 – 0.54 | 0.547 | -2.85 | -6.20 – 0.49 | 0.094 | -2.18 | -5.87 – 1.52 | 0.246 |
| ActiveDays | 0.01 | -0.01 – 0.04 | 0.305 | 0.02 | -0.01 – 0.05 | 0.129 | 0.02 | -0.01 – 0.05 | 0.112 |
| Reports | 0.02 | -0.05 – 0.10 | 0.513 | 0.04 | -0.03 – 0.11 | 0.266 | 0.05 | -0.04 – 0.13 | 0.261 |
| Activities | -0.02 | -0.05 – 0.01 | 0.175 | -0.03 | -0.07 – -0.00 | 0.048 | -0.04 | -0.07 – -0.00 | 0.030 |
| univ [Foothill] | 0.88 | -0.58 – 2.35 | 0.237 | 0.84 | -0.69 – 2.38 | 0.280 | |||
| univ [UW] | 0.73 | -0.20 – 1.66 | 0.125 | 0.67 | -0.34 – 1.69 | 0.193 | |||
| Sex [Woman] | -0.59 | -1.68 – 0.49 | 0.281 | -0.44 | -1.56 – 0.67 | 0.431 | |||
| Age | 0.09 | 0.00 – 0.18 | 0.048 | 0.08 | -0.01 – 0.17 | 0.100 | |||
| int student [No] | 0.88 | -0.79 – 2.56 | 0.298 | 0.59 | -1.28 – 2.47 | 0.534 | |||
| SES num | 0.01 | -0.37 – 0.38 | 0.976 | 0.05 | -0.35 – 0.45 | 0.812 | |||
| Ethnicity White | -0.43 | -1.66 – 0.80 | 0.491 | ||||||
| Ethnicity Hispanic | -1.01 | -2.67 – 0.65 | 0.232 | ||||||
| Ethnicity Black | 1.06 | -1.32 – 3.45 | 0.379 | ||||||
| Ethnicity East Asian | -0.45 | -2.01 – 1.10 | 0.565 | ||||||
| Ethnicity South Asian | -0.97 | -3.11 – 1.17 | 0.370 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
2.07 | -1.97 – 6.11 | 0.313 | ||||||
| Ethnicity Middle Eastern | -0.74 | -3.55 – 2.08 | 0.606 | ||||||
| Ethnicity American Indian | -0.15 | -6.44 – 6.14 | 0.963 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.018 / 0.000 | 0.089 / 0.037 | 0.119 / 0.019 | ||||||
Excluded Unreasonable Numbers
m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)| perceived stress diff | perceived stress diff | perceived stress diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.18 | -1.10 – 0.74 | 0.699 | -2.87 | -6.69 – 0.96 | 0.140 | -1.81 | -5.99 – 2.37 | 0.393 |
| ActiveDays | 0.02 | -0.03 – 0.08 | 0.449 | 0.03 | -0.03 – 0.09 | 0.269 | 0.03 | -0.03 – 0.09 | 0.310 |
| Reports | 0.02 | -0.08 – 0.12 | 0.683 | 0.03 | -0.07 – 0.13 | 0.518 | 0.04 | -0.08 – 0.15 | 0.525 |
| Activities | -0.03 | -0.06 – 0.01 | 0.156 | -0.04 | -0.08 – -0.00 | 0.036 | -0.05 | -0.09 – -0.01 | 0.022 |
| univ [Foothill] | 1.18 | -0.52 – 2.87 | 0.173 | 1.17 | -0.60 – 2.94 | 0.193 | |||
| univ [UW] | 0.90 | -0.19 – 1.98 | 0.104 | 0.77 | -0.41 – 1.95 | 0.197 | |||
| Sex [Woman] | -0.53 | -1.77 – 0.71 | 0.400 | -0.34 | -1.61 – 0.93 | 0.599 | |||
| Age | 0.07 | -0.02 – 0.17 | 0.132 | 0.05 | -0.05 – 0.16 | 0.296 | |||
| int student [No] | 1.30 | -0.85 – 3.46 | 0.234 | 0.96 | -1.34 – 3.26 | 0.410 | |||
| SES num | -0.05 | -0.47 – 0.38 | 0.826 | -0.05 | -0.49 – 0.40 | 0.841 | |||
| Ethnicity White | -0.25 | -1.62 – 1.12 | 0.719 | ||||||
| Ethnicity Hispanic | -1.16 | -2.99 – 0.68 | 0.216 | ||||||
| Ethnicity Black | 1.55 | -1.18 – 4.27 | 0.263 | ||||||
| Ethnicity East Asian | -0.38 | -2.09 – 1.34 | 0.665 | ||||||
| Ethnicity South Asian | -1.42 | -4.01 – 1.16 | 0.278 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.81 | -2.43 – 6.05 | 0.399 | ||||||
| Ethnicity Middle Eastern | -2.31 | -8.08 – 3.45 | 0.429 | ||||||
| Ethnicity American Indian | 0.04 | -7.10 – 7.17 | 0.992 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.018 / -0.003 | 0.085 / 0.022 | 0.130 / 0.009 | ||||||
SAS: Calm
Intention to Treat
m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)| SAS calm diff | SAS calm diff | SAS calm diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.21 | -0.91 – 0.50 | 0.565 | 2.17 | -0.78 – 5.11 | 0.148 | 2.79 | -0.47 – 6.04 | 0.093 |
| ActiveDays | -0.01 | -0.03 – 0.02 | 0.527 | -0.01 | -0.04 – 0.01 | 0.336 | -0.01 | -0.04 – 0.01 | 0.277 |
| Reports | 0.01 | -0.06 – 0.08 | 0.783 | -0.00 | -0.07 – 0.06 | 0.918 | -0.00 | -0.08 – 0.08 | 0.973 |
| Activities | 0.05 | 0.02 – 0.07 | 0.003 | 0.05 | 0.02 – 0.08 | 0.001 | 0.05 | 0.02 – 0.08 | 0.001 |
| univ [Foothill] | -1.23 | -2.57 – 0.11 | 0.071 | -0.87 | -2.26 – 0.51 | 0.215 | |||
| univ [UW] | -0.82 | -1.69 – 0.04 | 0.063 | -0.84 | -1.77 – 0.09 | 0.076 | |||
| Sex [Woman] | 0.37 | -0.62 – 1.36 | 0.466 | 0.28 | -0.73 – 1.28 | 0.588 | |||
| Age | -0.05 | -0.13 – 0.03 | 0.200 | -0.06 | -0.15 – 0.02 | 0.130 | |||
| int student [No] | -0.79 | -2.28 – 0.71 | 0.300 | -0.88 | -2.57 – 0.82 | 0.308 | |||
| SES num | -0.08 | -0.43 – 0.26 | 0.636 | -0.17 | -0.54 – 0.19 | 0.356 | |||
| Ethnicity White | 0.40 | -0.72 – 1.51 | 0.484 | ||||||
| Ethnicity Hispanic | -0.51 | -2.03 – 1.01 | 0.506 | ||||||
| Ethnicity Black | -0.09 | -2.29 – 2.11 | 0.933 | ||||||
| Ethnicity East Asian | -0.16 | -1.59 – 1.27 | 0.826 | ||||||
| Ethnicity South Asian | 0.11 | -1.82 – 2.04 | 0.912 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-4.69 | -8.41 – -0.97 | 0.014 | ||||||
| Ethnicity Middle Eastern | 0.28 | -2.31 – 2.87 | 0.832 | ||||||
| Ethnicity American Indian | -0.44 | -6.24 – 5.36 | 0.881 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.059 / 0.042 | 0.115 / 0.065 | 0.163 / 0.070 | ||||||
Excluded Preregistered
m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)| SAS calm diff | SAS calm diff | SAS calm diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.09 | -0.82 – 0.63 | 0.799 | 2.33 | -0.81 – 5.47 | 0.145 | 3.24 | -0.18 – 6.67 | 0.063 |
| ActiveDays | -0.01 | -0.03 – 0.02 | 0.502 | -0.01 | -0.04 – 0.01 | 0.320 | -0.02 | -0.04 – 0.01 | 0.242 |
| Reports | 0.01 | -0.06 – 0.08 | 0.822 | -0.00 | -0.07 – 0.06 | 0.892 | -0.00 | -0.08 – 0.07 | 0.904 |
| Activities | 0.04 | 0.01 – 0.07 | 0.006 | 0.05 | 0.02 – 0.08 | 0.002 | 0.05 | 0.02 – 0.08 | 0.002 |
| univ [Foothill] | -1.05 | -2.43 – 0.33 | 0.135 | -0.63 | -2.06 – 0.79 | 0.380 | |||
| univ [UW] | -0.81 | -1.69 – 0.06 | 0.069 | -0.84 | -1.78 – 0.10 | 0.079 | |||
| Sex [Woman] | 0.36 | -0.66 – 1.38 | 0.488 | 0.23 | -0.80 – 1.26 | 0.664 | |||
| Age | -0.06 | -0.14 – 0.03 | 0.173 | -0.07 | -0.16 – 0.01 | 0.097 | |||
| int student [No] | -0.82 | -2.39 – 0.75 | 0.304 | -0.98 | -2.72 – 0.76 | 0.266 | |||
| SES num | -0.08 | -0.43 – 0.27 | 0.660 | -0.17 | -0.54 – 0.20 | 0.371 | |||
| Ethnicity White | 0.31 | -0.82 – 1.45 | 0.586 | ||||||
| Ethnicity Hispanic | -0.68 | -2.22 – 0.86 | 0.382 | ||||||
| Ethnicity Black | -0.18 | -2.40 – 2.03 | 0.869 | ||||||
| Ethnicity East Asian | -0.30 | -1.74 – 1.15 | 0.685 | ||||||
| Ethnicity South Asian | 0.15 | -1.83 – 2.14 | 0.880 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-4.95 | -8.70 – -1.21 | 0.010 | ||||||
| Ethnicity Middle Eastern | 0.21 | -2.40 – 2.82 | 0.874 | ||||||
| Ethnicity American Indian | -0.25 | -6.08 – 5.58 | 0.933 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.050 / 0.033 | 0.103 / 0.052 | 0.157 / 0.061 | ||||||
Excluded Unreasonable Numbers
m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)| SAS calm diff | SAS calm diff | SAS calm diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.10 | -0.77 – 0.97 | 0.821 | 3.39 | -0.22 – 7.00 | 0.065 | 4.35 | 0.47 – 8.23 | 0.028 |
| ActiveDays | -0.02 | -0.08 – 0.03 | 0.379 | -0.04 | -0.09 – 0.02 | 0.188 | -0.04 | -0.10 – 0.01 | 0.109 |
| Reports | 0.01 | -0.08 – 0.11 | 0.758 | 0.00 | -0.09 – 0.09 | 0.999 | -0.00 | -0.11 – 0.11 | 1.000 |
| Activities | 0.04 | 0.01 – 0.08 | 0.019 | 0.06 | 0.02 – 0.10 | 0.002 | 0.06 | 0.02 – 0.10 | 0.002 |
| univ [Foothill] | -1.05 | -2.65 – 0.55 | 0.197 | -0.54 | -2.19 – 1.10 | 0.515 | |||
| univ [UW] | -1.06 | -2.08 – -0.03 | 0.043 | -1.21 | -2.31 – -0.12 | 0.031 | |||
| Sex [Woman] | 0.38 | -0.79 – 1.55 | 0.522 | 0.22 | -0.96 – 1.40 | 0.716 | |||
| Age | -0.08 | -0.17 – 0.02 | 0.102 | -0.09 | -0.19 – 0.00 | 0.052 | |||
| int student [No] | -1.21 | -3.24 – 0.83 | 0.243 | -1.13 | -3.27 – 1.00 | 0.295 | |||
| SES num | -0.10 | -0.50 – 0.31 | 0.638 | -0.19 | -0.60 – 0.23 | 0.368 | |||
| Ethnicity White | 0.24 | -1.03 – 1.51 | 0.710 | ||||||
| Ethnicity Hispanic | -1.02 | -2.73 – 0.69 | 0.240 | ||||||
| Ethnicity Black | -0.51 | -3.04 – 2.02 | 0.692 | ||||||
| Ethnicity East Asian | -0.15 | -1.74 – 1.44 | 0.853 | ||||||
| Ethnicity South Asian | 0.49 | -1.91 – 2.89 | 0.685 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-5.35 | -9.29 – -1.42 | 0.008 | ||||||
| Ethnicity Middle Eastern | -0.54 | -5.90 – 4.82 | 0.842 | ||||||
| Ethnicity American Indian | 0.55 | -6.09 – 7.18 | 0.871 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.041 / 0.020 | 0.111 / 0.050 | 0.181 / 0.067 | ||||||
SAS: Well-Being
Intention to Treat
m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)| SAS well being diff | SAS well being diff | SAS well being diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.45 | -1.12 – 0.22 | 0.184 | 3.88 | 1.13 – 6.63 | 0.006 | 3.57 | 0.49 – 6.66 | 0.024 |
| ActiveDays | -0.00 | -0.03 – 0.02 | 0.758 | -0.01 | -0.03 – 0.01 | 0.348 | -0.01 | -0.03 – 0.01 | 0.382 |
| Reports | 0.01 | -0.05 – 0.07 | 0.755 | -0.01 | -0.07 – 0.06 | 0.852 | -0.01 | -0.09 – 0.06 | 0.738 |
| Activities | 0.02 | -0.01 – 0.05 | 0.192 | 0.03 | -0.00 – 0.06 | 0.054 | 0.03 | -0.00 – 0.06 | 0.064 |
| univ [Foothill] | -0.97 | -2.22 – 0.28 | 0.129 | -0.79 | -2.10 – 0.52 | 0.237 | |||
| univ [UW] | -0.79 | -1.60 – 0.02 | 0.056 | -0.92 | -1.80 – -0.04 | 0.041 | |||
| Sex [Woman] | -0.10 | -1.02 – 0.83 | 0.835 | -0.10 | -1.05 – 0.84 | 0.829 | |||
| Age | -0.10 | -0.17 – -0.02 | 0.013 | -0.10 | -0.18 – -0.02 | 0.013 | |||
| int student [No] | -1.49 | -2.89 – -0.09 | 0.037 | -1.17 | -2.77 – 0.43 | 0.152 | |||
| SES num | -0.11 | -0.43 – 0.22 | 0.506 | -0.20 | -0.54 – 0.15 | 0.266 | |||
| Ethnicity White | 0.54 | -0.52 – 1.60 | 0.313 | ||||||
| Ethnicity Hispanic | -0.05 | -1.49 – 1.39 | 0.941 | ||||||
| Ethnicity Black | 0.38 | -1.70 – 2.47 | 0.718 | ||||||
| Ethnicity East Asian | 1.10 | -0.26 – 2.45 | 0.112 | ||||||
| Ethnicity South Asian | 0.55 | -1.27 – 2.38 | 0.551 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.94 | -4.46 – 2.59 | 0.599 | ||||||
| Ethnicity Middle Eastern | 0.92 | -1.53 – 3.37 | 0.460 | ||||||
| Ethnicity American Indian | 1.83 | -3.67 – 7.33 | 0.511 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.013 / -0.005 | 0.101 / 0.050 | 0.126 / 0.028 | ||||||
Excluded Preregistered
m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)| SAS well being diff | SAS well being diff | SAS well being diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.38 | -1.06 – 0.31 | 0.280 | 4.48 | 1.58 – 7.37 | 0.003 | 4.33 | 1.13 – 7.54 | 0.008 |
| ActiveDays | -0.00 | -0.03 – 0.02 | 0.735 | -0.01 | -0.04 – 0.01 | 0.287 | -0.01 | -0.04 – 0.01 | 0.290 |
| Reports | 0.01 | -0.05 – 0.07 | 0.781 | -0.01 | -0.07 – 0.06 | 0.808 | -0.02 | -0.09 – 0.06 | 0.649 |
| Activities | 0.02 | -0.01 – 0.05 | 0.240 | 0.03 | -0.00 – 0.06 | 0.054 | 0.03 | -0.00 – 0.06 | 0.063 |
| univ [Foothill] | -0.64 | -1.92 – 0.63 | 0.319 | -0.47 | -1.80 – 0.87 | 0.490 | |||
| univ [UW] | -0.86 | -1.67 – -0.05 | 0.037 | -1.03 | -1.91 – -0.15 | 0.023 | |||
| Sex [Woman] | -0.22 | -1.16 – 0.72 | 0.641 | -0.26 | -1.22 – 0.71 | 0.601 | |||
| Age | -0.11 | -0.18 – -0.03 | 0.005 | -0.11 | -0.19 – -0.04 | 0.005 | |||
| int student [No] | -1.75 | -3.20 – -0.30 | 0.018 | -1.40 | -3.03 – 0.23 | 0.092 | |||
| SES num | -0.09 | -0.42 – 0.23 | 0.582 | -0.17 | -0.52 – 0.18 | 0.330 | |||
| Ethnicity White | 0.38 | -0.69 – 1.44 | 0.484 | ||||||
| Ethnicity Hispanic | -0.28 | -1.72 – 1.17 | 0.705 | ||||||
| Ethnicity Black | 0.27 | -1.80 – 2.35 | 0.794 | ||||||
| Ethnicity East Asian | 0.94 | -0.41 – 2.29 | 0.172 | ||||||
| Ethnicity South Asian | 0.74 | -1.12 – 2.60 | 0.431 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.31 | -4.82 – 2.20 | 0.461 | ||||||
| Ethnicity Middle Eastern | 0.77 | -1.68 – 3.21 | 0.535 | ||||||
| Ethnicity American Indian | 2.02 | -3.45 – 7.48 | 0.467 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.010 / -0.008 | 0.105 / 0.053 | 0.130 / 0.031 | ||||||
Excluded Unreasonable Numbers
m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)| SAS well being diff | SAS well being diff | SAS well being diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.42 | -1.22 – 0.38 | 0.302 | 4.90 | 1.67 – 8.14 | 0.003 | 4.49 | 0.94 – 8.04 | 0.014 |
| ActiveDays | 0.02 | -0.03 – 0.07 | 0.494 | 0.00 | -0.05 – 0.05 | 0.984 | -0.00 | -0.05 – 0.05 | 0.889 |
| Reports | -0.02 | -0.11 – 0.06 | 0.591 | -0.03 | -0.12 – 0.05 | 0.413 | -0.05 | -0.15 – 0.05 | 0.317 |
| Activities | 0.01 | -0.02 – 0.04 | 0.505 | 0.03 | -0.00 – 0.06 | 0.078 | 0.03 | -0.00 – 0.07 | 0.064 |
| univ [Foothill] | -0.96 | -2.40 – 0.48 | 0.188 | -0.77 | -2.27 – 0.74 | 0.315 | |||
| univ [UW] | -1.01 | -1.93 – -0.09 | 0.031 | -1.18 | -2.18 – -0.17 | 0.022 | |||
| Sex [Woman] | -0.20 | -1.25 – 0.85 | 0.709 | -0.27 | -1.35 – 0.81 | 0.624 | |||
| Age | -0.11 | -0.20 – -0.03 | 0.007 | -0.12 | -0.20 – -0.03 | 0.009 | |||
| int student [No] | -2.30 | -4.12 – -0.47 | 0.014 | -1.84 | -3.79 – 0.12 | 0.065 | |||
| SES num | -0.03 | -0.39 – 0.33 | 0.865 | -0.09 | -0.47 – 0.29 | 0.639 | |||
| Ethnicity White | 0.46 | -0.70 – 1.63 | 0.433 | ||||||
| Ethnicity Hispanic | -0.12 | -1.68 – 1.44 | 0.880 | ||||||
| Ethnicity Black | -0.23 | -2.54 – 2.09 | 0.847 | ||||||
| Ethnicity East Asian | 0.96 | -0.49 – 2.42 | 0.194 | ||||||
| Ethnicity South Asian | 1.37 | -0.83 – 3.56 | 0.220 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.08 | -4.69 – 2.52 | 0.553 | ||||||
| Ethnicity Middle Eastern | 0.71 | -4.19 – 5.62 | 0.774 | ||||||
| Ethnicity American Indian | 2.84 | -3.23 – 8.90 | 0.357 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.013 / -0.009 | 0.130 / 0.070 | 0.165 / 0.049 | ||||||
SAS: Vigour
Intention to Treat
m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)| SAS vigour diff | SAS vigour diff | SAS vigour diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.81 | -1.47 – -0.14 | 0.017 | 2.41 | -0.36 – 5.18 | 0.088 | 2.32 | -0.79 – 5.44 | 0.143 |
| ActiveDays | 0.01 | -0.02 – 0.03 | 0.552 | 0.00 | -0.02 – 0.02 | 0.913 | 0.00 | -0.02 – 0.02 | 0.942 |
| Reports | 0.04 | -0.02 – 0.10 | 0.213 | 0.03 | -0.03 – 0.09 | 0.350 | 0.03 | -0.05 – 0.10 | 0.500 |
| Activities | 0.01 | -0.02 – 0.04 | 0.433 | 0.02 | -0.01 – 0.05 | 0.252 | 0.02 | -0.01 – 0.05 | 0.260 |
| univ [Foothill] | -0.79 | -2.05 – 0.47 | 0.219 | -0.76 | -2.09 – 0.57 | 0.259 | |||
| univ [UW] | -0.75 | -1.58 – 0.07 | 0.073 | -0.75 | -1.65 – 0.14 | 0.099 | |||
| Sex [Woman] | -0.15 | -1.10 – 0.79 | 0.747 | -0.22 | -1.19 – 0.75 | 0.652 | |||
| Age | -0.04 | -0.11 – 0.04 | 0.360 | -0.04 | -0.12 – 0.04 | 0.350 | |||
| int student [No] | -1.59 | -2.99 – -0.18 | 0.027 | -1.56 | -3.18 – 0.05 | 0.058 | |||
| SES num | -0.12 | -0.45 – 0.21 | 0.474 | -0.17 | -0.52 – 0.18 | 0.346 | |||
| Ethnicity White | 0.45 | -0.62 – 1.53 | 0.403 | ||||||
| Ethnicity Hispanic | 0.73 | -0.73 – 2.18 | 0.324 | ||||||
| Ethnicity Black | 0.55 | -1.56 – 2.66 | 0.609 | ||||||
| Ethnicity East Asian | 0.29 | -1.07 – 1.66 | 0.672 | ||||||
| Ethnicity South Asian | 0.55 | -1.29 – 2.40 | 0.555 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.05 | -5.61 – 1.51 | 0.257 | ||||||
| Ethnicity Middle Eastern | 0.66 | -1.82 – 3.14 | 0.599 | ||||||
| Ethnicity American Indian | 0.38 | -5.17 – 5.93 | 0.893 | ||||||
| Observations | 170 | 169 | 169 | ||||||
| R2 / R2 adjusted | 0.037 / 0.019 | 0.087 / 0.035 | 0.106 / 0.005 | ||||||
Excluded Preregistered
m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)| SAS vigour diff | SAS vigour diff | SAS vigour diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.73 | -1.42 – -0.04 | 0.037 | 2.58 | -0.38 – 5.54 | 0.087 | 2.65 | -0.64 – 5.93 | 0.114 |
| ActiveDays | 0.01 | -0.02 – 0.03 | 0.571 | 0.00 | -0.02 – 0.02 | 0.933 | 0.00 | -0.02 – 0.02 | 0.988 |
| Reports | 0.04 | -0.02 – 0.10 | 0.227 | 0.03 | -0.04 – 0.09 | 0.368 | 0.02 | -0.05 – 0.10 | 0.548 |
| Activities | 0.01 | -0.02 – 0.04 | 0.516 | 0.02 | -0.02 – 0.05 | 0.324 | 0.02 | -0.02 – 0.05 | 0.335 |
| univ [Foothill] | -0.69 | -1.99 – 0.62 | 0.299 | -0.65 | -2.02 – 0.72 | 0.350 | |||
| univ [UW] | -0.72 | -1.55 – 0.11 | 0.090 | -0.72 | -1.63 – 0.19 | 0.120 | |||
| Sex [Woman] | -0.16 | -1.13 – 0.81 | 0.749 | -0.25 | -1.26 – 0.75 | 0.620 | |||
| Age | -0.04 | -0.12 – 0.04 | 0.338 | -0.04 | -0.12 – 0.04 | 0.317 | |||
| int student [No] | -1.62 | -3.10 – -0.14 | 0.032 | -1.65 | -3.32 – 0.02 | 0.052 | |||
| SES num | -0.13 | -0.46 – 0.20 | 0.446 | -0.18 | -0.54 – 0.17 | 0.312 | |||
| Ethnicity White | 0.46 | -0.63 – 1.55 | 0.408 | ||||||
| Ethnicity Hispanic | 0.66 | -0.82 – 2.14 | 0.379 | ||||||
| Ethnicity Black | 0.53 | -1.60 – 2.66 | 0.623 | ||||||
| Ethnicity East Asian | 0.23 | -1.16 – 1.62 | 0.745 | ||||||
| Ethnicity South Asian | 0.61 | -1.29 – 2.52 | 0.526 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.18 | -5.77 – 1.42 | 0.233 | ||||||
| Ethnicity Middle Eastern | 0.70 | -1.81 – 3.20 | 0.583 | ||||||
| Ethnicity American Indian | 0.57 | -5.03 – 6.17 | 0.840 | ||||||
| Observations | 167 | 166 | 166 | ||||||
| R2 / R2 adjusted | 0.032 / 0.014 | 0.079 / 0.026 | 0.100 / -0.004 | ||||||
Excluded Unreasonable Numbers
m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)| SAS vigour diff | SAS vigour diff | SAS vigour diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.64 | -1.44 – 0.16 | 0.116 | 2.26 | -1.09 – 5.61 | 0.184 | 2.25 | -1.45 – 5.94 | 0.231 |
| ActiveDays | 0.01 | -0.04 – 0.05 | 0.831 | -0.01 | -0.06 – 0.04 | 0.759 | -0.01 | -0.06 – 0.04 | 0.750 |
| Reports | 0.06 | -0.02 – 0.15 | 0.146 | 0.06 | -0.03 – 0.14 | 0.199 | 0.06 | -0.04 – 0.16 | 0.259 |
| Activities | 0.00 | -0.03 – 0.03 | 0.909 | 0.01 | -0.02 – 0.05 | 0.419 | 0.02 | -0.02 – 0.05 | 0.381 |
| univ [Foothill] | -0.78 | -2.27 – 0.71 | 0.304 | -0.64 | -2.21 – 0.93 | 0.422 | |||
| univ [UW] | -0.90 | -1.87 – 0.06 | 0.066 | -0.87 | -1.92 – 0.19 | 0.107 | |||
| Sex [Woman] | -0.33 | -1.42 – 0.77 | 0.557 | -0.43 | -1.57 – 0.71 | 0.460 | |||
| Age | -0.04 | -0.12 – 0.05 | 0.405 | -0.04 | -0.13 – 0.05 | 0.438 | |||
| int student [No] | -1.29 | -3.18 – 0.60 | 0.178 | -1.31 | -3.35 – 0.73 | 0.205 | |||
| SES num | -0.05 | -0.42 – 0.32 | 0.782 | -0.09 | -0.49 – 0.30 | 0.644 | |||
| Ethnicity White | 0.34 | -0.87 – 1.56 | 0.578 | ||||||
| Ethnicity Hispanic | 0.27 | -1.36 – 1.90 | 0.743 | ||||||
| Ethnicity Black | -0.30 | -2.72 – 2.11 | 0.804 | ||||||
| Ethnicity East Asian | -0.09 | -1.60 – 1.43 | 0.909 | ||||||
| Ethnicity South Asian | 0.95 | -1.34 – 3.24 | 0.414 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.36 | -6.11 – 1.39 | 0.215 | ||||||
| Ethnicity Middle Eastern | 0.29 | -4.82 – 5.40 | 0.911 | ||||||
| Ethnicity American Indian | -0.61 | -6.93 – 5.70 | 0.848 | ||||||
| Observations | 139 | 139 | 139 | ||||||
| R2 / R2 adjusted | 0.028 / 0.006 | 0.067 / 0.002 | 0.093 / -0.035 | ||||||
SAS: Depression
Intention to Treat
m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)| SAS depression diff | SAS depression diff | SAS depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.10 | -0.84 – 0.63 | 0.780 | -2.64 | -5.72 – 0.44 | 0.092 | -1.89 | -5.36 – 1.59 | 0.286 |
| ActiveDays | -0.00 | -0.03 – 0.02 | 0.964 | 0.01 | -0.02 – 0.03 | 0.622 | 0.01 | -0.02 – 0.03 | 0.604 |
| Reports | 0.03 | -0.04 – 0.11 | 0.332 | 0.04 | -0.03 – 0.12 | 0.222 | 0.04 | -0.04 – 0.12 | 0.358 |
| Activities | -0.00 | -0.04 – 0.03 | 0.759 | -0.01 | -0.04 – 0.02 | 0.628 | -0.01 | -0.04 – 0.02 | 0.604 |
| univ [Foothill] | 1.23 | -0.17 – 2.63 | 0.086 | 1.23 | -0.25 – 2.71 | 0.102 | |||
| univ [UW] | 0.23 | -0.68 – 1.14 | 0.614 | 0.32 | -0.68 – 1.32 | 0.529 | |||
| Sex [Woman] | 0.06 | -0.99 – 1.11 | 0.909 | -0.03 | -1.11 – 1.05 | 0.953 | |||
| Age | 0.05 | -0.03 – 0.14 | 0.224 | 0.05 | -0.04 – 0.14 | 0.296 | |||
| int student [No] | 1.15 | -0.42 – 2.71 | 0.150 | 0.79 | -1.02 – 2.60 | 0.390 | |||
| SES num | -0.01 | -0.38 – 0.35 | 0.937 | 0.02 | -0.38 – 0.41 | 0.933 | |||
| Ethnicity White | -0.32 | -1.52 – 0.87 | 0.596 | ||||||
| Ethnicity Hispanic | -0.03 | -1.65 – 1.59 | 0.971 | ||||||
| Ethnicity Black | -0.25 | -2.60 – 2.10 | 0.833 | ||||||
| Ethnicity East Asian | -0.76 | -2.29 – 0.77 | 0.327 | ||||||
| Ethnicity South Asian | -0.78 | -2.84 – 1.29 | 0.458 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.97 | -5.94 – 2.01 | 0.330 | ||||||
| Ethnicity Middle Eastern | -0.59 | -3.36 – 2.17 | 0.673 | ||||||
| Ethnicity American Indian | 0.43 | -5.77 – 6.63 | 0.892 | ||||||
| Observations | 170 | 169 | 169 | ||||||
| R2 / R2 adjusted | 0.007 / -0.011 | 0.055 / 0.002 | 0.068 / -0.036 | ||||||
Excluded Preregistered
m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)| SAS depression diff | SAS depression diff | SAS depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.19 | -0.96 – 0.57 | 0.614 | -2.72 | -6.01 – 0.57 | 0.104 | -2.08 | -5.75 – 1.59 | 0.265 |
| ActiveDays | -0.00 | -0.03 – 0.03 | 0.987 | 0.01 | -0.02 – 0.03 | 0.606 | 0.01 | -0.02 – 0.03 | 0.581 |
| Reports | 0.04 | -0.03 – 0.11 | 0.314 | 0.05 | -0.03 – 0.12 | 0.215 | 0.04 | -0.04 – 0.12 | 0.343 |
| Activities | -0.00 | -0.03 – 0.03 | 0.866 | -0.01 | -0.04 – 0.03 | 0.686 | -0.01 | -0.04 – 0.03 | 0.654 |
| univ [Foothill] | 1.04 | -0.40 – 2.49 | 0.155 | 1.06 | -0.47 – 2.58 | 0.174 | |||
| univ [UW] | 0.23 | -0.69 – 1.15 | 0.628 | 0.32 | -0.69 – 1.34 | 0.531 | |||
| Sex [Woman] | 0.06 | -1.02 – 1.13 | 0.919 | -0.03 | -1.14 – 1.09 | 0.964 | |||
| Age | 0.06 | -0.03 – 0.14 | 0.201 | 0.05 | -0.04 – 0.14 | 0.263 | |||
| int student [No] | 1.15 | -0.50 – 2.80 | 0.169 | 0.81 | -1.06 – 2.68 | 0.393 | |||
| SES num | -0.03 | -0.40 – 0.34 | 0.893 | 0.00 | -0.40 – 0.40 | 0.991 | |||
| Ethnicity White | -0.23 | -1.45 – 0.99 | 0.711 | ||||||
| Ethnicity Hispanic | 0.11 | -1.54 – 1.76 | 0.896 | ||||||
| Ethnicity Black | -0.16 | -2.53 – 2.22 | 0.895 | ||||||
| Ethnicity East Asian | -0.65 | -2.20 – 0.90 | 0.406 | ||||||
| Ethnicity South Asian | -0.74 | -2.87 – 1.39 | 0.493 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.79 | -5.81 – 2.23 | 0.380 | ||||||
| Ethnicity Middle Eastern | -0.49 | -3.29 – 2.31 | 0.732 | ||||||
| Ethnicity American Indian | 0.37 | -5.89 – 6.62 | 0.908 | ||||||
| Observations | 167 | 166 | 166 | ||||||
| R2 / R2 adjusted | 0.008 / -0.011 | 0.051 / -0.004 | 0.063 / -0.044 | ||||||
Excluded Unreasonable Numbers
m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)| SAS depression diff | SAS depression diff | SAS depression diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.03 | -0.91 – 0.86 | 0.956 | -3.40 | -7.10 – 0.30 | 0.071 | -2.75 | -6.83 – 1.34 | 0.185 |
| ActiveDays | -0.04 | -0.09 – 0.02 | 0.172 | -0.03 | -0.09 – 0.03 | 0.294 | -0.03 | -0.09 – 0.02 | 0.261 |
| Reports | 0.10 | 0.00 – 0.19 | 0.046 | 0.10 | 0.00 – 0.20 | 0.042 | 0.09 | -0.02 – 0.20 | 0.118 |
| Activities | 0.00 | -0.03 – 0.04 | 0.929 | -0.01 | -0.04 – 0.03 | 0.722 | -0.01 | -0.05 – 0.03 | 0.726 |
| univ [Foothill] | 1.08 | -0.56 – 2.72 | 0.196 | 1.10 | -0.63 – 2.83 | 0.210 | |||
| univ [UW] | 0.24 | -0.81 – 1.29 | 0.653 | 0.41 | -0.75 – 1.56 | 0.488 | |||
| Sex [Woman] | -0.02 | -1.22 – 1.18 | 0.971 | -0.16 | -1.40 – 1.08 | 0.799 | |||
| Age | 0.07 | -0.02 – 0.17 | 0.120 | 0.08 | -0.02 – 0.18 | 0.133 | |||
| int student [No] | 2.15 | 0.07 – 4.24 | 0.043 | 1.82 | -0.43 – 4.07 | 0.112 | |||
| SES num | -0.12 | -0.53 – 0.29 | 0.556 | -0.10 | -0.54 – 0.33 | 0.645 | |||
| Ethnicity White | -0.17 | -1.51 – 1.17 | 0.799 | ||||||
| Ethnicity Hispanic | 0.21 | -1.58 – 2.01 | 0.815 | ||||||
| Ethnicity Black | -0.84 | -3.50 – 1.83 | 0.535 | ||||||
| Ethnicity East Asian | -0.83 | -2.51 – 0.84 | 0.327 | ||||||
| Ethnicity South Asian | -1.22 | -3.75 – 1.31 | 0.341 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.07 | -6.21 – 2.07 | 0.325 | ||||||
| Ethnicity Middle Eastern | -1.76 | -7.41 – 3.88 | 0.537 | ||||||
| Ethnicity American Indian | 0.33 | -6.65 – 7.31 | 0.926 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.031 / 0.009 | 0.097 / 0.035 | 0.122 / -0.000 | ||||||
SAS: Anxiety
Intention to Treat
m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)| SAS anxiety diff | SAS anxiety diff | SAS anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.36 | -1.22 – 0.51 | 0.418 | -3.55 | -7.20 – 0.11 | 0.057 | -3.38 | -7.51 – 0.75 | 0.108 |
| ActiveDays | 0.00 | -0.03 – 0.03 | 0.810 | 0.01 | -0.02 – 0.04 | 0.677 | 0.01 | -0.02 – 0.04 | 0.620 |
| Reports | 0.03 | -0.05 – 0.12 | 0.410 | 0.05 | -0.03 – 0.14 | 0.210 | 0.05 | -0.05 – 0.14 | 0.354 |
| Activities | -0.02 | -0.05 – 0.02 | 0.411 | -0.02 | -0.06 – 0.01 | 0.221 | -0.03 | -0.07 – 0.01 | 0.181 |
| univ [Foothill] | 0.53 | -1.13 – 2.19 | 0.528 | 0.39 | -1.37 – 2.15 | 0.661 | |||
| univ [UW] | 0.64 | -0.43 – 1.72 | 0.240 | 0.62 | -0.56 – 1.80 | 0.302 | |||
| Sex [Woman] | 0.17 | -1.06 – 1.39 | 0.790 | 0.23 | -1.04 – 1.51 | 0.716 | |||
| Age | 0.08 | -0.02 – 0.18 | 0.103 | 0.08 | -0.03 – 0.19 | 0.140 | |||
| int student [No] | 0.31 | -1.55 – 2.17 | 0.743 | 0.17 | -1.98 – 2.31 | 0.878 | |||
| SES num | 0.21 | -0.22 – 0.65 | 0.327 | 0.25 | -0.21 – 0.72 | 0.285 | |||
| Ethnicity White | -0.14 | -1.56 – 1.28 | 0.847 | ||||||
| Ethnicity Hispanic | -0.11 | -2.04 – 1.82 | 0.912 | ||||||
| Ethnicity Black | 0.91 | -1.88 – 3.71 | 0.519 | ||||||
| Ethnicity East Asian | -0.06 | -1.88 – 1.75 | 0.944 | ||||||
| Ethnicity South Asian | -0.18 | -2.62 – 2.27 | 0.888 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
2.10 | -2.63 – 6.82 | 0.382 | ||||||
| Ethnicity Middle Eastern | -0.14 | -3.43 – 3.15 | 0.933 | ||||||
| Ethnicity American Indian | 1.76 | -5.61 – 9.13 | 0.638 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.008 / -0.010 | 0.044 / -0.010 | 0.055 / -0.051 | ||||||
Excluded Preregistered
m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)| SAS anxiety diff | SAS anxiety diff | SAS anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.40 | -1.30 – 0.50 | 0.382 | -3.99 | -7.91 – -0.08 | 0.046 | -3.90 | -8.27 – 0.48 | 0.080 |
| ActiveDays | 0.00 | -0.03 – 0.03 | 0.802 | 0.01 | -0.02 – 0.04 | 0.642 | 0.01 | -0.02 – 0.04 | 0.566 |
| Reports | 0.04 | -0.05 – 0.12 | 0.406 | 0.06 | -0.03 – 0.14 | 0.205 | 0.05 | -0.05 – 0.15 | 0.328 |
| Activities | -0.01 | -0.05 – 0.02 | 0.450 | -0.02 | -0.06 – 0.02 | 0.235 | -0.03 | -0.07 – 0.01 | 0.192 |
| univ [Foothill] | 0.41 | -1.31 – 2.13 | 0.639 | 0.26 | -1.56 – 2.08 | 0.779 | |||
| univ [UW] | 0.68 | -0.42 – 1.77 | 0.223 | 0.67 | -0.53 – 1.87 | 0.271 | |||
| Sex [Woman] | 0.26 | -1.01 – 1.53 | 0.686 | 0.35 | -0.97 – 1.66 | 0.604 | |||
| Age | 0.09 | -0.01 – 0.19 | 0.085 | 0.09 | -0.02 – 0.19 | 0.114 | |||
| int student [No] | 0.50 | -1.46 – 2.46 | 0.613 | 0.35 | -1.87 – 2.57 | 0.757 | |||
| SES num | 0.22 | -0.22 – 0.66 | 0.329 | 0.26 | -0.22 – 0.73 | 0.289 | |||
| Ethnicity White | -0.11 | -1.56 – 1.35 | 0.886 | ||||||
| Ethnicity Hispanic | -0.03 | -2.00 – 1.93 | 0.973 | ||||||
| Ethnicity Black | 0.92 | -1.90 – 3.75 | 0.519 | ||||||
| Ethnicity East Asian | -0.01 | -1.85 – 1.83 | 0.992 | ||||||
| Ethnicity South Asian | -0.36 | -2.89 – 2.18 | 0.780 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
2.27 | -2.51 – 7.05 | 0.349 | ||||||
| Ethnicity Middle Eastern | -0.14 | -3.48 – 3.19 | 0.933 | ||||||
| Ethnicity American Indian | 1.59 | -5.86 – 9.03 | 0.674 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.008 / -0.010 | 0.045 / -0.010 | 0.057 / -0.051 | ||||||
Excluded Unreasonable Numbers
m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)| SAS anxiety diff | SAS anxiety diff | SAS anxiety diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.28 | -1.33 – 0.78 | 0.606 | -3.47 | -7.93 – 1.00 | 0.127 | -3.22 | -8.18 – 1.74 | 0.201 |
| ActiveDays | -0.02 | -0.09 – 0.04 | 0.491 | -0.02 | -0.08 – 0.05 | 0.656 | -0.01 | -0.08 – 0.06 | 0.695 |
| Reports | 0.08 | -0.04 – 0.19 | 0.186 | 0.09 | -0.02 – 0.21 | 0.122 | 0.07 | -0.07 – 0.21 | 0.300 |
| Activities | -0.01 | -0.05 – 0.03 | 0.648 | -0.02 | -0.07 – 0.02 | 0.314 | -0.03 | -0.07 – 0.02 | 0.291 |
| univ [Foothill] | -0.17 | -2.15 – 1.81 | 0.868 | -0.42 | -2.52 – 1.68 | 0.695 | |||
| univ [UW] | 0.52 | -0.74 – 1.79 | 0.415 | 0.58 | -0.82 – 1.98 | 0.418 | |||
| Sex [Woman] | -0.13 | -1.58 – 1.31 | 0.856 | -0.08 | -1.59 – 1.43 | 0.918 | |||
| Age | 0.11 | -0.01 – 0.22 | 0.066 | 0.11 | -0.01 – 0.23 | 0.074 | |||
| int student [No] | 0.34 | -2.17 – 2.86 | 0.787 | 0.11 | -2.62 – 2.84 | 0.937 | |||
| SES num | 0.20 | -0.30 – 0.70 | 0.432 | 0.26 | -0.27 – 0.79 | 0.333 | |||
| Ethnicity White | -0.36 | -1.98 – 1.27 | 0.666 | ||||||
| Ethnicity Hispanic | 0.05 | -2.13 – 2.23 | 0.963 | ||||||
| Ethnicity Black | 0.15 | -3.08 – 3.39 | 0.925 | ||||||
| Ethnicity East Asian | -0.51 | -2.55 – 1.52 | 0.617 | ||||||
| Ethnicity South Asian | -0.62 | -3.69 – 2.45 | 0.689 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
2.09 | -2.94 – 7.13 | 0.412 | ||||||
| Ethnicity Middle Eastern | -2.29 | -9.14 – 4.57 | 0.510 | ||||||
| Ethnicity American Indian | 1.56 | -6.92 – 10.03 | 0.717 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.016 / -0.006 | 0.052 / -0.014 | 0.067 / -0.063 | ||||||
SAS: Anger
Intention to Treat
m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)| SAS anger diff | SAS anger diff | SAS anger diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.21 | -0.90 – 0.49 | 0.559 | -2.93 | -5.89 – 0.02 | 0.052 | -3.03 | -6.29 – 0.23 | 0.068 |
| ActiveDays | -0.01 | -0.03 – 0.01 | 0.467 | -0.01 | -0.03 – 0.02 | 0.580 | -0.01 | -0.03 – 0.02 | 0.465 |
| Reports | 0.04 | -0.03 – 0.11 | 0.225 | 0.05 | -0.02 – 0.12 | 0.148 | 0.02 | -0.06 – 0.10 | 0.575 |
| Activities | 0.01 | -0.02 – 0.04 | 0.395 | 0.01 | -0.02 – 0.04 | 0.526 | 0.01 | -0.02 – 0.04 | 0.442 |
| univ [Foothill] | 0.03 | -1.31 – 1.38 | 0.963 | -0.02 | -1.41 – 1.36 | 0.974 | |||
| univ [UW] | 0.10 | -0.77 – 0.97 | 0.817 | 0.34 | -0.59 – 1.27 | 0.465 | |||
| Sex [Woman] | 0.39 | -0.60 – 1.38 | 0.440 | 0.21 | -0.79 – 1.22 | 0.676 | |||
| Age | 0.04 | -0.04 – 0.13 | 0.283 | 0.05 | -0.03 – 0.14 | 0.211 | |||
| int student [No] | 0.71 | -0.79 – 2.22 | 0.349 | 0.53 | -1.16 – 2.23 | 0.535 | |||
| SES num | 0.23 | -0.12 – 0.58 | 0.190 | 0.16 | -0.21 – 0.53 | 0.391 | |||
| Ethnicity White | 0.63 | -0.49 – 1.74 | 0.272 | ||||||
| Ethnicity Hispanic | 1.43 | -0.09 – 2.96 | 0.064 | ||||||
| Ethnicity Black | -0.30 | -2.50 – 1.91 | 0.792 | ||||||
| Ethnicity East Asian | 0.21 | -1.22 – 1.64 | 0.772 | ||||||
| Ethnicity South Asian | 0.53 | -1.40 – 2.46 | 0.586 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.07 | -5.79 – 1.66 | 0.275 | ||||||
| Ethnicity Middle Eastern | 2.78 | 0.18 – 5.37 | 0.036 | ||||||
| Ethnicity American Indian | 2.88 | -2.93 – 8.69 | 0.329 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.014 / -0.003 | 0.040 / -0.014 | 0.098 / -0.003 | ||||||
Excluded Preregistered
m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)| SAS anger diff | SAS anger diff | SAS anger diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.20 | -0.92 – 0.53 | 0.589 | -3.32 | -6.48 – -0.15 | 0.040 | -3.30 | -6.76 – 0.15 | 0.061 |
| ActiveDays | -0.01 | -0.03 – 0.02 | 0.469 | -0.01 | -0.03 – 0.02 | 0.614 | -0.01 | -0.03 – 0.02 | 0.504 |
| Reports | 0.04 | -0.03 – 0.11 | 0.230 | 0.05 | -0.02 – 0.12 | 0.145 | 0.02 | -0.05 – 0.10 | 0.554 |
| Activities | 0.01 | -0.02 – 0.04 | 0.410 | 0.01 | -0.02 – 0.04 | 0.519 | 0.01 | -0.02 – 0.04 | 0.447 |
| univ [Foothill] | 0.03 | -1.36 – 1.42 | 0.962 | -0.04 | -1.48 – 1.39 | 0.954 | |||
| univ [UW] | 0.12 | -0.76 – 1.00 | 0.791 | 0.37 | -0.58 – 1.32 | 0.443 | |||
| Sex [Woman] | 0.47 | -0.55 – 1.50 | 0.364 | 0.28 | -0.76 – 1.32 | 0.595 | |||
| Age | 0.05 | -0.04 – 0.13 | 0.259 | 0.06 | -0.03 – 0.14 | 0.197 | |||
| int student [No] | 0.88 | -0.70 – 2.47 | 0.272 | 0.64 | -1.11 – 2.40 | 0.470 | |||
| SES num | 0.25 | -0.11 – 0.60 | 0.168 | 0.17 | -0.20 – 0.54 | 0.371 | |||
| Ethnicity White | 0.60 | -0.54 – 1.75 | 0.300 | ||||||
| Ethnicity Hispanic | 1.43 | -0.13 – 2.98 | 0.071 | ||||||
| Ethnicity Black | -0.33 | -2.56 – 1.91 | 0.774 | ||||||
| Ethnicity East Asian | 0.21 | -1.25 – 1.66 | 0.779 | ||||||
| Ethnicity South Asian | 0.40 | -1.60 – 2.40 | 0.691 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-2.02 | -5.80 – 1.76 | 0.292 | ||||||
| Ethnicity Middle Eastern | 2.72 | 0.09 – 5.36 | 0.043 | ||||||
| Ethnicity American Indian | 2.78 | -3.10 – 8.65 | 0.352 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.014 / -0.004 | 0.043 / -0.012 | 0.099 / -0.004 | ||||||
Excluded Unreasonable Numbers
m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)| SAS anger diff | SAS anger diff | SAS anger diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.36 | -1.14 – 0.41 | 0.357 | -2.26 | -5.57 – 1.04 | 0.178 | -2.98 | -6.49 – 0.52 | 0.095 |
| ActiveDays | -0.02 | -0.07 – 0.03 | 0.382 | -0.02 | -0.07 – 0.03 | 0.479 | -0.02 | -0.06 – 0.03 | 0.546 |
| Reports | 0.07 | -0.02 – 0.15 | 0.113 | 0.07 | -0.01 – 0.16 | 0.101 | 0.04 | -0.06 – 0.14 | 0.429 |
| Activities | 0.02 | -0.01 – 0.05 | 0.183 | 0.02 | -0.02 – 0.05 | 0.366 | 0.02 | -0.02 – 0.05 | 0.307 |
| univ [Foothill] | -0.71 | -2.17 – 0.76 | 0.342 | -0.94 | -2.42 – 0.55 | 0.215 | |||
| univ [UW] | 0.08 | -0.86 – 1.01 | 0.873 | 0.41 | -0.58 – 1.40 | 0.413 | |||
| Sex [Woman] | 0.41 | -0.66 – 1.48 | 0.446 | 0.19 | -0.88 – 1.25 | 0.732 | |||
| Age | 0.06 | -0.02 – 0.15 | 0.139 | 0.09 | 0.00 – 0.17 | 0.047 | |||
| int student [No] | 0.06 | -1.81 – 1.93 | 0.949 | -0.04 | -1.97 – 1.89 | 0.967 | |||
| SES num | 0.09 | -0.27 – 0.46 | 0.611 | 0.12 | -0.26 – 0.49 | 0.540 | |||
| Ethnicity White | 0.42 | -0.73 – 1.58 | 0.467 | ||||||
| Ethnicity Hispanic | 2.16 | 0.62 – 3.70 | 0.006 | ||||||
| Ethnicity Black | -1.10 | -3.38 – 1.19 | 0.344 | ||||||
| Ethnicity East Asian | -0.20 | -1.63 – 1.24 | 0.788 | ||||||
| Ethnicity South Asian | 1.01 | -1.16 – 3.18 | 0.360 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.69 | -5.25 – 1.87 | 0.349 | ||||||
| Ethnicity Middle Eastern | -0.28 | -5.12 – 4.57 | 0.910 | ||||||
| Ethnicity American Indian | 2.18 | -3.82 – 8.17 | 0.474 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.031 / 0.010 | 0.056 / -0.010 | 0.153 / 0.035 | ||||||
SAS: Positive
Intention to Treat
m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)| SAS positive diff | SAS positive diff | SAS positive diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -1.35 | -3.01 – 0.31 | 0.111 | 8.31 | 1.50 – 15.13 | 0.017 | 8.64 | 1.02 – 16.26 | 0.026 |
| ActiveDays | -0.00 | -0.06 – 0.05 | 0.939 | -0.02 | -0.08 – 0.04 | 0.507 | -0.02 | -0.08 – 0.04 | 0.482 |
| Reports | 0.06 | -0.09 – 0.22 | 0.426 | 0.03 | -0.13 – 0.19 | 0.731 | 0.02 | -0.16 – 0.20 | 0.852 |
| Activities | 0.06 | -0.01 – 0.14 | 0.079 | 0.09 | 0.01 – 0.16 | 0.022 | 0.09 | 0.01 – 0.16 | 0.024 |
| univ [Foothill] | -2.84 | -5.95 – 0.26 | 0.073 | -2.31 | -5.56 – 0.93 | 0.161 | |||
| univ [UW] | -2.19 | -4.21 – -0.16 | 0.035 | -2.36 | -4.55 – -0.17 | 0.035 | |||
| Sex [Woman] | 0.32 | -1.99 – 2.64 | 0.783 | 0.15 | -2.22 – 2.52 | 0.900 | |||
| Age | -0.19 | -0.38 – -0.00 | 0.049 | -0.21 | -0.40 – -0.01 | 0.037 | |||
| int student [No] | -3.84 | -7.30 – -0.38 | 0.030 | -3.61 | -7.56 – 0.35 | 0.074 | |||
| SES num | -0.30 | -1.10 – 0.50 | 0.461 | -0.52 | -1.38 – 0.34 | 0.231 | |||
| Ethnicity White | 1.32 | -1.30 – 3.94 | 0.322 | ||||||
| Ethnicity Hispanic | 0.13 | -3.42 – 3.69 | 0.941 | ||||||
| Ethnicity Black | 1.00 | -4.16 – 6.15 | 0.703 | ||||||
| Ethnicity East Asian | 1.20 | -2.14 – 4.55 | 0.479 | ||||||
| Ethnicity South Asian | 1.14 | -3.37 – 5.65 | 0.619 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-7.61 | -16.31 – 1.10 | 0.086 | ||||||
| Ethnicity Middle Eastern | 1.83 | -4.23 – 7.89 | 0.552 | ||||||
| Ethnicity American Indian | 1.85 | -11.72 – 15.43 | 0.788 | ||||||
| Observations | 170 | 169 | 169 | ||||||
| R2 / R2 adjusted | 0.035 / 0.018 | 0.121 / 0.071 | 0.149 / 0.054 | ||||||
Excluded Preregistered
m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)| SAS positive diff | SAS positive diff | SAS positive diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -1.08 | -2.79 – 0.64 | 0.216 | 9.24 | 2.00 – 16.48 | 0.013 | 10.17 | 2.18 – 18.16 | 0.013 |
| ActiveDays | -0.00 | -0.06 – 0.05 | 0.909 | -0.02 | -0.08 – 0.04 | 0.462 | -0.02 | -0.08 – 0.03 | 0.405 |
| Reports | 0.06 | -0.10 – 0.22 | 0.455 | 0.02 | -0.13 – 0.18 | 0.767 | 0.01 | -0.17 – 0.19 | 0.940 |
| Activities | 0.06 | -0.01 – 0.13 | 0.118 | 0.08 | 0.01 – 0.16 | 0.031 | 0.08 | 0.01 – 0.16 | 0.035 |
| univ [Foothill] | -2.22 | -5.41 – 0.97 | 0.171 | -1.63 | -4.95 – 1.70 | 0.334 | |||
| univ [UW] | -2.20 | -4.24 – -0.16 | 0.034 | -2.43 | -4.65 – -0.22 | 0.032 | |||
| Sex [Woman] | 0.20 | -2.18 – 2.58 | 0.869 | -0.07 | -2.51 – 2.37 | 0.956 | |||
| Age | -0.21 | -0.40 – -0.02 | 0.032 | -0.23 | -0.43 – -0.03 | 0.021 | |||
| int student [No] | -4.16 | -7.78 – -0.54 | 0.025 | -4.03 | -8.08 – 0.03 | 0.052 | |||
| SES num | -0.29 | -1.10 – 0.52 | 0.486 | -0.51 | -1.37 – 0.36 | 0.248 | |||
| Ethnicity White | 1.07 | -1.59 – 3.73 | 0.427 | ||||||
| Ethnicity Hispanic | -0.34 | -3.93 – 3.26 | 0.854 | ||||||
| Ethnicity Black | 0.78 | -4.39 – 5.94 | 0.767 | ||||||
| Ethnicity East Asian | 0.84 | -2.53 – 4.20 | 0.625 | ||||||
| Ethnicity South Asian | 1.42 | -3.21 – 6.05 | 0.546 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-8.37 | -17.11 – 0.36 | 0.060 | ||||||
| Ethnicity Middle Eastern | 1.64 | -4.45 – 7.73 | 0.595 | ||||||
| Ethnicity American Indian | 2.42 | -11.17 – 16.02 | 0.725 | ||||||
| Observations | 167 | 166 | 166 | ||||||
| R2 / R2 adjusted | 0.028 / 0.010 | 0.113 / 0.062 | 0.145 / 0.047 | ||||||
Excluded Unreasonable Numbers
m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)| SAS positive diff | SAS positive diff | SAS positive diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.78 | -2.82 – 1.26 | 0.448 | 10.36 | 2.09 – 18.64 | 0.014 | 11.01 | 1.98 – 20.04 | 0.017 |
| ActiveDays | -0.00 | -0.13 – 0.12 | 0.972 | -0.04 | -0.17 – 0.09 | 0.527 | -0.05 | -0.18 – 0.08 | 0.413 |
| Reports | 0.06 | -0.16 – 0.28 | 0.578 | 0.03 | -0.19 – 0.24 | 0.799 | 0.01 | -0.24 – 0.26 | 0.926 |
| Activities | 0.04 | -0.04 – 0.13 | 0.313 | 0.09 | 0.00 – 0.18 | 0.044 | 0.10 | 0.01 – 0.19 | 0.035 |
| univ [Foothill] | -2.58 | -6.27 – 1.11 | 0.169 | -1.78 | -5.62 – 2.05 | 0.359 | |||
| univ [UW] | -2.74 | -5.13 – -0.36 | 0.025 | -3.06 | -5.64 – -0.48 | 0.021 | |||
| Sex [Woman] | 0.09 | -2.62 – 2.80 | 0.947 | -0.25 | -3.03 – 2.54 | 0.860 | |||
| Age | -0.23 | -0.44 – -0.02 | 0.031 | -0.25 | -0.47 – -0.03 | 0.026 | |||
| int student [No] | -4.71 | -9.38 – -0.04 | 0.048 | -4.23 | -9.20 – 0.75 | 0.095 | |||
| SES num | -0.17 | -1.10 – 0.75 | 0.709 | -0.37 | -1.33 – 0.60 | 0.454 | |||
| Ethnicity White | 0.98 | -1.98 – 3.95 | 0.514 | ||||||
| Ethnicity Hispanic | -0.89 | -4.86 – 3.09 | 0.659 | ||||||
| Ethnicity Black | -0.82 | -6.72 – 5.08 | 0.784 | ||||||
| Ethnicity East Asian | 0.69 | -3.01 – 4.38 | 0.714 | ||||||
| Ethnicity South Asian | 2.73 | -2.86 – 8.32 | 0.336 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-8.73 | -17.88 – 0.43 | 0.062 | ||||||
| Ethnicity Middle Eastern | 0.48 | -11.99 – 12.95 | 0.939 | ||||||
| Ethnicity American Indian | 2.94 | -12.49 – 18.38 | 0.706 | ||||||
| Observations | 139 | 139 | 139 | ||||||
| R2 / R2 adjusted | 0.015 / -0.007 | 0.112 / 0.050 | 0.157 / 0.038 | ||||||
SAS: Negative
Intention to Treat
m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)| SAS negative diff | SAS negative diff | SAS negative diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.65 | -2.45 – 1.15 | 0.475 | -9.12 | -16.63 – -1.61 | 0.018 | -8.28 | -16.78 – 0.21 | 0.056 |
| ActiveDays | -0.00 | -0.07 – 0.06 | 0.879 | 0.01 | -0.06 – 0.07 | 0.817 | 0.01 | -0.06 – 0.07 | 0.824 |
| Reports | 0.11 | -0.06 – 0.28 | 0.210 | 0.15 | -0.03 – 0.32 | 0.096 | 0.10 | -0.10 – 0.31 | 0.304 |
| Activities | -0.01 | -0.08 – 0.07 | 0.827 | -0.02 | -0.10 – 0.06 | 0.555 | -0.03 | -0.11 – 0.06 | 0.537 |
| univ [Foothill] | 1.79 | -1.62 – 5.21 | 0.301 | 1.62 | -2.00 – 5.23 | 0.378 | |||
| univ [UW] | 1.02 | -1.20 – 3.24 | 0.366 | 1.34 | -1.10 – 3.78 | 0.280 | |||
| Sex [Woman] | 0.54 | -2.01 – 3.10 | 0.674 | 0.33 | -2.31 – 2.98 | 0.803 | |||
| Age | 0.18 | -0.03 – 0.39 | 0.087 | 0.18 | -0.04 – 0.40 | 0.104 | |||
| int student [No] | 2.19 | -1.63 – 6.01 | 0.260 | 1.48 | -2.94 – 5.89 | 0.510 | |||
| SES num | 0.45 | -0.44 – 1.34 | 0.322 | 0.45 | -0.51 – 1.41 | 0.359 | |||
| Ethnicity White | 0.20 | -2.73 – 3.12 | 0.895 | ||||||
| Ethnicity Hispanic | 1.31 | -2.65 – 5.28 | 0.514 | ||||||
| Ethnicity Black | 0.35 | -5.40 – 6.09 | 0.905 | ||||||
| Ethnicity East Asian | -0.65 | -4.38 – 3.09 | 0.733 | ||||||
| Ethnicity South Asian | -0.46 | -5.50 – 4.58 | 0.857 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.93 | -11.63 – 7.78 | 0.696 | ||||||
| Ethnicity Middle Eastern | 2.01 | -4.74 – 8.77 | 0.557 | ||||||
| Ethnicity American Indian | 5.18 | -9.97 – 20.33 | 0.500 | ||||||
| Observations | 170 | 169 | 169 | ||||||
| R2 / R2 adjusted | 0.011 / -0.007 | 0.056 / 0.003 | 0.067 / -0.039 | ||||||
Excluded Preregistered
m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)| SAS negative diff | SAS negative diff | SAS negative diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.78 | -2.64 – 1.09 | 0.413 | -10.02 | -18.06 – -1.98 | 0.015 | -9.26 | -18.25 – -0.27 | 0.044 |
| ActiveDays | -0.00 | -0.07 – 0.06 | 0.892 | 0.01 | -0.05 – 0.07 | 0.779 | 0.01 | -0.06 – 0.08 | 0.768 |
| Reports | 0.11 | -0.06 – 0.29 | 0.206 | 0.15 | -0.03 – 0.33 | 0.092 | 0.11 | -0.09 – 0.31 | 0.282 |
| Activities | -0.01 | -0.08 – 0.07 | 0.889 | -0.02 | -0.10 – 0.06 | 0.593 | -0.02 | -0.11 – 0.06 | 0.566 |
| univ [Foothill] | 1.49 | -2.04 – 5.02 | 0.405 | 1.29 | -2.45 – 5.02 | 0.497 | |||
| univ [UW] | 1.06 | -1.19 – 3.31 | 0.354 | 1.42 | -1.06 – 3.91 | 0.260 | |||
| Sex [Woman] | 0.72 | -1.92 – 3.36 | 0.592 | 0.52 | -2.22 – 3.26 | 0.707 | |||
| Age | 0.19 | -0.02 – 0.40 | 0.072 | 0.19 | -0.03 – 0.42 | 0.086 | |||
| int student [No] | 2.55 | -1.47 – 6.58 | 0.212 | 1.79 | -2.78 – 6.35 | 0.441 | |||
| SES num | 0.46 | -0.45 – 1.36 | 0.322 | 0.45 | -0.53 – 1.42 | 0.370 | |||
| Ethnicity White | 0.30 | -2.69 – 3.29 | 0.843 | ||||||
| Ethnicity Hispanic | 1.52 | -2.52 – 5.57 | 0.459 | ||||||
| Ethnicity Black | 0.42 | -5.39 – 6.23 | 0.887 | ||||||
| Ethnicity East Asian | -0.49 | -4.28 – 3.31 | 0.800 | ||||||
| Ethnicity South Asian | -0.74 | -5.95 – 4.48 | 0.781 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.53 | -11.36 – 8.30 | 0.759 | ||||||
| Ethnicity Middle Eastern | 2.07 | -4.79 – 8.92 | 0.552 | ||||||
| Ethnicity American Indian | 4.84 | -10.47 – 20.16 | 0.533 | ||||||
| Observations | 167 | 166 | 166 | ||||||
| R2 / R2 adjusted | 0.011 / -0.007 | 0.058 / 0.003 | 0.068 / -0.039 | ||||||
Excluded Unreasonable Numbers
m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)| SAS negative diff | SAS negative diff | SAS negative diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.66 | -2.79 – 1.46 | 0.538 | -9.13 | -18.09 – -0.18 | 0.046 | -8.95 | -18.82 – 0.93 | 0.075 |
| ActiveDays | -0.08 | -0.21 – 0.05 | 0.218 | -0.06 | -0.20 – 0.07 | 0.359 | -0.06 | -0.20 – 0.08 | 0.381 |
| Reports | 0.24 | 0.01 – 0.47 | 0.039 | 0.26 | 0.03 – 0.49 | 0.027 | 0.20 | -0.07 – 0.48 | 0.148 |
| Activities | 0.01 | -0.07 – 0.10 | 0.765 | -0.01 | -0.11 – 0.08 | 0.753 | -0.02 | -0.11 – 0.08 | 0.754 |
| univ [Foothill] | 0.20 | -3.77 – 4.18 | 0.919 | -0.25 | -4.44 – 3.93 | 0.906 | |||
| univ [UW] | 0.84 | -1.70 – 3.38 | 0.515 | 1.39 | -1.40 – 4.18 | 0.325 | |||
| Sex [Woman] | 0.26 | -2.64 – 3.16 | 0.860 | -0.05 | -3.06 – 2.95 | 0.972 | |||
| Age | 0.24 | 0.02 – 0.47 | 0.036 | 0.27 | 0.03 – 0.51 | 0.027 | |||
| int student [No] | 2.56 | -2.50 – 7.61 | 0.319 | 1.89 | -3.55 – 7.33 | 0.493 | |||
| SES num | 0.17 | -0.83 – 1.17 | 0.736 | 0.27 | -0.78 – 1.33 | 0.607 | |||
| Ethnicity White | -0.10 | -3.35 – 3.14 | 0.949 | ||||||
| Ethnicity Hispanic | 2.42 | -1.92 – 6.77 | 0.272 | ||||||
| Ethnicity Black | -1.78 | -8.22 – 4.66 | 0.585 | ||||||
| Ethnicity East Asian | -1.54 | -5.59 – 2.50 | 0.452 | ||||||
| Ethnicity South Asian | -0.84 | -6.95 – 5.28 | 0.787 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.67 | -11.69 – 8.36 | 0.743 | ||||||
| Ethnicity Middle Eastern | -4.33 | -17.97 – 9.32 | 0.531 | ||||||
| Ethnicity American Indian | 4.06 | -12.82 – 20.94 | 0.635 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.031 / 0.010 | 0.073 / 0.009 | 0.101 / -0.024 | ||||||
Flourishing Scale
Intention to Treat
m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)| flourishing diff | flourishing diff | flourishing diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.10 | -1.34 – 1.14 | 0.878 | 1.79 | -3.50 – 7.09 | 0.504 | 1.52 | -4.43 – 7.46 | 0.615 |
| ActiveDays | -0.07 | -0.11 – -0.03 | 0.001 | -0.07 | -0.12 – -0.03 | 0.002 | -0.07 | -0.11 – -0.02 | 0.005 |
| Reports | 0.04 | -0.08 – 0.16 | 0.544 | 0.03 | -0.09 – 0.16 | 0.601 | 0.04 | -0.10 – 0.18 | 0.547 |
| Activities | 0.09 | 0.03 – 0.14 | 0.001 | 0.10 | 0.04 – 0.15 | 0.001 | 0.10 | 0.04 – 0.15 | 0.001 |
| univ [Foothill] | 0.99 | -1.42 – 3.40 | 0.419 | 1.10 | -1.43 – 3.63 | 0.392 | |||
| univ [UW] | -0.10 | -1.65 – 1.46 | 0.903 | 0.16 | -1.53 – 1.86 | 0.850 | |||
| Sex [Woman] | 0.49 | -1.29 – 2.27 | 0.589 | 0.40 | -1.43 – 2.23 | 0.664 | |||
| Age | -0.09 | -0.24 – 0.06 | 0.223 | -0.08 | -0.23 – 0.07 | 0.305 | |||
| int student [No] | -0.40 | -3.09 – 2.29 | 0.771 | -0.77 | -3.86 – 2.32 | 0.622 | |||
| SES num | -0.09 | -0.71 – 0.54 | 0.782 | 0.02 | -0.65 – 0.69 | 0.959 | |||
| Ethnicity White | 0.01 | -2.03 – 2.05 | 0.990 | ||||||
| Ethnicity Hispanic | 0.79 | -1.99 – 3.57 | 0.575 | ||||||
| Ethnicity Black | -1.84 | -5.86 – 2.18 | 0.368 | ||||||
| Ethnicity East Asian | -0.29 | -2.91 – 2.32 | 0.825 | ||||||
| Ethnicity South Asian | -1.73 | -5.25 – 1.80 | 0.334 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.58 | -6.22 – 7.37 | 0.867 | ||||||
| Ethnicity Middle Eastern | -2.62 | -7.35 – 2.10 | 0.275 | ||||||
| Ethnicity American Indian | -0.34 | -10.94 – 10.25 | 0.949 | ||||||
| Observations | 170 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.085 / 0.068 | 0.098 / 0.047 | 0.121 / 0.023 | ||||||
Excluded Preregistered
m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)| flourishing diff | flourishing diff | flourishing diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.10 | -1.38 – 1.18 | 0.875 | 3.08 | -2.54 – 8.70 | 0.281 | 2.48 | -3.77 – 8.73 | 0.435 |
| ActiveDays | -0.07 | -0.11 – -0.03 | 0.001 | -0.07 | -0.12 – -0.03 | 0.001 | -0.07 | -0.12 – -0.02 | 0.003 |
| Reports | 0.04 | -0.08 – 0.16 | 0.544 | 0.03 | -0.09 – 0.15 | 0.626 | 0.04 | -0.10 – 0.18 | 0.590 |
| Activities | 0.09 | 0.03 – 0.14 | 0.001 | 0.10 | 0.04 – 0.15 | 0.001 | 0.10 | 0.04 – 0.16 | 0.001 |
| univ [Foothill] | 1.27 | -1.20 – 3.73 | 0.312 | 1.31 | -1.28 – 3.91 | 0.319 | |||
| univ [UW] | -0.26 | -1.82 – 1.31 | 0.748 | -0.04 | -1.76 – 1.68 | 0.963 | |||
| Sex [Woman] | 0.17 | -1.65 – 2.00 | 0.853 | 0.14 | -1.74 – 2.02 | 0.884 | |||
| Age | -0.11 | -0.26 – 0.04 | 0.144 | -0.10 | -0.25 – 0.06 | 0.225 | |||
| int student [No] | -1.02 | -3.83 – 1.79 | 0.475 | -1.14 | -4.31 – 2.04 | 0.480 | |||
| SES num | -0.08 | -0.71 – 0.55 | 0.795 | 0.04 | -0.64 – 0.71 | 0.918 | |||
| Ethnicity White | -0.10 | -2.18 – 1.97 | 0.923 | ||||||
| Ethnicity Hispanic | 0.67 | -2.14 – 3.49 | 0.636 | ||||||
| Ethnicity Black | -1.84 | -5.88 – 2.20 | 0.369 | ||||||
| Ethnicity East Asian | -0.36 | -2.99 – 2.28 | 0.790 | ||||||
| Ethnicity South Asian | -1.31 | -4.94 – 2.31 | 0.475 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.27 | -6.56 – 7.11 | 0.937 | ||||||
| Ethnicity Middle Eastern | -2.69 | -7.45 – 2.07 | 0.266 | ||||||
| Ethnicity American Indian | -0.16 | -10.80 – 10.49 | 0.977 | ||||||
| Observations | 167 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.086 / 0.069 | 0.105 / 0.054 | 0.124 / 0.024 | ||||||
Excluded Unreasonable Numbers
m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)| flourishing diff | flourishing diff | flourishing diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.47 | -1.89 – 0.96 | 0.518 | 2.64 | -3.31 – 8.60 | 0.381 | 1.92 | -4.73 – 8.58 | 0.568 |
| ActiveDays | -0.02 | -0.11 – 0.06 | 0.577 | -0.01 | -0.10 – 0.08 | 0.891 | -0.00 | -0.10 – 0.09 | 0.968 |
| Reports | -0.01 | -0.16 – 0.14 | 0.906 | -0.03 | -0.18 – 0.12 | 0.704 | -0.03 | -0.22 – 0.16 | 0.746 |
| Activities | 0.08 | 0.03 – 0.14 | 0.004 | 0.10 | 0.04 – 0.16 | 0.002 | 0.10 | 0.04 – 0.16 | 0.003 |
| univ [Foothill] | 2.69 | 0.04 – 5.33 | 0.046 | 2.53 | -0.29 – 5.35 | 0.078 | |||
| univ [UW] | 0.35 | -1.34 – 2.04 | 0.681 | 0.35 | -1.53 – 2.23 | 0.710 | |||
| Sex [Woman] | 0.79 | -1.14 – 2.72 | 0.421 | 0.81 | -1.22 – 2.83 | 0.432 | |||
| Age | -0.16 | -0.31 – -0.01 | 0.034 | -0.16 | -0.32 – 0.01 | 0.058 | |||
| int student [No] | -1.56 | -4.93 – 1.80 | 0.359 | -1.36 | -5.03 – 2.30 | 0.464 | |||
| SES num | 0.04 | -0.62 – 0.70 | 0.905 | 0.04 | -0.67 – 0.75 | 0.916 | |||
| Ethnicity White | 0.14 | -2.04 – 2.33 | 0.898 | ||||||
| Ethnicity Hispanic | 0.76 | -2.17 – 3.69 | 0.610 | ||||||
| Ethnicity Black | 0.27 | -4.07 – 4.61 | 0.903 | ||||||
| Ethnicity East Asian | 0.50 | -2.22 – 3.23 | 0.716 | ||||||
| Ethnicity South Asian | 0.96 | -3.16 – 5.08 | 0.645 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.77 | -5.99 – 7.52 | 0.822 | ||||||
| Ethnicity Middle Eastern | 1.31 | -7.89 – 10.50 | 0.779 | ||||||
| Ethnicity American Indian | -0.27 | -11.65 – 11.10 | 0.962 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.064 / 0.044 | 0.118 / 0.057 | 0.122 / -0.000 | ||||||
Cohesion
Intention to Treat
m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)| cohesion diff | cohesion diff | cohesion diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.41 | -0.02 – 0.84 | 0.062 | -0.24 | -2.08 – 1.61 | 0.801 | 0.04 | -2.01 – 2.10 | 0.967 |
| ActiveDays | -0.01 | -0.02 – 0.01 | 0.477 | -0.00 | -0.02 – 0.01 | 0.703 | -0.00 | -0.02 – 0.01 | 0.797 |
| Reports | -0.01 | -0.05 – 0.04 | 0.784 | -0.01 | -0.05 – 0.04 | 0.792 | -0.01 | -0.06 – 0.04 | 0.592 |
| Activities | 0.01 | -0.01 – 0.02 | 0.507 | 0.00 | -0.02 – 0.02 | 0.703 | 0.00 | -0.02 – 0.02 | 0.688 |
| univ [Foothill] | 0.01 | -0.83 – 0.85 | 0.983 | 0.15 | -0.72 – 1.02 | 0.737 | |||
| univ [UW] | 0.03 | -0.52 – 0.57 | 0.924 | -0.07 | -0.66 – 0.52 | 0.816 | |||
| Sex [Woman] | 0.19 | -0.43 – 0.81 | 0.551 | 0.12 | -0.51 – 0.75 | 0.705 | |||
| Age | 0.03 | -0.02 – 0.08 | 0.245 | 0.03 | -0.03 – 0.08 | 0.328 | |||
| int student [No] | 0.22 | -0.72 – 1.15 | 0.650 | 0.37 | -0.70 – 1.43 | 0.498 | |||
| SES num | -0.10 | -0.32 – 0.11 | 0.351 | -0.10 | -0.33 – 0.13 | 0.408 | |||
| Ethnicity White | -0.29 | -1.00 – 0.41 | 0.411 | ||||||
| Ethnicity Hispanic | -0.62 | -1.58 – 0.34 | 0.202 | ||||||
| Ethnicity Black | -0.73 | -2.12 – 0.66 | 0.301 | ||||||
| Ethnicity East Asian | 0.01 | -0.89 – 0.92 | 0.976 | ||||||
| Ethnicity South Asian | -0.26 | -1.47 – 0.96 | 0.677 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.85 | -4.20 – 0.49 | 0.121 | ||||||
| Ethnicity Middle Eastern | -0.83 | -2.47 – 0.80 | 0.315 | ||||||
| Ethnicity American Indian | 1.65 | -2.01 – 5.31 | 0.374 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.006 / -0.012 | 0.023 / -0.032 | 0.064 / -0.041 | ||||||
Excluded Preregistered
m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)| cohesion diff | cohesion diff | cohesion diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.35 | -0.09 – 0.80 | 0.121 | -0.45 | -2.42 – 1.52 | 0.651 | -0.17 | -2.34 – 2.00 | 0.877 |
| ActiveDays | -0.01 | -0.02 – 0.01 | 0.500 | -0.00 | -0.02 – 0.01 | 0.738 | -0.00 | -0.02 – 0.01 | 0.844 |
| Reports | -0.00 | -0.05 – 0.04 | 0.817 | -0.00 | -0.05 – 0.04 | 0.824 | -0.01 | -0.06 – 0.04 | 0.643 |
| Activities | 0.01 | -0.01 – 0.03 | 0.425 | 0.01 | -0.01 – 0.02 | 0.606 | 0.01 | -0.01 – 0.03 | 0.598 |
| univ [Foothill] | -0.04 | -0.91 – 0.82 | 0.918 | 0.11 | -0.79 – 1.01 | 0.804 | |||
| univ [UW] | 0.01 | -0.54 – 0.56 | 0.967 | -0.09 | -0.68 – 0.51 | 0.769 | |||
| Sex [Woman] | 0.22 | -0.42 – 0.85 | 0.507 | 0.15 | -0.50 – 0.80 | 0.647 | |||
| Age | 0.03 | -0.02 – 0.08 | 0.221 | 0.03 | -0.03 – 0.08 | 0.308 | |||
| int student [No] | 0.28 | -0.70 – 1.27 | 0.570 | 0.44 | -0.66 – 1.54 | 0.432 | |||
| SES num | -0.09 | -0.31 – 0.13 | 0.413 | -0.08 | -0.32 – 0.15 | 0.498 | |||
| Ethnicity White | -0.33 | -1.05 – 0.39 | 0.373 | ||||||
| Ethnicity Hispanic | -0.61 | -1.59 – 0.37 | 0.218 | ||||||
| Ethnicity Black | -0.74 | -2.14 – 0.66 | 0.297 | ||||||
| Ethnicity East Asian | 0.03 | -0.88 – 0.95 | 0.945 | ||||||
| Ethnicity South Asian | -0.33 | -1.58 – 0.93 | 0.610 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.80 | -4.17 – 0.57 | 0.135 | ||||||
| Ethnicity Middle Eastern | -0.89 | -2.55 – 0.76 | 0.287 | ||||||
| Ethnicity American Indian | 1.52 | -2.17 – 5.21 | 0.418 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.006 / -0.012 | 0.023 / -0.033 | 0.064 / -0.043 | ||||||
Excluded Unreasonable Numbers
m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)| cohesion diff | cohesion diff | cohesion diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.16 | -0.32 – 0.65 | 0.508 | 0.12 | -1.95 – 2.19 | 0.911 | 0.12 | -2.17 – 2.40 | 0.920 |
| ActiveDays | 0.00 | -0.03 – 0.03 | 0.854 | 0.01 | -0.02 – 0.04 | 0.637 | 0.00 | -0.03 – 0.04 | 0.769 |
| Reports | 0.00 | -0.05 – 0.06 | 0.923 | 0.00 | -0.05 – 0.05 | 0.984 | -0.01 | -0.07 – 0.06 | 0.797 |
| Activities | 0.01 | -0.01 – 0.03 | 0.325 | 0.01 | -0.01 – 0.03 | 0.509 | 0.01 | -0.01 – 0.03 | 0.444 |
| univ [Foothill] | -0.21 | -1.13 – 0.71 | 0.650 | -0.15 | -1.12 – 0.82 | 0.761 | |||
| univ [UW] | 0.17 | -0.42 – 0.76 | 0.568 | 0.00 | -0.64 – 0.65 | 0.991 | |||
| Sex [Woman] | 0.40 | -0.27 – 1.07 | 0.238 | 0.35 | -0.34 – 1.05 | 0.315 | |||
| Age | 0.01 | -0.04 – 0.07 | 0.594 | 0.01 | -0.05 – 0.07 | 0.725 | |||
| int student [No] | -0.43 | -1.59 – 0.74 | 0.471 | -0.13 | -1.38 – 1.13 | 0.844 | |||
| SES num | -0.07 | -0.30 – 0.16 | 0.553 | -0.10 | -0.35 – 0.14 | 0.397 | |||
| Ethnicity White | 0.01 | -0.73 – 0.76 | 0.969 | ||||||
| Ethnicity Hispanic | -0.13 | -1.14 – 0.87 | 0.796 | ||||||
| Ethnicity Black | -0.02 | -1.51 – 1.46 | 0.974 | ||||||
| Ethnicity East Asian | 0.53 | -0.41 – 1.46 | 0.269 | ||||||
| Ethnicity South Asian | 0.39 | -1.02 – 1.80 | 0.587 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.23 | -3.54 – 1.09 | 0.296 | ||||||
| Ethnicity Middle Eastern | -0.35 | -3.51 – 2.80 | 0.826 | ||||||
| Ethnicity American Indian | 1.24 | -2.66 – 5.15 | 0.529 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.013 / -0.009 | 0.039 / -0.028 | 0.068 / -0.062 | ||||||
Mindfulness
Intention to Treat
m0_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_mindfulness_diff, m1_mindfulness_diff, m2_mindfulness_diff)| mindfulness diff | mindfulness diff | mindfulness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -1.54 | -3.14 – 0.06 | 0.060 | 3.42 | -3.31 – 10.14 | 0.317 | 3.63 | -3.88 – 11.14 | 0.342 |
| ActiveDays | -0.02 | -0.07 – 0.04 | 0.584 | -0.02 | -0.07 – 0.04 | 0.557 | -0.02 | -0.08 – 0.04 | 0.532 |
| Reports | -0.08 | -0.24 – 0.07 | 0.289 | -0.11 | -0.27 – 0.04 | 0.161 | -0.05 | -0.22 – 0.13 | 0.599 |
| Activities | 0.09 | 0.02 – 0.15 | 0.014 | 0.10 | 0.03 – 0.18 | 0.004 | 0.11 | 0.03 – 0.18 | 0.005 |
| univ [Foothill] | -0.55 | -3.61 – 2.51 | 0.724 | 0.12 | -3.08 – 3.31 | 0.943 | |||
| univ [UW] | -2.00 | -3.98 – -0.02 | 0.047 | -1.83 | -3.97 – 0.31 | 0.094 | |||
| Sex [Woman] | -1.08 | -3.34 – 1.18 | 0.345 | -0.87 | -3.18 – 1.44 | 0.456 | |||
| Age | -0.14 | -0.33 – 0.04 | 0.132 | -0.15 | -0.34 – 0.04 | 0.120 | |||
| int student [No] | 0.31 | -3.11 – 3.73 | 0.859 | -0.16 | -4.06 – 3.74 | 0.934 | |||
| SES num | -0.19 | -0.99 – 0.60 | 0.632 | -0.38 | -1.23 – 0.46 | 0.372 | |||
| Ethnicity White | 1.24 | -1.34 – 3.82 | 0.344 | ||||||
| Ethnicity Hispanic | -1.25 | -4.75 – 2.26 | 0.484 | ||||||
| Ethnicity Black | 0.05 | -5.03 – 5.12 | 0.986 | ||||||
| Ethnicity East Asian | 0.77 | -2.53 – 4.07 | 0.645 | ||||||
| Ethnicity South Asian | -1.69 | -6.14 – 2.76 | 0.454 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.30 | -7.28 – 9.88 | 0.766 | ||||||
| Ethnicity Middle Eastern | 1.50 | -4.47 – 7.47 | 0.620 | ||||||
| Ethnicity American Indian | -8.18 | -21.56 – 5.20 | 0.229 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.040 / 0.023 | 0.080 / 0.028 | 0.113 / 0.013 | ||||||
Excluded Preregistered
m0_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_mindfulness_diff, m1_mindfulness_diff, m2_mindfulness_diff)| mindfulness diff | mindfulness diff | mindfulness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -1.53 | -3.19 – 0.13 | 0.070 | 4.94 | -2.22 – 12.10 | 0.175 | 4.78 | -3.14 – 12.70 | 0.235 |
| ActiveDays | -0.02 | -0.07 – 0.04 | 0.585 | -0.02 | -0.08 – 0.04 | 0.499 | -0.02 | -0.08 – 0.04 | 0.470 |
| Reports | -0.08 | -0.24 – 0.07 | 0.291 | -0.11 | -0.27 – 0.04 | 0.154 | -0.05 | -0.23 – 0.13 | 0.561 |
| Activities | 0.08 | 0.02 – 0.15 | 0.015 | 0.11 | 0.04 – 0.18 | 0.004 | 0.11 | 0.03 – 0.18 | 0.004 |
| univ [Foothill] | -0.36 | -3.51 – 2.78 | 0.820 | 0.21 | -3.08 – 3.50 | 0.899 | |||
| univ [UW] | -2.17 | -4.16 – -0.17 | 0.033 | -2.02 | -4.20 – 0.16 | 0.069 | |||
| Sex [Woman] | -1.46 | -3.78 – 0.86 | 0.217 | -1.20 | -3.58 – 1.18 | 0.322 | |||
| Age | -0.16 | -0.35 – 0.03 | 0.092 | -0.17 | -0.36 – 0.03 | 0.094 | |||
| int student [No] | -0.43 | -4.01 – 3.16 | 0.815 | -0.65 | -4.67 – 3.38 | 0.752 | |||
| SES num | -0.21 | -1.01 – 0.59 | 0.610 | -0.40 | -1.26 – 0.46 | 0.357 | |||
| Ethnicity White | 1.25 | -1.38 – 3.88 | 0.348 | ||||||
| Ethnicity Hispanic | -1.24 | -4.80 – 2.33 | 0.494 | ||||||
| Ethnicity Black | 0.16 | -4.96 – 5.27 | 0.952 | ||||||
| Ethnicity East Asian | 0.79 | -2.55 – 4.13 | 0.640 | ||||||
| Ethnicity South Asian | -1.10 | -5.69 – 3.48 | 0.635 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.08 | -7.58 – 9.74 | 0.806 | ||||||
| Ethnicity Middle Eastern | 1.63 | -4.41 – 7.66 | 0.595 | ||||||
| Ethnicity American Indian | -7.86 | -21.34 – 5.62 | 0.251 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.040 / 0.023 | 0.086 / 0.034 | 0.115 / 0.014 | ||||||
Excluded Unreasonable Numbers
m0_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_mindfulness_diff, m1_mindfulness_diff, m2_mindfulness_diff)| mindfulness diff | mindfulness diff | mindfulness diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -1.45 | -3.30 – 0.40 | 0.124 | 7.57 | -0.08 – 15.23 | 0.052 | 5.61 | -2.70 – 13.93 | 0.184 |
| ActiveDays | -0.01 | -0.12 – 0.11 | 0.927 | -0.05 | -0.16 – 0.07 | 0.431 | -0.06 | -0.17 – 0.06 | 0.357 |
| Reports | -0.20 | -0.40 – 0.00 | 0.051 | -0.22 | -0.42 – -0.02 | 0.032 | -0.15 | -0.38 – 0.08 | 0.200 |
| Activities | 0.09 | 0.02 – 0.17 | 0.014 | 0.14 | 0.06 – 0.22 | 0.001 | 0.14 | 0.06 – 0.22 | 0.001 |
| univ [Foothill] | -0.09 | -3.49 – 3.31 | 0.960 | 0.83 | -2.69 – 4.35 | 0.642 | |||
| univ [UW] | -2.72 | -4.89 – -0.54 | 0.015 | -2.54 | -4.89 – -0.19 | 0.034 | |||
| Sex [Woman] | -1.72 | -4.20 – 0.76 | 0.172 | -1.72 | -4.25 – 0.81 | 0.181 | |||
| Age | -0.21 | -0.41 – -0.02 | 0.032 | -0.21 | -0.41 – -0.00 | 0.046 | |||
| int student [No] | -2.35 | -6.67 – 1.97 | 0.284 | -1.76 | -6.34 – 2.82 | 0.449 | |||
| SES num | 0.03 | -0.83 – 0.88 | 0.950 | -0.09 | -0.98 – 0.80 | 0.845 | |||
| Ethnicity White | 1.94 | -0.79 – 4.67 | 0.162 | ||||||
| Ethnicity Hispanic | -0.65 | -4.31 – 3.01 | 0.726 | ||||||
| Ethnicity Black | -1.30 | -6.72 – 4.13 | 0.637 | ||||||
| Ethnicity East Asian | 2.25 | -1.16 – 5.65 | 0.195 | ||||||
| Ethnicity South Asian | 0.09 | -5.05 – 5.24 | 0.972 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.46 | -6.98 – 9.90 | 0.732 | ||||||
| Ethnicity Middle Eastern | 9.65 | -1.84 – 21.14 | 0.099 | ||||||
| Ethnicity American Indian | -2.17 | -16.38 – 12.04 | 0.763 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.074 / 0.054 | 0.149 / 0.090 | 0.200 / 0.089 | ||||||
Emotional Resilience
Intention to Treat
m0_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_emo_res_diff, m1_emo_res_diff, m2_emo_res_diff)| emo res diff | emo res diff | emo res diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.80 | -0.11 – 1.71 | 0.083 | 1.06 | -2.83 – 4.95 | 0.592 | -1.20 | -5.48 – 3.08 | 0.579 |
| ActiveDays | -0.03 | -0.06 – 0.00 | 0.098 | -0.03 | -0.06 – 0.01 | 0.123 | -0.02 | -0.06 – 0.01 | 0.152 |
| Reports | 0.01 | -0.07 – 0.10 | 0.754 | 0.02 | -0.07 – 0.11 | 0.678 | 0.02 | -0.08 – 0.12 | 0.681 |
| Activities | 0.02 | -0.02 – 0.05 | 0.437 | 0.02 | -0.02 – 0.06 | 0.422 | 0.02 | -0.02 – 0.06 | 0.373 |
| univ [Foothill] | 0.98 | -0.79 – 2.75 | 0.277 | 0.96 | -0.86 – 2.78 | 0.298 | |||
| univ [UW] | 0.46 | -0.68 – 1.61 | 0.427 | 0.45 | -0.77 – 1.67 | 0.470 | |||
| Sex [Woman] | 0.32 | -0.99 – 1.62 | 0.634 | 0.31 | -1.01 – 1.63 | 0.643 | |||
| Age | -0.04 | -0.14 – 0.07 | 0.501 | -0.02 | -0.13 – 0.09 | 0.742 | |||
| int student [No] | -0.11 | -2.09 – 1.87 | 0.910 | 0.53 | -1.69 – 2.76 | 0.636 | |||
| SES num | -0.02 | -0.48 – 0.44 | 0.931 | -0.01 | -0.49 – 0.47 | 0.959 | |||
| Ethnicity White | 1.45 | -0.02 – 2.92 | 0.053 | ||||||
| Ethnicity Hispanic | 2.30 | 0.30 – 4.29 | 0.025 | ||||||
| Ethnicity Black | 0.50 | -2.39 – 3.39 | 0.734 | ||||||
| Ethnicity East Asian | 1.10 | -0.78 – 2.98 | 0.251 | ||||||
| Ethnicity South Asian | 2.80 | 0.26 – 5.33 | 0.031 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.31 | -4.58 – 5.20 | 0.899 | ||||||
| Ethnicity Middle Eastern | -0.75 | -4.16 – 2.65 | 0.663 | ||||||
| Ethnicity American Indian | 0.41 | -7.21 – 8.04 | 0.915 | ||||||
| Observations | 170 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.018 / -0.000 | 0.029 / -0.025 | 0.092 / -0.009 | ||||||
Excluded Preregistered
m0_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_emo_res_diff, m1_emo_res_diff, m2_emo_res_diff)| emo res diff | emo res diff | emo res diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.90 | -0.05 – 1.84 | 0.063 | 1.13 | -3.03 – 5.29 | 0.592 | -0.82 | -5.35 – 3.70 | 0.719 |
| ActiveDays | -0.03 | -0.06 – 0.00 | 0.095 | -0.03 | -0.06 – 0.01 | 0.121 | -0.02 | -0.06 – 0.01 | 0.141 |
| Reports | 0.01 | -0.08 – 0.10 | 0.780 | 0.02 | -0.07 – 0.11 | 0.699 | 0.02 | -0.08 – 0.12 | 0.729 |
| Activities | 0.01 | -0.03 – 0.05 | 0.512 | 0.01 | -0.03 – 0.06 | 0.500 | 0.02 | -0.03 – 0.06 | 0.447 |
| univ [Foothill] | 1.16 | -0.67 – 2.98 | 0.213 | 1.13 | -0.75 – 3.01 | 0.237 | |||
| univ [UW] | 0.50 | -0.66 – 1.66 | 0.395 | 0.47 | -0.77 – 1.72 | 0.451 | |||
| Sex [Woman] | 0.34 | -1.01 – 1.69 | 0.616 | 0.28 | -1.09 – 1.64 | 0.690 | |||
| Age | -0.04 | -0.15 – 0.07 | 0.478 | -0.02 | -0.13 – 0.09 | 0.685 | |||
| int student [No] | -0.09 | -2.17 – 1.99 | 0.934 | 0.44 | -1.86 – 2.73 | 0.707 | |||
| SES num | -0.02 | -0.49 – 0.45 | 0.934 | -0.02 | -0.51 – 0.47 | 0.926 | |||
| Ethnicity White | 1.42 | -0.08 – 2.93 | 0.063 | ||||||
| Ethnicity Hispanic | 2.18 | 0.15 – 4.22 | 0.036 | ||||||
| Ethnicity Black | 0.45 | -2.47 – 3.37 | 0.762 | ||||||
| Ethnicity East Asian | 1.00 | -0.91 – 2.90 | 0.303 | ||||||
| Ethnicity South Asian | 2.85 | 0.23 – 5.47 | 0.033 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.13 | -4.82 – 5.07 | 0.959 | ||||||
| Ethnicity Middle Eastern | -0.75 | -4.20 – 2.70 | 0.668 | ||||||
| Ethnicity American Indian | 0.62 | -7.08 – 8.32 | 0.874 | ||||||
| Observations | 167 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.019 / 0.001 | 0.033 / -0.022 | 0.094 / -0.009 | ||||||
Excluded Unreasonable Numbers
m0_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_emo_res_diff, m1_emo_res_diff, m2_emo_res_diff)| emo res diff | emo res diff | emo res diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.91 | -0.16 – 1.99 | 0.095 | 0.76 | -3.82 – 5.35 | 0.742 | -1.02 | -5.95 – 3.91 | 0.682 |
| ActiveDays | -0.03 | -0.09 – 0.04 | 0.383 | -0.02 | -0.09 – 0.05 | 0.603 | -0.02 | -0.09 – 0.05 | 0.653 |
| Reports | 0.03 | -0.09 – 0.15 | 0.615 | 0.03 | -0.09 – 0.15 | 0.663 | 0.03 | -0.11 – 0.16 | 0.719 |
| Activities | 0.01 | -0.03 – 0.06 | 0.582 | 0.02 | -0.03 – 0.06 | 0.510 | 0.02 | -0.03 – 0.06 | 0.521 |
| univ [Foothill] | 1.23 | -0.80 – 3.27 | 0.234 | 1.17 | -0.92 – 3.26 | 0.270 | |||
| univ [UW] | 0.39 | -0.92 – 1.69 | 0.559 | 0.72 | -0.67 – 2.11 | 0.306 | |||
| Sex [Woman] | 0.43 | -1.06 – 1.91 | 0.572 | 0.43 | -1.06 – 1.93 | 0.567 | |||
| Age | -0.06 | -0.18 – 0.06 | 0.306 | -0.04 | -0.16 – 0.08 | 0.462 | |||
| int student [No] | -0.42 | -3.00 – 2.17 | 0.752 | -0.45 | -3.17 – 2.26 | 0.743 | |||
| SES num | 0.27 | -0.24 – 0.78 | 0.299 | 0.16 | -0.36 – 0.69 | 0.537 | |||
| Ethnicity White | 2.23 | 0.61 – 3.84 | 0.007 | ||||||
| Ethnicity Hispanic | 2.68 | 0.51 – 4.85 | 0.016 | ||||||
| Ethnicity Black | 0.99 | -2.22 – 4.20 | 0.543 | ||||||
| Ethnicity East Asian | 1.37 | -0.65 – 3.39 | 0.182 | ||||||
| Ethnicity South Asian | 2.67 | -0.38 – 5.72 | 0.085 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
1.06 | -3.94 – 6.06 | 0.677 | ||||||
| Ethnicity Middle Eastern | -0.70 | -7.51 – 6.11 | 0.840 | ||||||
| Ethnicity American Indian | 0.70 | -7.72 – 9.13 | 0.869 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.006 / -0.016 | 0.030 / -0.037 | 0.108 / -0.017 | ||||||
School Satisfaction
Intention to Treat
m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)| school satis diff | school satis diff | school satis diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.01 | -0.19 – 0.21 | 0.934 | -0.13 | -0.99 – 0.73 | 0.762 | -0.11 | -1.06 – 0.85 | 0.829 |
| ActiveDays | -0.01 | -0.01 – 0.00 | 0.055 | -0.01 | -0.01 – 0.00 | 0.054 | -0.01 | -0.01 – 0.00 | 0.095 |
| Reports | 0.01 | -0.01 – 0.03 | 0.355 | 0.01 | -0.01 – 0.03 | 0.545 | 0.01 | -0.01 – 0.04 | 0.240 |
| Activities | 0.01 | 0.00 – 0.02 | 0.046 | 0.01 | 0.00 – 0.02 | 0.036 | 0.01 | 0.00 – 0.02 | 0.044 |
| univ [Foothill] | -0.24 | -0.63 – 0.15 | 0.229 | -0.15 | -0.56 – 0.26 | 0.464 | |||
| univ [UW] | -0.12 | -0.38 – 0.13 | 0.330 | -0.11 | -0.39 – 0.16 | 0.413 | |||
| Sex [Woman] | 0.04 | -0.25 – 0.33 | 0.776 | 0.05 | -0.24 – 0.35 | 0.726 | |||
| Age | -0.00 | -0.03 – 0.02 | 0.753 | -0.01 | -0.03 – 0.02 | 0.674 | |||
| int student [No] | 0.21 | -0.23 – 0.64 | 0.348 | 0.16 | -0.34 – 0.65 | 0.539 | |||
| SES num | 0.02 | -0.08 – 0.13 | 0.634 | 0.02 | -0.09 – 0.13 | 0.715 | |||
| Ethnicity White | 0.08 | -0.25 – 0.41 | 0.647 | ||||||
| Ethnicity Hispanic | -0.11 | -0.56 – 0.33 | 0.617 | ||||||
| Ethnicity Black | -0.14 | -0.78 – 0.51 | 0.680 | ||||||
| Ethnicity East Asian | 0.06 | -0.36 – 0.49 | 0.764 | ||||||
| Ethnicity South Asian | -0.35 | -0.92 – 0.22 | 0.222 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.05 | -1.15 – 1.04 | 0.921 | ||||||
| Ethnicity Middle Eastern | -0.37 | -1.14 – 0.39 | 0.335 | ||||||
| Ethnicity American Indian | -0.74 | -2.45 – 0.97 | 0.392 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.033 / 0.016 | 0.063 / 0.011 | 0.093 / -0.009 | ||||||
Excluded Preregistered
m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)| school satis diff | school satis diff | school satis diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.03 | -0.18 – 0.23 | 0.774 | 0.23 | -0.67 – 1.12 | 0.619 | 0.19 | -0.81 – 1.19 | 0.709 |
| ActiveDays | -0.01 | -0.01 – -0.00 | 0.048 | -0.01 | -0.01 – -0.00 | 0.033 | -0.01 | -0.01 – 0.00 | 0.059 |
| Reports | 0.01 | -0.01 – 0.03 | 0.364 | 0.01 | -0.01 – 0.03 | 0.584 | 0.01 | -0.01 – 0.03 | 0.290 |
| Activities | 0.01 | -0.00 – 0.02 | 0.058 | 0.01 | 0.00 – 0.02 | 0.029 | 0.01 | 0.00 – 0.02 | 0.034 |
| univ [Foothill] | -0.18 | -0.57 – 0.22 | 0.373 | -0.10 | -0.52 – 0.31 | 0.625 | |||
| univ [UW] | -0.16 | -0.41 – 0.09 | 0.201 | -0.16 | -0.44 – 0.11 | 0.245 | |||
| Sex [Woman] | -0.04 | -0.34 – 0.25 | 0.766 | -0.03 | -0.33 – 0.28 | 0.869 | |||
| Age | -0.01 | -0.03 – 0.01 | 0.468 | -0.01 | -0.03 – 0.02 | 0.449 | |||
| int student [No] | 0.04 | -0.41 – 0.49 | 0.866 | 0.04 | -0.47 – 0.55 | 0.875 | |||
| SES num | 0.02 | -0.08 – 0.12 | 0.665 | 0.02 | -0.09 – 0.13 | 0.729 | |||
| Ethnicity White | 0.06 | -0.27 – 0.39 | 0.706 | ||||||
| Ethnicity Hispanic | -0.13 | -0.58 – 0.32 | 0.558 | ||||||
| Ethnicity Black | -0.12 | -0.77 – 0.52 | 0.702 | ||||||
| Ethnicity East Asian | 0.05 | -0.37 – 0.47 | 0.802 | ||||||
| Ethnicity South Asian | -0.22 | -0.80 – 0.36 | 0.449 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.13 | -1.22 – 0.96 | 0.810 | ||||||
| Ethnicity Middle Eastern | -0.36 | -1.13 – 0.40 | 0.344 | ||||||
| Ethnicity American Indian | -0.66 | -2.36 – 1.03 | 0.441 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.033 / 0.015 | 0.055 / 0.001 | 0.077 / -0.028 | ||||||
Excluded Unreasonable Numbers
m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)| school satis diff | school satis diff | school satis diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | -0.10 | -0.33 – 0.12 | 0.368 | -0.13 | -1.11 – 0.85 | 0.792 | -0.23 | -1.31 – 0.84 | 0.671 |
| ActiveDays | 0.01 | -0.01 – 0.02 | 0.251 | 0.01 | -0.01 – 0.02 | 0.286 | 0.01 | -0.01 – 0.02 | 0.253 |
| Reports | -0.00 | -0.03 – 0.02 | 0.946 | -0.00 | -0.03 – 0.02 | 0.855 | 0.01 | -0.02 – 0.04 | 0.506 |
| Activities | 0.01 | -0.00 – 0.01 | 0.233 | 0.01 | -0.00 – 0.02 | 0.132 | 0.01 | -0.00 – 0.02 | 0.122 |
| univ [Foothill] | 0.07 | -0.36 – 0.51 | 0.746 | 0.13 | -0.32 – 0.59 | 0.563 | |||
| univ [UW] | -0.07 | -0.34 – 0.21 | 0.635 | -0.08 | -0.38 – 0.22 | 0.602 | |||
| Sex [Woman] | 0.11 | -0.21 – 0.42 | 0.506 | 0.13 | -0.20 – 0.46 | 0.434 | |||
| Age | -0.01 | -0.04 – 0.01 | 0.295 | -0.01 | -0.04 – 0.01 | 0.287 | |||
| int student [No] | 0.04 | -0.52 – 0.59 | 0.896 | 0.08 | -0.52 – 0.67 | 0.799 | |||
| SES num | 0.05 | -0.06 – 0.16 | 0.359 | 0.04 | -0.07 – 0.16 | 0.465 | |||
| Ethnicity White | 0.02 | -0.33 – 0.37 | 0.912 | ||||||
| Ethnicity Hispanic | -0.16 | -0.64 – 0.31 | 0.495 | ||||||
| Ethnicity Black | 0.02 | -0.68 – 0.73 | 0.946 | ||||||
| Ethnicity East Asian | 0.06 | -0.38 – 0.51 | 0.773 | ||||||
| Ethnicity South Asian | 0.13 | -0.54 – 0.79 | 0.704 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.03 | -1.12 – 1.06 | 0.957 | ||||||
| Ethnicity Middle Eastern | 1.02 | -0.47 – 2.50 | 0.179 | ||||||
| Ethnicity American Indian | -1.28 | -3.12 – 0.55 | 0.169 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.044 / 0.023 | 0.064 / -0.000 | 0.098 / -0.028 | ||||||
School Prioritizes Well-Being
Intention to Treat
m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)| wellbeing priority diff | wellbeing priority diff | wellbeing priority diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.15 | -0.17 – 0.47 | 0.348 | -0.62 | -1.97 – 0.74 | 0.370 | -0.74 | -2.25 – 0.78 | 0.340 |
| ActiveDays | -0.00 | -0.01 – 0.01 | 0.980 | 0.00 | -0.01 – 0.01 | 0.841 | 0.00 | -0.01 – 0.01 | 0.780 |
| Reports | -0.00 | -0.03 – 0.03 | 0.952 | -0.00 | -0.03 – 0.03 | 0.984 | -0.00 | -0.04 – 0.03 | 0.864 |
| Activities | -0.00 | -0.02 – 0.01 | 0.682 | -0.00 | -0.02 – 0.01 | 0.612 | -0.00 | -0.02 – 0.01 | 0.519 |
| univ [Foothill] | -0.10 | -0.72 – 0.52 | 0.749 | 0.01 | -0.64 – 0.65 | 0.981 | |||
| univ [UW] | 0.03 | -0.37 – 0.42 | 0.896 | 0.00 | -0.43 – 0.43 | 0.994 | |||
| Sex [Woman] | 0.35 | -0.10 – 0.81 | 0.127 | 0.38 | -0.09 – 0.85 | 0.111 | |||
| Age | 0.01 | -0.03 – 0.05 | 0.569 | 0.01 | -0.03 – 0.05 | 0.684 | |||
| int student [No] | 0.30 | -0.38 – 0.99 | 0.384 | 0.34 | -0.45 – 1.13 | 0.394 | |||
| SES num | -0.01 | -0.17 – 0.15 | 0.910 | -0.03 | -0.20 – 0.14 | 0.694 | |||
| Ethnicity White | 0.36 | -0.16 – 0.88 | 0.172 | ||||||
| Ethnicity Hispanic | -0.14 | -0.85 – 0.57 | 0.702 | ||||||
| Ethnicity Black | 0.36 | -0.67 – 1.39 | 0.489 | ||||||
| Ethnicity East Asian | 0.32 | -0.35 – 0.99 | 0.348 | ||||||
| Ethnicity South Asian | 0.27 | -0.63 – 1.17 | 0.555 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.18 | -1.56 – 1.92 | 0.838 | ||||||
| Ethnicity Middle Eastern | 0.17 | -1.04 – 1.38 | 0.781 | ||||||
| Ethnicity American Indian | 1.21 | -1.49 – 3.92 | 0.377 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.002 / -0.016 | 0.021 / -0.034 | 0.048 / -0.058 | ||||||
Excluded Preregistered
m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)| wellbeing priority diff | wellbeing priority diff | wellbeing priority diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.22 | -0.10 – 0.55 | 0.178 | -0.74 | -2.17 – 0.69 | 0.309 | -0.74 | -2.33 – 0.84 | 0.356 |
| ActiveDays | -0.00 | -0.01 – 0.01 | 0.935 | 0.00 | -0.01 – 0.01 | 0.828 | 0.00 | -0.01 – 0.01 | 0.775 |
| Reports | -0.00 | -0.03 – 0.03 | 0.896 | -0.00 | -0.03 – 0.03 | 0.965 | -0.00 | -0.04 – 0.03 | 0.837 |
| Activities | -0.00 | -0.02 – 0.01 | 0.512 | -0.01 | -0.02 – 0.01 | 0.484 | -0.01 | -0.02 – 0.01 | 0.391 |
| univ [Foothill] | -0.01 | -0.64 – 0.61 | 0.967 | 0.11 | -0.55 – 0.76 | 0.750 | |||
| univ [UW] | 0.06 | -0.34 – 0.46 | 0.765 | 0.04 | -0.39 – 0.48 | 0.846 | |||
| Sex [Woman] | 0.41 | -0.06 – 0.87 | 0.086 | 0.42 | -0.06 – 0.90 | 0.083 | |||
| Age | 0.01 | -0.03 – 0.05 | 0.561 | 0.01 | -0.03 – 0.05 | 0.702 | |||
| int student [No] | 0.39 | -0.32 – 1.11 | 0.280 | 0.39 | -0.42 – 1.19 | 0.344 | |||
| SES num | -0.00 | -0.17 – 0.16 | 0.951 | -0.03 | -0.20 – 0.14 | 0.733 | |||
| Ethnicity White | 0.32 | -0.21 – 0.85 | 0.231 | ||||||
| Ethnicity Hispanic | -0.22 | -0.94 – 0.49 | 0.538 | ||||||
| Ethnicity Black | 0.29 | -0.73 – 1.32 | 0.572 | ||||||
| Ethnicity East Asian | 0.25 | -0.42 – 0.91 | 0.469 | ||||||
| Ethnicity South Asian | 0.17 | -0.75 – 1.09 | 0.712 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.10 | -1.64 – 1.83 | 0.913 | ||||||
| Ethnicity Middle Eastern | 0.11 | -1.09 – 1.32 | 0.852 | ||||||
| Ethnicity American Indian | 1.25 | -1.45 – 3.94 | 0.363 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.005 / -0.014 | 0.027 / -0.028 | 0.055 / -0.053 | ||||||
Excluded Unreasonable Numbers
m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)| wellbeing priority diff | wellbeing priority diff | wellbeing priority diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.04 | -0.31 – 0.39 | 0.826 | -1.14 | -2.63 – 0.35 | 0.131 | -1.34 | -2.97 – 0.28 | 0.105 |
| ActiveDays | 0.01 | -0.01 – 0.03 | 0.545 | 0.01 | -0.01 – 0.04 | 0.237 | 0.01 | -0.01 – 0.04 | 0.251 |
| Reports | 0.01 | -0.03 – 0.05 | 0.579 | 0.01 | -0.03 – 0.05 | 0.661 | 0.01 | -0.03 – 0.06 | 0.538 |
| Activities | -0.00 | -0.02 – 0.01 | 0.574 | -0.01 | -0.02 – 0.01 | 0.424 | -0.01 | -0.02 – 0.01 | 0.431 |
| univ [Foothill] | 0.24 | -0.42 – 0.90 | 0.481 | 0.36 | -0.33 – 1.05 | 0.307 | |||
| univ [UW] | 0.19 | -0.23 – 0.61 | 0.367 | 0.16 | -0.30 – 0.61 | 0.504 | |||
| Sex [Woman] | 0.51 | 0.03 – 0.99 | 0.038 | 0.55 | 0.06 – 1.04 | 0.030 | |||
| Age | 0.01 | -0.02 – 0.05 | 0.487 | 0.01 | -0.03 – 0.05 | 0.615 | |||
| int student [No] | 0.21 | -0.63 – 1.05 | 0.618 | 0.30 | -0.59 – 1.20 | 0.504 | |||
| SES num | 0.03 | -0.13 – 0.20 | 0.681 | 0.01 | -0.17 – 0.18 | 0.938 | |||
| Ethnicity White | 0.31 | -0.23 – 0.84 | 0.257 | ||||||
| Ethnicity Hispanic | -0.22 | -0.93 – 0.50 | 0.549 | ||||||
| Ethnicity Black | 0.32 | -0.74 – 1.38 | 0.552 | ||||||
| Ethnicity East Asian | 0.34 | -0.32 – 1.01 | 0.310 | ||||||
| Ethnicity South Asian | 0.64 | -0.37 – 1.64 | 0.214 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.30 | -1.35 – 1.96 | 0.716 | ||||||
| Ethnicity Middle Eastern | 1.27 | -0.98 – 3.52 | 0.265 | ||||||
| Ethnicity American Indian | 0.06 | -2.72 – 2.85 | 0.964 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.010 / -0.012 | 0.051 / -0.015 | 0.093 / -0.033 | ||||||
Academic Self-Efficacy
Intention to Treat
m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)| acad selfefficacy diff | acad selfefficacy diff | acad selfefficacy diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.98 | -0.09 – 2.06 | 0.073 | 2.21 | -2.31 – 6.73 | 0.337 | 2.72 | -2.37 – 7.82 | 0.293 |
| ActiveDays | -0.02 | -0.06 – 0.01 | 0.236 | -0.03 | -0.07 – 0.01 | 0.126 | -0.03 | -0.07 – 0.01 | 0.176 |
| Reports | 0.02 | -0.08 – 0.12 | 0.709 | 0.01 | -0.10 – 0.11 | 0.898 | 0.02 | -0.10 – 0.14 | 0.690 |
| Activities | 0.00 | -0.04 – 0.05 | 0.908 | 0.01 | -0.04 – 0.06 | 0.683 | 0.01 | -0.04 – 0.06 | 0.799 |
| univ [Foothill] | -1.73 | -3.78 – 0.33 | 0.099 | -1.36 | -3.53 – 0.80 | 0.215 | |||
| univ [UW] | -0.87 | -2.20 – 0.46 | 0.197 | -0.95 | -2.40 – 0.51 | 0.200 | |||
| Sex [Woman] | -0.21 | -1.73 – 1.31 | 0.782 | -0.13 | -1.70 – 1.44 | 0.869 | |||
| Age | -0.05 | -0.17 – 0.07 | 0.422 | -0.07 | -0.20 – 0.06 | 0.304 | |||
| int student [No] | -0.24 | -2.54 – 2.06 | 0.836 | -0.45 | -3.10 – 2.20 | 0.739 | |||
| SES num | 0.28 | -0.25 – 0.82 | 0.299 | 0.22 | -0.36 – 0.79 | 0.458 | |||
| Ethnicity White | 0.44 | -1.31 – 2.19 | 0.622 | ||||||
| Ethnicity Hispanic | -0.93 | -3.31 – 1.45 | 0.442 | ||||||
| Ethnicity Black | 0.78 | -2.67 – 4.22 | 0.657 | ||||||
| Ethnicity East Asian | 0.47 | -1.77 – 2.71 | 0.678 | ||||||
| Ethnicity South Asian | -0.98 | -4.00 – 2.04 | 0.523 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.55 | -6.37 – 5.27 | 0.853 | ||||||
| Ethnicity Middle Eastern | -0.52 | -4.58 – 3.53 | 0.799 | ||||||
| Ethnicity American Indian | -0.82 | -9.90 – 8.26 | 0.859 | ||||||
| Observations | 170 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.010 / -0.008 | 0.051 / -0.003 | 0.067 / -0.037 | ||||||
Excluded Preregistered
m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)| acad selfefficacy diff | acad selfefficacy diff | acad selfefficacy diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 1.11 | -0.00 – 2.21 | 0.050 | 3.36 | -1.46 – 8.18 | 0.170 | 3.77 | -1.60 – 9.15 | 0.167 |
| ActiveDays | -0.02 | -0.06 – 0.01 | 0.226 | -0.03 | -0.07 – 0.01 | 0.104 | -0.03 | -0.07 – 0.01 | 0.140 |
| Reports | 0.02 | -0.09 – 0.12 | 0.737 | 0.00 | -0.10 – 0.11 | 0.935 | 0.02 | -0.10 – 0.14 | 0.763 |
| Activities | -0.00 | -0.05 – 0.05 | 0.991 | 0.01 | -0.04 – 0.06 | 0.695 | 0.01 | -0.04 – 0.06 | 0.805 |
| univ [Foothill] | -1.53 | -3.64 – 0.59 | 0.156 | -1.18 | -3.41 – 1.05 | 0.299 | |||
| univ [UW] | -0.96 | -2.30 – 0.38 | 0.159 | -1.06 | -2.54 – 0.41 | 0.157 | |||
| Sex [Woman] | -0.47 | -2.03 – 1.09 | 0.554 | -0.38 | -2.00 – 1.24 | 0.644 | |||
| Age | -0.07 | -0.19 – 0.06 | 0.307 | -0.08 | -0.21 – 0.05 | 0.225 | |||
| int student [No] | -0.76 | -3.17 – 1.65 | 0.534 | -0.84 | -3.57 – 1.89 | 0.543 | |||
| SES num | 0.26 | -0.28 – 0.80 | 0.335 | 0.20 | -0.38 – 0.78 | 0.502 | |||
| Ethnicity White | 0.42 | -1.36 – 2.21 | 0.640 | ||||||
| Ethnicity Hispanic | -1.01 | -3.42 – 1.41 | 0.412 | ||||||
| Ethnicity Black | 0.81 | -2.66 – 4.28 | 0.645 | ||||||
| Ethnicity East Asian | 0.41 | -1.85 – 2.68 | 0.719 | ||||||
| Ethnicity South Asian | -0.55 | -3.66 – 2.57 | 0.729 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-0.83 | -6.71 – 5.04 | 0.779 | ||||||
| Ethnicity Middle Eastern | -0.45 | -4.54 – 3.65 | 0.829 | ||||||
| Ethnicity American Indian | -0.46 | -9.60 – 8.69 | 0.921 | ||||||
| Observations | 167 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.012 / -0.006 | 0.051 / -0.003 | 0.066 / -0.041 | ||||||
Excluded Unreasonable Numbers
m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)| acad selfefficacy diff | acad selfefficacy diff | acad selfefficacy diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.84 | -0.39 – 2.07 | 0.181 | 3.99 | -1.14 – 9.12 | 0.127 | 2.91 | -2.78 – 8.60 | 0.313 |
| ActiveDays | 0.00 | -0.07 – 0.08 | 0.948 | -0.01 | -0.09 – 0.06 | 0.729 | -0.02 | -0.10 – 0.06 | 0.692 |
| Reports | -0.02 | -0.15 – 0.12 | 0.817 | -0.01 | -0.14 – 0.12 | 0.886 | -0.00 | -0.16 – 0.15 | 0.956 |
| Activities | -0.00 | -0.05 – 0.05 | 0.911 | 0.01 | -0.05 – 0.06 | 0.774 | 0.01 | -0.05 – 0.06 | 0.746 |
| univ [Foothill] | -2.03 | -4.31 – 0.25 | 0.081 | -1.82 | -4.23 – 0.59 | 0.138 | |||
| univ [UW] | -0.59 | -2.05 – 0.87 | 0.424 | -0.44 | -2.05 – 1.16 | 0.585 | |||
| Sex [Woman] | -0.31 | -1.98 – 1.35 | 0.708 | -0.32 | -2.05 – 1.41 | 0.719 | |||
| Age | -0.05 | -0.18 – 0.08 | 0.405 | -0.05 | -0.19 – 0.09 | 0.505 | |||
| int student [No] | -2.73 | -5.62 – 0.17 | 0.065 | -2.60 | -5.74 – 0.53 | 0.103 | |||
| SES num | 0.43 | -0.15 – 1.00 | 0.144 | 0.36 | -0.25 – 0.97 | 0.240 | |||
| Ethnicity White | 1.34 | -0.53 – 3.21 | 0.158 | ||||||
| Ethnicity Hispanic | 0.67 | -1.84 – 3.17 | 0.599 | ||||||
| Ethnicity Black | -0.11 | -3.82 – 3.60 | 0.952 | ||||||
| Ethnicity East Asian | 0.96 | -1.37 – 3.29 | 0.415 | ||||||
| Ethnicity South Asian | 1.38 | -2.14 – 4.91 | 0.438 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
0.96 | -4.82 – 6.73 | 0.744 | ||||||
| Ethnicity Middle Eastern | 0.79 | -7.07 – 8.66 | 0.842 | ||||||
| Ethnicity American Indian | 1.02 | -8.71 – 10.75 | 0.836 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.001 / -0.021 | 0.071 / 0.006 | 0.090 / -0.037 | ||||||
Closeness to School (IOS)
Intention to Treat
m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)| ios diff | ios diff | ios diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.05 | -0.29 – 0.40 | 0.759 | -1.05 | -2.51 – 0.41 | 0.157 | -0.50 | -2.05 – 1.06 | 0.529 |
| ActiveDays | -0.01 | -0.02 – 0.01 | 0.347 | -0.00 | -0.02 – 0.01 | 0.453 | -0.00 | -0.01 – 0.01 | 0.639 |
| Reports | 0.01 | -0.03 – 0.04 | 0.655 | 0.01 | -0.02 – 0.05 | 0.467 | 0.03 | -0.01 – 0.06 | 0.169 |
| Activities | 0.01 | -0.00 – 0.03 | 0.081 | 0.01 | -0.00 – 0.03 | 0.167 | 0.01 | -0.01 – 0.02 | 0.226 |
| univ [Foothill] | 0.02 | -0.64 – 0.69 | 0.941 | -0.13 | -0.79 – 0.53 | 0.707 | |||
| univ [UW] | 0.16 | -0.27 – 0.59 | 0.456 | 0.04 | -0.40 – 0.48 | 0.865 | |||
| Sex [Woman] | 0.33 | -0.16 – 0.81 | 0.192 | 0.32 | -0.15 – 0.80 | 0.182 | |||
| Age | 0.02 | -0.02 – 0.06 | 0.332 | 0.01 | -0.03 – 0.05 | 0.677 | |||
| int student [No] | 0.17 | -0.57 – 0.91 | 0.652 | 0.09 | -0.72 – 0.89 | 0.832 | |||
| SES num | 0.06 | -0.11 – 0.23 | 0.481 | 0.07 | -0.10 – 0.24 | 0.430 | |||
| Ethnicity White | -0.55 | -1.08 – -0.02 | 0.044 | ||||||
| Ethnicity Hispanic | 0.23 | -0.49 – 0.95 | 0.533 | ||||||
| Ethnicity Black | 0.83 | -0.22 – 1.87 | 0.122 | ||||||
| Ethnicity East Asian | 0.00 | -0.68 – 0.68 | 0.995 | ||||||
| Ethnicity South Asian | -0.91 | -1.83 – 0.01 | 0.053 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.16 | -2.93 – 0.61 | 0.197 | ||||||
| Ethnicity Middle Eastern | -0.76 | -2.00 – 0.47 | 0.223 | ||||||
| Ethnicity American Indian | -2.69 | -5.45 – 0.07 | 0.056 | ||||||
| Observations | 171 | 170 | 170 | ||||||
| R2 / R2 adjusted | 0.020 / 0.002 | 0.039 / -0.015 | 0.160 / 0.066 | ||||||
Excluded Preregistered
m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)| ios diff | ios diff | ios diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.08 | -0.28 – 0.43 | 0.668 | -0.94 | -2.50 – 0.62 | 0.236 | -0.55 | -2.19 – 1.09 | 0.510 |
| ActiveDays | -0.01 | -0.02 – 0.01 | 0.341 | -0.00 | -0.02 – 0.01 | 0.440 | -0.00 | -0.01 – 0.01 | 0.658 |
| Reports | 0.01 | -0.03 – 0.04 | 0.673 | 0.01 | -0.02 – 0.05 | 0.482 | 0.03 | -0.01 – 0.06 | 0.168 |
| Activities | 0.01 | -0.00 – 0.03 | 0.101 | 0.01 | -0.01 – 0.03 | 0.185 | 0.01 | -0.01 – 0.02 | 0.225 |
| univ [Foothill] | 0.05 | -0.64 – 0.74 | 0.887 | -0.12 | -0.80 – 0.56 | 0.729 | |||
| univ [UW] | 0.16 | -0.28 – 0.59 | 0.472 | 0.04 | -0.42 – 0.49 | 0.875 | |||
| Sex [Woman] | 0.30 | -0.20 – 0.81 | 0.238 | 0.34 | -0.16 – 0.83 | 0.182 | |||
| Age | 0.02 | -0.02 – 0.06 | 0.378 | 0.01 | -0.03 – 0.05 | 0.675 | |||
| int student [No] | 0.13 | -0.66 – 0.91 | 0.753 | 0.11 | -0.72 – 0.94 | 0.795 | |||
| SES num | 0.06 | -0.12 – 0.23 | 0.509 | 0.08 | -0.10 – 0.25 | 0.405 | |||
| Ethnicity White | -0.56 | -1.11 – -0.02 | 0.043 | ||||||
| Ethnicity Hispanic | 0.22 | -0.52 – 0.96 | 0.559 | ||||||
| Ethnicity Black | 0.81 | -0.25 – 1.87 | 0.133 | ||||||
| Ethnicity East Asian | -0.00 | -0.70 – 0.69 | 0.992 | ||||||
| Ethnicity South Asian | -0.94 | -1.89 – 0.02 | 0.054 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.16 | -2.96 – 0.64 | 0.203 | ||||||
| Ethnicity Middle Eastern | -0.79 | -2.04 – 0.46 | 0.215 | ||||||
| Ethnicity American Indian | -2.72 | -5.52 – 0.08 | 0.056 | ||||||
| Observations | 168 | 167 | 167 | ||||||
| R2 / R2 adjusted | 0.018 / -0.000 | 0.034 / -0.021 | 0.155 / 0.059 | ||||||
Excluded Unreasonable Numbers
m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)| ios diff | ios diff | ios diff | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 0.24 | -0.13 – 0.61 | 0.208 | -0.25 | -1.83 – 1.33 | 0.757 | -0.06 | -1.73 – 1.61 | 0.943 |
| ActiveDays | -0.04 | -0.06 – -0.02 | 0.001 | -0.04 | -0.06 – -0.01 | 0.004 | -0.03 | -0.06 – -0.01 | 0.006 |
| Reports | 0.04 | 0.00 – 0.08 | 0.037 | 0.04 | 0.00 – 0.08 | 0.049 | 0.06 | 0.01 – 0.10 | 0.018 |
| Activities | 0.02 | 0.01 – 0.04 | 0.008 | 0.02 | 0.00 – 0.04 | 0.014 | 0.02 | 0.00 – 0.04 | 0.015 |
| univ [Foothill] | 0.20 | -0.51 – 0.90 | 0.582 | 0.08 | -0.63 – 0.79 | 0.821 | |||
| univ [UW] | 0.15 | -0.30 – 0.60 | 0.504 | 0.04 | -0.44 – 0.51 | 0.883 | |||
| Sex [Woman] | 0.38 | -0.13 – 0.89 | 0.141 | 0.42 | -0.09 – 0.92 | 0.108 | |||
| Age | -0.01 | -0.05 – 0.03 | 0.710 | -0.02 | -0.06 – 0.02 | 0.431 | |||
| int student [No] | -0.16 | -1.05 – 0.73 | 0.724 | -0.08 | -1.00 – 0.84 | 0.869 | |||
| SES num | 0.10 | -0.08 – 0.27 | 0.283 | 0.07 | -0.11 – 0.24 | 0.465 | |||
| Ethnicity White | -0.23 | -0.77 – 0.32 | 0.417 | ||||||
| Ethnicity Hispanic | 0.30 | -0.43 – 1.04 | 0.414 | ||||||
| Ethnicity Black | 0.88 | -0.21 – 1.97 | 0.112 | ||||||
| Ethnicity East Asian | 0.31 | -0.37 – 1.00 | 0.366 | ||||||
| Ethnicity South Asian | -0.71 | -1.75 – 0.32 | 0.174 | ||||||
|
Ethnicity Native Hawaiian Pacific Islander |
-1.04 | -2.73 – 0.65 | 0.227 | ||||||
| Ethnicity Middle Eastern | -0.21 | -2.51 – 2.10 | 0.861 | ||||||
| Ethnicity American Indian | -2.43 | -5.29 – 0.42 | 0.094 | ||||||
| Observations | 140 | 140 | 140 | ||||||
| R2 / R2 adjusted | 0.094 / 0.074 | 0.122 / 0.061 | 0.220 / 0.111 | ||||||
Visualizations
Depression
Intention to Treat
ggplot(data_ITT, aes(x = time, y = depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Anxiety
Intention to Treat
ggplot(data_ITT, aes(x = time, y = anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety (0-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Loneliness
Intention to Treat
ggplot(data_ITT, aes(x = time, y = loneliness, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.1) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Loneliness (3-9)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = loneliness, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Loneliness (3-9)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = loneliness, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Loneliness (3-9)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Perceived Stress
Intention to Treat
ggplot(data_ITT, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Stress (0-16)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Stress (0-16)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Stress (0-16)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Calm
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Calm Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Calm Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Calm Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Well-Being
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Well-Being Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Well-Being Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Well-Being Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Vigour
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Vigour Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Vigour Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Vigour Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Depression
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Depression Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Anxiety
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anxiety Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Anger
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anger Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anger Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Anger Affect (0-12)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Positive
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Positive Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Positive Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Positive Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
SAS: Negative
Intention to Treat
ggplot(data_ITT, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Negative Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Negative Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Negative Emotions (0-36)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Flourishing Score
Intention to Treat
ggplot(data_ITT, aes(x = time, y = flourishing, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Flourishing Score (8-56)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
coord_cartesian(y=c(43.5, 46)) +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = flourishing, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Flourishing Score (8-56)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
coord_cartesian(y=c(43.5, 46)) +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = flourishing, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Flourishing Score (8-56)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
coord_cartesian(y=c(43.5, 46)) +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Social Fit
Intention to Treat
ggplot(data_ITT, aes(x = time, y = social_fit, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Social Fit (2-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = social_fit, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Social Fit (2-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = social_fit, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Social Fit (2-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Cohesion
Intention to Treat
ggplot(data_ITT, aes(x = time, y = cohesion, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Cohesion (0-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = cohesion, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Cohesion (0-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = cohesion, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Cohesion (0-10)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Mindfulness
Intention to Treat
ggplot(data_ITT, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Mindfulness (0-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Mindfulness (0-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Mindfulness (0-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Emotional Resilience
Intention to Treat
ggplot(data_ITT, aes(x = time, y = emo_res, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Emotional Resilience (6-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = emo_res, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Emotional Resilience (6-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = emo_res, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Emotional Resilience (6-30)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
School Satisfaction
Intention to Treat
ggplot(data_ITT, aes(x = time, y = school_satis, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Satisfaction (1-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = school_satis, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Satisfaction (1-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = school_satis, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Satisfaction (1-6)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
School Prioritizes Well-Being
Intention to Treat
ggplot(data_ITT, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Prioritizes Well-Being (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Prioritizes Well-Being (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "School Prioritizes Well-Being (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Academic Self-Efficacy
Intention to Treat
ggplot(data_ITT, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Academic Self-Efficacy (5-25)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Academic Self-Efficacy (5-25)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Academic Self-Efficacy (5-25)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Closeness to School (IOS)
Intention to Treat
ggplot(data_ITT, aes(x = time, y = ios, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Closeness to School (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Preregistered
ggplot(data_excluded, aes(x = time, y = ios, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Closeness to School (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Excluded Unreasonable Numbers
ggplot(data_excluded_unreasonable, aes(x = time, y = ios, color = cond, group = cond)) +
# geom_jitter(width = 0.1, size = 2, alpha = 0.6) + # Add individual data points with some jitter for better visibility
geom_errorbar(stat = "summary", fun.data = mean_se, width = 0.2) +
geom_line(stat = "summary", fun = mean, size = 1.75) + # Plot the mean anxiety per condition over time
geom_point(stat = "summary", fun = mean, size = 4) + # Add points for the mean at each time point
labs(x = "Time",
y = "Closeness to School (1-7)",
color = "Condition",
linetype = "Condition",
shape = "Condition") +
theme_minimal() +
scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6")) +
scale_color_manual(values = c("flourish" = "#3D97A7", "control" = "#F57266"),
labels = c("Control", "Flourish")) +
theme(
panel.grid.major = element_line(color = "#EAEAEA"),
panel.grid.minor = element_line(color = "#F5F5F5"),
axis.text = element_text(size = 13), # Adjust size of tick labels
axis.text.x = element_text(margin = margin(t = 8)), # Add space between x-axis tick labels and line
axis.text.y = element_text(margin = margin(r = 8)), # Add space between y-axis tick labels and line
axis.title = element_text(size = 16, face = "bold"), # Adjust size of axis labels
axis.line.x = element_line(color = "#7D7D7D", size = 0.5), # Ensure x-axis line is visible
axis.line.y = element_line(color = "#7D7D7D", size = 0.5), # Ensure y-axis line is visible
axis.title.x = element_text(margin = margin(t = 10)), # Increase space between x-axis ticks and label
axis.title.y = element_text(margin = margin(r = 10)), # Increase space between y-axis ticks and label
legend.title = element_text(size = 15, face = "bold"), # Make legend title match axis titles
legend.text = element_text(size = 13) # Make legend text match axis labels
)
Exploratory Analyses
Categorical time (Time 1 = -1, Time 2, 3, 4 = +1)
# Depression
model_depression <- lmer(depression ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_depression)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: depression ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 5044.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7842 -0.5459 -0.1457 0.4595 3.5544
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.35497 1.1640
## univ (Intercept) 0.02133 0.1460
## Residual 0.81505 0.9028
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.51470 0.10575 1.81958
## condflourish_vs_control -0.04297 0.05873 471.33418
## treatment_vs_baseline 0.04251 0.03890 1165.02258
## condflourish_vs_control:treatment_vs_baseline -0.05032 0.03885 1169.68822
## t value Pr(>|t|)
## (Intercept) 14.324 0.007 **
## condflourish_vs_control -0.732 0.465
## treatment_vs_baseline 1.093 0.275
## condflourish_vs_control:treatment_vs_baseline -1.295 0.195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.003
## trtmnt_vs_b 0.056 -0.008
## cndflr__:__ -0.005 0.105 0.000# Anxiety
model_anxiety <- lmer(anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_anxiety)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 |
## univ)
## Data: merged_data_long
##
## REML criterion at convergence: 5544.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2865 -0.5438 -0.0710 0.4841 4.1262
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.789 1.337
## univ (Intercept) 0.000 0.000
## Residual 1.137 1.066
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 2.339602 0.067802
## condflourish_vs_control -0.116692 0.067802
## treatment_vs_baseline -0.096062 0.045835
## condflourish_vs_control:treatment_vs_baseline -0.004634 0.045835
## df t value
## (Intercept) 478.337176 34.506
## condflourish_vs_control 478.337176 -1.721
## treatment_vs_baseline 1177.389442 -2.096
## condflourish_vs_control:treatment_vs_baseline 1177.389442 -0.101
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## condflourish_vs_control 0.0859 .
## treatment_vs_baseline 0.0363 *
## condflourish_vs_control:treatment_vs_baseline 0.9195
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.009
## trtmnt_vs_b 0.107 -0.009
## cndflr__:__ -0.009 0.107 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# Loneliness
model_loneliness <- lmer(loneliness ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00607822 (tol = 0.002, component 1)summary(model_loneliness)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 5332.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2396 -0.5381 -0.0525 0.4960 3.2355
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.792794664 1.338953
## univ (Intercept) 0.000003032 0.001741
## Residual 0.948536021 0.973928
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.27382 0.06682 102.63973
## condflourish_vs_control -0.08741 0.06681 484.86398
## treatment_vs_baseline -0.31115 0.04199 1173.16478
## condflourish_vs_control:treatment_vs_baseline -0.08088 0.04199 1173.29732
## t value Pr(>|t|)
## (Intercept) 78.927 < 0.0000000000000002 ***
## condflourish_vs_control -1.308 0.1914
## treatment_vs_baseline -7.411 0.00000000000024 ***
## condflourish_vs_control:treatment_vs_baseline -1.926 0.0543 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.009
## trtmnt_vs_b 0.102 -0.009
## cndflr__:__ -0.009 0.102 0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00607822 (tol = 0.002, component 1)# Perceived Stress
model_stress <- lmer(perceived_stress ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_stress)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: perceived_stress ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 7277.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09200 -0.59763 -0.03136 0.54016 3.06803
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 4.8211864462660 2.19572003
## univ (Intercept) 0.0000000008717 0.00002952
## Residual 3.5395894878169 1.88137968
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 6.62665 0.11289 469.97656
## condflourish_vs_control -0.07541 0.11289 469.98740
## treatment_vs_baseline -0.14549 0.08069 1178.01695
## condflourish_vs_control:treatment_vs_baseline -0.02625 0.08069 1178.01696
## t value Pr(>|t|)
## (Intercept) 58.701 <0.0000000000000002 ***
## condflourish_vs_control -0.668 0.5044
## treatment_vs_baseline -1.803 0.0716 .
## condflourish_vs_control:treatment_vs_baseline -0.325 0.7450
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.008
## trtmnt_vs_b 0.110 -0.009
## cndflr__:__ -0.009 0.110 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# Affect variables
model_calm <- lmer(SAS_calm ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_calm)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 |
## univ)
## Data: merged_data_long
##
## REML criterion at convergence: 7000.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4517 -0.6032 0.0401 0.5837 3.3752
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.56 1.887
## univ (Intercept) 0.00 0.000
## Residual 3.09 1.758
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.73116 0.09879 472.76369
## condflourish_vs_control 0.21667 0.09879 472.76369
## treatment_vs_baseline 0.09851 0.07518 1190.03073
## condflourish_vs_control:treatment_vs_baseline 0.13200 0.07518 1190.03073
## t value Pr(>|t|)
## (Intercept) 58.016 <0.0000000000000002 ***
## condflourish_vs_control 2.193 0.0288 *
## treatment_vs_baseline 1.310 0.1903
## condflourish_vs_control:treatment_vs_baseline 1.756 0.0794 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.007
## trtmnt_vs_b 0.114 -0.010
## cndflr__:__ -0.010 0.114 0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')model_well_being <- lmer(SAS_well_being ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_well_being)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 6786.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2100 -0.5604 0.0434 0.5449 3.6885
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.66679 1.9149
## univ (Intercept) 0.08877 0.2979
## Residual 2.56327 1.6010
## Number of obs: 1578, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 6.85023 0.20307 1.88149
## condflourish_vs_control 0.17425 0.09799 471.09802
## treatment_vs_baseline -0.20574 0.06884 1172.04354
## condflourish_vs_control:treatment_vs_baseline 0.06514 0.06874 1176.78345
## t value Pr(>|t|)
## (Intercept) 33.734 0.00124 **
## condflourish_vs_control 1.778 0.07599 .
## treatment_vs_baseline -2.989 0.00286 **
## condflourish_vs_control:treatment_vs_baseline 0.948 0.34352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.002
## trtmnt_vs_b 0.051 -0.008
## cndflr__:__ -0.004 0.109 0.000model_vigour <- lmer(SAS_vigour ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_vigour)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_vigour ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 7000.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1242 -0.5531 0.0049 0.5629 4.2960
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 4.58253 2.141
## univ (Intercept) 0.07234 0.269
## Residual 2.85468 1.690
## Number of obs: 1578, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 5.70803 0.19486 2.31559
## condflourish_vs_control 0.14773 0.10835 472.90286
## treatment_vs_baseline -0.20186 0.07277 1166.94493
## condflourish_vs_control:treatment_vs_baseline 0.11466 0.07267 1171.36286
## t value Pr(>|t|)
## (Intercept) 29.293 0.00050 ***
## condflourish_vs_control 1.363 0.17338
## treatment_vs_baseline -2.774 0.00563 **
## condflourish_vs_control:treatment_vs_baseline 1.578 0.11491
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.003
## trtmnt_vs_b 0.057 -0.008
## cndflr__:__ -0.005 0.106 0.000model_SAS_depression <- lmer(SAS_depression ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_SAS_depression)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_depression ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 7305.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9133 -0.5559 -0.0962 0.4772 3.5903
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 5.229 2.287
## univ (Intercept) 0.000 0.000
## Residual 3.542 1.882
## Number of obs: 1578, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.04924 0.11664 466.45091
## condflourish_vs_control -0.14410 0.11664 466.45091
## treatment_vs_baseline -0.12495 0.08083 1169.10057
## condflourish_vs_control:treatment_vs_baseline 0.04722 0.08083 1169.10057
## t value Pr(>|t|)
## (Intercept) 34.717 <0.0000000000000002 ***
## condflourish_vs_control -1.235 0.217
## treatment_vs_baseline -1.546 0.122
## condflourish_vs_control:treatment_vs_baseline 0.584 0.559
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.009
## trtmnt_vs_b 0.108 -0.009
## cndflr__:__ -0.009 0.108 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')model_SAS_anxiety <- lmer(SAS_anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_SAS_anxiety)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 7322.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7329 -0.6180 0.0034 0.5950 3.1698
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 4.475227523786 2.11547336
## univ (Intercept) 0.000000002722 0.00005217
## Residual 3.762685356039 1.93976425
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 6.00160 0.11036 471.53308
## condflourish_vs_control -0.16615 0.11036 471.56565
## treatment_vs_baseline -0.33351 0.08301 1187.16585
## condflourish_vs_control:treatment_vs_baseline -0.02800 0.08301 1187.16586
## t value Pr(>|t|)
## (Intercept) 54.384 < 0.0000000000000002 ***
## condflourish_vs_control -1.506 0.133
## treatment_vs_baseline -4.018 0.0000625 ***
## condflourish_vs_control:treatment_vs_baseline -0.337 0.736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.007
## trtmnt_vs_b 0.113 -0.010
## cndflr__:__ -0.010 0.113 0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')model_SAS_anger <- lmer(SAS_anger ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_SAS_anger)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_anger ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 6858.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8905 -0.5224 -0.1795 0.4539 4.5481
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.5798 1.8920
## univ (Intercept) 0.1994 0.4466
## Residual 2.7307 1.6525
## Number of obs: 1579, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 2.72113 0.28032 1.71286
## condflourish_vs_control 0.07234 0.09768 473.94960
## treatment_vs_baseline 0.03253 0.07096 1180.20218
## condflourish_vs_control:treatment_vs_baseline 0.01026 0.07084 1185.06363
## t value Pr(>|t|)
## (Intercept) 9.707 0.0169 *
## condflourish_vs_control 0.741 0.4593
## treatment_vs_baseline 0.458 0.6467
## condflourish_vs_control:treatment_vs_baseline 0.145 0.8849
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.001
## trtmnt_vs_b 0.037 -0.008
## cndflr__:__ -0.003 0.111 0.000model_SAS_positive <- lmer(SAS_positive ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_SAS_positive)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_positive ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 9836.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7688 -0.5524 -0.0048 0.5479 4.3459
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 29.4895 5.4304
## univ (Intercept) 0.2899 0.5384
## Residual 17.0428 4.1283
## Number of obs: 1577, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 18.3208 0.4264 2.2595
## condflourish_vs_control 0.5208 0.2730 474.0945
## treatment_vs_baseline -0.3136 0.1780 1162.8401
## condflourish_vs_control:treatment_vs_baseline 0.2953 0.1778 1167.2796
## t value Pr(>|t|)
## (Intercept) 42.970 0.000244 ***
## condflourish_vs_control 1.907 0.057082 .
## treatment_vs_baseline -1.762 0.078367 .
## condflourish_vs_control:treatment_vs_baseline 1.661 0.096990 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.004
## trtmnt_vs_b 0.065 -0.008
## cndflr__:__ -0.005 0.104 0.000model_SAS_negative <- lmer(SAS_negative ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)## boundary (singular) fit: see help('isSingular')summary(model_SAS_negative)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_negative ~ cond * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long
##
## REML criterion at convergence: 9884.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9870 -0.5606 -0.0423 0.5036 4.4829
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 29.428381327236 5.42479321
## univ (Intercept) 0.000000004503 0.00006711
## Residual 17.699955691132 4.20713153
## Number of obs: 1578, groups: unique_ID, 486; univ, 3
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 12.65757 0.27366 470.61364
## condflourish_vs_control -0.24101 0.27366 470.62413
## treatment_vs_baseline -0.42151 0.18104 1166.25449
## condflourish_vs_control:treatment_vs_baseline 0.02861 0.18104 1166.25449
## t value Pr(>|t|)
## (Intercept) 46.253 <0.0000000000000002 ***
## condflourish_vs_control -0.881 0.3789
## treatment_vs_baseline -2.328 0.0201 *
## condflourish_vs_control:treatment_vs_baseline 0.158 0.8745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cndf__ trtm__
## cndflrsh_v_ 0.009
## trtmnt_vs_b 0.105 -0.009
## cndflr__:__ -0.009 0.105 0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## VARIABLES COLLECTED JUST PRE POST (time 1 vs. 4)
merged_data_long_factor <- merged_data_long |>
dplyr::filter(time == 1 | time == 4) |>
dplyr::mutate(time_factor = as.factor(time)) |>
dplyr::mutate(cond_factor = as.factor(cond))
contrasts(merged_data_long_factor$time_factor) <- c(-1,1)
# flourishing score
model_flourishing <- lmer(flourishing ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_flourishing)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 5253.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7571 -0.3986 0.0676 0.4461 3.0516
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 29.894 5.468
## univ (Intercept) 1.302 1.141
## Residual 12.772 3.574
## Number of obs: 832, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 44.4189 0.7362
## cond_factorflourish_vs_control 0.2721 0.2969
## treatment_vs_baseline -0.0728 0.2001
## cond_factorflourish_vs_control:treatment_vs_baseline 0.4284 0.1996
## df t value Pr(>|t|)
## (Intercept) 2.2118 60.333 0.000133
## cond_factorflourish_vs_control 574.1968 0.916 0.359953
## treatment_vs_baseline 365.5110 -0.364 0.716140
## cond_factorflourish_vs_control:treatment_vs_baseline 368.1616 2.146 0.032499
##
## (Intercept) ***
## cond_factorflourish_vs_control
## treatment_vs_baseline
## cond_factorflourish_vs_control:treatment_vs_baseline *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.002
## trtmnt_vs_b 0.134 -0.013
## cnd_fc__:__ -0.007 0.336 -0.012# social fit
model_social_fit <- lmer(social_fit ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_social_fit)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: social_fit ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 2848
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72043 -0.48538 -0.04663 0.55927 2.75430
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.26945 1.1267
## univ (Intercept) 0.04065 0.2016
## Residual 0.84726 0.9205
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 6.178840 0.136769
## cond_factorflourish_vs_control 0.088005 0.065812
## treatment_vs_baseline -0.020711 0.051102
## cond_factorflourish_vs_control:treatment_vs_baseline -0.008842 0.050963
## df t value
## (Intercept) 2.403451 45.177
## cond_factorflourish_vs_control 603.709224 1.337
## treatment_vs_baseline 384.931296 -0.405
## cond_factorflourish_vs_control:treatment_vs_baseline 388.141953 -0.174
## Pr(>|t|)
## (Intercept) 0.00014 ***
## cond_factorflourish_vs_control 0.18165
## treatment_vs_baseline 0.68549
## cond_factorflourish_vs_control:treatment_vs_baseline 0.86235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004
## trtmnt_vs_b 0.181 -0.016
## cnd_fc__:__ -0.010 0.381 -0.014# cohesion (MARG)
model_cohesion <- lmer(cohesion ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_cohesion)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 3353.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37421 -0.42300 0.04007 0.50388 2.45014
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 3.45707 1.859
## univ (Intercept) 0.06864 0.262
## Residual 1.13318 1.065
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 5.74828 0.18469
## cond_factorflourish_vs_control 0.15055 0.09748
## treatment_vs_baseline 0.19043 0.05971
## cond_factorflourish_vs_control:treatment_vs_baseline 0.13002 0.05961
## df t value Pr(>|t|)
## (Intercept) 2.22992 31.124 0.000548
## cond_factorflourish_vs_control 566.17055 1.544 0.123032
## treatment_vs_baseline 368.72018 3.189 0.001549
## cond_factorflourish_vs_control:treatment_vs_baseline 370.88987 2.181 0.029800
##
## (Intercept) ***
## cond_factorflourish_vs_control
## treatment_vs_baseline **
## cond_factorflourish_vs_control:treatment_vs_baseline *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.001
## trtmnt_vs_b 0.160 -0.014
## cnd_fc__:__ -0.009 0.308 -0.015# mindfulness
model_mindfulness <- lmer(mindfulness ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)## boundary (singular) fit: see help('isSingular')summary(model_mindfulness)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 5200.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.41784 -0.49969 0.02324 0.45562 2.59292
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 21.51 4.638
## univ (Intercept) 0.00 0.000
## Residual 14.63 3.825
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 15.4551 0.2718
## cond_factorflourish_vs_control -0.3428 0.2718
## treatment_vs_baseline 0.7779 0.2117
## cond_factorflourish_vs_control:treatment_vs_baseline -0.4264 0.2117
## df t value
## (Intercept) 605.3701 56.859
## cond_factorflourish_vs_control 605.3701 -1.261
## treatment_vs_baseline 386.6873 3.675
## cond_factorflourish_vs_control:treatment_vs_baseline 386.6873 -2.015
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## cond_factorflourish_vs_control 0.207800
## treatment_vs_baseline 0.000271 ***
## cond_factorflourish_vs_control:treatment_vs_baseline 0.044633 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.003
## trtmnt_vs_b 0.383 -0.017
## cnd_fc__:__ -0.017 0.383 -0.013
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# emotional resilience
model_emo_res <- lmer(emo_res ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_emo_res)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: emo_res ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 4572.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9103 -0.4405 0.0100 0.4542 2.3547
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 14.7841258898 3.8450131
## univ (Intercept) 0.0000002239 0.0004732
## Residual 5.0927163395 2.2567048
## Number of obs: 832, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 19.05817 0.20276
## cond_factorflourish_vs_control 0.06153 0.20276
## treatment_vs_baseline 0.21211 0.12642
## cond_factorflourish_vs_control:treatment_vs_baseline 0.25205 0.12642
## df t value
## (Intercept) 570.01436 93.995
## cond_factorflourish_vs_control 572.66051 0.303
## treatment_vs_baseline 373.54061 1.678
## cond_factorflourish_vs_control:treatment_vs_baseline 373.54064 1.994
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## cond_factorflourish_vs_control 0.7617
## treatment_vs_baseline 0.0942 .
## cond_factorflourish_vs_control:treatment_vs_baseline 0.0469 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ 0.002
## trtmnt_vs_b 0.313 -0.015
## cnd_fc__:__ -0.015 0.313 -0.012# school satisfaction
model_school_satis <- lmer(school_satis ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_school_satis)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## school_satis ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 1971.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6788 -0.4027 0.1034 0.3724 1.9676
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 0.52213 0.7226
## univ (Intercept) 0.02205 0.1485
## Residual 0.25920 0.5091
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.55980 0.09650
## cond_factorflourish_vs_control 0.03757 0.04011
## treatment_vs_baseline 0.06718 0.02840
## cond_factorflourish_vs_control:treatment_vs_baseline 0.01438 0.02833
## df t value Pr(>|t|)
## (Intercept) 2.30284 47.253 0.000172
## cond_factorflourish_vs_control 586.53958 0.936 0.349407
## treatment_vs_baseline 375.33412 2.365 0.018524
## cond_factorflourish_vs_control:treatment_vs_baseline 378.28188 0.508 0.611982
##
## (Intercept) ***
## cond_factorflourish_vs_control
## treatment_vs_baseline *
## cond_factorflourish_vs_control:treatment_vs_baseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.003
## trtmnt_vs_b 0.144 -0.015
## cnd_fc__:__ -0.008 0.351 -0.014# school prioritizes wellbeing
model_wellbeing_priority <- lmer(wellbeing_priority ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_wellbeing_priority)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: wellbeing_priority ~ cond_factor * treatment_vs_baseline + (1 |
## unique_ID) + (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 2639.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0848 -0.4665 -0.1319 0.6356 2.4055
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 0.7458 0.8636
## univ (Intercept) 0.1151 0.3393
## Residual 0.7807 0.8836
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 4.59648 0.20545
## cond_factorflourish_vs_control 0.04781 0.05547
## treatment_vs_baseline 0.07153 0.04870
## cond_factorflourish_vs_control:treatment_vs_baseline 0.04028 0.04851
## df t value Pr(>|t|)
## (Intercept) 1.90111 22.373 0.00254
## cond_factorflourish_vs_control 621.62599 0.862 0.38909
## treatment_vs_baseline 393.84638 1.469 0.14270
## cond_factorflourish_vs_control:treatment_vs_baseline 397.72790 0.830 0.40693
##
## (Intercept) **
## cond_factorflourish_vs_control
## treatment_vs_baseline
## cond_factorflourish_vs_control:treatment_vs_baseline
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004
## trtmnt_vs_b 0.113 -0.017
## cnd_fc__:__ -0.006 0.423 -0.013# academic self-efficacy
model_acad_selfefficacy <- lmer(acad_selfefficacy ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)## boundary (singular) fit: see help('isSingular')summary(model_acad_selfefficacy)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: acad_selfefficacy ~ cond_factor * treatment_vs_baseline + (1 |
## unique_ID) + (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 4562
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.50620 -0.43145 0.07989 0.49916 2.08830
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 9.521 3.086
## univ (Intercept) 0.000 0.000
## Residual 7.132 2.671
## Number of obs: 831, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 24.11326 0.18426
## cond_factorflourish_vs_control 0.03749 0.18426
## treatment_vs_baseline 0.34149 0.14780
## cond_factorflourish_vs_control:treatment_vs_baseline 0.11531 0.14780
## df t value
## (Intercept) 608.55225 130.864
## cond_factorflourish_vs_control 608.55225 0.203
## treatment_vs_baseline 387.45747 2.310
## cond_factorflourish_vs_control:treatment_vs_baseline 387.45747 0.780
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## cond_factorflourish_vs_control 0.8388
## treatment_vs_baseline 0.0214 *
## cond_factorflourish_vs_control:treatment_vs_baseline 0.4358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004
## trtmnt_vs_b 0.391 -0.018
## cnd_fc__:__ -0.018 0.391 -0.009
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')# ios
model_ios <- lmer(ios ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_ios)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ios ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +
## (1 | univ)
## Data: merged_data_long_factor
##
## REML criterion at convergence: 2792.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0541 -0.5342 -0.0813 0.4512 3.3759
##
## Random effects:
## Groups Name Variance Std.Dev.
## unique_ID (Intercept) 1.184312 1.08826
## univ (Intercept) 0.008385 0.09157
## Residual 0.797026 0.89276
## Number of obs: 833, groups: unique_ID, 485; univ, 3
##
## Fixed effects:
## Estimate Std. Error
## (Intercept) 3.32986 0.08487
## cond_factorflourish_vs_control 0.09806 0.06366
## treatment_vs_baseline 0.10526 0.04950
## cond_factorflourish_vs_control:treatment_vs_baseline 0.08990 0.04942
## df t value Pr(>|t|)
## (Intercept) 2.17979 39.235 0.000379
## cond_factorflourish_vs_control 606.82385 1.540 0.123996
## treatment_vs_baseline 388.33945 2.126 0.034100
## cond_factorflourish_vs_control:treatment_vs_baseline 391.37465 1.819 0.069649
##
## (Intercept) ***
## cond_factorflourish_vs_control
## treatment_vs_baseline *
## cond_factorflourish_vs_control:treatment_vs_baseline .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004
## trtmnt_vs_b 0.284 -0.016
## cnd_fc__:__ -0.014 0.382 -0.013
Social Fit
Intention to Treat
time - 2 5
Pacific Islander
Excluded Preregistered
time - 2 5
Pacific Islander
Excluded Unreasonable Numbers
time - 2 5
Pacific Islander