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.

Prep 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    87
## 2                        2   142
## 3                        3    33
## 4                        4   276

# write.csv(data_ITT, "merged_no_exclusions.csv")

Preregistered 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   134
## 2                        3    37
## 3                        4   256

# write.csv(data_excluded, "merged_excluded_prereg.csv")

Additionally 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   130
## 2                        3    30
## 3                        4   235

# write.csv(data_excluded_unreasonable, "merged_excluded_unreasonable.csv")

Demographics

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

Excluded 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
20.31	4.2

Excluded 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
20.34	4.28

Sex

Intention to Treat

data_ITT_demog %>%
  group_by(Sex) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

Sex	count
Man	100
Woman	383
NA	55

Excluded Preregistered

data_excluded_demog %>%
  group_by(Sex) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

Sex	count
Man	69
Woman	318
NA	40

Excluded Unreasonable Numbers

data_excluded_unreasonable_demog %>%
  group_by(Sex) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

Sex	count
Man	63
Woman	293
NA	39

Gender

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	59

Excluded 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	44

Excluded 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	43

Ethnicity

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	39.78
Hispanic	48	8.92
Black	21	3.90
East_Asian	61	11.34
South_Asian	26	4.83
Native_Hawaiian_Pacific_Islander	2	0.37
Middle_Eastern	10	1.86
American_Indian	2	0.37
Mixed	86	15.99
Self_Identify	13	2.42

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	41.92
Hispanic	32	7.49
Black	16	3.75
East_Asian	51	11.94
South_Asian	22	5.15
Native_Hawaiian_Pacific_Islander	2	0.47
Middle_Eastern	9	2.11
American_Indian	2	0.47
Mixed	63	14.75
Self_Identify	11	2.58

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	42.28
Hispanic	31	7.85
Black	15	3.80
East_Asian	47	11.90
South_Asian	18	4.56
Native_Hawaiian_Pacific_Islander	2	0.51
Middle_Eastern	5	1.27
American_Indian	2	0.51
Mixed	58	14.68
Self_Identify	11	2.78

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	55

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	40

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	39

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	55

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	28
2	61
3	120
4	113
5	65
NA	40

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
3.33	1.14

Excluded Unreasonable Numbers

data_excluded_unreasonable_demog %>%
  group_by(SES) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

SES	count
1	27
2	56
3	114
4	103
5	56
NA	39

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
3.29	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
foothill	NA	52

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	33
Foothill	Bachelors	4
Foothill	Masters	1
Foothill	Non-degree student	7
Foothill	NA	4
UW	Associates	3
UW	Bachelors	143
UW	Masters	1
UW	Other	2
UW	Non-degree student	4
UW	NA	5
foothill	NA	38

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	31
Foothill	Bachelors	3
Foothill	Masters	1
Foothill	Non-degree student	7
Foothill	NA	4
UW	Associates	2
UW	Bachelors	131
UW	Masters	1
UW	Other	2
UW	Non-degree student	4
UW	NA	4
foothill	NA	38

Condition count

Intention to Treat

data_ITT_demog %>%
  group_by(cond) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

cond	count
control	272
flourish	266

Excluded Preregistered

data_excluded_demog %>%
  group_by(cond) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

cond	count
control	206
flourish	221

Excluded Unreasonable Numbers

data_excluded_unreasonable_demog %>%
  group_by(cond) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

cond	count
control	206
flourish	189

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
##   (56 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: 0.4599

Excluded Preregistered

lm(Age ~ cond, data = data_excluded) |> summary()

## 
## Call:
## lm(formula = Age ~ cond, data = data_excluded)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.279 -1.279 -1.021 -0.021 32.721 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             20.15026    0.09731 207.079   <2e-16 ***
## condflourish_vs_control  0.12909    0.09731   1.327    0.185    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.672 on 1427 degrees of freedom
##   (138 observations deleted due to missingness)
## Multiple R-squared:  0.001232,   Adjusted R-squared:  0.0005319 
## F-statistic:  1.76 on 1 and 1427 DF,  p-value: 0.1848

Excluded 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 
## -3.701 -1.701 -1.021 -0.021 32.299 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.3609     0.2270  89.710   <2e-16 ***
## condflourish_vs_control   0.3397     0.2270   1.497    0.135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.274 on 354 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.006289,   Adjusted R-squared:  0.003482 
## F-statistic:  2.24 on 1 and 354 DF,  p-value: 0.1353

Gender

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 = NA

Excluded 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 = NA

Excluded 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 = NA

Engagement

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
13.61	15.56	14.86	11.85

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
10.83	11.11	14.54	11.69

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
23.01	20.82	22.27	14.98

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
17.21	13.01	21.18	14.72

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 54 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            18.6

Excluded Preregistered

ggplot(subset(data_excluded, time == 1), aes(x = depression)) +
  geom_density(fill = "blue", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            83.0            17.0

Excluded Unreasonable Numbers

ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = depression)) +
  geom_density(fill = "blue", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            82.9            17.1

Anxiety

Intention to Treat

ggplot(subset(data_ITT, time == 1), aes(x = anxiety)) +
  geom_density(fill = "red", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 54 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            41.1

Excluded Preregistered

ggplot(subset(data_excluded, time == 1), aes(x = anxiety)) +
  geom_density(fill = "red", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            40.5

Excluded Unreasonable Numbers

ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = anxiety)) +
  geom_density(fill = "red", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            42.1

Loneliness

Intention to Treat

ggplot(subset(data_ITT, time == 1), aes(x = loneliness)) +
  geom_density(fill = "green", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 54 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             100

Excluded Preregistered

ggplot(subset(data_excluded, time == 1), aes(x = loneliness)) +
  geom_density(fill = "green", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            27.8            72.2

Excluded Unreasonable Numbers

ggplot(subset(data_excluded_unreasonable, time == 1), aes(x = loneliness)) +
  geom_density(fill = "green", alpha = 0.5) +
  theme_minimal()

## Warning: Removed 39 rows 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            26.4            73.6

ggplot(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            72.9

Analyses

Linear time (-1.5,-0.5,0.5,1.5)

Depression

Intention to Treat

m0 <- lmer(depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
m1 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
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_ITT)
tab_model(m0, m1, m2)

	depression			depression			depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.54	1.35 – 1.73	<0.001	2.96	1.98 – 3.95	<0.001	2.54	1.53 – 3.55	<0.001
condflourish vs control	-0.04	-0.16 – 0.07	0.432	-0.03	-0.14 – 0.08	0.600	-0.04	-0.15 – 0.07	0.499
time - 2 5	0.00	-0.04 – 0.05	0.861	-0.00	-0.05 – 0.04	0.870	-0.00	-0.05 – 0.04	0.879
condflourish vs control × time - 2 5	-0.03	-0.07 – 0.01	0.154	-0.03	-0.07 – 0.01	0.160	-0.03	-0.07 – 0.01	0.150
Sex [Woman]				0.08	-0.21 – 0.36	0.601	0.09	-0.20 – 0.37	0.554
Age				-0.03	-0.06 – 0.00	0.060	-0.03	-0.06 – 0.00	0.084
int student [No]				-0.12	-0.58 – 0.34	0.611	0.12	-0.37 – 0.61	0.633
SES num				-0.25	-0.35 – -0.15	<0.001	-0.24	-0.34 – -0.13	<0.001
Ethnicity White							-0.08	-0.39 – 0.22	0.587
Ethnicity Hispanic							0.04	-0.40 – 0.48	0.867
Ethnicity Black							0.72	0.12 – 1.33	0.019
Ethnicity East Asian							0.17	-0.24 – 0.58	0.418
Ethnicity South Asian							0.81	0.25 – 1.38	0.005
Ethnicity Native Hawaiian Pacific Islander							0.30	-1.45 – 2.05	0.733
Ethnicity Middle Eastern							0.41	-0.40 – 1.23	0.319
Ethnicity American Indian							0.79	-0.90 – 2.48	0.357
Random Effects
σ²	0.80			0.79			0.79
τ₀₀	1.41 _{unique_ID}			1.27 _{unique_ID}			1.25 _{unique_ID}
	0.02 _univ			0.03 _univ			0.01 _univ
ICC	0.64			0.62			0.61
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.001 / 0.643			0.042 / 0.636			0.071 / 0.640

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)	1.48	1.27 – 1.70	<0.001	3.10	2.06 – 4.14	<0.001	2.82	1.74 – 3.90	<0.001
condflourish vs control	-0.06	-0.17 – 0.06	0.366	-0.03	-0.15 – 0.09	0.598	-0.03	-0.15 – 0.09	0.606
time - 2 5	0.00	-0.04 – 0.05	0.986	-0.01	-0.05 – 0.04	0.798	-0.01	-0.05 – 0.04	0.786
condflourish vs control × time - 2 5	-0.02	-0.07 – 0.02	0.297	-0.02	-0.07 – 0.02	0.305	-0.02	-0.07 – 0.02	0.292
Sex [Woman]				0.02	-0.30 – 0.33	0.914	0.02	-0.30 – 0.34	0.915
Age				-0.03	-0.07 – -0.00	0.031	-0.03	-0.07 – -0.00	0.035
int student [No]				-0.07	-0.54 – 0.41	0.779	0.09	-0.42 – 0.59	0.739
SES num				-0.27	-0.38 – -0.17	<0.001	-0.27	-0.38 – -0.16	<0.001
Ethnicity White							0.02	-0.31 – 0.35	0.909
Ethnicity Hispanic							-0.04	-0.54 – 0.46	0.883
Ethnicity Black							0.64	-0.02 – 1.30	0.059
Ethnicity East Asian							0.18	-0.26 – 0.62	0.421
Ethnicity South Asian							0.65	0.06 – 1.24	0.030
Ethnicity Native Hawaiian Pacific Islander							0.31	-1.40 – 2.01	0.724
Ethnicity Middle Eastern							0.35	-0.48 – 1.18	0.412
Ethnicity American Indian							0.91	-0.75 – 2.57	0.285
Random Effects
σ²	0.80			0.80			0.80
τ₀₀	1.32 _{unique_ID}			1.16 _{unique_ID}			1.15 _{unique_ID}
	0.03 _univ			0.04 _univ			0.02 _univ
ICC	0.63			0.60			0.59
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.002 / 0.628			0.052 / 0.621			0.069 / 0.622

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)	1.49	1.26 – 1.72	<0.001	3.15	2.05 – 4.25	<0.001	2.92	1.79 – 4.05	<0.001
condflourish vs control	-0.06	-0.18 – 0.07	0.373	-0.04	-0.17 – 0.08	0.490	-0.03	-0.16 – 0.09	0.588
time - 2 5	-0.01	-0.06 – 0.04	0.716	-0.01	-0.06 – 0.03	0.550	-0.01	-0.06 – 0.03	0.544
condflourish vs control × time - 2 5	-0.03	-0.08 – 0.01	0.168	-0.03	-0.08 – 0.01	0.177	-0.03	-0.08 – 0.01	0.170
Sex [Woman]				0.01	-0.32 – 0.34	0.966	0.02	-0.31 – 0.35	0.917
Age				-0.04	-0.07 – -0.00	0.031	-0.03	-0.07 – -0.00	0.034
int student [No]				-0.04	-0.56 – 0.48	0.869	0.10	-0.44 – 0.65	0.708
SES num				-0.28	-0.39 – -0.18	<0.001	-0.28	-0.39 – -0.17	<0.001
Ethnicity White							-0.04	-0.38 – 0.29	0.796
Ethnicity Hispanic							-0.20	-0.71 – 0.30	0.429
Ethnicity Black							0.60	-0.08 – 1.28	0.085
Ethnicity East Asian							0.12	-0.33 – 0.56	0.608
Ethnicity South Asian							0.74	0.12 – 1.36	0.020
Ethnicity Native Hawaiian Pacific Islander							0.22	-1.48 – 1.91	0.801
Ethnicity Middle Eastern							-0.02	-1.09 – 1.05	0.968
Ethnicity American Indian							0.86	-0.79 – 2.50	0.307
Random Effects
σ²	0.80			0.79			0.79
τ₀₀	1.32 _{unique_ID}			1.15 _{unique_ID}			1.13 _{unique_ID}
	0.03 _univ			0.05 _univ			0.03 _univ
ICC	0.63			0.60			0.59
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.002 / 0.630			0.056 / 0.624			0.078 / 0.625

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)	2.31	2.19 – 2.44	<0.001	3.49	2.41 – 4.57	<0.001	3.57	2.42 – 4.71	<0.001
condflourish vs control	-0.11	-0.24 – 0.02	0.088	-0.09	-0.22 – 0.04	0.197	-0.09	-0.22 – 0.04	0.181
time - 2 5	-0.07	-0.12 – -0.02	0.010	-0.08	-0.13 – -0.03	0.003	-0.08	-0.13 – -0.03	0.003
condflourish vs control × time - 2 5	-0.01	-0.06 – 0.04	0.682	-0.01	-0.06 – 0.04	0.645	-0.01	-0.06 – 0.04	0.661
Sex [Woman]				0.56	0.23 – 0.89	0.001	0.56	0.23 – 0.89	0.001
Age				-0.04	-0.07 – -0.01	0.019	-0.04	-0.08 – -0.01	0.014
int student [No]				0.28	-0.24 – 0.80	0.289	0.23	-0.33 – 0.79	0.425
SES num				-0.32	-0.43 – -0.21	<0.001	-0.30	-0.42 – -0.19	<0.001
Ethnicity White							-0.10	-0.45 – 0.25	0.562
Ethnicity Hispanic							0.12	-0.39 – 0.63	0.650
Ethnicity Black							0.45	-0.24 – 1.14	0.200
Ethnicity East Asian							-0.38	-0.85 – 0.09	0.115
Ethnicity South Asian							0.13	-0.52 – 0.77	0.695
Ethnicity Native Hawaiian Pacific Islander							0.30	-1.70 – 2.31	0.767
Ethnicity Middle Eastern							0.74	-0.19 – 1.68	0.118
Ethnicity American Indian							0.54	-1.40 – 2.47	0.586
Random Effects
σ²	1.13			1.11			1.11
τ₀₀	1.81 _{unique_ID}			1.63 _{unique_ID}			1.62 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC							0.59
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.016 / NA			0.177 / NA			0.094 / 0.632

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)	2.30	2.16 – 2.44	<0.001	3.72	2.58 – 4.87	<0.001	4.01	2.79 – 5.23	<0.001
condflourish vs control	-0.09	-0.22 – 0.05	0.221	-0.06	-0.20 – 0.08	0.410	-0.05	-0.19 – 0.09	0.449
time - 2 5	-0.08	-0.13 – -0.03	0.003	-0.09	-0.14 – -0.03	0.001	-0.09	-0.14 – -0.03	0.001
condflourish vs control × time - 2 5	-0.00	-0.06 – 0.05	0.859	-0.01	-0.06 – 0.05	0.841	-0.01	-0.06 – 0.05	0.826
Sex [Woman]				0.54	0.17 – 0.90	0.004	0.51	0.15 – 0.88	0.006
Age				-0.05	-0.08 – -0.01	0.008	-0.05	-0.09 – -0.02	0.004
int student [No]				0.32	-0.21 – 0.86	0.236	0.17	-0.42 – 0.75	0.573
SES num				-0.36	-0.48 – -0.24	<0.001	-0.35	-0.48 – -0.23	<0.001
Ethnicity White							-0.08	-0.46 – 0.30	0.677
Ethnicity Hispanic							0.10	-0.48 – 0.67	0.734
Ethnicity Black							0.44	-0.31 – 1.20	0.251
Ethnicity East Asian							-0.39	-0.89 – 0.11	0.126
Ethnicity South Asian							-0.16	-0.83 – 0.51	0.645
Ethnicity Native Hawaiian Pacific Islander							0.18	-1.78 – 2.15	0.854
Ethnicity Middle Eastern							0.66	-0.30 – 1.61	0.178
Ethnicity American Indian							0.54	-1.38 – 2.45	0.584
Random Effects
σ²	1.15			1.13			1.13
τ₀₀	1.73 _{unique_ID}			1.52 _{unique_ID}			1.52 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.60			0.57
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.005 / 0.604			0.096 / 0.615			0.221 / 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)	2.31	2.16 – 2.45	<0.001	3.85	2.64 – 5.07	<0.001	4.08	2.80 – 5.36	<0.001
condflourish vs control	-0.08	-0.23 – 0.06	0.257	-0.07	-0.21 – 0.08	0.350	-0.06	-0.20 – 0.09	0.443
time - 2 5	-0.10	-0.16 – -0.05	<0.001	-0.11	-0.17 – -0.06	<0.001	-0.11	-0.17 – -0.06	<0.001
condflourish vs control × time - 2 5	-0.03	-0.09 – 0.03	0.287	-0.03	-0.09 – 0.03	0.288	-0.03	-0.09 – 0.03	0.283
Sex [Woman]				0.52	0.14 – 0.90	0.007	0.52	0.13 – 0.90	0.008
Age				-0.05	-0.09 – -0.01	0.007	-0.05	-0.09 – -0.02	0.004
int student [No]				0.27	-0.33 – 0.87	0.372	0.16	-0.47 – 0.80	0.620
SES num				-0.37	-0.50 – -0.25	<0.001	-0.36	-0.49 – -0.23	<0.001
Ethnicity White							-0.10	-0.49 – 0.30	0.625
Ethnicity Hispanic							-0.03	-0.62 – 0.57	0.933
Ethnicity Black							0.46	-0.33 – 1.26	0.252
Ethnicity East Asian							-0.44	-0.96 – 0.08	0.097
Ethnicity South Asian							-0.05	-0.77 – 0.68	0.897
Ethnicity Native Hawaiian Pacific Islander							0.14	-1.84 – 2.12	0.893
Ethnicity Middle Eastern							-0.01	-1.26 – 1.24	0.986
Ethnicity American Indian							0.51	-1.42 – 2.44	0.606
Random Effects
σ²	1.13			1.11			1.11
τ₀₀	1.75 _{unique_ID}			1.53 _{unique_ID}			1.54 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.018 / NA			0.206 / NA			0.222 / 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')

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)

## boundary (singular) fit: see help('isSingular')

## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+01

tab_model(m0, m1, m2)

	loneliness			loneliness			loneliness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.29	5.17 – 5.42	<0.001	6.09	4.99 – 7.19	<0.001	5.75	4.60 – 6.90	<0.001
condflourish vs control	-0.09	-0.22 – 0.03	0.154	-0.07	-0.20 – 0.06	0.264	-0.08	-0.21 – 0.05	0.221
time - 2 5	-0.13	-0.17 – -0.08	<0.001	-0.14	-0.18 – -0.09	<0.001	-0.14	-0.18 – -0.09	<0.001
condflourish vs control × time - 2 5	-0.05	-0.09 – -0.00	0.047	-0.05	-0.09 – 0.00	0.063	-0.05	-0.09 – 0.00	0.062
Sex [Woman]				0.17	-0.16 – 0.50	0.322	0.17	-0.16 – 0.50	0.322
Age				-0.03	-0.06 – 0.01	0.136	-0.02	-0.06 – 0.01	0.209
int student [No]				0.37	-0.15 – 0.90	0.163	0.56	-0.01 – 1.13	0.053
SES num				-0.22	-0.34 – -0.11	<0.001	-0.20	-0.32 – -0.09	0.001
Ethnicity White							-0.15	-0.50 – 0.20	0.401
Ethnicity Hispanic							0.17	-0.34 – 0.68	0.507
Ethnicity Black							0.13	-0.56 – 0.83	0.705
Ethnicity East Asian							0.04	-0.44 – 0.51	0.871
Ethnicity South Asian							0.57	-0.07 – 1.22	0.083
Ethnicity Native Hawaiian Pacific Islander							-0.15	-2.17 – 1.87	0.884
Ethnicity Middle Eastern							0.13	-0.81 – 1.07	0.789
Ethnicity American Indian							1.52	-0.44 – 3.48	0.128
Random Effects
σ²	0.97			0.98			0.98
τ₀₀	1.79 _{unique_ID}			1.69 _{unique_ID}			1.69 _{unique_ID}
	0.00 _univ			0.01 _univ			0.00 _univ
ICC				0.63			0.63
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.031 / NA			0.042 / 0.649			0.056 / 0.653

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	5.13 – 5.41	<0.001	6.27	5.06 – 7.48	<0.001	6.11	4.84 – 7.37	<0.001
condflourish vs control	-0.09	-0.23 – 0.05	0.219	-0.07	-0.21 – 0.08	0.372	-0.07	-0.22 – 0.07	0.328
time - 2 5	-0.13	-0.18 – -0.08	<0.001	-0.14	-0.19 – -0.09	<0.001	-0.14	-0.19 – -0.09	<0.001
condflourish vs control × time - 2 5	-0.05	-0.10 – -0.00	0.038	-0.05	-0.10 – 0.00	0.064	-0.05	-0.10 – 0.00	0.062
Sex [Woman]				-0.01	-0.39 – 0.37	0.975	-0.02	-0.40 – 0.36	0.922
Age				-0.03	-0.07 – 0.00	0.084	-0.03	-0.07 – 0.01	0.092
int student [No]				0.48	-0.09 – 1.04	0.097	0.61	0.01 – 1.22	0.048
SES num				-0.23	-0.36 – -0.11	<0.001	-0.21	-0.34 – -0.08	0.001
Ethnicity White							-0.22	-0.61 – 0.18	0.279
Ethnicity Hispanic							0.08	-0.52 – 0.68	0.798
Ethnicity Black							0.33	-0.46 – 1.12	0.408
Ethnicity East Asian							0.02	-0.50 – 0.54	0.940
Ethnicity South Asian							0.31	-0.39 – 1.01	0.382
Ethnicity Native Hawaiian Pacific Islander							-0.27	-2.31 – 1.78	0.799
Ethnicity Middle Eastern							0.08	-0.92 – 1.07	0.877
Ethnicity American Indian							1.70	-0.30 – 3.69	0.095
Random Effects
σ²	0.97			0.98			0.98
τ₀₀	1.85 _{unique_ID}			1.72 _{unique_ID}			1.72 _{unique_ID}
	0.00 _univ			0.01 _univ			0.00 _univ
ICC				0.64			0.64
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.031 / NA			0.045 / 0.655			0.059 / 0.658

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)
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_unreasonable)

## boundary (singular) fit: see help('isSingular')

tab_model(m0, m1, m2)

	loneliness			loneliness			loneliness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.28	5.14 – 5.43	<0.001	6.25	4.99 – 7.51	<0.001	6.12	4.81 – 7.44	<0.001
condflourish vs control	-0.08	-0.22 – 0.07	0.305	-0.07	-0.22 – 0.08	0.362	-0.07	-0.22 – 0.08	0.372
time - 2 5	-0.14	-0.19 – -0.09	<0.001	-0.15	-0.20 – -0.10	<0.001	-0.15	-0.20 – -0.10	<0.001
condflourish vs control × time - 2 5	-0.07	-0.12 – -0.01	0.013	-0.06	-0.12 – -0.01	0.017	-0.06	-0.12 – -0.01	0.017
Sex [Woman]				-0.04	-0.43 – 0.35	0.841	-0.05	-0.45 – 0.35	0.812
Age				-0.03	-0.07 – 0.01	0.115	-0.03	-0.07 – 0.01	0.133
int student [No]				0.40	-0.22 – 1.02	0.204	0.56	-0.10 – 1.21	0.096
SES num				-0.22	-0.35 – -0.09	0.001	-0.19	-0.33 – -0.06	0.005
Ethnicity White							-0.29	-0.70 – 0.12	0.161
Ethnicity Hispanic							-0.07	-0.68 – 0.54	0.819
Ethnicity Black							0.25	-0.57 – 1.06	0.552
Ethnicity East Asian							-0.10	-0.64 – 0.43	0.707
Ethnicity South Asian							0.39	-0.35 – 1.14	0.300
Ethnicity Native Hawaiian Pacific Islander							-0.33	-2.36 – 1.71	0.753
Ethnicity Middle Eastern							0.01	-1.28 – 1.30	0.988
Ethnicity American Indian							1.63	-0.36 – 3.62	0.108
Random Effects
σ²	0.96			0.97			0.97
τ₀₀	1.83 _{unique_ID}			1.71 _{unique_ID}			1.70 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.65			0.64
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.012 / 0.658			0.040 / 0.653			0.144 / NA

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')

m1 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0627871 (tol = 0.002, component 1)

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)	6.65	6.43 – 6.87	<0.001	9.06	7.27 – 10.85	<0.001	8.82	6.93 – 10.72	<0.001
condflourish vs control	-0.09	-0.31 – 0.12	0.407	-0.06	-0.28 – 0.15	0.575	-0.07	-0.29 – 0.14	0.507
time - 2 5	-0.09	-0.18 – -0.00	0.045	-0.11	-0.20 – -0.02	0.015	-0.11	-0.20 – -0.02	0.017
condflourish vs control × time - 2 5	-0.04	-0.12 – 0.05	0.430	-0.03	-0.12 – 0.06	0.512	-0.03	-0.12 – 0.06	0.516
Sex [Woman]				0.77	0.22 – 1.31	0.006	0.75	0.20 – 1.30	0.007
Age				-0.05	-0.11 – 0.01	0.083	-0.05	-0.10 – 0.01	0.102
int student [No]				-0.04	-0.90 – 0.82	0.931	0.26	-0.67 – 1.19	0.585
SES num				-0.62	-0.80 – -0.43	<0.001	-0.57	-0.77 – -0.38	<0.001
Ethnicity White							-0.51	-1.09 – 0.07	0.084
Ethnicity Hispanic							0.00	-0.84 – 0.85	0.995
Ethnicity Black							0.33	-0.81 – 1.48	0.568
Ethnicity East Asian							-0.46	-1.24 – 0.33	0.253
Ethnicity South Asian							0.78	-0.29 – 1.85	0.152
Ethnicity Native Hawaiian Pacific Islander							0.03	-3.30 – 3.35	0.987
Ethnicity Middle Eastern							0.64	-0.90 – 2.19	0.415
Ethnicity American Indian							0.68	-2.51 – 3.88	0.676
Random Effects
σ²	3.40			3.39			3.38
τ₀₀	5.05 _{unique_ID}			4.35 _{unique_ID}			4.33 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC				0.56
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.006 / NA			0.080 / 0.597			0.196 / 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)	6.59	6.36 – 6.82	<0.001	9.31	7.43 – 11.18	<0.001	9.31	7.31 – 11.31	<0.001
condflourish vs control	-0.09	-0.32 – 0.14	0.445	-0.05	-0.27 – 0.18	0.673	-0.05	-0.28 – 0.17	0.641
time - 2 5	-0.11	-0.20 – -0.02	0.018	-0.12	-0.22 – -0.03	0.008	-0.12	-0.21 – -0.03	0.009
condflourish vs control × time - 2 5	-0.04	-0.13 – 0.05	0.399	-0.03	-0.12 – 0.06	0.547	-0.03	-0.12 – 0.06	0.538
Sex [Woman]				0.62	0.02 – 1.22	0.042	0.57	-0.03 – 1.18	0.063
Age				-0.05	-0.11 – 0.00	0.073	-0.05	-0.11 – 0.00	0.067
int student [No]				0.05	-0.83 – 0.93	0.919	0.18	-0.78 – 1.14	0.719
SES num				-0.68	-0.87 – -0.48	<0.001	-0.64	-0.85 – -0.44	<0.001
Ethnicity White							-0.38	-1.00 – 0.25	0.240
Ethnicity Hispanic							0.14	-0.81 – 1.08	0.776
Ethnicity Black							0.45	-0.79 – 1.70	0.475
Ethnicity East Asian							-0.38	-1.20 – 0.44	0.364
Ethnicity South Asian							0.40	-0.71 – 1.50	0.481
Ethnicity Native Hawaiian Pacific Islander							-0.03	-3.27 – 3.20	0.983
Ethnicity Middle Eastern							0.62	-0.95 – 2.19	0.437
Ethnicity American Indian							0.70	-2.44 – 3.85	0.660
Random Effects
σ²	3.35			3.33			3.33
τ₀₀	4.82 _{unique_ID}			3.99 _{unique_ID}			4.00 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.007 / NA			0.182 / NA			0.200 / NA

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)	6.57	6.33 – 6.80	<0.001	9.52	7.55 – 11.48	<0.001	9.45	7.38 – 11.51	<0.001
condflourish vs control	-0.11	-0.35 – 0.13	0.359	-0.09	-0.32 – 0.15	0.468	-0.09	-0.33 – 0.15	0.456
time - 2 5	-0.12	-0.21 – -0.02	0.017	-0.14	-0.23 – -0.04	0.006	-0.13	-0.23 – -0.04	0.007
condflourish vs control × time - 2 5	-0.05	-0.14 – 0.05	0.342	-0.04	-0.14 – 0.06	0.422	-0.04	-0.14 – 0.06	0.420
Sex [Woman]				0.63	0.02 – 1.24	0.045	0.62	-0.00 – 1.24	0.052
Age				-0.06	-0.11 – 0.00	0.061	-0.06	-0.12 – 0.00	0.064
int student [No]				-0.05	-1.01 – 0.92	0.924	0.13	-0.89 – 1.16	0.798
SES num				-0.71	-0.91 – -0.50	<0.001	-0.67	-0.88 – -0.46	<0.001
Ethnicity White							-0.39	-1.03 – 0.25	0.229
Ethnicity Hispanic							-0.02	-0.97 – 0.94	0.973
Ethnicity Black							0.46	-0.82 – 1.74	0.480
Ethnicity East Asian							-0.34	-1.18 – 0.50	0.432
Ethnicity South Asian							0.49	-0.68 – 1.65	0.412
Ethnicity Native Hawaiian Pacific Islander							-0.04	-3.24 – 3.17	0.982
Ethnicity Middle Eastern							-0.91	-2.93 – 1.12	0.380
Ethnicity American Indian							0.71	-2.40 – 3.82	0.654
Random Effects
σ²	3.38			3.37			3.37
τ₀₀	4.74 _{unique_ID}			3.85 _{unique_ID}			3.88 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.58
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.004 / 0.585			0.190 / NA			0.206 / NA

SAS: Calm

Intention to Treat

m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0704422 (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_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 – 5.93	<0.001	4.51	2.92 – 6.11	<0.001	4.55	2.84 – 6.25	<0.001
condflourish vs control	0.24	0.05 – 0.43	0.012	0.20	0.01 – 0.40	0.036	0.21	0.02 – 0.40	0.034
time - 2 5	0.13	0.05 – 0.21	0.002	0.14	0.06 – 0.22	0.001	0.14	0.06 – 0.22	0.001
condflourish vs control × time - 2 5	0.11	0.03 – 0.19	0.007	0.11	0.02 – 0.19	0.012	0.11	0.02 – 0.19	0.013
Sex [Woman]				-0.68	-1.17 – -0.20	0.006	-0.67	-1.16 – -0.18	0.007
Age				0.03	-0.02 – 0.08	0.212	0.03	-0.02 – 0.08	0.209
int student [No]				-0.45	-1.22 – 0.31	0.243	-0.47	-1.31 – 0.36	0.267
SES num				0.48	0.31 – 0.64	<0.001	0.46	0.29 – 0.64	<0.001
Ethnicity White							0.06	-0.46 – 0.57	0.824
Ethnicity Hispanic							-0.36	-1.12 – 0.39	0.347
Ethnicity Black							-0.05	-1.07 – 0.98	0.931
Ethnicity East Asian							0.08	-0.62 – 0.78	0.822
Ethnicity South Asian							-0.07	-1.02 – 0.88	0.886
Ethnicity Native Hawaiian Pacific Islander							0.84	-2.13 – 3.81	0.579
Ethnicity Middle Eastern							-0.33	-1.71 – 1.05	0.644
Ethnicity American Indian							-0.51	-3.35 – 2.33	0.723
Random Effects
σ²	2.95			2.97			2.97
τ₀₀	3.76 _{unique_ID}			3.29 _{unique_ID}			3.33 _{unique_ID}
	0.00 _univ			0.01 _univ			0.01 _univ
ICC	0.56			0.53			0.53
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.013 / 0.566			0.073 / 0.560			0.076 / 0.565

Excluded Preregistered

m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
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)	5.72	5.52 – 5.92	<0.001	4.56	2.84 – 6.28	<0.001	4.44	2.59 – 6.28	<0.001
condflourish vs control	0.27	0.07 – 0.48	0.009	0.24	0.04 – 0.45	0.019	0.25	0.04 – 0.45	0.019
time - 2 5	0.16	0.08 – 0.25	<0.001	0.17	0.08 – 0.25	<0.001	0.17	0.08 – 0.25	<0.001
condflourish vs control × time - 2 5	0.11	0.02 – 0.19	0.013	0.10	0.01 – 0.18	0.026	0.10	0.01 – 0.18	0.026
Sex [Woman]				-0.57	-1.11 – -0.03	0.040	-0.53	-1.08 – 0.02	0.057
Age				0.02	-0.03 – 0.08	0.382	0.03	-0.03 – 0.08	0.343
int student [No]				-0.51	-1.31 – 0.29	0.214	-0.41	-1.28 – 0.46	0.353
SES num				0.49	0.31 – 0.67	<0.001	0.48	0.29 – 0.66	<0.001
Ethnicity White							0.04	-0.53 – 0.60	0.895
Ethnicity Hispanic							-0.57	-1.43 – 0.29	0.194
Ethnicity Black							-0.10	-1.24 – 1.03	0.857
Ethnicity East Asian							0.17	-0.59 – 0.92	0.665
Ethnicity South Asian							0.22	-0.78 – 1.23	0.661
Ethnicity Native Hawaiian Pacific Islander							0.85	-2.08 – 3.79	0.569
Ethnicity Middle Eastern							-0.22	-1.64 – 1.21	0.766
Ethnicity American Indian							-0.34	-3.18 – 2.51	0.816
Random Effects
σ²	2.93			2.95			2.95
τ₀₀	3.67 _{unique_ID}			3.16 _{unique_ID}			3.21 _{unique_ID}
	0.00 _univ			0.02 _univ			0.03 _univ
ICC	0.56			0.52			0.52
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.018 / 0.565			0.079 / 0.557			0.083 / 0.563

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)	5.69	5.46 – 5.93	<0.001	4.60	2.79 – 6.40	<0.001	4.62	2.71 – 6.53	<0.001
condflourish vs control	0.25	0.04 – 0.46	0.019	0.24	0.03 – 0.45	0.023	0.25	0.04 – 0.47	0.022
time - 2 5	0.15	0.07 – 0.24	0.001	0.16	0.07 – 0.25	0.001	0.15	0.06 – 0.25	0.001
condflourish vs control × time - 2 5	0.10	0.01 – 0.19	0.030	0.09	-0.00 – 0.18	0.059	0.09	-0.00 – 0.18	0.060
Sex [Woman]				-0.55	-1.10 – 0.01	0.054	-0.52	-1.09 – 0.04	0.069
Age				0.02	-0.04 – 0.07	0.512	0.02	-0.04 – 0.07	0.488
int student [No]				-0.34	-1.22 – 0.53	0.443	-0.32	-1.25 – 0.62	0.507
SES num				0.46	0.28 – 0.65	<0.001	0.45	0.26 – 0.64	<0.001
Ethnicity White							-0.03	-0.61 – 0.55	0.910
Ethnicity Hispanic							-0.57	-1.44 – 0.30	0.197
Ethnicity Black							-0.22	-1.39 – 0.95	0.710
Ethnicity East Asian							0.06	-0.70 – 0.83	0.873
Ethnicity South Asian							-0.05	-1.11 – 1.02	0.931
Ethnicity Native Hawaiian Pacific Islander							0.73	-2.18 – 3.65	0.622
Ethnicity Middle Eastern							0.34	-1.51 – 2.18	0.721
Ethnicity American Indian							-0.42	-3.24 – 2.40	0.770
Random Effects
σ²	2.90			2.92			2.92
τ₀₀	3.54 _{unique_ID}			3.08 _{unique_ID}			3.14 _{unique_ID}
	0.01 _univ			0.03 _univ			0.04 _univ
ICC	0.55			0.52			0.52
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.015 / 0.557			0.068 / 0.549			0.072 / 0.555

SAS: Well-Being

Intention to Treat

m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
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)	6.79	6.42 – 7.17	<0.001	3.90	2.22 – 5.59	<0.001	4.37	2.63 – 6.11	<0.001
condflourish vs control	0.20	0.02 – 0.39	0.034	0.18	-0.01 – 0.37	0.061	0.20	0.01 – 0.39	0.038
time - 2 5	-0.05	-0.12 – 0.03	0.218	-0.05	-0.12 – 0.03	0.238	-0.05	-0.12 – 0.03	0.230
condflourish vs control × time - 2 5	0.09	0.02 – 0.17	0.013	0.09	0.01 – 0.16	0.020	0.09	0.02 – 0.17	0.018
Sex [Woman]				0.25	-0.23 – 0.72	0.308	0.23	-0.25 – 0.71	0.347
Age				0.07	0.02 – 0.12	0.009	0.07	0.01 – 0.12	0.012
int student [No]				-0.26	-1.02 – 0.51	0.507	-0.55	-1.37 – 0.27	0.189
SES num				0.47	0.31 – 0.63	<0.001	0.45	0.28 – 0.62	<0.001
Ethnicity White							0.24	-0.27 – 0.74	0.361
Ethnicity Hispanic							0.01	-0.73 – 0.75	0.983
Ethnicity Black							-0.80	-1.81 – 0.21	0.121
Ethnicity East Asian							-0.31	-1.00 – 0.38	0.378
Ethnicity South Asian							-0.71	-1.65 – 0.23	0.140
Ethnicity Native Hawaiian Pacific Islander							-1.12	-4.03 – 1.80	0.452
Ethnicity Middle Eastern							-0.58	-1.94 – 0.78	0.403
Ethnicity American Indian							-1.46	-4.26 – 1.34	0.307
Random Effects
σ²	2.46			2.44			2.43
τ₀₀	3.91 _{unique_ID}			3.38 _{unique_ID}			3.36 _{unique_ID}
	0.10 _univ			0.18 _univ			0.12 _univ
ICC	0.62			0.59			0.59
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1578			1474			1474
Marginal R² / Conditional R²	0.008 / 0.622			0.059 / 0.618			0.075 / 0.619

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)
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)	6.81	6.43 – 7.20	<0.001	3.67	1.87 – 5.47	<0.001	4.10	2.23 – 5.96	<0.001
condflourish vs control	0.22	0.01 – 0.42	0.036	0.18	-0.02 – 0.38	0.073	0.21	0.00 – 0.41	0.045
time - 2 5	-0.04	-0.12 – 0.03	0.272	-0.04	-0.12 – 0.04	0.312	-0.04	-0.12 – 0.04	0.314
condflourish vs control × time - 2 5	0.08	0.00 – 0.16	0.046	0.07	-0.01 – 0.15	0.080	0.07	-0.01 – 0.15	0.078
Sex [Woman]				0.27	-0.26 – 0.81	0.310	0.28	-0.26 – 0.82	0.305
Age				0.08	0.02 – 0.13	0.005	0.07	0.02 – 0.13	0.009
int student [No]				-0.31	-1.10 – 0.48	0.445	-0.55	-1.40 – 0.30	0.206
SES num				0.51	0.33 – 0.68	<0.001	0.48	0.29 – 0.66	<0.001
Ethnicity White							0.25	-0.31 – 0.80	0.382
Ethnicity Hispanic							-0.19	-1.04 – 0.65	0.651
Ethnicity Black							-0.54	-1.65 – 0.57	0.340
Ethnicity East Asian							-0.32	-1.05 – 0.42	0.396
Ethnicity South Asian							-0.42	-1.40 – 0.57	0.407
Ethnicity Native Hawaiian Pacific Islander							-1.06	-3.93 – 1.81	0.469
Ethnicity Middle Eastern							-0.05	-1.44 – 1.35	0.946
Ethnicity American Indian							-1.42	-4.21 – 1.36	0.316
Random Effects
σ²	2.42			2.40			2.40
τ₀₀	3.77 _{unique_ID}			3.19 _{unique_ID}			3.20 _{unique_ID}
	0.09 _univ			0.22 _univ			0.14 _univ
ICC	0.61			0.59			0.58
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1406			1324			1324
Marginal R² / Conditional R²	0.009 / 0.618			0.070 / 0.616			0.081 / 0.615

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)	6.79	6.41 – 7.17	<0.001	3.62	1.75 – 5.50	<0.001	4.18	2.26 – 6.09	<0.001
condflourish vs control	0.19	-0.02 – 0.40	0.079	0.17	-0.04 – 0.38	0.108	0.19	-0.02 – 0.41	0.072
time - 2 5	-0.04	-0.12 – 0.04	0.304	-0.04	-0.12 – 0.04	0.358	-0.04	-0.12 – 0.04	0.362
condflourish vs control × time - 2 5	0.08	-0.00 – 0.16	0.051	0.07	-0.01 – 0.15	0.082	0.07	-0.01 – 0.15	0.083
Sex [Woman]				0.30	-0.25 – 0.85	0.289	0.29	-0.27 – 0.84	0.306
Age				0.07	0.02 – 0.13	0.011	0.06	0.01 – 0.12	0.021
int student [No]				-0.16	-1.04 – 0.71	0.717	-0.46	-1.38 – 0.46	0.326
SES num				0.50	0.32 – 0.68	<0.001	0.47	0.28 – 0.66	<0.001
Ethnicity White							0.25	-0.32 – 0.82	0.389
Ethnicity Hispanic							-0.11	-0.96 – 0.75	0.807
Ethnicity Black							-0.54	-1.69 – 0.60	0.353
Ethnicity East Asian							-0.29	-1.04 – 0.47	0.456
Ethnicity South Asian							-0.86	-1.91 – 0.19	0.107
Ethnicity Native Hawaiian Pacific Islander							-1.06	-3.92 – 1.79	0.466
Ethnicity Middle Eastern							0.90	-0.91 – 2.71	0.329
Ethnicity American Indian							-1.41	-4.18 – 1.36	0.318
Random Effects
σ²	2.37			2.34			2.34
τ₀₀	3.76 _{unique_ID}			3.19 _{unique_ID}			3.17 _{unique_ID}
	0.09 _univ			0.20 _univ			0.11 _univ
ICC	0.62			0.59			0.58
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.007 / 0.621			0.066 / 0.618			0.082 / 0.618

SAS: Vigour

Intention to Treat

m0 <- lmer(SAS_vigour ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
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)	5.69	5.32 – 6.06	<0.001	3.65	1.81 – 5.50	<0.001	3.91	2.00 – 5.81	<0.001
condflourish vs control	0.16	-0.05 – 0.37	0.131	0.13	-0.09 – 0.34	0.241	0.13	-0.08 – 0.34	0.231
time - 2 5	-0.05	-0.13 – 0.03	0.193	-0.04	-0.12 – 0.04	0.312	-0.04	-0.13 – 0.04	0.281
condflourish vs control × time - 2 5	0.06	-0.02 – 0.13	0.172	0.05	-0.03 – 0.13	0.219	0.05	-0.03 – 0.13	0.201
Sex [Woman]				-0.01	-0.55 – 0.53	0.970	-0.02	-0.56 – 0.51	0.932
Age				0.06	-0.00 – 0.11	0.052	0.06	0.00 – 0.11	0.049
int student [No]				-0.41	-1.27 – 0.45	0.351	-0.73	-1.65 – 0.20	0.124
SES num				0.39	0.21 – 0.58	<0.001	0.37	0.18 – 0.56	<0.001
Ethnicity White							0.37	-0.20 – 0.95	0.197
Ethnicity Hispanic							0.39	-0.44 – 1.22	0.357
Ethnicity Black							-0.84	-1.97 – 0.30	0.148
Ethnicity East Asian							-0.23	-1.01 – 0.54	0.557
Ethnicity South Asian							-0.55	-1.61 – 0.51	0.306
Ethnicity Native Hawaiian Pacific Islander							0.47	-2.82 – 3.75	0.781
Ethnicity Middle Eastern							1.02	-0.52 – 2.55	0.193
Ethnicity American Indian							-0.92	-4.09 – 2.25	0.570
Random Effects
σ²	2.80			2.77			2.77
τ₀₀	4.73 _{unique_ID}			4.41 _{unique_ID}			4.39 _{unique_ID}
	0.08 _univ			0.11 _univ			0.05 _univ
ICC	0.63			0.62			0.62
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1578			1474			1474
Marginal R² / Conditional R²	0.004 / 0.634			0.036 / 0.634			0.052 / 0.636

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)	5.67	5.34 – 6.00	<0.001	3.35	1.40 – 5.30	0.001	3.52	1.50 – 5.54	0.001
condflourish vs control	0.25	0.03 – 0.47	0.024	0.23	0.01 – 0.46	0.041	0.24	0.02 – 0.47	0.034
time - 2 5	-0.05	-0.13 – 0.03	0.228	-0.04	-0.13 – 0.04	0.328	-0.04	-0.13 – 0.04	0.318
condflourish vs control × time - 2 5	0.04	-0.04 – 0.13	0.301	0.04	-0.05 – 0.12	0.369	0.04	-0.04 – 0.12	0.355
Sex [Woman]				0.20	-0.40 – 0.79	0.514	0.21	-0.39 – 0.81	0.491
Age				0.06	0.00 – 0.12	0.045	0.06	0.00 – 0.12	0.044
int student [No]				-0.60	-1.49 – 0.29	0.186	-0.90	-1.85 – 0.06	0.065
SES num				0.45	0.25 – 0.64	<0.001	0.40	0.20 – 0.61	<0.001
Ethnicity White							0.53	-0.09 – 1.15	0.096
Ethnicity Hispanic							0.35	-0.59 – 1.29	0.461
Ethnicity Black							-0.40	-1.63 – 0.84	0.531
Ethnicity East Asian							-0.17	-0.99 – 0.66	0.693
Ethnicity South Asian							-0.15	-1.25 – 0.95	0.789
Ethnicity Native Hawaiian Pacific Islander							0.68	-2.52 – 3.89	0.676
Ethnicity Middle Eastern							1.69	0.13 – 3.25	0.034
Ethnicity American Indian							-0.45	-3.57 – 2.67	0.775
Random Effects
σ²	2.79			2.76			2.77
τ₀₀	4.42 _{unique_ID}			4.11 _{unique_ID}			4.08 _{unique_ID}
	0.05 _univ			0.11 _univ			0.05 _univ
ICC	0.62			0.60			0.60
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1406			1324			1324
Marginal R² / Conditional R²	0.009 / 0.619			0.053 / 0.625			0.069 / 0.627

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)
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	5.28 – 5.91	<0.001	3.50	1.45 – 5.56	0.001	3.68	1.58 – 5.78	0.001
condflourish vs control	0.17	-0.06 – 0.40	0.139	0.17	-0.07 – 0.40	0.162	0.19	-0.05 – 0.43	0.121
time - 2 5	-0.07	-0.16 – 0.01	0.093	-0.07	-0.16 – 0.02	0.131	-0.07	-0.16 – 0.02	0.127
condflourish vs control × time - 2 5	0.02	-0.07 – 0.11	0.627	0.01	-0.07 – 0.10	0.755	0.01	-0.07 – 0.10	0.743
Sex [Woman]				0.26	-0.37 – 0.88	0.418	0.27	-0.35 – 0.90	0.390
Age				0.05	-0.01 – 0.11	0.081	0.05	-0.01 – 0.11	0.098
int student [No]				-0.56	-1.55 – 0.42	0.263	-0.91	-1.95 – 0.12	0.083
SES num				0.39	0.19 – 0.60	<0.001	0.36	0.15 – 0.58	0.001
Ethnicity White							0.63	-0.01 – 1.27	0.054
Ethnicity Hispanic							0.50	-0.46 – 1.46	0.309
Ethnicity Black							-0.39	-1.68 – 0.90	0.550
Ethnicity East Asian							-0.08	-0.93 – 0.77	0.856
Ethnicity South Asian							-0.47	-1.65 – 0.71	0.437
Ethnicity Native Hawaiian Pacific Islander							0.81	-2.41 – 4.02	0.623
Ethnicity Middle Eastern							2.08	0.04 – 4.12	0.046
Ethnicity American Indian							-0.33	-3.46 – 2.80	0.834
Random Effects
σ²	2.76			2.72			2.72
τ₀₀	4.41 _{unique_ID}			4.17 _{unique_ID}			4.12 _{unique_ID}
	0.04 _univ			0.10 _univ			0.03 _univ
ICC	0.62			0.61			0.60
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1292			1213			1213
Marginal R² / Conditional R²	0.005 / 0.619			0.039 / 0.626			0.061 / 0.628

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)

## 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: -1.2e+02

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)	4.05	3.83 – 4.28	<0.001	5.85	3.95 – 7.74	<0.001	5.03	3.03 – 7.03	<0.001
condflourish vs control	-0.13	-0.36 – 0.09	0.249	-0.13	-0.36 – 0.10	0.265	-0.15	-0.37 – 0.08	0.211
time - 2 5	-0.08	-0.17 – 0.00	0.057	-0.10	-0.19 – -0.01	0.023	-0.10	-0.19 – -0.02	0.021
condflourish vs control × time - 2 5	0.02	-0.06 – 0.11	0.580	0.02	-0.07 – 0.11	0.667	0.02	-0.07 – 0.11	0.663
Sex [Woman]				0.40	-0.18 – 0.98	0.174	0.43	-0.15 – 1.00	0.146
Age				-0.07	-0.13 – -0.01	0.025	-0.06	-0.12 – -0.00	0.044
int student [No]				0.55	-0.37 – 1.46	0.241	0.98	-0.01 – 1.96	0.053
SES num				-0.40	-0.59 – -0.20	<0.001	-0.39	-0.59 – -0.18	<0.001
Ethnicity White							0.05	-0.56 – 0.66	0.876
Ethnicity Hispanic							0.29	-0.60 – 1.18	0.519
Ethnicity Black							0.52	-0.69 – 1.73	0.401
Ethnicity East Asian							0.19	-0.63 – 1.02	0.649
Ethnicity South Asian							1.61	0.49 – 2.74	0.005
Ethnicity Native Hawaiian Pacific Islander							1.45	-2.07 – 4.96	0.420
Ethnicity Middle Eastern							1.59	-0.04 – 3.23	0.056
Ethnicity American Indian							2.26	-1.13 – 5.65	0.191
Random Effects
σ²	3.37			3.31			3.30
τ₀₀	5.54 _{unique_ID}			5.06 _{unique_ID}			5.00 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC				0.60			0.60
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1578			1474			1474
Marginal R² / Conditional R²	0.008 / NA			0.040 / 0.621			0.063 / 0.627

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)	3.93	3.63 – 4.22	<0.001	5.85	3.82 – 7.88	<0.001	5.29	3.14 – 7.43	<0.001
condflourish vs control	-0.15	-0.39 – 0.09	0.229	-0.13	-0.37 – 0.11	0.276	-0.13	-0.37 – 0.11	0.276
time - 2 5	-0.07	-0.16 – 0.02	0.129	-0.08	-0.17 – 0.01	0.079	-0.08	-0.17 – 0.01	0.076
condflourish vs control × time - 2 5	0.01	-0.08 – 0.10	0.819	0.01	-0.08 – 0.10	0.789	0.01	-0.08 – 0.10	0.784
Sex [Woman]				0.34	-0.30 – 0.97	0.296	0.36	-0.28 – 1.00	0.270
Age				-0.07	-0.13 – -0.01	0.024	-0.07	-0.13 – -0.00	0.040
int student [No]				0.64	-0.30 – 1.58	0.184	0.81	-0.20 – 1.83	0.117
SES num				-0.42	-0.63 – -0.21	<0.001	-0.42	-0.64 – -0.21	<0.001
Ethnicity White							0.23	-0.43 – 0.89	0.496
Ethnicity Hispanic							0.23	-0.77 – 1.23	0.651
Ethnicity Black							0.44	-0.88 – 1.76	0.509
Ethnicity East Asian							0.12	-0.76 – 0.99	0.791
Ethnicity South Asian							1.21	0.04 – 2.38	0.043
Ethnicity Native Hawaiian Pacific Islander							1.59	-1.82 – 5.00	0.360
Ethnicity Middle Eastern							1.32	-0.34 – 2.98	0.120
Ethnicity American Indian							2.45	-0.87 – 5.77	0.148
Random Effects
σ²	3.26			3.23			3.23
τ₀₀	5.22 _{unique_ID}			4.61 _{unique_ID}			4.60 _{unique_ID}
	0.02 _univ			0.05 _univ			0.04 _univ
ICC	0.62			0.59			0.59
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1406			1324			1324
Marginal R² / Conditional R²	0.003 / 0.618			0.047 / 0.609			0.061 / 0.614

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)	3.93	3.64 – 4.22	<0.001	5.81	3.67 – 7.96	<0.001	5.26	3.02 – 7.50	<0.001
condflourish vs control	-0.14	-0.39 – 0.11	0.263	-0.14	-0.39 – 0.11	0.261	-0.12	-0.38 – 0.13	0.341
time - 2 5	-0.08	-0.18 – 0.01	0.081	-0.10	-0.19 – -0.00	0.041	-0.10	-0.20 – -0.01	0.039
condflourish vs control × time - 2 5	-0.00	-0.10 – 0.09	0.933	-0.01	-0.10 – 0.09	0.901	-0.01	-0.10 – 0.09	0.916
Sex [Woman]				0.42	-0.24 – 1.08	0.215	0.46	-0.21 – 1.12	0.178
Age				-0.07	-0.13 – -0.00	0.035	-0.06	-0.13 – 0.00	0.064
int student [No]				0.57	-0.48 – 1.61	0.288	0.70	-0.40 – 1.80	0.210
SES num				-0.43	-0.65 – -0.21	<0.001	-0.41	-0.64 – -0.19	<0.001
Ethnicity White							0.17	-0.52 – 0.85	0.629
Ethnicity Hispanic							0.01	-1.01 – 1.04	0.977
Ethnicity Black							0.26	-1.11 – 1.63	0.708
Ethnicity East Asian							-0.07	-0.98 – 0.83	0.872
Ethnicity South Asian							1.46	0.21 – 2.72	0.022
Ethnicity Native Hawaiian Pacific Islander							1.54	-1.89 – 4.96	0.379
Ethnicity Middle Eastern							0.70	-1.47 – 2.87	0.529
Ethnicity American Indian							2.42	-0.91 – 5.75	0.154
Random Effects
σ²	3.26			3.24			3.24
τ₀₀	5.28 _{unique_ID}			4.64 _{unique_ID}			4.62 _{unique_ID}
	0.02 _univ			0.05 _univ			0.04 _univ
ICC	0.62			0.59			0.59
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.003 / 0.620			0.046 / 0.610			0.062 / 0.616

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)	5.99	5.78 – 6.20	<0.001	6.87	5.12 – 8.63	<0.001	6.75	4.88 – 8.61	<0.001
condflourish vs control	-0.15	-0.36 – 0.06	0.164	-0.17	-0.38 – 0.04	0.120	-0.18	-0.39 – 0.03	0.098
time - 2 5	-0.13	-0.22 – -0.04	0.004	-0.14	-0.23 – -0.05	0.003	-0.14	-0.23 – -0.04	0.004
condflourish vs control × time - 2 5	-0.03	-0.13 – 0.06	0.460	-0.04	-0.14 – 0.05	0.360	-0.04	-0.14 – 0.05	0.372
Sex [Woman]				1.13	0.60 – 1.67	<0.001	1.14	0.60 – 1.68	<0.001
Age				-0.05	-0.10 – 0.01	0.081	-0.05	-0.11 – 0.00	0.064
int student [No]				0.87	0.03 – 1.71	0.043	0.87	-0.04 – 1.79	0.062
SES num				-0.48	-0.67 – -0.30	<0.001	-0.45	-0.64 – -0.26	<0.001
Ethnicity White							-0.03	-0.60 – 0.54	0.918
Ethnicity Hispanic							0.60	-0.23 – 1.43	0.155
Ethnicity Black							0.70	-0.43 – 1.83	0.226
Ethnicity East Asian							-0.13	-0.89 – 0.64	0.748
Ethnicity South Asian							0.27	-0.78 – 1.31	0.613
Ethnicity Native Hawaiian Pacific Islander							0.08	-3.18 – 3.34	0.962
Ethnicity Middle Eastern							0.49	-1.02 – 2.01	0.524
Ethnicity American Indian							2.32	-0.80 – 5.44	0.145
Random Effects
σ²	3.76			3.79			3.79
τ₀₀	4.49 _{unique_ID}			3.96 _{unique_ID}			3.97 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.012 / NA			0.152 / NA			0.167 / 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)	5.96	5.73 – 6.18	<0.001	6.70	4.83 – 8.58	<0.001	6.93	4.94 – 8.93	<0.001
condflourish vs control	-0.16	-0.38 – 0.07	0.174	-0.14	-0.37 – 0.08	0.209	-0.14	-0.37 – 0.08	0.213
time - 2 5	-0.14	-0.24 – -0.05	0.003	-0.15	-0.24 – -0.05	0.003	-0.14	-0.24 – -0.05	0.004
condflourish vs control × time - 2 5	-0.05	-0.14 – 0.05	0.334	-0.05	-0.15 – 0.05	0.326	-0.05	-0.15 – 0.05	0.321
Sex [Woman]				1.08	0.48 – 1.67	<0.001	1.07	0.47 – 1.67	0.001
Age				-0.04	-0.10 – 0.01	0.129	-0.05	-0.11 – 0.01	0.086
int student [No]				1.08	0.20 – 1.96	0.016	0.90	-0.06 – 1.86	0.065
SES num				-0.52	-0.72 – -0.32	<0.001	-0.49	-0.70 – -0.29	<0.001
Ethnicity White							-0.07	-0.70 – 0.55	0.814
Ethnicity Hispanic							0.49	-0.45 – 1.43	0.308
Ethnicity Black							0.67	-0.57 – 1.92	0.291
Ethnicity East Asian							-0.33	-1.15 – 0.49	0.434
Ethnicity South Asian							-0.25	-1.35 – 0.84	0.650
Ethnicity Native Hawaiian Pacific Islander							-0.09	-3.32 – 3.14	0.957
Ethnicity Middle Eastern							0.27	-1.30 – 1.83	0.736
Ethnicity American Indian							2.50	-0.63 – 5.63	0.117
Random Effects
σ²	3.73			3.77			3.78
τ₀₀	4.41 _{unique_ID}			3.83 _{unique_ID}			3.83 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.54
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.006 / 0.545			0.163 / NA			0.179 / 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)	6.00	5.76 – 6.23	<0.001	6.76	4.76 – 8.76	<0.001	6.88	4.78 – 8.98	<0.001
condflourish vs control	-0.12	-0.35 – 0.12	0.337	-0.12	-0.36 – 0.12	0.326	-0.12	-0.36 – 0.12	0.326
time - 2 5	-0.15	-0.25 – -0.05	0.002	-0.16	-0.26 – -0.06	0.002	-0.16	-0.26 – -0.06	0.002
condflourish vs control × time - 2 5	-0.06	-0.16 – 0.04	0.250	-0.06	-0.17 – 0.04	0.216	-0.06	-0.17 – 0.04	0.216
Sex [Woman]				1.10	0.48 – 1.73	0.001	1.12	0.49 – 1.75	0.001
Age				-0.04	-0.10 – 0.02	0.155	-0.05	-0.11 – 0.01	0.115
int student [No]				0.93	-0.05 – 1.91	0.063	0.82	-0.22 – 1.87	0.122
SES num				-0.50	-0.71 – -0.29	<0.001	-0.48	-0.69 – -0.26	<0.001
Ethnicity White							-0.04	-0.69 – 0.61	0.898
Ethnicity Hispanic							0.41	-0.56 – 1.38	0.408
Ethnicity Black							0.75	-0.55 – 2.06	0.258
Ethnicity East Asian							-0.36	-1.22 – 0.49	0.406
Ethnicity South Asian							-0.02	-1.20 – 1.16	0.974
Ethnicity Native Hawaiian Pacific Islander							-0.10	-3.36 – 3.15	0.950
Ethnicity Middle Eastern							-0.90	-2.96 – 1.15	0.390
Ethnicity American Indian							2.51	-0.65 – 5.67	0.120
Random Effects
σ²	3.70			3.75			3.76
τ₀₀	4.43 _{unique_ID}			3.90 _{unique_ID}			3.91 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.012 / NA			0.151 / NA			0.168 / 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)	2.79	2.35 – 3.24	<0.001	3.36	1.64 – 5.09	<0.001	2.61	0.83 – 4.39	0.004
condflourish vs control	0.09	-0.10 – 0.28	0.344	0.07	-0.12 – 0.27	0.445	0.06	-0.13 – 0.25	0.551
time - 2 5	0.05	-0.02 – 0.13	0.182	0.05	-0.03 – 0.13	0.188	0.05	-0.03 – 0.13	0.202
condflourish vs control × time - 2 5	0.01	-0.07 – 0.08	0.867	0.01	-0.07 – 0.09	0.786	0.01	-0.07 – 0.09	0.761
Sex [Woman]				0.12	-0.36 – 0.60	0.624	0.12	-0.36 – 0.61	0.624
Age				-0.02	-0.07 – 0.03	0.397	-0.01	-0.07 – 0.04	0.594
int student [No]				0.42	-0.35 – 1.20	0.286	0.68	-0.15 – 1.51	0.107
SES num				-0.21	-0.37 – -0.04	0.015	-0.19	-0.36 – -0.02	0.033
Ethnicity White							0.12	-0.39 – 0.63	0.652
Ethnicity Hispanic							0.72	-0.03 – 1.47	0.060
Ethnicity Black							0.21	-0.82 – 1.23	0.692
Ethnicity East Asian							0.10	-0.59 – 0.80	0.771
Ethnicity South Asian							1.21	0.26 – 2.16	0.012
Ethnicity Native Hawaiian Pacific Islander							0.83	-2.12 – 3.79	0.579
Ethnicity Middle Eastern							1.10	-0.27 – 2.48	0.115
Ethnicity American Indian							1.30	-1.53 – 4.13	0.369
Random Effects
σ²	2.65			2.64			2.64
τ₀₀	3.78 _{unique_ID}			3.43 _{unique_ID}			3.39 _{unique_ID}
	0.15 _univ			0.22 _univ			0.16 _univ
ICC	0.60			0.58			0.57
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1579			1475			1475
Marginal R² / Conditional R²	0.002 / 0.598			0.014 / 0.586			0.031 / 0.587

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)	2.74	2.30 – 3.18	<0.001	3.57	1.73 – 5.42	<0.001	2.67	0.77 – 4.57	0.006
condflourish vs control	0.15	-0.06 – 0.35	0.154	0.14	-0.06 – 0.35	0.177	0.13	-0.08 – 0.33	0.238
time - 2 5	0.05	-0.03 – 0.13	0.247	0.04	-0.04 – 0.13	0.293	0.04	-0.04 – 0.12	0.326
condflourish vs control × time - 2 5	-0.01	-0.09 – 0.07	0.806	-0.00	-0.09 – 0.08	0.924	-0.00	-0.08 – 0.08	0.948
Sex [Woman]				0.05	-0.50 – 0.61	0.845	0.04	-0.52 – 0.59	0.893
Age				-0.02	-0.07 – 0.04	0.524	-0.01	-0.06 – 0.05	0.822
int student [No]				0.25	-0.57 – 1.07	0.553	0.45	-0.43 – 1.33	0.320
SES num				-0.25	-0.44 – -0.07	0.006	-0.26	-0.44 – -0.07	0.007
Ethnicity White							0.42	-0.15 – 0.99	0.148
Ethnicity Hispanic							1.15	0.28 – 2.02	0.009
Ethnicity Black							0.15	-1.00 – 1.30	0.799
Ethnicity East Asian							0.43	-0.33 – 1.19	0.267
Ethnicity South Asian							1.32	0.30 – 2.34	0.011
Ethnicity Native Hawaiian Pacific Islander							0.99	-1.97 – 3.95	0.512
Ethnicity Middle Eastern							1.54	0.09 – 2.98	0.037
Ethnicity American Indian							1.62	-1.25 – 4.50	0.269
Random Effects
σ²	2.64			2.64			2.64
τ₀₀	3.76 _{unique_ID}			3.45 _{unique_ID}			3.38 _{unique_ID}
	0.14 _univ			0.16 _univ			0.10 _univ
ICC	0.60			0.58			0.57
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1407			1325			1325
Marginal R² / Conditional R²	0.004 / 0.598			0.018 / 0.586			0.041 / 0.587

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)	2.73	2.22 – 3.25	<0.001	3.30	1.35 – 5.25	0.001	2.46	0.45 – 4.47	0.017
condflourish vs control	0.10	-0.11 – 0.32	0.335	0.09	-0.13 – 0.30	0.435	0.07	-0.15 – 0.29	0.508
time - 2 5	0.04	-0.05 – 0.12	0.369	0.03	-0.05 – 0.12	0.450	0.03	-0.05 – 0.11	0.482
condflourish vs control × time - 2 5	-0.02	-0.10 – 0.06	0.625	-0.02	-0.10 – 0.07	0.678	-0.02	-0.10 – 0.07	0.715
Sex [Woman]				0.18	-0.39 – 0.75	0.531	0.18	-0.40 – 0.76	0.539
Age				-0.01	-0.07 – 0.04	0.647	-0.00	-0.06 – 0.06	0.988
int student [No]				0.45	-0.45 – 1.36	0.327	0.61	-0.35 – 1.56	0.211
SES num				-0.30	-0.49 – -0.11	0.002	-0.28	-0.48 – -0.09	0.005
Ethnicity White							0.33	-0.27 – 0.92	0.279
Ethnicity Hispanic							1.00	0.11 – 1.89	0.027
Ethnicity Black							-0.21	-1.40 – 0.98	0.734
Ethnicity East Asian							0.32	-0.47 – 1.10	0.426
Ethnicity South Asian							1.22	0.13 – 2.31	0.028
Ethnicity Native Hawaiian Pacific Islander							0.90	-2.07 – 3.87	0.552
Ethnicity Middle Eastern							0.61	-1.27 – 2.48	0.528
Ethnicity American Indian							1.61	-1.27 – 4.49	0.273
Random Effects
σ²	2.53			2.53			2.53
τ₀₀	3.80 _{unique_ID}			3.44 _{unique_ID}			3.42 _{unique_ID}
	0.21 _univ			0.24 _univ			0.18 _univ
ICC	0.61			0.59			0.59
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.002 / 0.614			0.023 / 0.602			0.041 / 0.604

SAS: Positive

Intention to Treat

m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00358245 (tol = 0.002, component 1)

tab_model(m0, m1, m2)

	SAS_positive			SAS_positive			SAS_positive
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	18.25	17.38 – 19.11	<0.001	12.15	7.61 – 16.69	<0.001	12.91	8.16 – 17.65	<0.001
condflourish vs control	0.59	0.07 – 1.12	0.026	0.50	-0.03 – 1.02	0.063	0.53	-0.00 – 1.06	0.051
time - 2 5	0.03	-0.16 – 0.22	0.750	0.05	-0.15 – 0.25	0.614	0.05	-0.15 – 0.24	0.641
condflourish vs control × time - 2 5	0.25	0.06 – 0.44	0.010	0.24	0.04 – 0.43	0.017	0.24	0.05 – 0.43	0.016
Sex [Woman]				-0.42	-1.75 – 0.90	0.533	-0.44	-1.77 – 0.90	0.522
Age				0.15	0.01 – 0.29	0.032	0.15	0.01 – 0.29	0.035
int student [No]				-1.14	-3.26 – 0.99	0.294	-1.74	-4.04 – 0.55	0.136
SES num				1.34	0.89 – 1.80	<0.001	1.29	0.82 – 1.77	<0.001
Ethnicity White							0.65	-0.77 – 2.06	0.369
Ethnicity Hispanic							0.02	-2.04 – 2.09	0.982
Ethnicity Black							-1.68	-4.50 – 1.13	0.242
Ethnicity East Asian							-0.45	-2.38 – 1.48	0.647
Ethnicity South Asian							-1.37	-4.00 – 1.26	0.307
Ethnicity Native Hawaiian Pacific Islander							0.14	-8.02 – 8.30	0.973
Ethnicity Middle Eastern							0.01	-3.80 – 3.81	0.997
Ethnicity American Indian							-2.87	-10.76 – 5.01	0.475
Random Effects
σ²	16.15			16.09			16.09
τ₀₀	31.33 _{unique_ID}			27.24 _{unique_ID}			27.47 _{unique_ID}
	0.41 _univ			0.60 _univ			0.35 _univ
ICC	0.66			0.63			0.63
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1577			1473			1473
Marginal R² / Conditional R²	0.008 / 0.666			0.065 / 0.658			0.074 / 0.661

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)	18.21	17.34 – 19.08	<0.001	11.62	6.75 – 16.48	<0.001	12.08	6.98 – 17.18	<0.001
condflourish vs control	0.74	0.17 – 1.30	0.010	0.65	0.10 – 1.21	0.021	0.69	0.13 – 1.25	0.016
time - 2 5	0.07	-0.13 – 0.27	0.506	0.08	-0.12 – 0.28	0.434	0.08	-0.12 – 0.28	0.440
condflourish vs control × time - 2 5	0.22	0.02 – 0.42	0.028	0.20	-0.00 – 0.40	0.051	0.20	0.00 – 0.40	0.049
Sex [Woman]				-0.06	-1.54 – 1.42	0.938	-0.01	-1.50 – 1.49	0.994
Age				0.16	0.01 – 0.30	0.033	0.16	0.01 – 0.30	0.037
int student [No]				-1.46	-3.66 – 0.74	0.195	-1.87	-4.25 – 0.51	0.124
SES num				1.44	0.95 – 1.93	<0.001	1.36	0.85 – 1.86	<0.001
Ethnicity White							0.77	-0.78 – 2.32	0.330
Ethnicity Hispanic							-0.45	-2.80 – 1.89	0.705
Ethnicity Black							-1.02	-4.12 – 2.08	0.519
Ethnicity East Asian							-0.30	-2.36 – 1.75	0.772
Ethnicity South Asian							-0.34	-3.10 – 2.41	0.807
Ethnicity Native Hawaiian Pacific Islander							0.45	-7.55 – 8.46	0.911
Ethnicity Middle Eastern							1.36	-2.55 – 5.26	0.495
Ethnicity American Indian							-2.20	-9.99 – 5.60	0.581
Random Effects
σ²	16.05			15.98			15.99
τ₀₀	29.83 _{unique_ID}			25.55 _{unique_ID}			25.89 _{unique_ID}
	0.37 _univ			0.85 _univ			0.55 _univ
ICC	0.65			0.62			0.62
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1405			1323			1323
Marginal R² / Conditional R²	0.013 / 0.657			0.080 / 0.653			0.085 / 0.655

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)	18.08	17.21 – 18.96	<0.001	11.72	6.63 – 16.81	<0.001	12.47	7.20 – 17.73	<0.001
condflourish vs control	0.61	0.03 – 1.19	0.040	0.58	0.00 – 1.16	0.050	0.63	0.04 – 1.22	0.037
time - 2 5	0.04	-0.17 – 0.24	0.729	0.05	-0.16 – 0.26	0.669	0.05	-0.16 – 0.26	0.672
condflourish vs control × time - 2 5	0.19	-0.01 – 0.40	0.066	0.17	-0.04 – 0.38	0.115	0.17	-0.04 – 0.38	0.115
Sex [Woman]				0.03	-1.50 – 1.56	0.970	0.06	-1.49 – 1.61	0.939
Age				0.14	-0.01 – 0.29	0.059	0.14	-0.02 – 0.29	0.078
int student [No]				-1.12	-3.54 – 1.31	0.366	-1.71	-4.27 – 0.84	0.189
SES num				1.35	0.84 – 1.86	<0.001	1.29	0.76 – 1.81	<0.001
Ethnicity White							0.80	-0.79 – 2.40	0.321
Ethnicity Hispanic							-0.21	-2.59 – 2.17	0.862
Ethnicity Black							-1.13	-4.32 – 2.06	0.489
Ethnicity East Asian							-0.29	-2.40 – 1.82	0.786
Ethnicity South Asian							-1.38	-4.30 – 1.55	0.356
Ethnicity Native Hawaiian Pacific Islander							0.47	-7.49 – 8.43	0.908
Ethnicity Middle Eastern							3.26	-1.79 – 8.32	0.205
Ethnicity American Indian							-2.16	-9.91 – 5.59	0.585
Random Effects
σ²	15.69			15.56			15.57
τ₀₀	29.29 _{unique_ID}			25.42 _{unique_ID}			25.61 _{unique_ID}
	0.37 _univ			0.89 _univ			0.50 _univ
ICC	0.65			0.63			0.63
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1292			1213			1213
Marginal R² / Conditional R²	0.009 / 0.657			0.067 / 0.653			0.077 / 0.655

SAS: Negative

Intention to Treat

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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0257716 (tol = 0.002, component 1)

tab_model(m0, m1, m2)

	SAS_negative			SAS_negative			SAS_negative
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.69	12.17 – 13.22	<0.001	15.58	11.21 – 19.95	<0.001	13.98	9.37 – 18.59	<0.001
condflourish vs control	-0.19	-0.71 – 0.34	0.481	-0.23	-0.76 – 0.30	0.389	-0.28	-0.81 – 0.25	0.299
time - 2 5	-0.17	-0.36 – 0.03	0.096	-0.20	-0.40 – 0.00	0.054	-0.20	-0.40 – 0.00	0.054
condflourish vs control × time - 2 5	-0.01	-0.20 – 0.19	0.952	-0.01	-0.21 – 0.18	0.884	-0.01	-0.21 – 0.19	0.904
Sex [Woman]				1.66	0.33 – 2.99	0.014	1.69	0.36 – 3.02	0.013
Age				-0.12	-0.26 – 0.02	0.085	-0.11	-0.25 – 0.02	0.108
int student [No]				1.82	-0.29 – 3.92	0.091	2.51	0.23 – 4.78	0.031
SES num				-1.09	-1.55 – -0.63	<0.001	-1.02	-1.49 – -0.55	<0.001
Ethnicity White							0.11	-1.30 – 1.52	0.876
Ethnicity Hispanic							1.72	-0.33 – 3.77	0.099
Ethnicity Black							1.39	-1.40 – 4.18	0.330
Ethnicity East Asian							0.10	-1.81 – 2.01	0.918
Ethnicity South Asian							3.07	0.47 – 5.67	0.021
Ethnicity Native Hawaiian Pacific Islander							2.64	-5.46 – 10.75	0.523
Ethnicity Middle Eastern							3.30	-0.47 – 7.07	0.086
Ethnicity American Indian							5.80	-2.04 – 13.63	0.147
Random Effects
σ²	17.08			16.96			16.94
τ₀₀	31.03 _{unique_ID}			27.14 _{unique_ID}			26.83 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC				0.62			0.61
N	538 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1578			1474			1474
Marginal R² / Conditional R²	0.004 / NA			0.056 / 0.637			0.077 / 0.643

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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00314788 (tol = 0.002, component 1)

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)	12.46	11.90 – 13.03	<0.001	15.70	11.06 – 20.34	<0.001	14.58	9.66 – 19.51	<0.001
condflourish vs control	-0.15	-0.72 – 0.41	0.598	-0.14	-0.70 – 0.42	0.617	-0.16	-0.72 – 0.40	0.572
time - 2 5	-0.16	-0.37 – 0.04	0.113	-0.19	-0.40 – 0.02	0.070	-0.19	-0.40 – 0.02	0.071
condflourish vs control × time - 2 5	-0.05	-0.25 – 0.16	0.652	-0.04	-0.25 – 0.17	0.700	-0.04	-0.25 – 0.17	0.707
Sex [Woman]				1.46	-0.02 – 2.95	0.053	1.46	-0.03 – 2.95	0.055
Age				-0.12	-0.26 – 0.02	0.104	-0.11	-0.25 – 0.03	0.127
int student [No]				1.91	-0.27 – 4.09	0.086	2.12	-0.25 – 4.48	0.080
SES num				-1.19	-1.68 – -0.70	<0.001	-1.17	-1.67 – -0.66	<0.001
Ethnicity White							0.56	-0.98 – 2.10	0.476
Ethnicity Hispanic							1.95	-0.37 – 4.28	0.100
Ethnicity Black							1.26	-1.81 – 4.33	0.420
Ethnicity East Asian							0.19	-1.84 – 2.22	0.853
Ethnicity South Asian							2.26	-0.46 – 4.99	0.103
Ethnicity Native Hawaiian Pacific Islander							2.69	-5.27 – 10.65	0.508
Ethnicity Middle Eastern							3.15	-0.73 – 7.02	0.111
Ethnicity American Indian							6.50	-1.27 – 14.27	0.101
Random Effects
σ²	16.76			16.77			16.77
τ₀₀	29.90 _{unique_ID}			25.59 _{unique_ID}			25.46 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC				0.60
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1406			1324			1324
Marginal R² / Conditional R²	0.004 / NA			0.063 / 0.629			0.175 / 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)	12.47	11.88 – 13.06	<0.001	15.35	10.44 – 20.26	<0.001	14.17	9.01 – 19.33	<0.001
condflourish vs control	-0.14	-0.73 – 0.45	0.636	-0.18	-0.77 – 0.40	0.538	-0.18	-0.78 – 0.41	0.552
time - 2 5	-0.20	-0.41 – 0.01	0.063	-0.24	-0.45 – -0.02	0.031	-0.24	-0.45 – -0.02	0.031
condflourish vs control × time - 2 5	-0.08	-0.29 – 0.13	0.448	-0.09	-0.30 – 0.13	0.431	-0.08	-0.30 – 0.13	0.445
Sex [Woman]				1.67	0.13 – 3.22	0.034	1.73	0.17 – 3.29	0.030
Age				-0.10	-0.25 – 0.04	0.162	-0.09	-0.24 – 0.05	0.218
int student [No]				1.88	-0.54 – 4.30	0.129	2.07	-0.50 – 4.64	0.114
SES num				-1.22	-1.73 – -0.71	<0.001	-1.16	-1.69 – -0.63	<0.001
Ethnicity White							0.44	-1.17 – 2.04	0.594
Ethnicity Hispanic							1.56	-0.83 – 3.94	0.202
Ethnicity Black							0.80	-2.41 – 4.00	0.626
Ethnicity East Asian							-0.17	-2.28 – 1.94	0.874
Ethnicity South Asian							2.63	-0.30 – 5.57	0.078
Ethnicity Native Hawaiian Pacific Islander							2.62	-5.39 – 10.63	0.521
Ethnicity Middle Eastern							0.45	-4.63 – 5.53	0.863
Ethnicity American Indian							6.44	-1.38 – 14.25	0.106
Random Effects
σ²	16.27			16.35			16.35
τ₀₀	30.39 _{unique_ID}			25.84 _{unique_ID}			25.87 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC				0.61
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	1293			1214			1214
Marginal R² / Conditional R²	0.005 / NA			0.065 / 0.638			0.180 / 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)
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)	44.36	43.20 – 45.52	<0.001	37.74	32.84 – 42.64	<0.001	39.52	34.51 – 44.53	<0.001
condflourish vs control	0.16	-0.38 – 0.70	0.558	0.04	-0.50 – 0.58	0.892	0.11	-0.43 – 0.65	0.697
time - 2 5	-0.04	-0.22 – 0.13	0.622	-0.04	-0.22 – 0.14	0.660	-0.04	-0.22 – 0.13	0.634
condflourish vs control × time - 2 5	0.18	0.00 – 0.35	0.048	0.16	-0.02 – 0.34	0.075	0.16	-0.01 – 0.34	0.070
Sex [Woman]				0.31	-1.05 – 1.66	0.658	0.26	-1.09 – 1.62	0.701
Age				0.10	-0.04 – 0.25	0.171	0.09	-0.06 – 0.23	0.253
int student [No]				-0.40	-2.60 – 1.80	0.721	-1.66	-4.00 – 0.69	0.167
SES num				1.38	0.91 – 1.85	<0.001	1.30	0.82 – 1.78	<0.001
Ethnicity White							1.07	-0.37 – 2.51	0.145
Ethnicity Hispanic							0.12	-1.97 – 2.20	0.912
Ethnicity Black							-1.21	-4.09 – 1.66	0.406
Ethnicity East Asian							-1.16	-3.13 – 0.80	0.245
Ethnicity South Asian							-2.55	-5.23 – 0.13	0.062
Ethnicity Native Hawaiian Pacific Islander							-4.19	-12.64 – 4.26	0.331
Ethnicity Middle Eastern							0.08	-3.83 – 3.99	0.966
Ethnicity American Indian							-3.19	-11.27 – 4.89	0.439
Random Effects
σ²	11.50			11.46			11.48
τ₀₀	31.59 _{unique_ID}			27.75 _{unique_ID}			27.29 _{unique_ID}
	0.98 _univ			1.86 _univ			1.21 _univ
ICC	0.74			0.72			0.71
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	832			782			782
Marginal R² / Conditional R²	0.002 / 0.740			0.060 / 0.738			0.085 / 0.737

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)	44.51	43.29 – 45.73	<0.001	37.06	31.75 – 42.37	<0.001	38.92	33.48 – 44.37	<0.001
condflourish vs control	0.19	-0.41 – 0.78	0.538	0.01	-0.58 – 0.60	0.982	0.10	-0.49 – 0.69	0.737
time - 2 5	-0.05	-0.24 – 0.13	0.568	-0.05	-0.23 – 0.14	0.604	-0.05	-0.24 – 0.14	0.601
condflourish vs control × time - 2 5	0.17	-0.01 – 0.36	0.061	0.15	-0.04 – 0.33	0.115	0.15	-0.03 – 0.34	0.108
Sex [Woman]				1.15	-0.42 – 2.71	0.150	1.17	-0.39 – 2.74	0.142
Age				0.11	-0.04 – 0.27	0.153	0.10	-0.05 – 0.26	0.199
int student [No]				-0.25	-2.58 – 2.08	0.832	-1.40	-3.90 – 1.09	0.270
SES num				1.31	0.79 – 1.83	<0.001	1.21	0.68 – 1.74	<0.001
Ethnicity White							0.80	-0.82 – 2.42	0.332
Ethnicity Hispanic							-0.59	-3.03 – 1.86	0.639
Ethnicity Black							-2.06	-5.31 – 1.20	0.215
Ethnicity East Asian							-1.78	-3.94 – 0.37	0.104
Ethnicity South Asian							-2.07	-4.95 – 0.81	0.158
Ethnicity Native Hawaiian Pacific Islander							-4.48	-12.88 – 3.93	0.296
Ethnicity Middle Eastern							-0.09	-4.19 – 4.01	0.965
Ethnicity American Indian							-3.25	-11.26 – 4.77	0.427
Random Effects
σ²	11.69			11.66			11.71
τ₀₀	30.81 _{unique_ID}			26.82 _{unique_ID}			26.38 _{unique_ID}
	1.03 _univ			1.88 _univ			1.07 _univ
ICC	0.73			0.71			0.70
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	709			669			669
Marginal R² / Conditional R²	0.002 / 0.732			0.056 / 0.727			0.082 / 0.726

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)	44.48	43.32 – 45.63	<0.001	36.98	31.55 – 42.42	<0.001	38.75	33.23 – 44.26	<0.001
condflourish vs control	0.16	-0.44 – 0.77	0.600	0.02	-0.59 – 0.62	0.959	0.11	-0.50 – 0.72	0.721
time - 2 5	0.01	-0.18 – 0.20	0.924	0.01	-0.18 – 0.20	0.899	0.01	-0.18 – 0.20	0.904
condflourish vs control × time - 2 5	0.24	0.05 – 0.43	0.013	0.21	0.02 – 0.40	0.030	0.21	0.02 – 0.40	0.030
Sex [Woman]				0.96	-0.64 – 2.55	0.239	0.95	-0.65 – 2.55	0.245
Age				0.10	-0.06 – 0.26	0.212	0.08	-0.08 – 0.24	0.306
int student [No]				0.38	-2.15 – 2.92	0.766	-0.76	-3.41 – 1.89	0.573
SES num				1.29	0.76 – 1.82	<0.001	1.20	0.66 – 1.74	<0.001
Ethnicity White							1.05	-0.59 – 2.69	0.210
Ethnicity Hispanic							0.08	-2.38 – 2.54	0.951
Ethnicity Black							-1.62	-4.93 – 1.69	0.338
Ethnicity East Asian							-1.50	-3.68 – 0.68	0.176
Ethnicity South Asian							-2.34	-5.35 – 0.67	0.128
Ethnicity Native Hawaiian Pacific Islander							-4.26	-12.52 – 4.00	0.312
Ethnicity Middle Eastern							2.28	-2.99 – 7.54	0.396
Ethnicity American Indian							-3.14	-11.02 – 4.73	0.434
Random Effects
σ²	11.42			11.35			11.40
τ₀₀	29.90 _{unique_ID}			25.83 _{unique_ID}			25.31 _{unique_ID}
	0.87 _univ			1.57 _univ			0.78 _univ
ICC	0.73			0.71			0.70
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.003 / 0.730			0.056 / 0.723			0.087 / 0.722

Social Fit

Intention to Treat

m0 <- lmer(social_fit ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m1 <- lmer(social_fit ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(social_fit ~ 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)

	social_fit			social_fit			social_fit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.87	6.62 – 7.12	<0.001	6.49	5.63 – 7.34	<0.001	6.73	5.84 – 7.61	<0.001
condflourish vs control	0.05	-0.04 – 0.14	0.274	0.05	-0.04 – 0.15	0.288	0.06	-0.03 – 0.16	0.201
time - 2 5	0.03	-0.02 – 0.07	0.205	0.03	-0.02 – 0.07	0.220	0.03	-0.02 – 0.08	0.191
condflourish vs control × time - 2 5	-0.00	-0.05 – 0.04	0.841	-0.01	-0.05 – 0.04	0.776	-0.01	-0.05 – 0.04	0.774
Sex [Woman]				0.45	0.21 – 0.69	<0.001	0.43	0.19 – 0.67	<0.001
Age				-0.02	-0.05 – 0.01	0.125	-0.02	-0.05 – 0.00	0.072
int student [No]				0.21	-0.17 – 0.60	0.273	0.17	-0.25 – 0.58	0.428
SES num				0.09	0.01 – 0.17	0.026	0.09	0.01 – 0.17	0.037
Ethnicity White							-0.06	-0.31 – 0.19	0.628
Ethnicity Hispanic							0.03	-0.34 – 0.40	0.865
Ethnicity Black							-0.17	-0.68 – 0.34	0.518
Ethnicity East Asian							-0.05	-0.39 – 0.30	0.786
Ethnicity South Asian							-0.29	-0.76 – 0.17	0.212
Ethnicity Native Hawaiian Pacific Islander							-2.67	-4.16 – -1.18	<0.001
Ethnicity Middle Eastern							-0.49	-1.17 – 0.20	0.162
Ethnicity American Indian							-0.47	-1.84 – 0.90	0.504
Random Effects
σ²	0.80			0.80			0.80
τ₀₀	0.60 _{unique_ID}			0.56 _{unique_ID}			0.55 _{unique_ID}
	0.05 _univ			0.04 _univ			0.03 _univ
ICC	0.45			0.43			0.42
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.003 / 0.450			0.040 / 0.448			0.064 / 0.455

Excluded Preregistered

m0 <- lmer(social_fit ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(social_fit ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(social_fit ~ 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)

	social_fit			social_fit			social_fit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.82	6.51 – 7.14	<0.001	6.30	5.36 – 7.25	<0.001	6.57	5.59 – 7.55	<0.001
condflourish vs control	0.03	-0.07 – 0.13	0.599	0.03	-0.07 – 0.14	0.539	0.04	-0.06 – 0.14	0.441
time - 2 5	0.02	-0.03 – 0.07	0.381	0.02	-0.03 – 0.07	0.346	0.02	-0.02 – 0.07	0.327
condflourish vs control × time - 2 5	0.00	-0.04 – 0.05	0.925	-0.00	-0.05 – 0.05	0.939	-0.00	-0.05 – 0.05	0.950
Sex [Woman]				0.49	0.21 – 0.76	0.001	0.45	0.18 – 0.72	0.001
Age				-0.02	-0.05 – 0.01	0.136	-0.02	-0.05 – 0.00	0.100
int student [No]				0.30	-0.11 – 0.71	0.154	0.25	-0.19 – 0.68	0.267
SES num				0.11	0.02 – 0.21	0.013	0.12	0.02 – 0.21	0.015
Ethnicity White							-0.13	-0.41 – 0.15	0.366
Ethnicity Hispanic							0.10	-0.33 – 0.53	0.650
Ethnicity Black							-0.35	-0.93 – 0.23	0.235
Ethnicity East Asian							-0.17	-0.54 – 0.21	0.380
Ethnicity South Asian							-0.34	-0.83 – 0.16	0.186
Ethnicity Native Hawaiian Pacific Islander							-2.64	-4.13 – -1.15	0.001
Ethnicity Middle Eastern							-0.54	-1.25 – 0.18	0.143
Ethnicity American Indian							-0.52	-1.89 – 0.85	0.454
Random Effects
σ²	0.82			0.82			0.82
τ₀₀	0.58 _{unique_ID}			0.54 _{unique_ID}			0.52 _{unique_ID}
	0.09 _univ			0.06 _univ			0.05 _univ
ICC	0.45			0.42			0.41
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.001 / 0.450			0.043 / 0.446			0.072 / 0.453

Excluded Unreasonable Numbers

m0 <- lmer(social_fit ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(social_fit ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(social_fit ~ 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)

	social_fit			social_fit			social_fit
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.82	6.48 – 7.17	<0.001	6.22	5.24 – 7.21	<0.001	6.56	5.54 – 7.57	<0.001
condflourish vs control	0.04	-0.06 – 0.14	0.426	0.04	-0.06 – 0.15	0.411	0.05	-0.06 – 0.16	0.367
time - 2 5	0.03	-0.02 – 0.08	0.259	0.03	-0.02 – 0.08	0.236	0.03	-0.02 – 0.08	0.224
condflourish vs control × time - 2 5	0.01	-0.04 – 0.06	0.691	0.01	-0.04 – 0.05	0.833	0.01	-0.04 – 0.05	0.838
Sex [Woman]				0.46	0.18 – 0.74	0.001	0.41	0.14 – 0.69	0.004
Age				-0.02	-0.05 – 0.01	0.216	-0.02	-0.05 – 0.01	0.162
int student [No]				0.34	-0.11 – 0.78	0.141	0.26	-0.20 – 0.73	0.265
SES num				0.12	0.02 – 0.21	0.014	0.11	0.02 – 0.20	0.021
Ethnicity White							-0.14	-0.43 – 0.14	0.332
Ethnicity Hispanic							0.07	-0.36 – 0.51	0.736
Ethnicity Black							-0.39	-0.98 – 0.20	0.193
Ethnicity East Asian							-0.20	-0.58 – 0.18	0.313
Ethnicity South Asian							-0.53	-1.05 – -0.01	0.044
Ethnicity Native Hawaiian Pacific Islander							-2.67	-4.13 – -1.20	<0.001
Ethnicity Middle Eastern							-0.20	-1.12 – 0.73	0.678
Ethnicity American Indian							-0.57	-1.91 – 0.77	0.406
Random Effects
σ²	0.79			0.79			0.79
τ₀₀	0.56 _{unique_ID}			0.53 _{unique_ID}			0.50 _{unique_ID}
	0.10 _univ			0.06 _univ			0.06 _univ
ICC	0.45			0.43			0.41
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.002 / 0.455			0.043 / 0.450			0.077 / 0.459

Cohesion

Intention to Treat

m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
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)	5.64	5.32 – 5.95	<0.001	4.42	2.86 – 5.98	<0.001	4.30	2.68 – 5.92	<0.001
condflourish vs control	0.14	-0.04 – 0.32	0.127	0.12	-0.06 – 0.30	0.193	0.16	-0.02 – 0.34	0.087
time - 2 5	0.07	0.02 – 0.13	0.006	0.07	0.02 – 0.12	0.008	0.07	0.02 – 0.12	0.009
condflourish vs control × time - 2 5	0.06	0.01 – 0.11	0.018	0.06	0.01 – 0.11	0.032	0.06	0.01 – 0.11	0.030
Sex [Woman]				0.72	0.26 – 1.17	0.002	0.70	0.24 – 1.15	0.003
Age				-0.03	-0.07 – 0.02	0.309	-0.02	-0.07 – 0.03	0.417
int student [No]				0.39	-0.35 – 1.12	0.305	0.45	-0.33 – 1.24	0.260
SES num				0.25	0.10 – 0.41	0.002	0.23	0.07 – 0.39	0.006
Ethnicity White							0.34	-0.14 – 0.83	0.164
Ethnicity Hispanic							-0.15	-0.85 – 0.54	0.664
Ethnicity Black							-0.84	-1.80 – 0.12	0.085
Ethnicity East Asian							-0.16	-0.82 – 0.50	0.628
Ethnicity South Asian							0.55	-0.34 – 1.45	0.226
Ethnicity Native Hawaiian Pacific Islander							-2.13	-4.96 – 0.70	0.139
Ethnicity Middle Eastern							-0.92	-2.23 – 0.39	0.169
Ethnicity American Indian							-1.66	-4.38 – 1.07	0.234
Random Effects
σ²	1.01			1.01			1.01
τ₀₀	3.78 _{unique_ID}			3.33 _{unique_ID}			3.26 _{unique_ID}
	0.06 _univ			0.05 _univ			0.04 _univ
ICC	0.79			0.77			0.76
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.007 / 0.793			0.045 / 0.780			0.077 / 0.783

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)	5.53	5.10 – 5.97	<0.001	3.87	2.09 – 5.64	<0.001	3.66	1.85 – 5.48	<0.001
condflourish vs control	0.19	-0.01 – 0.39	0.067	0.18	-0.03 – 0.38	0.087	0.21	0.01 – 0.41	0.041
time - 2 5	0.06	0.01 – 0.12	0.028	0.06	0.01 – 0.12	0.028	0.06	0.01 – 0.12	0.029
condflourish vs control × time - 2 5	0.06	0.01 – 0.12	0.027	0.06	0.00 – 0.11	0.047	0.06	0.00 – 0.11	0.043
Sex [Woman]				0.92	0.39 – 1.46	0.001	0.94	0.40 – 1.47	0.001
Age				-0.01	-0.06 – 0.04	0.696	-0.01	-0.06 – 0.04	0.759
int student [No]				0.40	-0.39 – 1.20	0.319	0.55	-0.30 – 1.40	0.207
SES num				0.24	0.06 – 0.41	0.009	0.20	0.02 – 0.38	0.034
Ethnicity White							0.47	-0.08 – 1.02	0.096
Ethnicity Hispanic							-0.22	-1.05 – 0.62	0.608
Ethnicity Black							-0.63	-1.73 – 0.48	0.265
Ethnicity East Asian							0.07	-0.66 – 0.80	0.849
Ethnicity South Asian							0.98	-0.01 – 1.96	0.051
Ethnicity Native Hawaiian Pacific Islander							-1.90	-4.75 – 0.95	0.191
Ethnicity Middle Eastern							-0.78	-2.18 – 0.62	0.272
Ethnicity American Indian							-1.44	-4.19 – 1.31	0.304
Random Effects
σ²	1.03			1.03			1.03
τ₀₀	3.84 _{unique_ID}			3.35 _{unique_ID}			3.27 _{unique_ID}
	0.14 _univ			0.14 _univ			0.06 _univ
ICC	0.79			0.77			0.76
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.010 / 0.796			0.048 / 0.782			0.085 / 0.783

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)	5.52	5.08 – 5.97	<0.001	3.50	1.62 – 5.38	<0.001	3.43	1.51 – 5.36	<0.001
condflourish vs control	0.18	-0.04 – 0.39	0.101	0.16	-0.05 – 0.38	0.144	0.19	-0.02 – 0.41	0.081
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	0.08	0.02 – 0.13	0.006	0.07	0.02 – 0.13	0.012	0.07	0.02 – 0.13	0.011
Sex [Woman]				0.92	0.35 – 1.48	0.002	0.93	0.36 – 1.50	0.001
Age				-0.01	-0.06 – 0.05	0.808	-0.01	-0.06 – 0.05	0.823
int student [No]				0.73	-0.17 – 1.63	0.112	0.79	-0.15 – 1.73	0.101
SES num				0.24	0.05 – 0.43	0.014	0.19	-0.00 – 0.38	0.055
Ethnicity White							0.46	-0.13 – 1.04	0.126
Ethnicity Hispanic							-0.22	-1.10 – 0.65	0.619
Ethnicity Black							-0.68	-1.85 – 0.49	0.256
Ethnicity East Asian							0.12	-0.66 – 0.89	0.767
Ethnicity South Asian							0.80	-0.27 – 1.87	0.144
Ethnicity Native Hawaiian Pacific Islander							-1.89	-4.81 – 1.04	0.206
Ethnicity Middle Eastern							-0.38	-2.25 – 1.49	0.693
Ethnicity American Indian							-1.44	-4.27 – 1.38	0.317
Random Effects
σ²	0.97			0.97			0.97
τ₀₀	4.07 _{unique_ID}			3.55 _{unique_ID}			3.51 _{unique_ID}
	0.14 _univ			0.12 _univ			0.05 _univ
ICC	0.81			0.79			0.79
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.010 / 0.815			0.051 / 0.802			0.081 / 0.804

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')

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)	19.83	19.35 – 20.32	<0.001	16.03	12.01 – 20.04	<0.001	16.32	12.06 – 20.58	<0.001
condflourish vs control	0.17	-0.32 – 0.65	0.498	0.16	-0.32 – 0.64	0.508	0.18	-0.30 – 0.67	0.464
time - 2 5	-0.33	-0.53 – -0.14	0.001	-0.30	-0.50 – -0.10	0.003	-0.30	-0.50 – -0.11	0.003
condflourish vs control × time - 2 5	0.20	0.01 – 0.39	0.044	0.23	0.04 – 0.43	0.021	0.23	0.03 – 0.43	0.022
Sex [Woman]				-1.87	-3.07 – -0.66	0.002	-1.90	-3.11 – -0.69	0.002
Age				0.24	0.11 – 0.37	<0.001	0.24	0.12 – 0.37	<0.001
int student [No]				-2.57	-4.48 – -0.65	0.009	-2.49	-4.58 – -0.40	0.020
SES num				0.86	0.44 – 1.27	<0.001	0.80	0.37 – 1.23	<0.001
Ethnicity White							-0.13	-1.42 – 1.15	0.839
Ethnicity Hispanic							-1.31	-3.17 – 0.55	0.168
Ethnicity Black							-0.84	-3.40 – 1.73	0.522
Ethnicity East Asian							0.20	-1.54 – 1.94	0.822
Ethnicity South Asian							-0.45	-2.81 – 1.92	0.712
Ethnicity Native Hawaiian Pacific Islander							0.63	-6.92 – 8.18	0.870
Ethnicity Middle Eastern							-0.92	-4.40 – 2.55	0.602
Ethnicity American Indian							-6.56	-13.66 – 0.54	0.070
Random Effects
σ²	14.96			14.78			14.76
τ₀₀	21.75 _{unique_ID}			17.93 _{unique_ID}			18.07 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.022 / 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)	19.95	19.42 – 20.48	<0.001	15.48	11.17 – 19.78	<0.001	15.04	10.45 – 19.64	<0.001
condflourish vs control	0.23	-0.30 – 0.76	0.396	0.18	-0.33 – 0.70	0.481	0.18	-0.34 – 0.70	0.497
time - 2 5	-0.37	-0.57 – -0.17	<0.001	-0.34	-0.54 – -0.13	0.001	-0.34	-0.54 – -0.14	0.001
condflourish vs control × time - 2 5	0.24	0.04 – 0.45	0.017	0.27	0.06 – 0.47	0.011	0.27	0.06 – 0.47	0.010
Sex [Woman]				-1.35	-2.71 – 0.00	0.051	-1.37	-2.74 – 0.01	0.051
Age				0.24	0.10 – 0.37	0.001	0.25	0.11 – 0.38	<0.001
int student [No]				-2.74	-4.73 – -0.74	0.007	-2.24	-4.42 – -0.05	0.045
SES num				1.01	0.56 – 1.46	<0.001	1.02	0.55 – 1.48	<0.001
Ethnicity White							-0.25	-1.67 – 1.17	0.727
Ethnicity Hispanic							-0.98	-3.12 – 1.16	0.370
Ethnicity Black							-0.85	-3.71 – 2.01	0.559
Ethnicity East Asian							0.33	-1.54 – 2.20	0.727
Ethnicity South Asian							0.57	-1.92 – 3.05	0.655
Ethnicity Native Hawaiian Pacific Islander							0.92	-6.49 – 8.34	0.807
Ethnicity Middle Eastern							-1.86	-5.45 – 1.73	0.310
Ethnicity American Indian							-6.45	-13.41 – 0.52	0.070
Random Effects
σ²	14.64			14.45			14.45
τ₀₀	21.37 _{unique_ID}			16.94 _{unique_ID}			17.07 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.030 / NA			0.198 / NA			0.216 / 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)	19.89	19.35 – 20.44	<0.001	14.87	10.36 – 19.39	<0.001	14.56	9.81 – 19.31	<0.001
condflourish vs control	0.17	-0.38 – 0.72	0.538	0.18	-0.35 – 0.72	0.503	0.19	-0.35 – 0.73	0.497
time - 2 5	-0.38	-0.59 – -0.18	<0.001	-0.36	-0.57 – -0.15	0.001	-0.36	-0.57 – -0.15	0.001
condflourish vs control × time - 2 5	0.23	0.02 – 0.44	0.031	0.24	0.04 – 0.45	0.022	0.24	0.03 – 0.45	0.024
Sex [Woman]				-1.22	-2.62 – 0.18	0.088	-1.32	-2.73 – 0.10	0.068
Age				0.22	0.08 – 0.35	0.002	0.22	0.08 – 0.36	0.002
int student [No]				-2.21	-4.41 – -0.01	0.049	-1.91	-4.25 – 0.44	0.111
SES num				1.12	0.65 – 1.59	<0.001	1.09	0.62 – 1.57	<0.001
Ethnicity White							0.05	-1.40 – 1.51	0.943
Ethnicity Hispanic							-0.39	-2.56 – 1.78	0.724
Ethnicity Black							-0.19	-3.14 – 2.75	0.898
Ethnicity East Asian							0.92	-0.99 – 2.84	0.344
Ethnicity South Asian							0.37	-2.27 – 3.01	0.784
Ethnicity Native Hawaiian Pacific Islander							1.33	-6.02 – 8.68	0.722
Ethnicity Middle Eastern							3.73	-0.94 – 8.39	0.117
Ethnicity American Indian							-6.07	-12.97 – 0.84	0.085
Random Effects
σ²	14.17			13.99			14.00
τ₀₀	21.19 _{unique_ID}			16.76 _{unique_ID}			16.79 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.033 / NA			0.197 / NA			0.220 / NA

Emotional Resilience

Intention to Treat

m0 <- lmer(emo_res ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
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)	18.19	17.98 – 18.40	<0.001	18.62	17.31 – 19.93	<0.001	18.27	16.88 – 19.66	<0.001
condflourish vs control	0.02	-0.13 – 0.16	0.826	-0.00	-0.15 – 0.15	0.992	0.00	-0.15 – 0.15	0.998
time - 2 5	0.01	-0.07 – 0.09	0.863	0.01	-0.07 – 0.09	0.719	0.02	-0.06 – 0.10	0.691
condflourish vs control × time - 2 5	0.03	-0.05 – 0.11	0.425	0.03	-0.04 – 0.11	0.387	0.04	-0.04 – 0.11	0.379
Sex [Woman]				0.10	-0.28 – 0.48	0.613	0.06	-0.31 – 0.44	0.737
Age				-0.02	-0.06 – 0.02	0.365	-0.01	-0.06 – 0.03	0.553
int student [No]				-0.04	-0.64 – 0.57	0.906	0.32	-0.33 – 0.97	0.334
SES num				-0.02	-0.15 – 0.11	0.732	-0.02	-0.15 – 0.12	0.795
Ethnicity White							-0.20	-0.60 – 0.19	0.314
Ethnicity Hispanic							0.10	-0.48 – 0.69	0.727
Ethnicity Black							-1.03	-1.84 – -0.22	0.013
Ethnicity East Asian							0.12	-0.42 – 0.67	0.658
Ethnicity South Asian							0.68	-0.05 – 1.41	0.068
Ethnicity Native Hawaiian Pacific Islander							-1.74	-4.10 – 0.63	0.150
Ethnicity Middle Eastern							0.01	-1.07 – 1.09	0.984
Ethnicity American Indian							0.02	-2.12 – 2.17	0.983
Random Effects
σ²	2.59			2.50			2.50
τ₀₀	1.21 _{unique_ID}			1.11 _{unique_ID}			1.06 _{unique_ID}
	0.02 _univ			0.02 _univ			0.04 _univ
ICC	0.32			0.31			0.31
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	832			782			782
Marginal R² / Conditional R²	0.001 / 0.323			0.003 / 0.313			0.027 / 0.325

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)	18.11	17.95 – 18.26	<0.001	18.74	17.39 – 20.10	<0.001	18.06	16.62 – 19.49	<0.001
condflourish vs control	0.00	-0.15 – 0.16	0.957	0.00	-0.16 – 0.16	0.964	-0.00	-0.16 – 0.15	0.956
time - 2 5	0.02	-0.06 – 0.10	0.577	0.03	-0.06 – 0.11	0.548	0.02	-0.06 – 0.10	0.631
condflourish vs control × time - 2 5	0.04	-0.04 – 0.12	0.305	0.04	-0.04 – 0.12	0.354	0.04	-0.04 – 0.12	0.320
Sex [Woman]				0.03	-0.39 – 0.45	0.871	0.01	-0.41 – 0.43	0.947
Age				-0.02	-0.06 – 0.03	0.463	-0.00	-0.05 – 0.04	0.896
int student [No]				-0.16	-0.77 – 0.46	0.621	0.24	-0.43 – 0.92	0.475
SES num				-0.06	-0.20 – 0.08	0.429	-0.06	-0.20 – 0.08	0.415
Ethnicity White							-0.02	-0.45 – 0.42	0.930
Ethnicity Hispanic							0.23	-0.42 – 0.89	0.485
Ethnicity Black							-0.96	-1.85 – -0.08	0.033
Ethnicity East Asian							0.37	-0.20 – 0.95	0.199
Ethnicity South Asian							0.91	0.15 – 1.66	0.019
Ethnicity Native Hawaiian Pacific Islander							-1.49	-3.79 – 0.82	0.206
Ethnicity Middle Eastern							0.13	-0.97 – 1.23	0.822
Ethnicity American Indian							0.18	-1.91 – 2.26	0.868
Random Effects
σ²	2.46			2.43			2.42
τ₀₀	1.03 _{unique_ID}			1.01 _{unique_ID}			0.97 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	709			669			669
Marginal R² / Conditional R²	0.002 / NA			0.006 / NA			0.045 / NA

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)	18.11	17.95 – 18.27	<0.001	18.99	17.57 – 20.41	<0.001	18.24	16.77 – 19.70	<0.001
condflourish vs control	0.01	-0.15 – 0.17	0.908	0.02	-0.15 – 0.18	0.854	0.01	-0.16 – 0.17	0.936
time - 2 5	0.04	-0.04 – 0.13	0.324	0.05	-0.04 – 0.13	0.281	0.04	-0.05 – 0.13	0.352
condflourish vs control × time - 2 5	0.06	-0.02 – 0.15	0.151	0.06	-0.03 – 0.15	0.164	0.07	-0.02 – 0.15	0.127
Sex [Woman]				-0.04	-0.48 – 0.39	0.840	-0.04	-0.47 – 0.38	0.844
Age				-0.02	-0.06 – 0.03	0.453	-0.00	-0.05 – 0.04	0.956
int student [No]				-0.24	-0.92 – 0.44	0.487	0.16	-0.55 – 0.88	0.654
SES num				-0.08	-0.22 – 0.06	0.278	-0.09	-0.23 – 0.06	0.246
Ethnicity White							0.02	-0.42 – 0.46	0.929
Ethnicity Hispanic							0.21	-0.45 – 0.86	0.537
Ethnicity Black							-1.03	-1.94 – -0.13	0.026
Ethnicity East Asian							0.39	-0.18 – 0.97	0.182
Ethnicity South Asian							1.13	0.35 – 1.92	0.005
Ethnicity Native Hawaiian Pacific Islander							-1.51	-3.76 – 0.74	0.189
Ethnicity Middle Eastern							-1.40	-2.82 – 0.01	0.052
Ethnicity American Indian							0.15	-1.88 – 2.18	0.884
Random Effects
σ²	2.52			2.48			2.47
τ₀₀	0.94 _{unique_ID}			0.91 _{unique_ID}			0.82 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC							0.25
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.005 / NA			0.010 / NA			0.052 / 0.288

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)	4.52	4.37 – 4.68	<0.001	3.35	2.69 – 4.01	<0.001	3.60	2.94 – 4.25	<0.001
condflourish vs control	0.04	-0.03 – 0.11	0.271	0.04	-0.03 – 0.12	0.272	0.05	-0.02 – 0.13	0.152
time - 2 5	0.03	0.01 – 0.06	0.017	0.03	0.01 – 0.06	0.018	0.03	0.00 – 0.06	0.023
condflourish vs control × time - 2 5	0.01	-0.01 – 0.04	0.317	0.01	-0.01 – 0.04	0.376	0.01	-0.01 – 0.04	0.344
Sex [Woman]				0.18	-0.01 – 0.36	0.058	0.16	-0.02 – 0.34	0.089
Age				0.02	-0.00 – 0.04	0.057	0.02	0.00 – 0.04	0.050
int student [No]				0.05	-0.25 – 0.35	0.742	-0.10	-0.41 – 0.21	0.539
SES num				0.18	0.12 – 0.25	<0.001	0.16	0.10 – 0.23	<0.001
Ethnicity White							0.12	-0.07 – 0.31	0.232
Ethnicity Hispanic							0.06	-0.22 – 0.34	0.678
Ethnicity Black							-0.76	-1.15 – -0.38	<0.001
Ethnicity East Asian							-0.14	-0.40 – 0.13	0.309
Ethnicity South Asian							-0.42	-0.77 – -0.06	0.022
Ethnicity Native Hawaiian Pacific Islander							-1.53	-2.65 – -0.40	0.008
Ethnicity Middle Eastern							0.15	-0.37 – 0.67	0.566
Ethnicity American Indian							-0.46	-1.53 – 0.61	0.396
Random Effects
σ²	0.25			0.25			0.25
τ₀₀	0.53 _{unique_ID}			0.49 _{unique_ID}			0.45 _{unique_ID}
	0.02 _univ			0.02 _univ			0.01 _univ
ICC	0.68			0.67			0.64
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.005 / 0.686			0.062 / 0.689			0.122 / 0.688

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)	4.53	4.36 – 4.70	<0.001	3.27	2.54 – 3.99	<0.001	3.48	2.76 – 4.20	<0.001
condflourish vs control	0.07	-0.01 – 0.15	0.092	0.06	-0.02 – 0.14	0.169	0.07	-0.01 – 0.14	0.102
time - 2 5	0.02	-0.01 – 0.05	0.120	0.02	-0.01 – 0.05	0.120	0.02	-0.01 – 0.05	0.129
condflourish vs control × time - 2 5	0.02	-0.01 – 0.05	0.135	0.02	-0.01 – 0.05	0.186	0.02	-0.01 – 0.05	0.165
Sex [Woman]				0.20	-0.01 – 0.41	0.066	0.19	-0.02 – 0.40	0.076
Age				0.02	0.00 – 0.04	0.046	0.02	0.00 – 0.04	0.034
int student [No]				0.07	-0.24 – 0.39	0.653	-0.05	-0.38 – 0.29	0.788
SES num				0.18	0.11 – 0.25	<0.001	0.16	0.09 – 0.23	<0.001
Ethnicity White							0.10	-0.12 – 0.31	0.372
Ethnicity Hispanic							0.04	-0.29 – 0.37	0.815
Ethnicity Black							-0.75	-1.19 – -0.32	0.001
Ethnicity East Asian							-0.16	-0.44 – 0.13	0.284
Ethnicity South Asian							-0.30	-0.69 – 0.08	0.118
Ethnicity Native Hawaiian Pacific Islander							-1.54	-2.66 – -0.41	0.007
Ethnicity Middle Eastern							0.21	-0.34 – 0.76	0.448
Ethnicity American Indian							-0.47	-1.53 – 0.60	0.390
Random Effects
σ²	0.26			0.26			0.26
τ₀₀	0.52 _{unique_ID}			0.47 _{unique_ID}			0.44 _{unique_ID}
	0.02 _univ			0.03 _univ			0.01 _univ
ICC	0.68			0.66			0.64
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.008 / 0.679			0.062 / 0.684			0.116 / 0.681

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)	4.51	4.34 – 4.68	<0.001	3.28	2.52 – 4.03	<0.001	3.45	2.70 – 4.20	<0.001
condflourish vs control	0.05	-0.03 – 0.13	0.200	0.05	-0.04 – 0.13	0.271	0.05	-0.03 – 0.14	0.213
time - 2 5	0.03	-0.00 – 0.05	0.071	0.03	-0.00 – 0.06	0.062	0.03	-0.00 – 0.06	0.066
condflourish vs control × time - 2 5	0.03	-0.00 – 0.05	0.072	0.02	-0.00 – 0.05	0.088	0.03	-0.00 – 0.05	0.080
Sex [Woman]				0.19	-0.04 – 0.41	0.099	0.17	-0.05 – 0.38	0.130
Age				0.02	-0.00 – 0.04	0.067	0.02	-0.00 – 0.04	0.059
int student [No]				0.14	-0.21 – 0.49	0.431	0.04	-0.32 – 0.40	0.830
SES num				0.16	0.09 – 0.23	<0.001	0.15	0.07 – 0.22	<0.001
Ethnicity White							0.11	-0.11 – 0.34	0.315
Ethnicity Hispanic							0.17	-0.16 – 0.50	0.321
Ethnicity Black							-0.70	-1.15 – -0.25	0.002
Ethnicity East Asian							-0.08	-0.38 – 0.21	0.582
Ethnicity South Asian							-0.39	-0.79 – 0.02	0.061
Ethnicity Native Hawaiian Pacific Islander							-1.50	-2.62 – -0.38	0.009
Ethnicity Middle Eastern							0.26	-0.45 – 0.98	0.468
Ethnicity American Indian							-0.45	-1.51 – 0.61	0.401
Random Effects
σ²	0.25			0.25			0.26
τ₀₀	0.50 _{unique_ID}			0.47 _{unique_ID}			0.43 _{unique_ID}
	0.02 _univ			0.04 _univ			0.02 _univ
ICC	0.67			0.66			0.64
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.007 / 0.676			0.053 / 0.682			0.111 / 0.678

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)	4.65	4.30 – 5.00	<0.001	4.20	3.23 – 5.17	<0.001	4.15	3.12 – 5.18	<0.001
condflourish vs control	0.04	-0.06 – 0.14	0.399	0.03	-0.08 – 0.13	0.619	0.03	-0.07 – 0.13	0.566
time - 2 5	0.04	-0.01 – 0.09	0.091	0.04	-0.01 – 0.08	0.114	0.04	-0.01 – 0.08	0.113
condflourish vs control × time - 2 5	0.02	-0.03 – 0.06	0.405	0.01	-0.03 – 0.06	0.550	0.01	-0.03 – 0.06	0.562
Sex [Woman]				0.05	-0.20 – 0.31	0.687	0.05	-0.21 – 0.30	0.725
Age				0.01	-0.01 – 0.04	0.330	0.01	-0.02 – 0.04	0.350
int student [No]				-0.40	-0.81 – 0.01	0.058	-0.33	-0.77 – 0.12	0.152
SES num				0.13	0.04 – 0.21	0.005	0.11	0.02 – 0.20	0.019
Ethnicity White							0.09	-0.18 – 0.37	0.506
Ethnicity Hispanic							-0.05	-0.45 – 0.35	0.807
Ethnicity Black							-0.15	-0.70 – 0.40	0.590
Ethnicity East Asian							0.22	-0.15 – 0.59	0.245
Ethnicity South Asian							0.14	-0.36 – 0.65	0.581
Ethnicity Native Hawaiian Pacific Islander							-0.70	-2.32 – 0.91	0.392
Ethnicity Middle Eastern							0.00	-0.74 – 0.74	0.998
Ethnicity American Indian							-0.75	-2.24 – 0.75	0.327
Random Effects
σ²	0.81			0.81			0.81
τ₀₀	0.72 _{unique_ID}			0.71 _{unique_ID}			0.72 _{unique_ID}
	0.11 _univ			0.12 _univ			0.14 _univ
ICC	0.51			0.50			0.51
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.003 / 0.510			0.022 / 0.515			0.029 / 0.528

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)	4.60	4.31 – 4.89	<0.001	4.19	3.19 – 5.19	<0.001	3.98	2.92 – 5.05	<0.001
condflourish vs control	0.04	-0.07 – 0.15	0.472	0.02	-0.09 – 0.13	0.730	0.02	-0.09 – 0.13	0.732
time - 2 5	0.03	-0.02 – 0.08	0.226	0.03	-0.02 – 0.07	0.294	0.02	-0.02 – 0.07	0.305
condflourish vs control × time - 2 5	0.02	-0.03 – 0.06	0.457	0.01	-0.03 – 0.06	0.586	0.01	-0.03 – 0.06	0.564
Sex [Woman]				0.00	-0.29 – 0.30	0.974	-0.01	-0.31 – 0.29	0.960
Age				0.02	-0.01 – 0.05	0.254	0.02	-0.01 – 0.05	0.194
int student [No]				-0.56	-1.00 – -0.11	0.014	-0.46	-0.94 – 0.02	0.061
SES num				0.14	0.04 – 0.24	0.006	0.13	0.02 – 0.23	0.015
Ethnicity White							0.14	-0.17 – 0.45	0.367
Ethnicity Hispanic							0.18	-0.29 – 0.65	0.451
Ethnicity Black							-0.27	-0.90 – 0.36	0.402
Ethnicity East Asian							0.21	-0.21 – 0.62	0.329
Ethnicity South Asian							0.37	-0.18 – 0.91	0.191
Ethnicity Native Hawaiian Pacific Islander							-0.53	-2.16 – 1.10	0.524
Ethnicity Middle Eastern							0.10	-0.69 – 0.89	0.803
Ethnicity American Indian							-0.71	-2.23 – 0.81	0.358
Random Effects
σ²	0.77			0.78			0.78
τ₀₀	0.78 _{unique_ID}			0.76 _{unique_ID}			0.76 _{unique_ID}
	0.07 _univ			0.04 _univ			0.05 _univ
ICC	0.52			0.51			0.51
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.002 / 0.525			0.033 / 0.522			0.043 / 0.533

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)	4.57	4.29 – 4.85	<0.001	4.18	3.14 – 5.21	<0.001	3.93	2.84 – 5.02	<0.001
condflourish vs control	0.02	-0.09 – 0.13	0.718	0.00	-0.11 – 0.12	0.939	-0.00	-0.12 – 0.12	0.993
time - 2 5	0.03	-0.02 – 0.08	0.267	0.02	-0.03 – 0.07	0.366	0.02	-0.03 – 0.07	0.385
condflourish vs control × time - 2 5	0.02	-0.03 – 0.06	0.480	0.01	-0.04 – 0.06	0.622	0.01	-0.04 – 0.06	0.589
Sex [Woman]				-0.06	-0.37 – 0.25	0.716	-0.07	-0.39 – 0.24	0.640
Age				0.02	-0.01 – 0.05	0.234	0.02	-0.01 – 0.05	0.170
int student [No]				-0.48	-0.97 – 0.01	0.056	-0.40	-0.91 – 0.12	0.136
SES num				0.12	0.01 – 0.22	0.026	0.11	0.00 – 0.21	0.046
Ethnicity White							0.17	-0.15 – 0.49	0.301
Ethnicity Hispanic							0.37	-0.12 – 0.85	0.136
Ethnicity Black							-0.24	-0.90 – 0.41	0.465
Ethnicity East Asian							0.28	-0.15 – 0.70	0.199
Ethnicity South Asian							0.35	-0.23 – 0.94	0.234
Ethnicity Native Hawaiian Pacific Islander							-0.47	-2.09 – 1.16	0.574
Ethnicity Middle Eastern							-0.01	-1.05 – 1.02	0.981
Ethnicity American Indian							-0.70	-2.22 – 0.81	0.363
Random Effects
σ²	0.75			0.75			0.75
τ₀₀	0.78 _{unique_ID}			0.77 _{unique_ID}			0.77 _{unique_ID}
	0.06 _univ			0.03 _univ			0.03 _univ
ICC	0.53			0.51			0.52
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.002 / 0.529			0.024 / 0.526			0.039 / 0.535

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')

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	23.67 – 24.33	<0.001	20.74	17.88 – 23.60	<0.001	21.68	18.65 – 24.70	<0.001
condflourish vs control	0.05	-0.28 – 0.38	0.778	-0.03	-0.37 – 0.31	0.861	0.02	-0.32 – 0.37	0.904
time - 2 5	0.17	0.04 – 0.30	0.010	0.19	0.05 – 0.32	0.006	0.19	0.05 – 0.32	0.007
condflourish vs control × time - 2 5	0.07	-0.06 – 0.20	0.321	0.05	-0.09 – 0.18	0.499	0.05	-0.09 – 0.18	0.490
Sex [Woman]				-0.41	-1.27 – 0.44	0.343	-0.40	-1.26 – 0.46	0.363
Age				0.08	-0.01 – 0.17	0.079	0.08	-0.01 – 0.17	0.098
int student [No]				0.54	-0.84 – 1.92	0.442	-0.21	-1.72 – 1.29	0.782
SES num				0.45	0.16 – 0.75	0.003	0.42	0.11 – 0.72	0.007
Ethnicity White							0.41	-0.50 – 1.32	0.374
Ethnicity Hispanic							-0.38	-1.70 – 0.94	0.570
Ethnicity Black							-0.38	-2.19 – 1.44	0.684
Ethnicity East Asian							-0.75	-1.98 – 0.49	0.235
Ethnicity South Asian							-1.35	-3.03 – 0.33	0.115
Ethnicity Native Hawaiian Pacific Islander							-2.54	-7.90 – 2.81	0.351
Ethnicity Middle Eastern							-0.81	-3.27 – 1.66	0.522
Ethnicity American Indian							1.48	-3.57 – 6.53	0.565
Random Effects
σ²	6.86			6.85			6.84
τ₀₀	9.88 _{unique_ID}			9.52 _{unique_ID}			9.48 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.59
N	531 _{unique_ID}			481 _{unique_ID}			481 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	831			781			781
Marginal R² / Conditional R²	0.005 / 0.592			0.062 / NA			0.099 / 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	23.70 – 24.43	<0.001	20.00	16.88 – 23.13	<0.001	20.70	17.38 – 24.03	<0.001
condflourish vs control	0.03	-0.33 – 0.39	0.880	-0.07	-0.45 – 0.30	0.700	-0.04	-0.41 – 0.34	0.854
time - 2 5	0.18	0.04 – 0.32	0.011	0.19	0.05 – 0.33	0.007	0.19	0.05 – 0.33	0.008
condflourish vs control × time - 2 5	0.09	-0.05 – 0.23	0.204	0.07	-0.07 – 0.21	0.310	0.08	-0.06 – 0.22	0.291
Sex [Woman]				-0.05	-1.04 – 0.94	0.919	-0.00	-1.00 – 0.99	0.997
Age				0.08	-0.01 – 0.18	0.086	0.09	-0.01 – 0.18	0.084
int student [No]				0.87	-0.59 – 2.32	0.242	0.23	-1.35 – 1.81	0.776
SES num				0.49	0.16 – 0.82	0.003	0.45	0.11 – 0.79	0.009
Ethnicity White							0.39	-0.64 – 1.42	0.459
Ethnicity Hispanic							-0.22	-1.77 – 1.33	0.783
Ethnicity Black							-1.26	-3.33 – 0.80	0.230
Ethnicity East Asian							-0.62	-1.98 – 0.73	0.368
Ethnicity South Asian							-1.22	-3.02 – 0.59	0.185
Ethnicity Native Hawaiian Pacific Islander							-2.39	-7.75 – 2.97	0.382
Ethnicity Middle Eastern							-0.98	-3.58 – 1.62	0.460
Ethnicity American Indian							1.53	-3.53 – 6.59	0.553
Random Effects
σ²	6.89			6.87			6.87
τ₀₀	9.84 _{unique_ID}			9.42 _{unique_ID}			9.39 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	709			669			669
Marginal R² / Conditional R²	0.013 / NA			0.067 / NA			0.104 / 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)	24.11	23.74 – 24.48	<0.001	19.77	16.54 – 23.01	<0.001	20.45	17.04 – 23.85	<0.001
condflourish vs control	0.07	-0.30 – 0.44	0.704	-0.03	-0.41 – 0.35	0.882	-0.01	-0.40 – 0.38	0.959
time - 2 5	0.19	0.05 – 0.33	0.009	0.21	0.06 – 0.35	0.005	0.20	0.06 – 0.35	0.006
condflourish vs control × time - 2 5	0.10	-0.04 – 0.24	0.169	0.09	-0.06 – 0.23	0.244	0.09	-0.06 – 0.23	0.239
Sex [Woman]				-0.38	-1.39 – 0.63	0.459	-0.34	-1.36 – 0.68	0.513
Age				0.09	-0.01 – 0.19	0.073	0.09	-0.01 – 0.18	0.091
int student [No]				1.32	-0.26 – 2.90	0.101	0.79	-0.90 – 2.47	0.358
SES num				0.50	0.17 – 0.84	0.003	0.46	0.11 – 0.80	0.010
Ethnicity White							0.39	-0.66 – 1.43	0.468
Ethnicity Hispanic							-0.15	-1.71 – 1.41	0.854
Ethnicity Black							-0.75	-2.86 – 1.36	0.488
Ethnicity East Asian							-0.29	-1.67 – 1.08	0.675
Ethnicity South Asian							-1.44	-3.34 – 0.46	0.137
Ethnicity Native Hawaiian Pacific Islander							-2.52	-7.79 – 2.76	0.349
Ethnicity Middle Eastern							-0.56	-3.91 – 2.79	0.743
Ethnicity American Indian							1.40	-3.57 – 6.38	0.580
Random Effects
σ²	6.53			6.50			6.50
τ₀₀	9.65 _{unique_ID}			9.07 _{unique_ID}			9.12 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.60
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.006 / 0.599			0.084 / NA			0.116 / 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)
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)	3.30	3.15 – 3.44	<0.001	2.87	1.88 – 3.86	<0.001	3.08	2.04 – 4.13	<0.001
condflourish vs control	0.06	-0.05 – 0.18	0.274	0.05	-0.07 – 0.16	0.433	0.07	-0.05 – 0.19	0.251
time - 2 5	0.06	0.01 – 0.10	0.010	0.06	0.02 – 0.10	0.008	0.06	0.02 – 0.10	0.008
condflourish vs control × time - 2 5	0.03	-0.02 – 0.07	0.231	0.02	-0.02 – 0.06	0.356	0.02	-0.02 – 0.06	0.361
Sex [Woman]				0.43	0.14 – 0.73	0.004	0.41	0.12 – 0.71	0.006
Age				-0.01	-0.04 – 0.02	0.385	-0.01	-0.05 – 0.02	0.369
int student [No]				0.09	-0.38 – 0.56	0.701	0.04	-0.47 – 0.55	0.885
SES num				0.09	-0.01 – 0.19	0.072	0.08	-0.02 – 0.18	0.134
Ethnicity White							0.01	-0.31 – 0.32	0.970
Ethnicity Hispanic							-0.37	-0.83 – 0.08	0.105
Ethnicity Black							0.06	-0.56 – 0.68	0.851
Ethnicity East Asian							-0.35	-0.78 – 0.07	0.102
Ethnicity South Asian							0.05	-0.53 – 0.63	0.864
Ethnicity Native Hawaiian Pacific Islander							-1.55	-3.38 – 0.28	0.097
Ethnicity Middle Eastern							-0.18	-1.02 – 0.67	0.679
Ethnicity American Indian							-1.78	-3.52 – -0.04	0.045
Random Effects
σ²	0.70			0.69			0.69
τ₀₀	1.34 _{unique_ID}			1.20 _{unique_ID}			1.18 _{unique_ID}
	0.01 _univ			0.00 _univ			0.01 _univ
ICC	0.66			0.63			0.63
N	532 _{unique_ID}			482 _{unique_ID}			482 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	833			782			782
Marginal R² / Conditional R²	0.006 / 0.660			0.028 / 0.644			0.051 / 0.650

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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00231824 (tol = 0.002, component 1)

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)	3.35	3.18 – 3.51	<0.001	3.06	1.96 – 4.16	<0.001	3.44	2.28 – 4.61	<0.001
condflourish vs control	0.09	-0.04 – 0.22	0.187	0.07	-0.06 – 0.20	0.295	0.09	-0.04 – 0.22	0.162
time - 2 5	0.05	0.01 – 0.10	0.021	0.06	0.01 – 0.10	0.013	0.06	0.01 – 0.10	0.012
condflourish vs control × time - 2 5	0.03	-0.01 – 0.07	0.185	0.03	-0.02 – 0.07	0.263	0.02	-0.02 – 0.07	0.276
Sex [Woman]				0.43	0.08 – 0.77	0.016	0.38	0.04 – 0.73	0.029
Age				-0.01	-0.04 – 0.02	0.513	-0.02	-0.05 – 0.02	0.359
int student [No]				0.09	-0.42 – 0.60	0.738	0.00	-0.55 – 0.55	0.992
SES num				0.04	-0.08 – 0.15	0.528	0.03	-0.09 – 0.15	0.621
Ethnicity White							-0.09	-0.45 – 0.27	0.612
Ethnicity Hispanic							-0.40	-0.94 – 0.14	0.149
Ethnicity Black							0.24	-0.48 – 0.96	0.508
Ethnicity East Asian							-0.43	-0.91 – 0.04	0.072
Ethnicity South Asian							-0.03	-0.67 – 0.60	0.917
Ethnicity Native Hawaiian Pacific Islander							-1.75	-3.61 – 0.11	0.066
Ethnicity Middle Eastern							-0.26	-1.16 – 0.65	0.578
Ethnicity American Indian							-1.91	-3.68 – -0.14	0.034
Random Effects
σ²	0.70			0.70			0.69
τ₀₀	1.36 _{unique_ID}			1.24 _{unique_ID}			1.22 _{unique_ID}
	0.01 _univ			0.00 _univ			0.01 _univ
ICC	0.66			0.64			0.64
N	427 _{unique_ID}			387 _{unique_ID}			387 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	710			669			669
Marginal R² / Conditional R²	0.007 / 0.664			0.022 / 0.649			0.049 / 0.656

Excluded Unreasonable Numbers

m0 <- lmer(ios  ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(ios  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_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_unreasonable_factor)
tab_model(m0, m1, m2)

	ios			ios			ios
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.32	3.18 – 3.47	<0.001	3.05	1.91 – 4.18	<0.001	3.44	2.25 – 4.63	<0.001
condflourish vs control	0.06	-0.08 – 0.19	0.406	0.03	-0.10 – 0.17	0.628	0.06	-0.08 – 0.19	0.411
time - 2 5	0.04	-0.00 – 0.09	0.052	0.05	0.00 – 0.09	0.044	0.05	0.00 – 0.09	0.042
condflourish vs control × time - 2 5	0.02	-0.02 – 0.07	0.335	0.02	-0.03 – 0.06	0.505	0.01	-0.03 – 0.06	0.522
Sex [Woman]				0.39	0.03 – 0.74	0.032	0.36	0.00 – 0.71	0.049
Age				-0.01	-0.05 – 0.02	0.468	-0.02	-0.05 – 0.02	0.291
int student [No]				0.16	-0.40 – 0.71	0.582	0.05	-0.54 – 0.63	0.880
SES num				0.03	-0.09 – 0.15	0.634	0.01	-0.11 – 0.13	0.823
Ethnicity White							-0.01	-0.37 – 0.36	0.975
Ethnicity Hispanic							-0.30	-0.84 – 0.25	0.284
Ethnicity Black							0.36	-0.38 – 1.10	0.340
Ethnicity East Asian							-0.33	-0.82 – 0.15	0.177
Ethnicity South Asian							-0.07	-0.73 – 0.60	0.845
Ethnicity Native Hawaiian Pacific Islander							-1.66	-3.50 – 0.18	0.077
Ethnicity Middle Eastern							-0.58	-1.76 – 0.59	0.328
Ethnicity American Indian							-1.84	-3.59 – -0.09	0.040
Random Effects
σ²	0.65			0.64			0.64
τ₀₀	1.38 _{unique_ID}			1.23 _{unique_ID}			1.22 _{unique_ID}
	0.00 _univ			0.00 _univ			0.00 _univ
ICC	0.68			0.66			0.66
N	395 _{unique_ID}			356 _{unique_ID}			356 _{unique_ID}
	4 _univ			3 _univ			3 _univ
Observations	652			613			613
Marginal R² / Conditional R²	0.004 / 0.682			0.017 / 0.664			0.046 / 0.672

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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (147 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   <2e-16 ***
## 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
##   (183 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: 0.3125

# 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   <2e-16 ***
## 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
##   (189 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: 0.103

# Flourish cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "flourish")
## 
## REML criterion at convergence: 2687.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.05734 -0.53530 -0.00015  0.46749  3.02025 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.7764   1.3328  
##  Residual              0.9621   0.9808  
## Number of obs: 787, groups:  unique_ID, 266
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.20260    0.09054 264.58586  57.462  < 2e-16 ***
## I(time - 2.5)  -0.17501    0.03350 580.52828  -5.224 2.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.082

# Control cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 2720.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0768 -0.5370 -0.0060  0.5302  3.2248 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.8100   1.3454  
##  Residual              0.9806   0.9903  
## Number of obs: 792, groups:  unique_ID, 272
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.38558    0.09097 271.87199  59.202   <2e-16 ***
## I(time - 2.5)  -0.08009    0.03399 583.74709  -2.356   0.0188 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.106

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 
## -2.593 -1.566  0.407  1.407  3.434 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.57955    0.08425  66.223   <2e-16 ***
## condflourish_vs_control  0.01341    0.08425   0.159    0.874    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.659 on 386 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  6.566e-05,  Adjusted R-squared:  -0.002525 
## F-statistic: 0.02535 on 1 and 386 DF,  p-value: 0.8736

# 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 
## -2.2581 -1.2581 -0.1087  0.8913  3.8913 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.18338    0.08289  62.535   <2e-16 ***
## condflourish_vs_control -0.07468    0.08289  -0.901    0.368    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.594 on 368 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.002201,   Adjusted R-squared:  -0.0005101 
## F-statistic: 0.8119 on 1 and 368 DF,  p-value: 0.3682

# 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 
## -2.1656 -1.1656 -0.1656  1.0235  4.0235 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.07104    0.09370  54.120   <2e-16 ***
## condflourish_vs_control -0.09457    0.09370  -1.009    0.314    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.693 on 325 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.003124,   Adjusted R-squared:  5.7e-05 
## F-statistic: 1.019 on 1 and 325 DF,  p-value: 0.3136

# 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 
## -2.3571 -1.3571 -0.1071  0.8929  3.8929 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.23214    0.09547  54.803   <2e-16 ***
## condflourish_vs_control -0.12500    0.09547  -1.309    0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.712 on 320 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.005328,   Adjusted R-squared:  0.00222 
## F-statistic: 1.714 on 1 and 320 DF,  p-value: 0.1914

# Flourish cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "flourish")
## 
## REML criterion at convergence: 2452.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.05085 -0.53471  0.00599  0.49068  3.01997 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.8154   1.3474  
##  Residual              0.9636   0.9816  
## Number of obs: 721, groups:  unique_ID, 221
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.18447    0.09837 217.58316  52.706  < 2e-16 ***
## I(time - 2.5)  -0.17932    0.03462 529.11062  -5.179 3.17e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.038

# Control cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "control")
## 
## REML criterion at convergence: 2341.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0771 -0.5364 -0.0036  0.5279  3.2258 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.882    1.3719  
##  Residual              0.976    0.9879  
## Number of obs: 686, groups:  unique_ID, 206
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.35986    0.10348 204.32730  51.796   <2e-16 ***
## I(time - 2.5)  -0.07556    0.03601 507.87381  -2.098   0.0364 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.050

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 
## -2.6527 -1.5661  0.3473  1.3473  3.4339 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.60942    0.08728  64.272   <2e-16 ***
## condflourish_vs_control  0.04328    0.08728   0.496     0.62    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.644 on 354 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.0006941,  Adjusted R-squared:  -0.002129 
## F-statistic: 0.2459 on 1 and 354 DF,  p-value: 0.6203

# 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 
## -2.2581 -1.2581 -0.1474  0.8526  3.8526 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.20275    0.08699  59.806   <2e-16 ***
## condflourish_vs_control -0.05531    0.08699  -0.636    0.525    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.603 on 340 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.001188,   Adjusted R-squared:  -0.00175 
## F-statistic: 0.4043 on 1 and 340 DF,  p-value: 0.5253

# 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 
## -2.1656 -1.1656 -0.1656  0.9789  3.9789 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.09337    0.09800  51.976   <2e-16 ***
## condflourish_vs_control -0.07224    0.09800  -0.737    0.462    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.692 on 297 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.001826,   Adjusted R-squared:  -0.001534 
## F-statistic: 0.5434 on 1 and 297 DF,  p-value: 0.4616

# 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 
## -2.3571 -1.3571 -0.1127  0.8873  3.8873 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.23491    0.09854   53.13   <2e-16 ***
## condflourish_vs_control -0.12223    0.09854   -1.24    0.216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.694 on 294 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.005207,   Adjusted R-squared:  0.001823 
## F-statistic: 1.539 on 1 and 294 DF,  p-value: 0.2158

# Flourish cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "flourish")
## 
## REML criterion at convergence: 2057.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.87343 -0.57068 -0.00884  0.48711  2.85576 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.7629   1.3278  
##  Residual              0.9516   0.9755  
## Number of obs: 607, groups:  unique_ID, 189
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.20821    0.10511 186.07719  49.551  < 2e-16 ***
## I(time - 2.5)  -0.20553    0.03778 446.62458  -5.441 8.76e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.038

# Control cond: over time
lmer(loneliness ~ 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: loneliness ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "control")
## 
## REML criterion at convergence: 2341.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0771 -0.5364 -0.0036  0.5279  3.2258 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.882    1.3719  
##  Residual              0.976    0.9879  
## Number of obs: 686, groups:  unique_ID, 206
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.35986    0.10348 204.32730  51.796   <2e-16 ***
## I(time - 2.5)  -0.07556    0.03601 507.87381  -2.098   0.0364 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.050

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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (147 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   <2e-16 ***
## 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
##   (183 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: 0.04464

# 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   <2e-16 ***
## 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
##   (189 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: 0.002197

# Flourish cond: over time
lmer(SAS_calm ~ 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_calm ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "flourish")
## 
## REML criterion at convergence: 3514.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7764 -0.5803  0.0245  0.5393  3.3834 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.969    1.992   
##  Residual              3.023    1.739   
## Number of obs: 787, groups:  unique_ID, 266
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)     5.9833     0.1400 253.7528  42.730  < 2e-16 ***
## I(time - 2.5)   0.2400     0.0589 586.3649   4.075 5.25e-05 ***
## ---
## 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
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: 3484.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6689 -0.5452  0.0357  0.6021  3.3729 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.551    1.884   
##  Residual              2.873    1.695   
## Number of obs: 792, groups:  unique_ID, 272
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.49569    0.13294 266.98158  41.341   <2e-16 ***
## I(time - 2.5)   0.01621    0.05758 598.97538   0.282    0.778    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.117

Excluded 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 
## -5.7236 -1.7236  0.2764  1.5767  6.5767 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5734     0.1279  43.562   <2e-16 ***
## condflourish_vs_control   0.1502     0.1279   1.174    0.241    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.519 on 386 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.003556,   Adjusted R-squared:  0.0009749 
## F-statistic: 1.378 on 1 and 386 DF,  p-value: 0.2412

# 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  1.6559  6.6559 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5335     0.1333  41.516   <2e-16 ***
## condflourish_vs_control   0.1894     0.1333   1.421    0.156    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.564 on 368 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.005455,   Adjusted R-squared:  0.002753 
## F-statistic: 2.019 on 1 and 368 DF,  p-value: 0.1562

# 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 
## -6.0588 -2.0588 -0.0588  1.9412  6.4650 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.7969     0.1454  39.876   <2e-16 ***
## condflourish_vs_control   0.2619     0.1454   1.802   0.0725 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.627 on 325 degrees of freedom
##   (49 observations deleted due to missingness)
## Multiple R-squared:  0.009887,   Adjusted R-squared:  0.006841 
## F-statistic: 3.245 on 1 and 325 DF,  p-value: 0.07255

# 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 
## -6.381 -1.474 -0.381  1.619  6.526 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.9275     0.1390  42.634  < 2e-16 ***
## condflourish_vs_control   0.4535     0.1390   3.262  0.00123 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.492 on 320 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.03217,    Adjusted R-squared:  0.02915 
## F-statistic: 10.64 on 1 and 320 DF,  p-value: 0.001228

# Flourish cond: over time
lmer(SAS_calm ~ 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_calm ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "flourish")
## 
## REML criterion at convergence: 3202.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8055 -0.5973  0.0167  0.5401  3.3793 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.856    1.964   
##  Residual              3.013    1.736   
## Number of obs: 721, groups:  unique_ID, 221
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.99353    0.14828 212.35236  40.421  < 2e-16 ***
## I(time - 2.5)   0.27111    0.06087 534.57474   4.454 1.03e-05 ***
## ---
## 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
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: 2994.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7172 -0.5443  0.0360  0.6177  3.4125 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.478    1.865   
##  Residual              2.833    1.683   
## Number of obs: 686, groups:  unique_ID, 206
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.44722    0.14633 205.69868  37.227   <2e-16 ***
## I(time - 2.5)   0.05510    0.06096 520.10105   0.904    0.366    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.058

SAS: 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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (147 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   <2e-16 ***
## 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
##   (184 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: 0.03486

# 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  < 2e-16 ***
## 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
##   (189 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: 0.004551

# 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: 3386.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9324 -0.5354  0.0444  0.5658  3.6691 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.662    1.914   
##  Residual              2.514    1.586   
## Number of obs: 786, groups:  unique_ID, 266
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     7.10952    0.13300 250.68612  53.454   <2e-16 ***
## I(time - 2.5)   0.04003    0.05386 577.68682   0.743    0.458    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.088

# Control cond: over time
lmer(SAS_well_being ~ 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_well_being ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 3422.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2409 -0.5655  0.0079  0.5119  2.8167 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.275    2.068   
##  Residual              2.413    1.553   
## Number of obs: 792, groups:  unique_ID, 272
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     6.71164    0.14034 264.42162  47.823  < 2e-16 ***
## I(time - 2.5)  -0.14802    0.05327 579.16987  -2.779  0.00563 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.108

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 
## -7.2965 -1.4710  0.0053  1.7035  5.0053 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.1456     0.1245  57.387   <2e-16 ***
## condflourish_vs_control   0.1509     0.1245   1.212    0.226    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.452 on 386 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.00379,    Adjusted R-squared:  0.001209 
## F-statistic: 1.468 on 1 and 386 DF,  p-value: 0.2263

# 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 
## -6.6667 -1.6667  0.1793  2.1793  5.3333 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              6.74366    0.12485  54.013   <2e-16 ***
## condflourish_vs_control  0.07699    0.12485   0.617    0.538    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.402 on 368 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.001032,   Adjusted R-squared:  -0.001682 
## F-statistic: 0.3803 on 1 and 368 DF,  p-value: 0.5378

# 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 
## -7.1657 -1.6561 -0.1657  1.8343  5.3439 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.9109     0.1405  49.181   <2e-16 ***
## condflourish_vs_control   0.2548     0.1405   1.813   0.0707 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.535 on 324 degrees of freedom
##   (50 observations deleted due to missingness)
## Multiple R-squared:  0.01005,    Adjusted R-squared:  0.006992 
## F-statistic: 3.288 on 1 and 324 DF,  p-value: 0.0707

# 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 
## -7.214 -1.539  0.461  1.786  5.461 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.8766     0.1370  50.191   <2e-16 ***
## condflourish_vs_control   0.3377     0.1370   2.465   0.0142 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.456 on 320 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.01863,    Adjusted R-squared:  0.01556 
## F-statistic: 6.074 on 1 and 320 DF,  p-value: 0.01424

# 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: 3074.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9586 -0.5359  0.0503  0.5721  3.7107 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.572    1.890   
##  Residual              2.459    1.568   
## Number of obs: 720, groups:  unique_ID, 221
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     7.13215    0.14099 211.86024  50.586   <2e-16 ***
## I(time - 2.5)   0.03080    0.05511 529.48934   0.559    0.576    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.041

# Control cond: over time
lmer(SAS_well_being ~ 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_well_being ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "control")
## 
## REML criterion at convergence: 2934.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.72309 -0.60277  0.00424  0.52802  2.85555 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.108    2.027   
##  Residual              2.390    1.546   
## Number of obs: 686, groups:  unique_ID, 206
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     6.69361    0.15423 201.80462  43.401   <2e-16 ***
## I(time - 2.5)  -0.12510    0.05627 508.20397  -2.223   0.0266 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.052

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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (147 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: 0.09625

# 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   <2e-16 ***
## 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
##   (184 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: 0.02812

# 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  < 2e-16 ***
## 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
##   (190 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: 0.002317

# 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: 4910.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9742 -0.5378  0.0034  0.5281  4.3244 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 29.44    5.426   
##  Residual              16.84    4.104   
## Number of obs: 785, groups:  unique_ID, 266
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    18.9661     0.3705 255.3271  51.188   <2e-16 ***
## I(time - 2.5)   0.2648     0.1403 572.7023   1.888   0.0595 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.085

# Control cond: over time
lmer(SAS_positive ~ 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_positive ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 4936.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.04532 -0.54391 -0.01418  0.51250  2.77743 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 33.76    5.810   
##  Residual              15.46    3.932   
## Number of obs: 792, groups:  unique_ID, 272
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    17.8048     0.3872 265.7032  45.983   <2e-16 ***
## I(time - 2.5)  -0.2335     0.1355 570.9649  -1.723   0.0854 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.102

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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (190 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: 2621
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5752 -0.3694  0.1053  0.3985  1.8574 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 32.69    5.717   
##  Residual              10.92    3.304   
## Number of obs: 415, groups:  unique_ID, 265
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.7874     0.3921 261.5572 114.225   <2e-16 ***
## I(time - 2.5)   0.1181     0.1221 168.6677   0.968    0.335    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.099

# Control cond: over time
lmer(flourishing ~ 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: flourishing ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 2651.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8641 -0.4033  0.0545  0.4560  3.1404 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 32.39    5.691   
##  Residual              12.02    3.467   
## Number of obs: 417, groups:  unique_ID, 267
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.5152     0.3941 267.2960 112.941   <2e-16 ***
## I(time - 2.5)  -0.2376     0.1279 171.4431  -1.857    0.065 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.126

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 
## -22.419  -3.562   1.011   4.011  11.581 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              44.7043     0.3345 133.637   <2e-16 ***
## condflourish_vs_control  -0.2851     0.3345  -0.852    0.395    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.3 on 354 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.002048,   Adjusted R-squared:  -0.000771 
## F-statistic: 0.7265 on 1 and 354 DF,  p-value: 0.3946

# 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 
## -21.3766  -4.0986   0.9014   3.9014  11.6234 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              44.7376     0.3838 116.560   <2e-16 ***
## condflourish_vs_control   0.3610     0.3838   0.941    0.348    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.598 on 294 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.003,  Adjusted R-squared:  -0.0003914 
## F-statistic: 0.8846 on 1 and 294 DF,  p-value: 0.3477

# Flourish cond: over time
lmer(flourishing ~ 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: flourishing ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "flourish")
## 
## REML criterion at convergence: 1923.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.60598 -0.33082  0.07326  0.38895  1.85776 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 30.45    5.518   
##  Residual              10.25    3.201   
## Number of obs: 309, groups:  unique_ID, 189
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.8488     0.4448 183.4261 100.819   <2e-16 ***
## I(time - 2.5)   0.2405     0.1332 130.3332   1.805   0.0733 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.054

# 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: 2168.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8280 -0.4071  0.0407  0.4660  3.0705 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 31.03    5.570   
##  Residual              12.40    3.521   
## Number of obs: 343, groups:  unique_ID, 206
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.5099     0.4368 205.1264 101.903   <2e-16 ***
## I(time - 2.5)  -0.2375     0.1373 153.1745  -1.729   0.0858 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.072

Cohesion

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   <2e-16 ***
## 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
##   (54 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   <2e-16 ***
## 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
##   (189 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: 0.06143

# Flourish cond: over time
lmer(cohesion ~ 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: cohesion ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "flourish")
## 
## REML criterion at convergence: 1723.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.28366 -0.44007  0.07678  0.45598  1.95476 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.849    1.962   
##  Residual              1.177    1.085   
## Number of obs: 416, groups:  unique_ID, 265
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.84528    0.13339 258.33398  43.821  < 2e-16 ***
## I(time - 2.5)   0.13400    0.04007 164.74189   3.344  0.00102 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.095

# 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: 1664.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.60031 -0.43042  0.05761  0.43676  2.80242 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.8078   1.9514  
##  Residual              0.8495   0.9217  
## Number of obs: 417, groups:  unique_ID, 267
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.572e+00  1.292e-01 2.666e+02  43.115   <2e-16 ***
## I(time - 2.5) 6.757e-03  3.447e-02 1.627e+02   0.196    0.845    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.106

Excluded 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 
## -5.5988 -1.5132  0.4012  1.4868  4.4868 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.55601    0.11628  47.783   <2e-16 ***
## condflourish_vs_control  0.04279    0.11628   0.368    0.713    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.19 on 354 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.0003824,  Adjusted R-squared:  -0.002441 
## F-statistic: 0.1354 on 1 and 354 DF,  p-value: 0.7131

# 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 
## -6.0915 -1.6234  0.3766  1.3766  4.3766 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.8575     0.1293  45.310   <2e-16 ***
## condflourish_vs_control   0.2341     0.1293   1.811   0.0712 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.222 on 294 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.01103,    Adjusted R-squared:  0.007666 
## F-statistic: 3.279 on 1 and 294 DF,  p-value: 0.0712

# Flourish cond: over time
lmer(cohesion ~ 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: cohesion ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "flourish")
## 
## REML criterion at convergence: 1278.8
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.22957 -0.42263  0.03401  0.43643  2.04829 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.261    2.064   
##  Residual              1.090    1.044   
## Number of obs: 309, groups:  unique_ID, 189
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.83446    0.16272 182.95006  35.856  < 2e-16 ***
## I(time - 2.5)   0.15548    0.04372 126.70724   3.556  0.00053 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.050

# 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: 1368.2
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.58463 -0.43091  0.05394  0.46814  2.79786 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.0528   2.0131  
##  Residual              0.8623   0.9286  
## Number of obs: 343, groups:  unique_ID, 206
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   5.470e+00  1.500e-01 2.015e+02  36.467   <2e-16 ***
## I(time - 2.5) 5.579e-04  3.666e-02 1.426e+02   0.015    0.988    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.059

Mindfulness

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)             20.31880    0.27130  74.895   <2e-16 ***
## 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
##   (54 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)              19.3656     0.3243  59.713   <2e-16 ***
## 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
##   (189 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: 0.05334

# 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: 2645.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4398 -0.5280 -0.0226  0.5034  1.9370 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 20.67    4.546   
##  Residual              17.66    4.203   
## Number of obs: 416, groups:  unique_ID, 265
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.0110     0.3542 247.3046  56.494   <2e-16 ***
## I(time - 2.5)  -0.1301     0.1499 177.8006  -0.868    0.387    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.123

# Control cond: over time
lmer(mindfulness_rev ~ 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: mindfulness_rev ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 2585.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.32883 -0.44359 -0.02184  0.48374  2.18111 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 22.49    4.743   
##  Residual              12.44    3.527   
## Number of obs: 417, groups:  unique_ID, 267
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.6732     0.3449 268.8923  57.034  < 2e-16 ***
## I(time - 2.5)  -0.5319     0.1285 181.9746  -4.139 5.32e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.140

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 
## -15.6032  -3.6032  -0.6032   3.6080  14.6080 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.4976     0.2985  68.677   <2e-16 ***
## condflourish_vs_control  -0.1056     0.2985  -0.354    0.724    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.877 on 386 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.0003242,  Adjusted R-squared:  -0.002266 
## F-statistic: 0.1252 on 1 and 386 DF,  p-value: 0.7237

# 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 
## -15.0893  -3.7013  -0.0893   4.2987  14.9107 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              19.3953     0.3362  57.687   <2e-16 ***
## condflourish_vs_control   0.6940     0.3362   2.064   0.0398 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.028 on 320 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.01314,    Adjusted R-squared:  0.01006 
## F-statistic: 4.261 on 1 and 320 DF,  p-value: 0.03981

# 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: 2323.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.47031 -0.50906 -0.04136  0.51762  1.92402 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 19.73    4.442   
##  Residual              17.68    4.204   
## Number of obs: 367, groups:  unique_ID, 221
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.1854     0.3758 205.7977  53.706   <2e-16 ***
## I(time - 2.5)  -0.1217     0.1553 163.9435  -0.783    0.435    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.072

# 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: 2107.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36368 -0.44908 -0.02073  0.51386  2.05350 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 22.81    4.776   
##  Residual              11.57    3.401   
## Number of obs: 343, groups:  unique_ID, 206
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.7284     0.3847 202.2959  51.283  < 2e-16 ***
## I(time - 2.5)  -0.6118     0.1319 153.6137  -4.638 7.48e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.077

Excluded 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 
## -15.6032  -3.6032  -0.6032   3.3968  14.6826 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.4603     0.3066  66.739   <2e-16 ***
## condflourish_vs_control  -0.1429     0.3066  -0.466    0.641    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.773 on 354 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.0006134,  Adjusted R-squared:  -0.00221 
## F-statistic: 0.2173 on 1 and 354 DF,  p-value: 0.6414

# 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 
## -14.937  -3.701  -0.319   4.299  14.299 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              19.3190     0.3506  55.110   <2e-16 ***
## condflourish_vs_control   0.6177     0.3506   1.762   0.0791 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.026 on 294 degrees of freedom
##   (52 observations deleted due to missingness)
## Multiple R-squared:  0.01045,    Adjusted R-squared:  0.007083 
## F-statistic: 3.104 on 1 and 294 DF,  p-value: 0.07912

# 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: 1948
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.48273 -0.50656 -0.04624  0.48176  1.77908 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 18.93    4.351   
##  Residual              17.33    4.163   
## Number of obs: 309, groups:  unique_ID, 189
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.0721     0.4009 174.4634  50.064   <2e-16 ***
## I(time - 2.5)  -0.1519     0.1684 136.6501  -0.902    0.369    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.069

# 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: 2107.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.36368 -0.44908 -0.02073  0.51386  2.05350 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 22.81    4.776   
##  Residual              11.57    3.401   
## Number of obs: 343, groups:  unique_ID, 206
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.7284     0.3847 202.2959  51.283  < 2e-16 ***
## I(time - 2.5)  -0.6118     0.1319 153.6137  -4.638 7.48e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.077

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)	-0.22	-0.65 – 0.21	0.318	-0.73	-3.23 – 1.77	0.563	-0.70	-3.47 – 2.07	0.618
ActiveDays	-0.00	-0.02 – 0.01	0.894	-0.00	-0.02 – 0.02	0.991	-0.00	-0.02 – 0.02	0.932
Reports	-0.01	-0.05 – 0.03	0.785	-0.01	-0.05 – 0.04	0.807	-0.01	-0.06 – 0.04	0.597
Activities	0.01	-0.01 – 0.03	0.284	0.01	-0.01 – 0.03	0.272	0.01	-0.01 – 0.03	0.219
univ [UW]				0.19	-0.31 – 0.69	0.456	0.22	-0.33 – 0.78	0.425
Sex [Woman]				0.02	-0.62 – 0.67	0.943	0.01	-0.65 – 0.68	0.965
Age				-0.02	-0.11 – 0.08	0.741	-0.01	-0.11 – 0.09	0.871
int student [No]				0.42	-0.69 – 1.52	0.457	0.43	-0.82 – 1.67	0.497
SES num				0.08	-0.14 – 0.29	0.472	0.05	-0.18 – 0.28	0.650
Ethnicity White							-0.17	-0.87 – 0.53	0.635
Ethnicity Hispanic							0.03	-1.02 – 1.08	0.960
Ethnicity Black							-0.72	-2.23 – 0.79	0.347
Ethnicity East Asian							0.03	-0.89 – 0.94	0.956
Ethnicity South Asian							-0.23	-1.47 – 1.01	0.718
Ethnicity Native Hawaiian Pacific Islander							-1.89	-4.89 – 1.10	0.213
Ethnicity Middle Eastern							0.87	-0.83 – 2.56	0.315
Ethnicity American Indian							0.29	-3.21 – 3.79	0.869
Observations	147			146			146
R² / R² adjusted	0.009 / -0.012			0.020 / -0.037			0.052 / -0.066

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)	-0.22	-0.66 – 0.21	0.314	-0.73	-3.24 – 1.78	0.564	-0.70	-3.48 – 2.08	0.618
ActiveDays	-0.00	-0.02 – 0.01	0.897	-0.00	-0.02 – 0.02	0.992	-0.00	-0.02 – 0.02	0.934
Reports	-0.01	-0.05 – 0.03	0.788	-0.01	-0.05 – 0.04	0.809	-0.01	-0.06 – 0.04	0.601
Activities	0.01	-0.01 – 0.03	0.281	0.01	-0.01 – 0.03	0.268	0.01	-0.01 – 0.03	0.216
univ [UW]				0.19	-0.32 – 0.69	0.469	0.22	-0.34 – 0.78	0.442
Sex [Woman]				0.02	-0.63 – 0.67	0.949	0.01	-0.65 – 0.68	0.970
Age				-0.02	-0.11 – 0.08	0.735	-0.01	-0.11 – 0.09	0.864
int student [No]				0.42	-0.69 – 1.53	0.458	0.43	-0.82 – 1.68	0.495
SES num				0.08	-0.14 – 0.30	0.464	0.06	-0.18 – 0.29	0.639
Ethnicity White							-0.17	-0.88 – 0.53	0.625
Ethnicity Hispanic							0.03	-1.03 – 1.08	0.961
Ethnicity Black							-0.72	-2.23 – 0.79	0.349
Ethnicity East Asian							0.03	-0.89 – 0.95	0.955
Ethnicity South Asian							-0.23	-1.47 – 1.02	0.719
Ethnicity Native Hawaiian Pacific Islander							-1.89	-4.90 – 1.12	0.215
Ethnicity Middle Eastern							0.86	-0.85 – 2.56	0.323
Ethnicity American Indian							0.28	-3.23 – 3.79	0.875
Observations	146			145			145
R² / R² adjusted	0.009 / -0.012			0.020 / -0.038			0.052 / -0.066

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)	0.05	-0.46 – 0.55	0.853	-1.10	-4.04 – 1.84	0.461	-1.65	-4.89 – 1.59	0.316
ActiveDays	-0.01	-0.04 – 0.02	0.455	-0.01	-0.04 – 0.02	0.607	-0.01	-0.04 – 0.02	0.495
Reports	-0.02	-0.07 – 0.04	0.538	-0.02	-0.07 – 0.04	0.562	-0.04	-0.11 – 0.03	0.286
Activities	0.01	-0.01 – 0.02	0.593	0.00	-0.02 – 0.03	0.711	0.01	-0.02 – 0.03	0.552
univ [UW]				0.32	-0.24 – 0.89	0.263	0.38	-0.24 – 1.00	0.230
Sex [Woman]				0.00	-0.71 – 0.71	0.996	-0.10	-0.82 – 0.61	0.780
Age				-0.02	-0.13 – 0.10	0.765	0.02	-0.10 – 0.14	0.787
int student [No]				1.37	-0.13 – 2.87	0.074	1.46	-0.17 – 3.08	0.078
SES num				-0.01	-0.25 – 0.23	0.926	0.01	-0.23 – 0.26	0.908
Ethnicity White							-0.17	-0.92 – 0.57	0.644
Ethnicity Hispanic							0.31	-0.82 – 1.45	0.582
Ethnicity Black							-1.69	-3.43 – 0.04	0.056
Ethnicity East Asian							-0.05	-1.01 – 0.92	0.922
Ethnicity South Asian							-0.37	-1.82 – 1.08	0.611
Ethnicity Native Hawaiian Pacific Islander							-2.09	-5.08 – 0.90	0.169
Ethnicity Middle Eastern							-0.08	-3.05 – 2.89	0.959
Ethnicity American Indian							1.97	-1.92 – 5.86	0.317
Observations	120			120			120
R² / R² adjusted	0.017 / -0.008			0.051 / -0.017			0.120 / -0.016

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)	-0.39	-0.86 – 0.09	0.107	1.22	-1.48 – 3.93	0.373	1.37	-1.64 – 4.37	0.370
ActiveDays	0.01	-0.00 – 0.03	0.102	0.01	-0.00 – 0.03	0.109	0.01	-0.00 – 0.03	0.102
Reports	-0.01	-0.05 – 0.03	0.629	-0.01	-0.06 – 0.03	0.520	-0.02	-0.07 – 0.03	0.482
Activities	-0.00	-0.02 – 0.01	0.683	-0.00	-0.02 – 0.02	0.974	-0.00	-0.02 – 0.02	0.908
univ [UW]				0.25	-0.30 – 0.79	0.371	0.09	-0.51 – 0.70	0.756
Sex [Woman]				-0.11	-0.81 – 0.59	0.749	-0.13	-0.85 – 0.59	0.721
Age				-0.09	-0.19 – 0.01	0.091	-0.10	-0.21 – 0.01	0.071
int student [No]				0.11	-1.09 – 1.30	0.861	0.36	-0.99 – 1.72	0.594
SES num				-0.02	-0.26 – 0.21	0.845	-0.00	-0.25 – 0.25	0.986
Ethnicity White							-0.38	-1.14 – 0.38	0.322
Ethnicity Hispanic							-0.03	-1.17 – 1.11	0.956
Ethnicity Black							0.64	-0.99 – 2.28	0.439
Ethnicity East Asian							0.19	-0.81 – 1.18	0.708
Ethnicity South Asian							0.18	-1.17 – 1.52	0.795
Ethnicity Native Hawaiian Pacific Islander							0.28	-2.98 – 3.53	0.866
Ethnicity Middle Eastern							-0.19	-2.04 – 1.65	0.835
Ethnicity American Indian							0.34	-3.46 – 4.14	0.860
Observations	147			146			146
R² / R² adjusted	0.020 / -0.000			0.046 / -0.010			0.072 / -0.043

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)	-0.42	-0.90 – 0.06	0.084	1.22	-1.49 – 3.93	0.376	1.35	-1.65 – 4.35	0.375
ActiveDays	0.01	-0.00 – 0.03	0.098	0.01	-0.00 – 0.03	0.109	0.01	-0.00 – 0.03	0.100
Reports	-0.01	-0.05 – 0.03	0.645	-0.01	-0.06 – 0.03	0.525	-0.02	-0.07 – 0.04	0.497
Activities	-0.00	-0.02 – 0.02	0.742	0.00	-0.02 – 0.02	0.943	0.00	-0.02 – 0.02	0.986
univ [UW]				0.22	-0.32 – 0.77	0.418	0.06	-0.55 – 0.66	0.855
Sex [Woman]				-0.13	-0.82 – 0.57	0.724	-0.14	-0.86 – 0.57	0.691
Age				-0.09	-0.19 – 0.01	0.084	-0.10	-0.21 – 0.01	0.063
int student [No]				0.11	-1.09 – 1.31	0.859	0.39	-0.96 – 1.74	0.567
SES num				-0.01	-0.25 – 0.22	0.915	0.01	-0.24 – 0.27	0.914
Ethnicity White							-0.42	-1.18 – 0.34	0.275
Ethnicity Hispanic							-0.04	-1.18 – 1.10	0.948
Ethnicity Black							0.64	-0.99 – 2.28	0.440
Ethnicity East Asian							0.19	-0.80 – 1.19	0.701
Ethnicity South Asian							0.18	-1.17 – 1.52	0.796
Ethnicity Native Hawaiian Pacific Islander							0.28	-2.97 – 3.53	0.864
Ethnicity Middle Eastern							-0.26	-2.10 – 1.59	0.784
Ethnicity American Indian							0.26	-3.54 – 4.06	0.892
Observations	146			145			145
R² / R² adjusted	0.022 / 0.001			0.047 / -0.009			0.077 / -0.039

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)	-0.14	-0.69 – 0.42	0.626	0.70	-2.48 – 3.89	0.663	0.87	-2.72 – 4.45	0.632
ActiveDays	-0.01	-0.04 – 0.03	0.714	-0.01	-0.04 – 0.03	0.678	-0.01	-0.04 – 0.03	0.656
Reports	-0.00	-0.06 – 0.05	0.876	-0.01	-0.06 – 0.05	0.859	-0.03	-0.10 – 0.05	0.507
Activities	-0.01	-0.03 – 0.02	0.583	-0.00	-0.03 – 0.02	0.860	-0.00	-0.03 – 0.02	0.849
univ [UW]				0.25	-0.36 – 0.87	0.415	0.15	-0.53 – 0.84	0.664
Sex [Woman]				-0.35	-1.12 – 0.41	0.362	-0.41	-1.20 – 0.38	0.310
Age				-0.09	-0.21 – 0.03	0.146	-0.09	-0.23 – 0.04	0.165
int student [No]				1.27	-0.36 – 2.90	0.125	1.53	-0.26 – 3.33	0.093
SES num				-0.05	-0.31 – 0.21	0.683	-0.02	-0.30 – 0.25	0.868
Ethnicity White							-0.46	-1.28 – 0.36	0.271
Ethnicity Hispanic							0.01	-1.24 – 1.26	0.985
Ethnicity Black							0.06	-1.86 – 1.98	0.950
Ethnicity East Asian							0.07	-0.99 – 1.14	0.890
Ethnicity South Asian							-0.64	-2.24 – 0.96	0.432
Ethnicity Native Hawaiian Pacific Islander							-0.05	-3.35 – 3.25	0.976
Ethnicity Middle Eastern							-1.05	-4.33 – 2.23	0.527
Ethnicity American Indian							1.75	-2.55 – 6.05	0.421
Observations	120			120			120
R² / R² adjusted	0.010 / -0.016			0.057 / -0.011			0.091 / -0.050

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)	-0.31	-0.74 – 0.11	0.145	-1.27	-3.70 – 1.16	0.304	-2.25	-4.91 – 0.41	0.097
ActiveDays	0.01	0.00 – 0.03	0.043	0.02	0.00 – 0.03	0.014	0.02	0.00 – 0.03	0.014
Reports	-0.03	-0.07 – 0.01	0.184	-0.02	-0.06 – 0.02	0.230	-0.01	-0.05 – 0.04	0.814
Activities	-0.02	-0.03 – 0.00	0.072	-0.01	-0.03 – 0.00	0.117	-0.01	-0.03 – 0.01	0.208
univ [UW]				0.23	-0.26 – 0.71	0.362	0.17	-0.37 – 0.70	0.534
Sex [Woman]				0.12	-0.50 – 0.75	0.696	0.19	-0.45 – 0.82	0.563
Age				-0.02	-0.11 – 0.07	0.650	-0.00	-0.10 – 0.09	0.954
int student [No]				0.96	-0.11 – 2.04	0.079	1.43	0.23 – 2.63	0.020
SES num				0.04	-0.17 – 0.25	0.723	0.02	-0.20 – 0.24	0.872
Ethnicity White							-0.05	-0.72 – 0.62	0.890
Ethnicity Hispanic							0.21	-0.80 – 1.22	0.681
Ethnicity Black							-0.66	-2.11 – 0.79	0.371
Ethnicity East Asian							0.59	-0.30 – 1.47	0.190
Ethnicity South Asian							-0.03	-1.22 – 1.17	0.965
Ethnicity Native Hawaiian Pacific Islander							-0.21	-3.09 – 2.68	0.887
Ethnicity Middle Eastern							-0.32	-1.95 – 1.32	0.703
Ethnicity American Indian							-2.75	-6.12 – 0.62	0.108
Observations	147			146			146
R² / R² adjusted	0.040 / 0.020			0.065 / 0.011			0.112 / 0.002

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)	-0.32	-0.75 – 0.11	0.142	-1.27	-3.71 – 1.17	0.306	-2.25	-4.93 – 0.42	0.098
ActiveDays	0.01	0.00 – 0.03	0.043	0.02	0.00 – 0.03	0.015	0.02	0.00 – 0.03	0.014
Reports	-0.03	-0.07 – 0.01	0.187	-0.02	-0.06 – 0.02	0.232	-0.01	-0.05 – 0.04	0.821
Activities	-0.02	-0.03 – 0.00	0.077	-0.01	-0.03 – 0.00	0.127	-0.01	-0.03 – 0.01	0.231
univ [UW]				0.22	-0.27 – 0.71	0.376	0.16	-0.38 – 0.70	0.567
Sex [Woman]				0.12	-0.51 – 0.75	0.703	0.18	-0.46 – 0.82	0.575
Age				-0.02	-0.11 – 0.07	0.644	-0.00	-0.10 – 0.09	0.938
int student [No]				0.96	-0.12 – 2.04	0.080	1.44	0.23 – 2.64	0.020
SES num				0.04	-0.17 – 0.25	0.709	0.02	-0.20 – 0.25	0.838
Ethnicity White							-0.06	-0.74 – 0.62	0.861
Ethnicity Hispanic							0.21	-0.81 – 1.22	0.685
Ethnicity Black							-0.66	-2.12 – 0.80	0.372
Ethnicity East Asian							0.59	-0.30 – 1.47	0.191
Ethnicity South Asian							-0.03	-1.22 – 1.17	0.964
Ethnicity Native Hawaiian Pacific Islander							-0.21	-3.10 – 2.69	0.888
Ethnicity Middle Eastern							-0.33	-1.98 – 1.31	0.688
Ethnicity American Indian							-2.78	-6.16 – 0.60	0.107
Observations	146			145			145
R² / R² adjusted	0.039 / 0.019			0.065 / 0.010			0.113 / 0.002

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)	-0.24	-0.74 – 0.26	0.352	-0.88	-3.82 – 2.07	0.556	-2.08	-5.29 – 1.13	0.202
ActiveDays	0.00	-0.03 – 0.03	0.982	0.00	-0.03 – 0.03	0.898	0.00	-0.03 – 0.03	0.946
Reports	-0.01	-0.06 – 0.05	0.774	-0.01	-0.06 – 0.05	0.751	0.02	-0.05 – 0.09	0.585
Activities	-0.01	-0.03 – 0.00	0.141	-0.01	-0.04 – 0.01	0.217	-0.01	-0.03 – 0.01	0.366
univ [UW]				0.09	-0.47 – 0.66	0.746	-0.02	-0.63 – 0.59	0.948
Sex [Woman]				0.12	-0.58 – 0.83	0.728	0.15	-0.55 – 0.86	0.666
Age				-0.01	-0.12 – 0.10	0.839	0.03	-0.09 – 0.15	0.588
int student [No]				0.71	-0.79 – 2.22	0.351	1.09	-0.52 – 2.69	0.182
SES num				0.00	-0.24 – 0.24	1.000	-0.00	-0.25 – 0.24	0.995
Ethnicity White							-0.31	-1.04 – 0.43	0.413
Ethnicity Hispanic							-0.16	-1.28 – 0.96	0.779
Ethnicity Black							-1.61	-3.33 – 0.11	0.067
Ethnicity East Asian							0.24	-0.72 – 1.19	0.625
Ethnicity South Asian							0.12	-1.32 – 1.55	0.871
Ethnicity Native Hawaiian Pacific Islander							-0.63	-3.59 – 2.33	0.674
Ethnicity Middle Eastern							-2.67	-5.61 – 0.27	0.074
Ethnicity American Indian							-3.18	-7.03 – 0.67	0.104
Observations	120			120			120
R² / R² adjusted	0.027 / 0.002			0.036 / -0.034			0.127 / -0.009

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)	-0.45	-1.25 – 0.35	0.266	-3.31	-7.87 – 1.25	0.154	-2.87	-7.99 – 2.24	0.268
ActiveDays	0.02	-0.01 – 0.05	0.164	0.02	-0.01 – 0.05	0.116	0.02	-0.01 – 0.05	0.115
Reports	0.01	-0.07 – 0.08	0.834	0.02	-0.05 – 0.10	0.537	0.01	-0.08 – 0.11	0.752
Activities	-0.02	-0.05 – 0.01	0.205	-0.03	-0.07 – 0.00	0.074	-0.03	-0.07 – 0.00	0.081
univ [UW]				0.66	-0.26 – 1.57	0.158	0.80	-0.23 – 1.83	0.125
Sex [Woman]				-0.22	-1.39 – 0.96	0.718	-0.25	-1.47 – 0.98	0.690
Age				0.10	-0.07 – 0.28	0.237	0.10	-0.08 – 0.28	0.284
int student [No]				0.62	-1.39 – 2.64	0.542	0.32	-1.98 – 2.62	0.785
SES num				0.04	-0.35 – 0.43	0.847	0.06	-0.36 – 0.49	0.769
Ethnicity White							-0.17	-1.45 – 1.12	0.799
Ethnicity Hispanic							0.12	-1.82 – 2.06	0.899
Ethnicity Black							-0.37	-3.16 – 2.41	0.792
Ethnicity East Asian							-0.36	-2.05 – 1.34	0.678
Ethnicity South Asian							-0.81	-3.10 – 1.48	0.486
Ethnicity Native Hawaiian Pacific Islander							0.24	-5.30 – 5.78	0.932
Ethnicity Middle Eastern							0.23	-2.90 – 3.37	0.883
Ethnicity American Indian							1.15	-5.31 – 7.62	0.724
Observations	147			146			146
R² / R² adjusted	0.021 / 0.001			0.055 / -0.000			0.061 / -0.055

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)	-0.42	-1.23 – 0.40	0.314	-3.30	-7.87 – 1.27	0.155	-2.86	-7.98 – 2.26	0.272
ActiveDays	0.02	-0.01 – 0.05	0.169	0.02	-0.01 – 0.05	0.117	0.02	-0.01 – 0.05	0.117
Reports	0.01	-0.07 – 0.08	0.846	0.02	-0.05 – 0.10	0.541	0.01	-0.08 – 0.11	0.765
Activities	-0.02	-0.05 – 0.01	0.190	-0.03	-0.07 – 0.00	0.063	-0.03	-0.07 – 0.00	0.069
univ [UW]				0.69	-0.23 – 1.61	0.141	0.85	-0.19 – 1.88	0.108
Sex [Woman]				-0.20	-1.38 – 0.98	0.740	-0.23	-1.46 – 1.00	0.712
Age				0.11	-0.07 – 0.28	0.224	0.10	-0.08 – 0.29	0.268
int student [No]				0.62	-1.40 – 2.64	0.544	0.29	-2.02 – 2.59	0.807
SES num				0.02	-0.37 – 0.42	0.906	0.04	-0.38 – 0.47	0.838
Ethnicity White							-0.12	-1.42 – 1.18	0.857
Ethnicity Hispanic							0.13	-1.81 – 2.07	0.894
Ethnicity Black							-0.37	-3.16 – 2.42	0.794
Ethnicity East Asian							-0.36	-2.06 – 1.34	0.674
Ethnicity South Asian							-0.81	-3.10 – 1.49	0.488
Ethnicity Native Hawaiian Pacific Islander							0.23	-5.31 – 5.78	0.934
Ethnicity Middle Eastern							0.30	-2.84 – 3.45	0.848
Ethnicity American Indian							1.25	-5.23 – 7.73	0.704
Observations	146			145			145
R² / R² adjusted	0.021 / 0.001			0.056 / 0.001			0.063 / -0.054

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)	-0.36	-1.31 – 0.60	0.462	-1.92	-7.48 – 3.64	0.495	-1.17	-7.47 – 5.14	0.714
ActiveDays	0.03	-0.03 – 0.09	0.289	0.04	-0.02 – 0.10	0.192	0.04	-0.03 – 0.10	0.256
Reports	-0.01	-0.11 – 0.09	0.870	-0.00	-0.11 – 0.10	0.965	-0.03	-0.16 – 0.11	0.705
Activities	-0.03	-0.06 – 0.01	0.162	-0.04	-0.08 – 0.01	0.085	-0.04	-0.08 – 0.01	0.085
univ [UW]				0.85	-0.22 – 1.92	0.118	0.97	-0.24 – 2.17	0.116
Sex [Woman]				-0.11	-1.45 – 1.23	0.876	-0.16	-1.55 – 1.23	0.819
Age				0.03	-0.19 – 0.24	0.816	0.00	-0.23 – 0.24	0.989
int student [No]				1.03	-1.81 – 3.87	0.475	0.83	-2.32 – 3.98	0.603
SES num				-0.07	-0.52 – 0.38	0.755	-0.06	-0.54 – 0.42	0.817
Ethnicity White							0.18	-1.27 – 1.63	0.805
Ethnicity Hispanic							0.37	-1.83 – 2.56	0.742
Ethnicity Black							0.35	-3.03 – 3.72	0.839
Ethnicity East Asian							-0.22	-2.10 – 1.65	0.814
Ethnicity South Asian							-1.16	-3.98 – 1.66	0.416
Ethnicity Native Hawaiian Pacific Islander							0.14	-5.67 – 5.96	0.961
Ethnicity Middle Eastern							-2.12	-7.90 – 3.65	0.468
Ethnicity American Indian							2.39	-5.17 – 9.95	0.532
Observations	120			120			120
R² / R² adjusted	0.019 / -0.006			0.045 / -0.024			0.064 / -0.081

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)	0.08	-0.67 – 0.83	0.831	0.64	-3.68 – 4.95	0.770	1.60	-3.17 – 6.36	0.509
ActiveDays	-0.01	-0.03 – 0.02	0.673	-0.01	-0.04 – 0.02	0.498	-0.01	-0.04 – 0.02	0.412
Reports	-0.00	-0.07 – 0.07	0.981	-0.01	-0.08 – 0.07	0.873	0.00	-0.08 – 0.09	0.979
Activities	0.04	0.01 – 0.07	0.012	0.04	0.01 – 0.07	0.015	0.04	0.00 – 0.07	0.026
univ [UW]				-0.80	-1.67 – 0.06	0.069	-0.98	-1.94 – -0.02	0.045
Sex [Woman]				0.32	-0.79 – 1.43	0.569	0.39	-0.75 – 1.53	0.501
Age				0.05	-0.12 – 0.21	0.577	0.03	-0.14 – 0.20	0.729
int student [No]				-1.00	-2.91 – 0.91	0.304	-1.19	-3.33 – 0.95	0.275
SES num				-0.09	-0.46 – 0.29	0.651	-0.13	-0.52 – 0.27	0.522
Ethnicity White							-0.07	-1.27 – 1.13	0.911
Ethnicity Hispanic							-1.23	-3.04 – 0.57	0.178
Ethnicity Black							0.82	-1.78 – 3.41	0.534
Ethnicity East Asian							-0.44	-2.02 – 1.14	0.581
Ethnicity South Asian							0.09	-2.04 – 2.22	0.933
Ethnicity Native Hawaiian Pacific Islander							-3.75	-8.90 – 1.41	0.153
Ethnicity Middle Eastern							-0.46	-3.38 – 2.46	0.755
Ethnicity American Indian							-0.50	-6.52 – 5.52	0.870
Observations	147			146			146
R² / R² adjusted	0.049 / 0.029			0.082 / 0.028			0.116 / 0.007

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)	0.11	-0.66 – 0.87	0.782	0.64	-3.69 – 4.97	0.770	1.60	-3.17 – 6.38	0.507
ActiveDays	-0.01	-0.03 – 0.02	0.667	-0.01	-0.04 – 0.02	0.498	-0.01	-0.04 – 0.02	0.412
Reports	-0.00	-0.07 – 0.07	0.973	-0.01	-0.08 – 0.07	0.871	0.00	-0.08 – 0.09	0.987
Activities	0.04	0.01 – 0.07	0.013	0.04	0.01 – 0.07	0.019	0.04	0.00 – 0.07	0.031
univ [UW]				-0.79	-1.66 – 0.09	0.077	-0.95	-1.92 – 0.01	0.053
Sex [Woman]				0.33	-0.79 – 1.45	0.561	0.40	-0.75 – 1.54	0.492
Age				0.05	-0.12 – 0.21	0.565	0.03	-0.14 – 0.20	0.712
int student [No]				-1.00	-2.91 – 0.92	0.304	-1.21	-3.36 – 0.95	0.269
SES num				-0.09	-0.47 – 0.28	0.626	-0.14	-0.54 – 0.26	0.493
Ethnicity White							-0.04	-1.25 – 1.17	0.946
Ethnicity Hispanic							-1.23	-3.04 – 0.58	0.181
Ethnicity Black							0.82	-1.78 – 3.42	0.535
Ethnicity East Asian							-0.44	-2.03 – 1.14	0.580
Ethnicity South Asian							0.09	-2.05 – 2.23	0.933
Ethnicity Native Hawaiian Pacific Islander							-3.75	-8.92 – 1.42	0.154
Ethnicity Middle Eastern							-0.42	-3.36 – 2.51	0.776
Ethnicity American Indian							-0.45	-6.49 – 5.59	0.884
Observations	146			145			145
R² / R² adjusted	0.047 / 0.027			0.079 / 0.025			0.114 / 0.004

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)	0.38	-0.55 – 1.31	0.417	1.93	-3.41 – 7.27	0.476	3.21	-2.73 – 9.14	0.286
ActiveDays	-0.03	-0.08 – 0.03	0.337	-0.04	-0.09 – 0.02	0.216	-0.04	-0.10 – 0.01	0.137
Reports	0.01	-0.09 – 0.11	0.850	0.00	-0.10 – 0.10	0.981	0.00	-0.12 – 0.13	0.946
Activities	0.04	0.00 – 0.07	0.034	0.04	0.00 – 0.08	0.030	0.04	0.00 – 0.08	0.034
univ [UW]				-1.07	-2.10 – -0.05	0.040	-1.40	-2.54 – -0.27	0.016
Sex [Woman]				0.23	-1.06 – 1.52	0.725	0.32	-0.99 – 1.63	0.633
Age				0.05	-0.15 – 0.26	0.614	0.04	-0.18 – 0.26	0.719
int student [No]				-1.89	-4.62 – 0.83	0.172	-2.20	-5.17 – 0.77	0.145
SES num				-0.09	-0.53 – 0.34	0.669	-0.18	-0.63 – 0.28	0.444
Ethnicity White							-0.12	-1.49 – 1.24	0.858
Ethnicity Hispanic							-1.59	-3.67 – 0.48	0.130
Ethnicity Black							0.62	-2.56 – 3.80	0.698
Ethnicity East Asian							-0.36	-2.13 – 1.40	0.683
Ethnicity South Asian							0.86	-1.79 – 3.52	0.520
Ethnicity Native Hawaiian Pacific Islander							-4.36	-9.84 – 1.12	0.117
Ethnicity Middle Eastern							-0.83	-6.27 – 4.61	0.763
Ethnicity American Indian							0.59	-6.53 – 7.71	0.870
Observations	120			120			120
R² / R² adjusted	0.038 / 0.013			0.088 / 0.022			0.141 / 0.007

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)	-0.16	-0.86 – 0.54	0.659	4.32	0.38 – 8.26	0.032	4.42	0.11 – 8.73	0.044
ActiveDays	-0.01	-0.03 – 0.01	0.353	-0.02	-0.05 – 0.00	0.093	-0.02	-0.04 – 0.01	0.121
Reports	0.02	-0.04 – 0.09	0.476	0.01	-0.05 – 0.08	0.684	0.02	-0.06 – 0.10	0.587
Activities	0.02	-0.01 – 0.04	0.279	0.02	-0.01 – 0.05	0.200	0.01	-0.02 – 0.04	0.347
univ [UW]				-0.83	-1.62 – -0.04	0.039	-1.13	-2.00 – -0.27	0.011
Sex [Woman]				-0.33	-1.35 – 0.69	0.522	-0.24	-1.27 – 0.79	0.644
Age				-0.04	-0.19 – 0.10	0.552	-0.05	-0.21 – 0.10	0.502
int student [No]				-2.40	-4.14 – -0.65	0.007	-2.13	-4.06 – -0.19	0.032
SES num				-0.13	-0.47 – 0.21	0.449	-0.17	-0.53 – 0.19	0.351
Ethnicity White							0.10	-0.98 – 1.19	0.854
Ethnicity Hispanic							-1.45	-3.08 – 0.19	0.082
Ethnicity Black							1.43	-0.92 – 3.77	0.231
Ethnicity East Asian							0.44	-0.99 – 1.86	0.544
Ethnicity South Asian							0.38	-1.55 – 2.31	0.699
Ethnicity Native Hawaiian Pacific Islander							0.08	-4.58 – 4.75	0.972
Ethnicity Middle Eastern							-0.88	-3.52 – 1.76	0.512
Ethnicity American Indian							0.93	-4.51 – 6.38	0.735
Observations	147			146			146
R² / R² adjusted	0.012 / -0.008			0.084 / 0.030			0.133 / 0.026

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)	-0.21	-0.92 – 0.50	0.563	4.31	0.38 – 8.25	0.032	4.40	0.10 – 8.71	0.045
ActiveDays	-0.01	-0.03 – 0.01	0.364	-0.02	-0.04 – 0.00	0.094	-0.02	-0.04 – 0.01	0.123
Reports	0.02	-0.04 – 0.09	0.463	0.01	-0.05 – 0.08	0.678	0.02	-0.05 – 0.10	0.571
Activities	0.02	-0.01 – 0.04	0.247	0.02	-0.01 – 0.05	0.161	0.02	-0.01 – 0.05	0.288
univ [UW]				-0.87	-1.66 – -0.08	0.032	-1.19	-2.06 – -0.31	0.008
Sex [Woman]				-0.35	-1.37 – 0.66	0.496	-0.26	-1.29 – 0.77	0.616
Age				-0.05	-0.20 – 0.10	0.516	-0.06	-0.21 – 0.10	0.467
int student [No]				-2.39	-4.13 – -0.65	0.007	-2.09	-4.02 – -0.15	0.035
SES num				-0.11	-0.45 – 0.23	0.519	-0.15	-0.51 – 0.21	0.422
Ethnicity White							0.04	-1.05 – 1.13	0.937
Ethnicity Hispanic							-1.46	-3.09 – 0.18	0.080
Ethnicity Black							1.43	-0.92 – 3.77	0.231
Ethnicity East Asian							0.44	-0.98 – 1.87	0.538
Ethnicity South Asian							0.38	-1.55 – 2.30	0.700
Ethnicity Native Hawaiian Pacific Islander							0.09	-4.57 – 4.75	0.970
Ethnicity Middle Eastern							-0.96	-3.61 – 1.68	0.472
Ethnicity American Indian							0.82	-4.63 – 6.27	0.766
Observations	146			145			145
R² / R² adjusted	0.014 / -0.007			0.087 / 0.033			0.137 / 0.029

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)	-0.21	-1.05 – 0.63	0.621	4.93	0.18 – 9.68	0.042	5.24	-0.05 – 10.53	0.052
ActiveDays	0.01	-0.04 – 0.06	0.682	-0.00	-0.05 – 0.05	0.877	-0.01	-0.06 – 0.04	0.791
Reports	-0.01	-0.10 – 0.08	0.855	-0.01	-0.10 – 0.07	0.755	-0.02	-0.13 – 0.09	0.759
Activities	0.01	-0.02 – 0.04	0.587	0.02	-0.01 – 0.06	0.240	0.02	-0.02 – 0.05	0.308
univ [UW]				-1.02	-1.94 – -0.11	0.029	-1.31	-2.32 – -0.30	0.012
Sex [Woman]				-0.35	-1.49 – 0.80	0.551	-0.28	-1.45 – 0.89	0.635
Age				-0.07	-0.25 – 0.12	0.477	-0.07	-0.26 – 0.13	0.505
int student [No]				-3.12	-5.55 – -0.70	0.012	-3.06	-5.71 – -0.42	0.024
SES num				-0.00	-0.39 – 0.39	0.999	-0.06	-0.47 – 0.34	0.759
Ethnicity White							0.12	-1.10 – 1.33	0.850
Ethnicity Hispanic							-1.43	-3.27 – 0.42	0.128
Ethnicity Black							0.90	-1.94 – 3.73	0.531
Ethnicity East Asian							0.30	-1.28 – 1.87	0.710
Ethnicity South Asian							1.24	-1.12 – 3.61	0.300
Ethnicity Native Hawaiian Pacific Islander							0.21	-4.67 – 5.08	0.933
Ethnicity Middle Eastern							0.38	-4.47 – 5.22	0.878
Ethnicity American Indian							1.78	-4.56 – 8.12	0.579
Observations	120			120			120
R² / R² adjusted	0.008 / -0.018			0.093 / 0.028			0.145 / 0.013

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)	-0.67	-1.39 – 0.05	0.068	3.93	-0.07 – 7.93	0.054	4.88	0.43 – 9.33	0.032
ActiveDays	0.01	-0.02 – 0.03	0.524	-0.00	-0.03 – 0.02	0.879	-0.00	-0.03 – 0.02	0.815
Reports	0.04	-0.03 – 0.10	0.247	0.03	-0.03 – 0.10	0.339	0.03	-0.05 – 0.11	0.463
Activities	0.01	-0.02 – 0.04	0.655	0.01	-0.02 – 0.04	0.564	0.01	-0.03 – 0.04	0.706
univ [UW]				-0.82	-1.63 – -0.01	0.048	-0.94	-1.84 – -0.04	0.042
Sex [Woman]				-0.20	-1.24 – 0.84	0.703	-0.21	-1.28 – 0.86	0.697
Age				-0.03	-0.18 – 0.12	0.673	-0.06	-0.23 – 0.10	0.429
int student [No]				-2.66	-4.43 – -0.89	0.003	-2.91	-4.91 – -0.91	0.005
SES num				-0.19	-0.53 – 0.15	0.277	-0.18	-0.55 – 0.19	0.338
Ethnicity White							0.12	-1.01 – 1.24	0.839
Ethnicity Hispanic							-0.04	-1.72 – 1.64	0.962
Ethnicity Black							1.70	-0.72 – 4.13	0.166
Ethnicity East Asian							-0.34	-1.82 – 1.13	0.644
Ethnicity South Asian							0.49	-1.49 – 2.48	0.624
Ethnicity Native Hawaiian Pacific Islander							-1.30	-6.10 – 3.51	0.594
Ethnicity Middle Eastern							-0.45	-3.17 – 2.27	0.742
Ethnicity American Indian							0.44	-5.17 – 6.05	0.877
Observations	146			145			145
R² / R² adjusted	0.033 / 0.013			0.114 / 0.062			0.138 / 0.031

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)	-0.61	-1.34 – 0.12	0.098	3.94	-0.06 – 7.94	0.054	4.90	0.45 – 9.35	0.031
ActiveDays	0.01	-0.02 – 0.03	0.539	-0.00	-0.03 – 0.02	0.878	-0.00	-0.03 – 0.02	0.809
Reports	0.04	-0.03 – 0.10	0.257	0.03	-0.03 – 0.10	0.342	0.03	-0.05 – 0.11	0.476
Activities	0.01	-0.02 – 0.04	0.720	0.01	-0.02 – 0.04	0.652	0.00	-0.03 – 0.04	0.802
univ [UW]				-0.78	-1.60 – 0.04	0.061	-0.88	-1.79 – 0.02	0.056
Sex [Woman]				-0.18	-1.22 – 0.86	0.735	-0.19	-1.26 – 0.88	0.728
Age				-0.03	-0.18 – 0.12	0.708	-0.06	-0.22 – 0.10	0.460
int student [No]				-2.66	-4.43 – -0.89	0.003	-2.95	-4.94 – -0.95	0.004
SES num				-0.21	-0.55 – 0.14	0.238	-0.20	-0.57 – 0.17	0.286
Ethnicity White							0.17	-0.96 – 1.30	0.766
Ethnicity Hispanic							-0.03	-1.72 – 1.65	0.969
Ethnicity Black							1.71	-0.71 – 4.13	0.165
Ethnicity East Asian							-0.35	-1.82 – 1.12	0.637
Ethnicity South Asian							0.49	-1.49 – 2.48	0.624
Ethnicity Native Hawaiian Pacific Islander							-1.30	-6.11 – 3.50	0.592
Ethnicity Middle Eastern							-0.37	-3.10 – 2.36	0.789
Ethnicity American Indian							0.55	-5.06 – 6.16	0.847
Observations	145			144			144
R² / R² adjusted	0.030 / 0.010			0.111 / 0.059			0.136 / 0.028

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)	-0.41	-1.28 – 0.46	0.350	3.95	-0.96 – 8.86	0.114	5.51	-0.01 – 11.02	0.050
ActiveDays	-0.00	-0.05 – 0.05	0.881	-0.02	-0.07 – 0.03	0.512	-0.02	-0.07 – 0.04	0.530
Reports	0.07	-0.02 – 0.16	0.115	0.07	-0.02 – 0.16	0.148	0.07	-0.05 – 0.18	0.236
Activities	-0.00	-0.04 – 0.03	0.909	0.01	-0.03 – 0.05	0.669	0.01	-0.03 – 0.04	0.758
univ [UW]				-0.98	-1.94 – -0.02	0.045	-1.02	-2.09 – 0.04	0.059
Sex [Woman]				-0.40	-1.60 – 0.79	0.508	-0.30	-1.53 – 0.92	0.623
Age				-0.03	-0.22 – 0.16	0.788	-0.06	-0.27 – 0.15	0.579
int student [No]				-2.78	-5.29 – -0.28	0.030	-3.56	-6.30 – -0.82	0.011
SES num				-0.08	-0.48 – 0.32	0.682	-0.13	-0.54 – 0.29	0.554
Ethnicity White							0.14	-1.12 – 1.40	0.825
Ethnicity Hispanic							-0.53	-2.45 – 1.38	0.581
Ethnicity Black							1.13	-1.83 – 4.09	0.452
Ethnicity East Asian							-0.79	-2.43 – 0.84	0.338
Ethnicity South Asian							1.40	-1.06 – 3.85	0.262
Ethnicity Native Hawaiian Pacific Islander							-1.38	-6.44 – 3.67	0.589
Ethnicity Middle Eastern							-0.12	-5.14 – 4.90	0.963
Ethnicity American Indian							-0.51	-7.09 – 6.07	0.879
Observations	119			119			119
R² / R² adjusted	0.027 / 0.002			0.093 / 0.027			0.135 / -0.001

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)	-0.45	-1.25 – 0.35	0.266	-1.87	-6.46 – 2.72	0.421	-1.32	-6.41 – 3.77	0.609
ActiveDays	0.00	-0.02 – 0.03	0.750	0.01	-0.02 – 0.04	0.454	0.01	-0.02 – 0.04	0.461
Reports	0.02	-0.05 – 0.09	0.567	0.02	-0.05 – 0.10	0.550	0.00	-0.09 – 0.09	0.950
Activities	0.00	-0.03 – 0.03	0.938	0.00	-0.03 – 0.04	0.970	0.00	-0.03 – 0.04	0.939
univ [UW]				0.18	-0.74 – 1.11	0.697	0.28	-0.75 – 1.31	0.596
Sex [Woman]				-0.15	-1.35 – 1.05	0.805	-0.25	-1.48 – 0.99	0.695
Age				-0.00	-0.17 – 0.17	0.993	-0.02	-0.20 – 0.17	0.861
int student [No]				1.37	-0.66 – 3.40	0.185	1.01	-1.28 – 3.30	0.383
SES num				0.02	-0.38 – 0.41	0.938	0.03	-0.39 – 0.46	0.877
Ethnicity White							0.18	-1.10 – 1.46	0.780
Ethnicity Hispanic							1.23	-0.70 – 3.16	0.210
Ethnicity Black							0.30	-2.47 – 3.07	0.831
Ethnicity East Asian							-0.14	-1.82 – 1.55	0.872
Ethnicity South Asian							-0.18	-2.46 – 2.10	0.875
Ethnicity Native Hawaiian Pacific Islander							-3.28	-8.79 – 2.23	0.241
Ethnicity Middle Eastern							0.33	-2.78 – 3.45	0.832
Ethnicity American Indian							1.85	-4.58 – 8.29	0.570
Observations	146			145			145
R² / R² adjusted	0.007 / -0.014			0.024 / -0.034			0.052 / -0.066

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)	-0.46	-1.27 – 0.35	0.261	-1.87	-6.48 – 2.73	0.423	-1.32	-6.43 – 3.79	0.610
ActiveDays	0.00	-0.02 – 0.03	0.748	0.01	-0.02 – 0.04	0.456	0.01	-0.02 – 0.04	0.462
Reports	0.02	-0.05 – 0.10	0.565	0.02	-0.05 – 0.10	0.551	0.00	-0.09 – 0.09	0.948
Activities	0.00	-0.03 – 0.03	0.927	0.00	-0.03 – 0.04	0.954	0.00	-0.03 – 0.04	0.927
univ [UW]				0.17	-0.76 – 1.11	0.711	0.27	-0.77 – 1.31	0.611
Sex [Woman]				-0.15	-1.36 – 1.05	0.801	-0.25	-1.49 – 0.99	0.693
Age				-0.00	-0.18 – 0.17	0.986	-0.02	-0.20 – 0.17	0.856
int student [No]				1.37	-0.67 – 3.41	0.187	1.02	-1.28 – 3.32	0.382
SES num				0.02	-0.38 – 0.42	0.924	0.04	-0.39 – 0.47	0.866
Ethnicity White							0.17	-1.12 – 1.47	0.792
Ethnicity Hispanic							1.23	-0.71 – 3.17	0.212
Ethnicity Black							0.30	-2.48 – 3.08	0.832
Ethnicity East Asian							-0.14	-1.83 – 1.56	0.873
Ethnicity South Asian							-0.18	-2.47 – 2.10	0.875
Ethnicity Native Hawaiian Pacific Islander							-3.28	-8.81 – 2.25	0.243
Ethnicity Middle Eastern							0.32	-2.82 – 3.46	0.839
Ethnicity American Indian							1.83	-4.63 – 8.30	0.575
Observations	145			144			144
R² / R² adjusted	0.007 / -0.014			0.024 / -0.034			0.053 / -0.067

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)	-0.31	-1.26 – 0.65	0.526	-3.35	-8.89 – 2.18	0.233	-2.96	-9.13 – 3.20	0.342
ActiveDays	-0.03	-0.09 – 0.02	0.275	-0.03	-0.09 – 0.03	0.309	-0.03	-0.09 – 0.03	0.273
Reports	0.08	-0.02 – 0.18	0.126	0.08	-0.02 – 0.18	0.124	0.04	-0.09 – 0.17	0.505
Activities	0.01	-0.03 – 0.04	0.755	0.00	-0.04 – 0.04	0.904	0.01	-0.04 – 0.05	0.802
univ [UW]				0.19	-0.88 – 1.26	0.725	0.40	-0.78 – 1.58	0.502
Sex [Woman]				-0.21	-1.55 – 1.12	0.751	-0.32	-1.68 – 1.04	0.639
Age				0.04	-0.18 – 0.25	0.729	0.02	-0.21 – 0.25	0.846
int student [No]				2.73	-0.10 – 5.55	0.059	2.53	-0.56 – 5.61	0.107
SES num				-0.06	-0.51 – 0.39	0.788	-0.05	-0.52 – 0.42	0.819
Ethnicity White							0.36	-1.05 – 1.78	0.613
Ethnicity Hispanic							1.65	-0.50 – 3.80	0.131
Ethnicity Black							-0.45	-3.75 – 2.85	0.787
Ethnicity East Asian							-0.08	-1.92 – 1.75	0.928
Ethnicity South Asian							-0.80	-3.55 – 1.96	0.567
Ethnicity Native Hawaiian Pacific Islander							-3.57	-9.25 – 2.12	0.216
Ethnicity Middle Eastern							-1.37	-7.02 – 4.28	0.631
Ethnicity American Indian							2.42	-4.97 – 9.81	0.518
Observations	120			120			120
R² / R² adjusted	0.021 / -0.004			0.059 / -0.009			0.111 / -0.027

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)	-0.48	-1.46 – 0.51	0.339	-4.04	-9.66 – 1.58	0.157	-4.14	-10.43 – 2.16	0.196
ActiveDays	0.00	-0.03 – 0.03	0.972	0.00	-0.03 – 0.04	0.793	0.01	-0.03 – 0.04	0.689
Reports	0.03	-0.06 – 0.12	0.473	0.05	-0.04 – 0.14	0.307	0.04	-0.08 – 0.15	0.532
Activities	-0.01	-0.05 – 0.03	0.606	-0.01	-0.05 – 0.03	0.559	-0.01	-0.06 – 0.03	0.528
univ [UW]				0.69	-0.43 – 1.82	0.226	0.76	-0.50 – 2.03	0.236
Sex [Woman]				0.44	-1.01 – 1.89	0.552	0.38	-1.13 – 1.89	0.619
Age				0.02	-0.19 – 0.24	0.826	0.02	-0.21 – 0.24	0.885
int student [No]				1.27	-1.22 – 3.75	0.316	1.18	-1.65 – 4.01	0.411
SES num				0.32	-0.16 – 0.80	0.193	0.35	-0.17 – 0.88	0.185
Ethnicity White							0.26	-1.33 – 1.85	0.746
Ethnicity Hispanic							0.82	-1.57 – 3.20	0.500
Ethnicity Black							0.60	-2.83 – 4.03	0.729
Ethnicity East Asian							0.30	-1.78 – 2.38	0.776
Ethnicity South Asian							-0.14	-2.96 – 2.67	0.919
Ethnicity Native Hawaiian Pacific Islander							1.75	-5.07 – 8.56	0.613
Ethnicity Middle Eastern							-0.03	-3.89 – 3.83	0.988
Ethnicity American Indian							2.22	-5.74 – 10.17	0.582
Observations	147			146			146
R² / R² adjusted	0.005 / -0.015			0.033 / -0.023			0.041 / -0.078

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)	-0.46	-1.46 – 0.53	0.360	-4.04	-9.68 – 1.60	0.159	-4.13	-10.45 – 2.18	0.198
ActiveDays	0.00	-0.03 – 0.03	0.975	0.00	-0.03 – 0.04	0.794	0.01	-0.03 – 0.04	0.692
Reports	0.03	-0.06 – 0.12	0.477	0.05	-0.05 – 0.14	0.309	0.04	-0.08 – 0.15	0.536
Activities	-0.01	-0.05 – 0.03	0.599	-0.01	-0.06 – 0.03	0.551	-0.01	-0.06 – 0.03	0.521
univ [UW]				0.70	-0.43 – 1.84	0.224	0.77	-0.51 – 2.05	0.234
Sex [Woman]				0.44	-1.01 – 1.90	0.550	0.38	-1.13 – 1.90	0.616
Age				0.02	-0.19 – 0.24	0.821	0.02	-0.21 – 0.25	0.879
int student [No]				1.26	-1.23 – 3.76	0.318	1.17	-1.67 – 4.02	0.416
SES num				0.32	-0.17 – 0.81	0.202	0.35	-0.18 – 0.88	0.195
Ethnicity White							0.27	-1.33 – 1.87	0.738
Ethnicity Hispanic							0.82	-1.58 – 3.22	0.500
Ethnicity Black							0.60	-2.84 – 4.04	0.730
Ethnicity East Asian							0.30	-1.79 – 2.39	0.778
Ethnicity South Asian							-0.14	-2.97 – 2.68	0.920
Ethnicity Native Hawaiian Pacific Islander							1.74	-5.10 – 8.59	0.615
Ethnicity Middle Eastern							-0.01	-3.89 – 3.87	0.995
Ethnicity American Indian							2.24	-5.75 – 10.23	0.580
Observations	146			145			145
R² / R² adjusted	0.006 / -0.015			0.033 / -0.024			0.041 / -0.079

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)	-0.34	-1.53 – 0.85	0.573	-3.89	-10.82 – 3.04	0.269	-3.70	-11.56 – 4.17	0.353
ActiveDays	-0.02	-0.09 – 0.05	0.572	-0.01	-0.09 – 0.06	0.723	-0.01	-0.09 – 0.06	0.747
Reports	0.07	-0.06 – 0.19	0.308	0.07	-0.06 – 0.20	0.263	0.04	-0.13 – 0.20	0.649
Activities	-0.01	-0.05 – 0.04	0.723	-0.01	-0.06 – 0.04	0.676	-0.01	-0.06 – 0.04	0.677
univ [UW]				0.58	-0.76 – 1.91	0.393	0.78	-0.72 – 2.29	0.305
Sex [Woman]				0.15	-1.52 – 1.82	0.857	0.04	-1.70 – 1.77	0.968
Age				0.01	-0.26 – 0.27	0.953	0.00	-0.29 – 0.30	0.985
int student [No]				1.89	-1.65 – 5.43	0.293	1.76	-2.17 – 5.69	0.377
SES num				0.32	-0.25 – 0.88	0.267	0.38	-0.22 – 0.98	0.216
Ethnicity White							-0.01	-1.82 – 1.79	0.991
Ethnicity Hispanic							0.98	-1.76 – 3.72	0.480
Ethnicity Black							-0.47	-4.68 – 3.74	0.825
Ethnicity East Asian							-0.24	-2.58 – 2.10	0.839
Ethnicity South Asian							-0.99	-4.51 – 2.53	0.577
Ethnicity Native Hawaiian Pacific Islander							1.31	-5.94 – 8.57	0.720
Ethnicity Middle Eastern							-2.07	-9.27 – 5.14	0.570
Ethnicity American Indian							2.85	-6.58 – 12.28	0.550
Observations	120			120			120
R² / R² adjusted	0.011 / -0.014			0.035 / -0.035			0.052 / -0.095

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)	-0.18	-0.93 – 0.57	0.634	-3.73	-8.04 – 0.58	0.089	-4.32	-9.05 – 0.41	0.073
ActiveDays	-0.02	-0.04 – 0.01	0.134	-0.02	-0.04 – 0.01	0.226	-0.02	-0.04 – 0.01	0.212
Reports	0.05	-0.02 – 0.12	0.173	0.06	-0.01 – 0.13	0.119	0.03	-0.06 – 0.11	0.512
Activities	0.02	-0.01 – 0.05	0.187	0.02	-0.01 – 0.05	0.287	0.02	-0.01 – 0.05	0.226
univ [UW]				0.08	-0.79 – 0.94	0.861	0.22	-0.73 – 1.17	0.648
Sex [Woman]				0.13	-0.98 – 1.24	0.819	0.02	-1.11 – 1.16	0.967
Age				0.08	-0.08 – 0.25	0.315	0.09	-0.08 – 0.27	0.272
int student [No]				1.03	-0.88 – 2.94	0.288	0.92	-1.21 – 3.05	0.394
SES num				0.22	-0.15 – 0.60	0.235	0.18	-0.21 – 0.57	0.370
Ethnicity White							0.90	-0.29 – 2.10	0.136
Ethnicity Hispanic							1.68	-0.11 – 3.48	0.066
Ethnicity Black							-0.21	-2.79 – 2.37	0.873
Ethnicity East Asian							0.62	-0.94 – 2.19	0.432
Ethnicity South Asian							0.92	-1.20 – 3.04	0.390
Ethnicity Native Hawaiian Pacific Islander							-2.11	-7.24 – 3.01	0.417
Ethnicity Middle Eastern							1.73	-1.17 – 4.63	0.240
Ethnicity American Indian							2.92	-3.06 – 8.90	0.336
Observations	147			146			146
R² / R² adjusted	0.027 / 0.006			0.053 / -0.002			0.098 / -0.014

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)	-0.19	-0.95 – 0.58	0.631	-3.73	-8.06 – 0.59	0.090	-4.32	-9.07 – 0.43	0.075
ActiveDays	-0.02	-0.04 – 0.01	0.136	-0.02	-0.04 – 0.01	0.228	-0.02	-0.04 – 0.01	0.214
Reports	0.05	-0.02 – 0.12	0.175	0.06	-0.02 – 0.13	0.121	0.03	-0.06 – 0.11	0.514
Activities	0.02	-0.01 – 0.05	0.188	0.02	-0.01 – 0.05	0.284	0.02	-0.01 – 0.05	0.231
univ [UW]				0.07	-0.80 – 0.94	0.873	0.22	-0.74 – 1.18	0.652
Sex [Woman]				0.13	-0.99 – 1.24	0.824	0.02	-1.11 – 1.16	0.967
Age				0.08	-0.08 – 0.25	0.321	0.09	-0.08 – 0.27	0.274
int student [No]				1.03	-0.88 – 2.94	0.289	0.92	-1.22 – 3.06	0.396
SES num				0.23	-0.15 – 0.60	0.233	0.18	-0.22 – 0.58	0.376
Ethnicity White							0.90	-0.30 – 2.11	0.139
Ethnicity Hispanic							1.68	-0.12 – 3.49	0.067
Ethnicity Black							-0.21	-2.80 – 2.38	0.873
Ethnicity East Asian							0.62	-0.95 – 2.20	0.433
Ethnicity South Asian							0.92	-1.20 – 3.05	0.392
Ethnicity Native Hawaiian Pacific Islander							-2.11	-7.26 – 3.04	0.419
Ethnicity Middle Eastern							1.73	-1.19 – 4.65	0.243
Ethnicity American Indian							2.92	-3.09 – 8.93	0.338
Observations	146			145			145
R² / R² adjusted	0.027 / 0.006			0.054 / -0.002			0.098 / -0.015

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)	-0.34	-1.18 – 0.50	0.426	-3.63	-8.53 – 1.28	0.146	-4.38	-9.66 – 0.90	0.103
ActiveDays	-0.03	-0.07 – 0.02	0.314	-0.02	-0.07 – 0.03	0.401	-0.02	-0.07 – 0.03	0.493
Reports	0.06	-0.02 – 0.15	0.153	0.07	-0.02 – 0.16	0.132	0.01	-0.10 – 0.13	0.796
Activities	0.03	-0.01 – 0.06	0.110	0.02	-0.02 – 0.06	0.318	0.02	-0.01 – 0.06	0.204
univ [UW]				0.03	-0.92 – 0.97	0.954	0.37	-0.64 – 1.39	0.464
Sex [Woman]				0.18	-1.00 – 1.36	0.761	0.02	-1.15 – 1.19	0.972
Age				0.12	-0.06 – 0.31	0.197	0.15	-0.05 – 0.35	0.132
int student [No]				0.40	-2.10 – 2.91	0.751	0.05	-2.60 – 2.69	0.973
SES num				0.11	-0.29 – 0.51	0.577	0.14	-0.27 – 0.54	0.501
Ethnicity White							0.63	-0.59 – 1.84	0.309
Ethnicity Hispanic							2.50	0.66 – 4.34	0.008
Ethnicity Black							-1.83	-4.66 – 0.99	0.201
Ethnicity East Asian							0.01	-1.56 – 1.59	0.987
Ethnicity South Asian							1.37	-0.99 – 3.73	0.252
Ethnicity Native Hawaiian Pacific Islander							-2.20	-7.07 – 2.67	0.372
Ethnicity Middle Eastern							-0.15	-4.99 – 4.69	0.951
Ethnicity American Indian							3.29	-3.05 – 9.62	0.306
Observations	120			120			120
R² / R² adjusted	0.038 / 0.013			0.056 / -0.012			0.167 / 0.037

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)	-0.62	-2.36 – 1.12	0.484	9.08	-0.60 – 18.76	0.066	11.36	0.70 – 22.02	0.037
ActiveDays	-0.01	-0.06 – 0.05	0.833	-0.03	-0.09 – 0.03	0.340	-0.03	-0.09 – 0.03	0.323
Reports	0.07	-0.09 – 0.23	0.414	0.05	-0.11 – 0.21	0.571	0.06	-0.13 – 0.25	0.553
Activities	0.05	-0.02 – 0.12	0.175	0.06	-0.02 – 0.13	0.127	0.05	-0.03 – 0.12	0.229
univ [UW]				-2.28	-4.24 – -0.31	0.024	-2.87	-5.03 – -0.72	0.009
Sex [Woman]				0.01	-2.51 – 2.53	0.994	0.16	-2.41 – 2.73	0.901
Age				-0.05	-0.42 – 0.31	0.774	-0.12	-0.50 – 0.27	0.547
int student [No]				-6.02	-10.30 – -1.74	0.006	-6.24	-11.02 – -1.45	0.011
SES num				-0.40	-1.23 – 0.44	0.349	-0.46	-1.34 – 0.43	0.308
Ethnicity White							0.02	-2.67 – 2.71	0.990
Ethnicity Hispanic							-2.77	-6.80 – 1.26	0.177
Ethnicity Black							4.21	-1.60 – 10.02	0.154
Ethnicity East Asian							-0.45	-3.98 – 3.07	0.800
Ethnicity South Asian							0.81	-3.95 – 5.58	0.737
Ethnicity Native Hawaiian Pacific Islander							-4.91	-16.43 – 6.60	0.400
Ethnicity Middle Eastern							-1.82	-8.34 – 4.70	0.581
Ethnicity American Indian							0.94	-12.50 – 14.38	0.890
Observations	146			145			145
R² / R² adjusted	0.026 / 0.005			0.106 / 0.053			0.147 / 0.040

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)	-0.59	-2.36 – 1.18	0.514	9.09	-0.63 – 18.80	0.067	11.37	0.67 – 22.07	0.037
ActiveDays	-0.01	-0.06 – 0.05	0.830	-0.03	-0.09 – 0.03	0.341	-0.03	-0.09 – 0.03	0.324
Reports	0.07	-0.09 – 0.23	0.419	0.05	-0.12 – 0.21	0.573	0.06	-0.13 – 0.25	0.557
Activities	0.05	-0.02 – 0.12	0.185	0.06	-0.02 – 0.13	0.137	0.05	-0.03 – 0.12	0.245
univ [UW]				-2.26	-4.24 – -0.28	0.026	-2.85	-5.03 – -0.67	0.011
Sex [Woman]				0.02	-2.52 – 2.55	0.988	0.17	-2.41 – 2.76	0.895
Age				-0.05	-0.42 – 0.32	0.781	-0.12	-0.50 – 0.27	0.556
int student [No]				-6.02	-10.32 – -1.73	0.006	-6.25	-11.06 – -1.45	0.011
SES num				-0.40	-1.24 – 0.44	0.345	-0.47	-1.36 – 0.43	0.302
Ethnicity White							0.05	-2.67 – 2.76	0.974
Ethnicity Hispanic							-2.77	-6.81 – 1.28	0.179
Ethnicity Black							4.21	-1.62 – 10.04	0.155
Ethnicity East Asian							-0.46	-3.99 – 3.08	0.799
Ethnicity South Asian							0.81	-3.97 – 5.59	0.738
Ethnicity Native Hawaiian Pacific Islander							-4.91	-16.47 – 6.65	0.402
Ethnicity Middle Eastern							-1.78	-8.34 – 4.78	0.592
Ethnicity American Indian							1.00	-12.50 – 14.50	0.884
Observations	145			144			144
R² / R² adjusted	0.024 / 0.004			0.105 / 0.052			0.146 / 0.038

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)	-0.05	-2.19 – 2.10	0.964	11.20	-0.79 – 23.19	0.067	14.77	1.42 – 28.12	0.030
ActiveDays	-0.02	-0.14 – 0.10	0.740	-0.05	-0.18 – 0.07	0.403	-0.06	-0.19 – 0.07	0.328
Reports	0.08	-0.14 – 0.31	0.470	0.06	-0.16 – 0.28	0.594	0.06	-0.22 – 0.34	0.680
Activities	0.03	-0.05 – 0.12	0.446	0.06	-0.03 – 0.15	0.177	0.06	-0.04 – 0.15	0.231
univ [UW]				-2.85	-5.19 – -0.51	0.018	-3.51	-6.08 – -0.94	0.008
Sex [Woman]				-0.27	-3.20 – 2.65	0.852	-0.01	-2.98 – 2.95	0.992
Age				-0.07	-0.54 – 0.39	0.752	-0.14	-0.64 – 0.37	0.595
int student [No]				-7.71	-13.83 – -1.60	0.014	-8.79	-15.42 – -2.16	0.010
SES num				-0.18	-1.15 – 0.79	0.715	-0.36	-1.37 – 0.65	0.479
Ethnicity White							0.06	-2.98 – 3.11	0.968
Ethnicity Hispanic							-3.53	-8.15 – 1.10	0.134
Ethnicity Black							3.16	-4.00 – 10.33	0.383
Ethnicity East Asian							-0.96	-4.91 – 2.99	0.631
Ethnicity South Asian							3.39	-2.55 – 9.32	0.260
Ethnicity Native Hawaiian Pacific Islander							-5.48	-17.71 – 6.76	0.377
Ethnicity Middle Eastern							-0.59	-12.74 – 11.56	0.924
Ethnicity American Indian							2.10	-13.81 – 18.01	0.794
Observations	119			119			119
R² / R² adjusted	0.011 / -0.015			0.098 / 0.033			0.157 / 0.025

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)	-1.10	-3.10 – 0.91	0.282	-9.59	-20.99 – 1.82	0.099	-9.68	-22.35 – 2.99	0.133
ActiveDays	-0.01	-0.08 – 0.05	0.692	0.00	-0.07 – 0.07	0.986	0.00	-0.07 – 0.08	0.935
Reports	0.10	-0.08 – 0.29	0.279	0.13	-0.06 – 0.32	0.189	0.06	-0.16 – 0.29	0.576
Activities	0.01	-0.07 – 0.09	0.799	0.00	-0.08 – 0.09	0.927	0.01	-0.08 – 0.09	0.901
univ [UW]				0.99	-1.31 – 3.29	0.395	1.32	-1.24 – 3.89	0.309
Sex [Woman]				0.33	-2.65 – 3.31	0.826	0.05	-3.02 – 3.13	0.973
Age				0.10	-0.33 – 0.53	0.640	0.09	-0.37 – 0.55	0.695
int student [No]				3.70	-1.35 – 8.75	0.149	3.11	-2.58 – 8.81	0.281
SES num				0.58	-0.41 – 1.56	0.250	0.59	-0.47 – 1.65	0.274
Ethnicity White							1.38	-1.82 – 4.57	0.395
Ethnicity Hispanic							3.76	-1.04 – 8.57	0.124
Ethnicity Black							0.68	-6.21 – 7.58	0.845
Ethnicity East Asian							0.75	-3.45 – 4.95	0.724
Ethnicity South Asian							0.55	-5.12 – 6.22	0.848
Ethnicity Native Hawaiian Pacific Islander							-3.56	-17.28 – 10.16	0.609
Ethnicity Middle Eastern							2.02	-5.75 – 9.78	0.608
Ethnicity American Indian							7.14	-8.88 – 23.16	0.380
Observations	146			145			145
R² / R² adjusted	0.010 / -0.011			0.039 / -0.017			0.064 / -0.053

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)	-1.10	-3.13 – 0.94	0.288	-9.59	-21.04 – 1.86	0.100	-9.68	-22.40 – 3.04	0.135
ActiveDays	-0.01	-0.08 – 0.05	0.693	0.00	-0.07 – 0.07	0.986	0.00	-0.07 – 0.08	0.935
Reports	0.10	-0.08 – 0.29	0.281	0.13	-0.06 – 0.32	0.190	0.06	-0.16 – 0.29	0.578
Activities	0.01	-0.07 – 0.09	0.799	0.00	-0.08 – 0.09	0.923	0.01	-0.08 – 0.10	0.905
univ [UW]				0.99	-1.33 – 3.30	0.402	1.33	-1.27 – 3.92	0.313
Sex [Woman]				0.33	-2.67 – 3.32	0.828	0.05	-3.03 – 3.14	0.972
Age				0.10	-0.33 – 0.54	0.644	0.09	-0.37 – 0.55	0.696
int student [No]				3.70	-1.37 – 8.77	0.151	3.11	-2.61 – 8.83	0.284
SES num				0.58	-0.42 – 1.58	0.252	0.59	-0.48 – 1.66	0.280
Ethnicity White							1.38	-1.84 – 4.61	0.397
Ethnicity Hispanic							3.76	-1.06 – 8.59	0.125
Ethnicity Black							0.68	-6.24 – 7.61	0.846
Ethnicity East Asian							0.75	-3.46 – 4.96	0.725
Ethnicity South Asian							0.55	-5.14 – 6.24	0.849
Ethnicity Native Hawaiian Pacific Islander							-3.56	-17.33 – 10.21	0.610
Ethnicity Middle Eastern							2.02	-5.79 – 9.83	0.609
Ethnicity American Indian							7.14	-8.95 – 23.24	0.381
Observations	145			144			144
R² / R² adjusted	0.010 / -0.012			0.039 / -0.018			0.064 / -0.054

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)	-0.98	-3.33 – 1.37	0.409	-10.87	-24.57 – 2.83	0.119	-11.04	-26.24 – 4.16	0.153
ActiveDays	-0.08	-0.21 – 0.06	0.277	-0.07	-0.21 – 0.08	0.373	-0.06	-0.21 – 0.08	0.396
Reports	0.21	-0.04 – 0.46	0.100	0.22	-0.03 – 0.48	0.085	0.10	-0.22 – 0.42	0.551
Activities	0.02	-0.07 – 0.11	0.602	0.01	-0.09 – 0.11	0.845	0.02	-0.09 – 0.12	0.742
univ [UW]				0.79	-1.84 – 3.43	0.551	1.56	-1.35 – 4.47	0.291
Sex [Woman]				0.12	-3.18 – 3.42	0.943	-0.27	-3.62 – 3.09	0.875
Age				0.17	-0.36 – 0.69	0.526	0.18	-0.39 – 0.75	0.539
int student [No]				5.02	-1.98 – 12.01	0.158	4.33	-3.27 – 11.94	0.261
SES num				0.37	-0.74 – 1.48	0.514	0.46	-0.70 – 1.62	0.433
Ethnicity White							0.98	-2.51 – 4.47	0.580
Ethnicity Hispanic							5.13	-0.17 – 10.44	0.058
Ethnicity Black							-2.76	-10.90 – 5.39	0.504
Ethnicity East Asian							-0.31	-4.84 – 4.22	0.892
Ethnicity South Asian							-0.42	-7.22 – 6.38	0.903
Ethnicity Native Hawaiian Pacific Islander							-4.46	-18.48 – 9.56	0.530
Ethnicity Middle Eastern							-3.59	-17.52 – 10.34	0.610
Ethnicity American Indian							8.56	-9.68 – 26.79	0.354
Observations	120			120			120
R² / R² adjusted	0.024 / -0.001			0.051 / -0.018			0.109 / -0.030

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)	-0.24	-1.61 – 1.12	0.725	3.53	-4.36 – 11.42	0.378	3.95	-4.83 – 12.73	0.375
ActiveDays	-0.06	-0.10 – -0.01	0.010	-0.06	-0.11 – -0.01	0.011	-0.06	-0.11 – -0.01	0.018
Reports	0.01	-0.11 – 0.14	0.839	0.01	-0.12 – 0.14	0.928	0.00	-0.15 – 0.16	0.954
Activities	0.09	0.03 – 0.14	0.002	0.09	0.03 – 0.15	0.003	0.09	0.03 – 0.15	0.004
univ [UW]				-0.20	-1.78 – 1.39	0.808	0.29	-1.48 – 2.05	0.747
Sex [Woman]				-0.38	-2.41 – 1.66	0.714	-0.44	-2.54 – 1.66	0.681
Age				-0.13	-0.43 – 0.17	0.400	-0.12	-0.44 – 0.19	0.449
int student [No]				-0.90	-4.39 – 2.59	0.611	-1.84	-5.79 – 2.11	0.359
SES num				-0.00	-0.68 – 0.68	0.999	0.09	-0.64 – 0.82	0.803
Ethnicity White							0.22	-1.99 – 2.44	0.843
Ethnicity Hispanic							0.52	-2.81 – 3.85	0.759
Ethnicity Black							-1.53	-6.31 – 3.25	0.527
Ethnicity East Asian							-1.17	-4.08 – 1.74	0.427
Ethnicity South Asian							-1.68	-5.61 – 2.25	0.398
Ethnicity Native Hawaiian Pacific Islander							2.04	-7.47 – 11.55	0.672
Ethnicity Middle Eastern							-1.22	-6.61 – 4.17	0.655
Ethnicity American Indian							1.49	-9.61 – 12.59	0.791
Observations	146			146			146
R² / R² adjusted	0.077 / 0.057			0.084 / 0.031			0.103 / -0.008

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)	-0.37	-1.75 – 1.01	0.597	3.50	-4.37 – 11.38	0.380	3.91	-4.87 – 12.68	0.380
ActiveDays	-0.06	-0.10 – -0.01	0.011	-0.06	-0.11 – -0.01	0.011	-0.06	-0.11 – -0.01	0.018
Reports	0.01	-0.11 – 0.14	0.817	0.01	-0.12 – 0.14	0.920	0.01	-0.15 – 0.16	0.934
Activities	0.09	0.03 – 0.14	0.002	0.10	0.04 – 0.16	0.002	0.10	0.03 – 0.16	0.003
univ [UW]				-0.29	-1.87 – 1.30	0.722	0.17	-1.60 – 1.95	0.848
Sex [Woman]				-0.43	-2.46 – 1.61	0.679	-0.48	-2.58 – 1.62	0.651
Age				-0.14	-0.44 – 0.16	0.366	-0.13	-0.45 – 0.19	0.414
int student [No]				-0.89	-4.38 – 2.59	0.613	-1.76	-5.71 – 2.19	0.381
SES num				0.04	-0.64 – 0.72	0.902	0.14	-0.59 – 0.87	0.705
Ethnicity White							0.10	-2.12 – 2.32	0.930
Ethnicity Hispanic							0.50	-2.83 – 3.83	0.768
Ethnicity Black							-1.54	-6.32 – 3.24	0.526
Ethnicity East Asian							-1.16	-4.06 – 1.75	0.432
Ethnicity South Asian							-1.69	-5.61 – 2.24	0.397
Ethnicity Native Hawaiian Pacific Islander							2.05	-7.45 – 11.55	0.670
Ethnicity Middle Eastern							-1.40	-6.80 – 3.99	0.607
Ethnicity American Indian							1.25	-9.84 – 12.35	0.823
Observations	145			145			145
R² / R² adjusted	0.079 / 0.060			0.088 / 0.034			0.106 / -0.006

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)	-0.87	-2.40 – 0.66	0.263	5.31	-3.59 – 14.21	0.240	6.45	-3.65 – 16.55	0.208
ActiveDays	-0.00	-0.09 – 0.08	0.916	-0.01	-0.10 – 0.09	0.898	0.00	-0.10 – 0.10	0.965
Reports	-0.04	-0.21 – 0.12	0.602	-0.05	-0.21 – 0.12	0.580	-0.08	-0.29 – 0.14	0.480
Activities	0.09	0.03 – 0.15	0.004	0.10	0.04 – 0.17	0.003	0.10	0.03 – 0.17	0.006
univ [UW]				0.26	-1.45 – 1.98	0.760	0.48	-1.45 – 2.42	0.621
Sex [Woman]				-0.11	-2.26 – 2.03	0.917	-0.11	-2.34 – 2.12	0.925
Age				-0.25	-0.59 – 0.09	0.146	-0.31	-0.69 – 0.07	0.111
int student [No]				-1.91	-6.46 – 2.63	0.406	-2.57	-7.62 – 2.48	0.315
SES num				0.11	-0.61 – 0.83	0.763	0.11	-0.66 – 0.88	0.775
Ethnicity White							0.68	-1.64 – 3.00	0.562
Ethnicity Hispanic							1.04	-2.49 – 4.56	0.561
Ethnicity Black							2.12	-3.29 – 7.54	0.438
Ethnicity East Asian							-0.25	-3.26 – 2.76	0.868
Ethnicity South Asian							1.35	-3.17 – 5.87	0.554
Ethnicity Native Hawaiian Pacific Islander							3.15	-6.17 – 12.47	0.505
Ethnicity Middle Eastern							1.54	-7.72 – 10.80	0.742
Ethnicity American Indian							2.05	-10.06 – 14.17	0.738
Observations	120			120			120
R² / R² adjusted	0.084 / 0.060			0.112 / 0.048			0.128 / -0.008

Social Fit

Intention to Treat

m0_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_social_fit_diff <- lm(social_fit_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_social_fit_diff, m1_social_fit_diff, m2_social_fit_diff)

	social_fit_diff			social_fit_diff			social_fit_diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.03	-0.36 – 0.43	0.870	0.02	-2.24 – 2.28	0.988	1.09	-1.30 – 3.49	0.369
ActiveDays	-0.00	-0.01 – 0.01	0.821	-0.01	-0.02 – 0.01	0.475	-0.01	-0.02 – 0.01	0.298
Reports	-0.01	-0.05 – 0.02	0.502	-0.01	-0.04 – 0.03	0.774	-0.02	-0.06 – 0.02	0.408
Activities	0.01	-0.01 – 0.02	0.492	0.00	-0.01 – 0.02	0.724	0.00	-0.01 – 0.02	0.679
univ [UW]				0.22	-0.23 – 0.68	0.333	0.30	-0.18 – 0.78	0.221
Sex [Woman]				0.35	-0.24 – 0.93	0.242	0.29	-0.28 – 0.87	0.311
Age				0.02	-0.07 – 0.10	0.724	-0.00	-0.09 – 0.09	0.993
int student [No]				-0.86	-1.86 – 0.14	0.091	-1.41	-2.49 – -0.33	0.011
SES num				0.06	-0.13 – 0.26	0.512	0.07	-0.13 – 0.27	0.487
Ethnicity White							0.00	-0.60 – 0.61	0.990
Ethnicity Hispanic							0.19	-0.71 – 1.10	0.673
Ethnicity Black							-0.02	-1.32 – 1.29	0.980
Ethnicity East Asian							-0.79	-1.58 – 0.00	0.051
Ethnicity South Asian							-0.18	-1.25 – 0.89	0.744
Ethnicity Native Hawaiian Pacific Islander							-4.12	-6.72 – -1.53	0.002
Ethnicity Middle Eastern							0.07	-1.40 – 1.54	0.930
Ethnicity American Indian							1.00	-2.03 – 4.03	0.514
Observations	147			146			146
R² / R² adjusted	0.007 / -0.014			0.056 / 0.001			0.162 / 0.058

Excluded Preregistered

m0_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_social_fit_diff <- lm(social_fit_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_social_fit_diff, m1_social_fit_diff, m2_social_fit_diff)

	social_fit_diff			social_fit_diff			social_fit_diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.03	-0.37 – 0.44	0.869	0.02	-2.25 – 2.29	0.987	1.09	-1.31 – 3.50	0.369
ActiveDays	-0.00	-0.01 – 0.01	0.821	-0.01	-0.02 – 0.01	0.476	-0.01	-0.02 – 0.01	0.298
Reports	-0.01	-0.05 – 0.02	0.503	-0.01	-0.04 – 0.03	0.774	-0.02	-0.06 – 0.02	0.405
Activities	0.01	-0.01 – 0.02	0.497	0.00	-0.01 – 0.02	0.738	0.00	-0.01 – 0.02	0.713
univ [UW]				0.23	-0.23 – 0.68	0.331	0.31	-0.18 – 0.80	0.211
Sex [Woman]				0.35	-0.24 – 0.93	0.242	0.30	-0.28 – 0.87	0.307
Age				0.02	-0.07 – 0.10	0.721	0.00	-0.09 – 0.09	0.993
int student [No]				-0.86	-1.87 – 0.14	0.092	-1.42	-2.50 – -0.34	0.011
SES num				0.06	-0.13 – 0.26	0.524	0.07	-0.14 – 0.27	0.516
Ethnicity White							0.01	-0.60 – 0.62	0.965
Ethnicity Hispanic							0.20	-0.72 – 1.11	0.672
Ethnicity Black							-0.02	-1.33 – 1.29	0.981
Ethnicity East Asian							-0.79	-1.59 – 0.01	0.052
Ethnicity South Asian							-0.18	-1.25 – 0.90	0.745
Ethnicity Native Hawaiian Pacific Islander							-4.12	-6.73 – -1.52	0.002
Ethnicity Middle Eastern							0.08	-1.40 – 1.56	0.914
Ethnicity American Indian							1.02	-2.02 – 4.06	0.507
Observations	146			145			145
R² / R² adjusted	0.007 / -0.014			0.056 / 0.000			0.163 / 0.058

Excluded Unreasonable Numbers

m0_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_social_fit_diff <- lm(social_fit_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_social_fit_diff <- lm(social_fit_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_social_fit_diff, m1_social_fit_diff, m2_social_fit_diff)

	social_fit_diff			social_fit_diff			social_fit_diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.22	-0.23 – 0.67	0.329	1.24	-1.36 – 3.84	0.346	3.02	0.30 – 5.75	0.030
ActiveDays	-0.02	-0.05 – 0.01	0.113	-0.02	-0.05 – 0.01	0.168	-0.02	-0.05 – 0.00	0.069
Reports	0.00	-0.04 – 0.05	0.840	0.00	-0.04 – 0.05	0.848	-0.01	-0.07 – 0.04	0.620
Activities	0.01	-0.01 – 0.03	0.294	0.01	-0.01 – 0.03	0.317	0.01	-0.01 – 0.03	0.307
univ [UW]				0.06	-0.44 – 0.56	0.804	0.07	-0.46 – 0.59	0.803
Sex [Woman]				0.21	-0.42 – 0.83	0.517	0.21	-0.39 – 0.81	0.486
Age				-0.02	-0.12 – 0.08	0.685	-0.07	-0.17 – 0.04	0.205
int student [No]				-1.06	-2.39 – 0.27	0.116	-1.64	-3.00 – -0.28	0.019
SES num				0.05	-0.16 – 0.26	0.638	0.01	-0.20 – 0.22	0.907
Ethnicity White							0.17	-0.46 – 0.79	0.596
Ethnicity Hispanic							0.14	-0.81 – 1.09	0.768
Ethnicity Black							1.03	-0.43 – 2.49	0.166
Ethnicity East Asian							-0.61	-1.42 – 0.20	0.137
Ethnicity South Asian							-0.20	-1.41 – 1.02	0.751
Ethnicity Native Hawaiian Pacific Islander							-4.43	-6.95 – -1.92	0.001
Ethnicity Middle Eastern							-0.35	-2.85 – 2.14	0.779
Ethnicity American Indian							1.48	-1.79 – 4.75	0.371
Observations	120			120			120
R² / R² adjusted	0.027 / 0.001			0.063 / -0.005			0.215 / 0.093

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	-0.05 – 0.87	0.080	-1.73	-4.34 – 0.88	0.192	-2.22	-5.05 – 0.61	0.124
ActiveDays	-0.01	-0.02 – 0.01	0.216	-0.01	-0.02 – 0.01	0.348	-0.01	-0.02 – 0.01	0.415
Reports	-0.01	-0.05 – 0.03	0.635	-0.01	-0.05 – 0.04	0.726	-0.01	-0.06 – 0.04	0.707
Activities	0.01	-0.01 – 0.03	0.249	0.00	-0.02 – 0.02	0.722	0.00	-0.02 – 0.02	0.761
univ [UW]				0.00	-0.52 – 0.52	0.998	-0.16	-0.73 – 0.41	0.578
Sex [Woman]				-0.01	-0.68 – 0.66	0.979	0.00	-0.68 – 0.68	0.996
Age				0.12	0.02 – 0.22	0.016	0.14	0.04 – 0.24	0.008
int student [No]				0.28	-0.87 – 1.44	0.630	0.60	-0.68 – 1.87	0.356
SES num				-0.13	-0.35 – 0.10	0.261	-0.13	-0.36 – 0.11	0.294
Ethnicity White							-0.05	-0.76 – 0.67	0.894
Ethnicity Hispanic							-0.65	-1.72 – 0.43	0.237
Ethnicity Black							-0.65	-2.19 – 0.90	0.409
Ethnicity East Asian							0.29	-0.65 – 1.23	0.540
Ethnicity South Asian							0.25	-1.02 – 1.52	0.694
Ethnicity Native Hawaiian Pacific Islander							-1.77	-4.84 – 1.30	0.256
Ethnicity Middle Eastern							-1.45	-3.19 – 0.29	0.102
Ethnicity American Indian							1.83	-1.75 – 5.42	0.313
Observations	147			146			146
R² / R² adjusted	0.021 / 0.000			0.078 / 0.025			0.142 / 0.035

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)	0.37	-0.09 – 0.84	0.116	-1.74	-4.35 – 0.88	0.191	-2.23	-5.07 – 0.60	0.122
ActiveDays	-0.01	-0.02 – 0.01	0.225	-0.01	-0.02 – 0.01	0.351	-0.01	-0.02 – 0.01	0.420
Reports	-0.01	-0.05 – 0.03	0.653	-0.01	-0.05 – 0.04	0.732	-0.01	-0.06 – 0.04	0.722
Activities	0.01	-0.01 – 0.03	0.216	0.00	-0.02 – 0.02	0.648	0.00	-0.02 – 0.02	0.680
univ [UW]				-0.02	-0.55 – 0.51	0.939	-0.19	-0.76 – 0.38	0.512
Sex [Woman]				-0.02	-0.70 – 0.65	0.952	-0.01	-0.69 – 0.67	0.976
Age				0.12	0.02 – 0.22	0.019	0.14	0.03 – 0.24	0.010
int student [No]				0.28	-0.87 – 1.44	0.628	0.62	-0.66 – 1.90	0.340
SES num				-0.12	-0.34 – 0.11	0.303	-0.11	-0.35 – 0.12	0.349
Ethnicity White							-0.08	-0.80 – 0.64	0.825
Ethnicity Hispanic							-0.65	-1.73 – 0.43	0.234
Ethnicity Black							-0.65	-2.19 – 0.90	0.408
Ethnicity East Asian							0.29	-0.64 – 1.23	0.536
Ethnicity South Asian							0.25	-1.02 – 1.52	0.695
Ethnicity Native Hawaiian Pacific Islander							-1.77	-4.84 – 1.31	0.257
Ethnicity Middle Eastern							-1.50	-3.24 – 0.25	0.092
Ethnicity American Indian							1.77	-1.82 – 5.36	0.330
Observations	146			145			145
R² / R² adjusted	0.021 / 0.000			0.076 / 0.022			0.140 / 0.033

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)	0.14	-0.37 – 0.66	0.585	-0.46	-3.48 – 2.56	0.762	-0.72	-4.09 – 2.65	0.672
ActiveDays	0.01	-0.02 – 0.04	0.468	0.01	-0.02 – 0.05	0.359	0.01	-0.02 – 0.05	0.455
Reports	-0.02	-0.08 – 0.03	0.457	-0.02	-0.08 – 0.04	0.461	-0.04	-0.11 – 0.03	0.246
Activities	0.01	-0.01 – 0.03	0.290	0.01	-0.02 – 0.03	0.573	0.01	-0.02 – 0.03	0.545
univ [UW]				0.17	-0.41 – 0.75	0.559	-0.03	-0.67 – 0.62	0.930
Sex [Woman]				0.18	-0.54 – 0.91	0.619	0.18	-0.57 – 0.92	0.638
Age				0.03	-0.08 – 0.15	0.597	0.04	-0.09 – 0.16	0.554
int student [No]				0.06	-1.49 – 1.60	0.944	0.35	-1.33 – 2.04	0.679
SES num				-0.08	-0.33 – 0.16	0.504	-0.13	-0.39 – 0.13	0.317
Ethnicity White							0.19	-0.58 – 0.97	0.622
Ethnicity Hispanic							-0.15	-1.32 – 1.03	0.804
Ethnicity Black							0.22	-1.58 – 2.03	0.807
Ethnicity East Asian							0.64	-0.36 – 1.64	0.209
Ethnicity South Asian							0.96	-0.55 – 2.47	0.208
Ethnicity Native Hawaiian Pacific Islander							-1.40	-4.51 – 1.71	0.373
Ethnicity Middle Eastern							-0.30	-3.38 – 2.79	0.850
Ethnicity American Indian							2.34	-1.70 – 6.38	0.253
Observations	120			120			120
R² / R² adjusted	0.025 / -0.000			0.036 / -0.034			0.085 / -0.057

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)	-1.73	-3.46 – 0.00	0.050	3.57	-6.29 – 13.44	0.475	3.29	-7.47 – 14.06	0.546
ActiveDays	-0.02	-0.08 – 0.04	0.527	-0.02	-0.08 – 0.04	0.533	-0.02	-0.08 – 0.04	0.550
Reports	-0.04	-0.20 – 0.12	0.617	-0.07	-0.23 – 0.10	0.410	0.03	-0.16 – 0.23	0.731
Activities	0.09	0.02 – 0.16	0.013	0.10	0.03 – 0.18	0.007	0.10	0.02 – 0.17	0.011
univ [UW]				-1.99	-3.97 – -0.01	0.049	-1.85	-4.02 – 0.31	0.092
Sex [Woman]				-1.50	-4.04 – 1.05	0.247	-1.00	-3.57 – 1.58	0.446
Age				-0.11	-0.48 – 0.27	0.572	-0.09	-0.47 – 0.30	0.659
int student [No]				-0.28	-4.64 – 4.09	0.900	-0.50	-5.34 – 4.34	0.838
SES num				-0.22	-1.07 – 0.63	0.607	-0.42	-1.31 – 0.48	0.355
Ethnicity White							0.46	-2.25 – 3.18	0.737
Ethnicity Hispanic							-3.24	-7.32 – 0.84	0.119
Ethnicity Black							0.12	-5.74 – 5.98	0.968
Ethnicity East Asian							0.01	-3.56 – 3.57	0.998
Ethnicity South Asian							-2.65	-7.47 – 2.16	0.278
Ethnicity Native Hawaiian Pacific Islander							2.96	-8.70 – 14.62	0.616
Ethnicity Middle Eastern							0.58	-6.02 – 7.19	0.862
Ethnicity American Indian							-11.85	-25.46 – 1.76	0.087
Observations	147			146			146
R² / R² adjusted	0.044 / 0.024			0.078 / 0.024			0.133 / 0.025

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)	-1.82	-3.58 – -0.06	0.042	3.55	-6.32 – 13.43	0.478	3.26	-7.52 – 14.04	0.551
ActiveDays	-0.02	-0.08 – 0.04	0.538	-0.02	-0.08 – 0.04	0.535	-0.02	-0.08 – 0.04	0.555
Reports	-0.04	-0.20 – 0.12	0.628	-0.07	-0.23 – 0.10	0.415	0.04	-0.16 – 0.23	0.719
Activities	0.09	0.02 – 0.16	0.012	0.11	0.03 – 0.18	0.006	0.10	0.03 – 0.18	0.009
univ [UW]				-2.07	-4.06 – -0.08	0.042	-1.95	-4.13 – 0.23	0.079
Sex [Woman]				-1.54	-4.09 – 1.01	0.235	-1.03	-3.61 – 1.55	0.430
Age				-0.11	-0.49 – 0.26	0.544	-0.09	-0.48 – 0.29	0.631
int student [No]				-0.27	-4.64 – 4.10	0.903	-0.43	-5.29 – 4.42	0.860
SES num				-0.18	-1.04 – 0.67	0.670	-0.38	-1.28 – 0.52	0.407
Ethnicity White							0.36	-2.37 – 3.09	0.794
Ethnicity Hispanic							-3.26	-7.35 – 0.83	0.118
Ethnicity Black							0.11	-5.76 – 5.99	0.969
Ethnicity East Asian							0.02	-3.56 – 3.59	0.993
Ethnicity South Asian							-2.66	-7.48 – 2.17	0.278
Ethnicity Native Hawaiian Pacific Islander							2.97	-8.71 – 14.65	0.616
Ethnicity Middle Eastern							0.43	-6.20 – 7.06	0.898
Ethnicity American Indian							-12.05	-25.69 – 1.59	0.083
Observations	146			145			145
R² / R² adjusted	0.046 / 0.025			0.082 / 0.028			0.135 / 0.027

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)	-1.82	-3.78 – 0.15	0.070	8.06	-3.09 – 19.22	0.155	3.76	-8.49 – 16.00	0.544
ActiveDays	-0.02	-0.13 – 0.10	0.742	-0.07	-0.18 – 0.05	0.262	-0.07	-0.19 – 0.05	0.239
Reports	-0.13	-0.34 – 0.08	0.222	-0.14	-0.35 – 0.07	0.194	0.00	-0.25 – 0.26	0.975
Activities	0.10	0.03 – 0.18	0.007	0.14	0.05 – 0.22	0.001	0.14	0.06 – 0.22	0.001
univ [UW]				-2.78	-4.93 – -0.64	0.012	-2.63	-4.98 – -0.29	0.028
Sex [Woman]				-2.18	-4.87 – 0.51	0.111	-2.01	-4.72 – 0.69	0.142
Age				-0.15	-0.58 – 0.27	0.477	-0.04	-0.50 – 0.42	0.854
int student [No]				-3.82	-9.51 – 1.88	0.187	-2.75	-8.88 – 3.37	0.375
SES num				0.06	-0.84 – 0.97	0.888	-0.03	-0.96 – 0.90	0.949
Ethnicity White							1.17	-1.64 – 3.98	0.412
Ethnicity Hispanic							-2.52	-6.79 – 1.75	0.245
Ethnicity Black							-2.30	-8.86 – 4.26	0.488
Ethnicity East Asian							1.64	-2.00 – 5.29	0.373
Ethnicity South Asian							-0.94	-6.42 – 4.53	0.733
Ethnicity Native Hawaiian Pacific Islander							3.67	-7.62 – 14.96	0.521
Ethnicity Middle Eastern							9.68	-1.54 – 20.90	0.090
Ethnicity American Indian							-8.04	-22.72 – 6.65	0.280
Observations	120			120			120
R² / R² adjusted	0.077 / 0.053			0.149 / 0.088			0.219 / 0.098

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)	0.21	-0.45 – 0.86	0.539	-0.46	-4.20 – 3.27	0.807	-0.27	-4.29 – 3.75	0.894
ActiveDays	-0.02	-0.04 – 0.01	0.143	-0.02	-0.04 – 0.01	0.136	-0.02	-0.04 – 0.00	0.083
Reports	0.05	-0.01 – 0.11	0.102	0.05	-0.01 – 0.11	0.121	0.04	-0.03 – 0.11	0.283
Activities	0.01	-0.02 – 0.03	0.653	0.00	-0.03 – 0.03	0.970	0.01	-0.02 – 0.03	0.711
univ [UW]				-0.53	-1.28 – 0.22	0.162	-0.54	-1.34 – 0.27	0.190
Sex [Woman]				0.11	-0.85 – 1.07	0.819	-0.01	-0.98 – 0.95	0.977
Age				0.12	-0.02 – 0.26	0.099	0.10	-0.04 – 0.25	0.166
int student [No]				-0.64	-2.29 – 1.01	0.444	-0.54	-2.35 – 1.27	0.557
SES num				-0.22	-0.54 – 0.10	0.176	-0.16	-0.49 – 0.17	0.347
Ethnicity White							-0.40	-1.41 – 0.62	0.439
Ethnicity Hispanic							1.65	0.13 – 3.18	0.034
Ethnicity Black							0.11	-2.08 – 2.30	0.919
Ethnicity East Asian							-0.30	-1.63 – 1.03	0.659
Ethnicity South Asian							0.93	-0.87 – 2.73	0.308
Ethnicity Native Hawaiian Pacific Islander							-0.85	-5.20 – 3.51	0.701
Ethnicity Middle Eastern							0.41	-2.06 – 2.87	0.744
Ethnicity American Indian							-1.55	-6.63 – 3.53	0.547
Observations	146			146			146
R² / R² adjusted	0.023 / 0.003			0.069 / 0.015			0.148 / 0.042

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)	0.09	-0.57 – 0.75	0.786	-0.48	-4.16 – 3.20	0.796	-0.31	-4.25 – 3.63	0.875
ActiveDays	-0.02	-0.04 – 0.01	0.151	-0.02	-0.04 – 0.01	0.133	-0.02	-0.04 – 0.00	0.081
Reports	0.05	-0.01 – 0.11	0.087	0.05	-0.01 – 0.11	0.112	0.04	-0.03 – 0.11	0.250
Activities	0.01	-0.02 – 0.03	0.518	0.00	-0.02 – 0.03	0.764	0.01	-0.02 – 0.04	0.498
univ [UW]				-0.61	-1.35 – 0.13	0.107	-0.65	-1.45 – 0.14	0.107
Sex [Woman]				0.07	-0.88 – 1.02	0.883	-0.06	-1.00 – 0.88	0.902
Age				0.11	-0.03 – 0.25	0.118	0.09	-0.05 – 0.23	0.201
int student [No]				-0.64	-2.26 – 0.99	0.442	-0.46	-2.23 – 1.32	0.612
SES num				-0.19	-0.50 – 0.13	0.253	-0.11	-0.44 – 0.22	0.506
Ethnicity White							-0.52	-1.52 – 0.48	0.304
Ethnicity Hispanic							1.63	0.14 – 3.13	0.032
Ethnicity Black							0.11	-2.04 – 2.25	0.921
Ethnicity East Asian							-0.28	-1.59 – 1.02	0.667
Ethnicity South Asian							0.93	-0.84 – 2.69	0.300
Ethnicity Native Hawaiian Pacific Islander							-0.83	-5.10 – 3.43	0.700
Ethnicity Middle Eastern							0.22	-2.20 – 2.64	0.856
Ethnicity American Indian							-1.79	-6.77 – 3.19	0.479
Observations	145			145			145
R² / R² adjusted	0.026 / 0.005			0.071 / 0.016			0.160 / 0.055

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)	0.14	-0.65 – 0.93	0.722	-2.15	-6.68 – 2.38	0.349	-1.89	-6.79 – 3.00	0.445
ActiveDays	-0.01	-0.06 – 0.04	0.651	-0.02	-0.06 – 0.03	0.530	-0.01	-0.05 – 0.04	0.771
Reports	0.05	-0.03 – 0.14	0.228	0.05	-0.03 – 0.14	0.237	0.04	-0.06 – 0.14	0.461
Activities	0.00	-0.03 – 0.03	0.776	-0.00	-0.03 – 0.03	0.942	0.00	-0.03 – 0.04	0.903
univ [UW]				-0.58	-1.45 – 0.29	0.191	-0.53	-1.47 – 0.40	0.262
Sex [Woman]				-0.08	-1.17 – 1.01	0.885	-0.10	-1.18 – 0.98	0.849
Age				0.15	-0.02 – 0.33	0.084	0.15	-0.04 – 0.33	0.114
int student [No]				0.27	-2.04 – 2.58	0.817	0.03	-2.41 – 2.48	0.978
SES num				-0.13	-0.50 – 0.24	0.480	-0.07	-0.44 – 0.30	0.717
Ethnicity White							-0.64	-1.77 – 0.48	0.257
Ethnicity Hispanic							1.53	-0.18 – 3.24	0.079
Ethnicity Black							-0.48	-3.10 – 2.14	0.716
Ethnicity East Asian							-0.59	-2.05 – 0.87	0.426
Ethnicity South Asian							0.85	-1.34 – 3.04	0.444
Ethnicity Native Hawaiian Pacific Islander							-0.75	-5.26 – 3.77	0.743
Ethnicity Middle Eastern							-2.61	-7.10 – 1.87	0.250
Ethnicity American Indian							-2.21	-8.08 – 3.66	0.456
Observations	120			120			120
R² / R² adjusted	0.014 / -0.012			0.065 / -0.002			0.168 / 0.038

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)	0.07	-0.14 – 0.29	0.513	0.09	-1.15 – 1.32	0.891	0.00	-1.35 – 1.36	0.998
ActiveDays	-0.01	-0.01 – 0.00	0.132	-0.01	-0.01 – 0.00	0.104	-0.01	-0.01 – 0.00	0.129
Reports	0.01	-0.01 – 0.03	0.418	0.01	-0.01 – 0.03	0.568	0.01	-0.01 – 0.04	0.281
Activities	0.01	-0.00 – 0.01	0.143	0.01	-0.00 – 0.02	0.102	0.01	-0.00 – 0.02	0.101
univ [UW]				-0.14	-0.39 – 0.11	0.264	-0.14	-0.41 – 0.14	0.324
Sex [Woman]				0.06	-0.26 – 0.38	0.714	0.10	-0.23 – 0.42	0.552
Age				0.00	-0.05 – 0.05	0.989	0.00	-0.04 – 0.05	0.864
int student [No]				-0.07	-0.62 – 0.48	0.800	-0.09	-0.70 – 0.52	0.774
SES num				0.02	-0.09 – 0.13	0.713	0.00	-0.11 – 0.11	0.977
Ethnicity White							0.09	-0.26 – 0.43	0.619
Ethnicity Hispanic							-0.10	-0.62 – 0.41	0.695
Ethnicity Black							-0.17	-0.91 – 0.57	0.648
Ethnicity East Asian							0.07	-0.38 – 0.52	0.759
Ethnicity South Asian							-0.32	-0.92 – 0.29	0.302
Ethnicity Native Hawaiian Pacific Islander							-1.23	-2.69 – 0.24	0.101
Ethnicity Middle Eastern							-0.22	-1.05 – 0.61	0.595
Ethnicity American Indian							-0.72	-2.43 – 0.99	0.409
Observations	147			146			146
R² / R² adjusted	0.023 / 0.002			0.036 / -0.020			0.083 / -0.030

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)	0.05	-0.17 – 0.26	0.656	0.08	-1.15 – 1.31	0.896	-0.01	-1.35 – 1.34	0.993
ActiveDays	-0.01	-0.01 – 0.00	0.139	-0.01	-0.01 – 0.00	0.103	-0.01	-0.01 – 0.00	0.131
Reports	0.01	-0.01 – 0.03	0.400	0.01	-0.01 – 0.03	0.558	0.01	-0.01 – 0.04	0.266
Activities	0.01	-0.00 – 0.02	0.115	0.01	-0.00 – 0.02	0.072	0.01	-0.00 – 0.02	0.072
univ [UW]				-0.16	-0.41 – 0.09	0.211	-0.16	-0.43 – 0.11	0.254
Sex [Woman]				0.05	-0.27 – 0.37	0.755	0.09	-0.23 – 0.41	0.586
Age				-0.00	-0.05 – 0.05	0.952	0.00	-0.05 – 0.05	0.922
int student [No]				-0.07	-0.61 – 0.47	0.803	-0.07	-0.68 – 0.53	0.813
SES num				0.03	-0.08 – 0.13	0.607	0.01	-0.10 – 0.12	0.851
Ethnicity White							0.06	-0.28 – 0.40	0.717
Ethnicity Hispanic							-0.11	-0.62 – 0.41	0.684
Ethnicity Black							-0.17	-0.91 – 0.56	0.645
Ethnicity East Asian							0.07	-0.37 – 0.52	0.750
Ethnicity South Asian							-0.32	-0.92 – 0.29	0.300
Ethnicity Native Hawaiian Pacific Islander							-1.22	-2.68 – 0.24	0.100
Ethnicity Middle Eastern							-0.26	-1.09 – 0.57	0.539
Ethnicity American Indian							-0.76	-2.47 – 0.94	0.379
Observations	146			145			145
R² / R² adjusted	0.025 / 0.004			0.041 / -0.016			0.087 / -0.027

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	-0.35 – 0.14	0.397	-0.18	-1.59 – 1.24	0.806	-0.33	-1.90 – 1.23	0.671
ActiveDays	0.01	-0.01 – 0.02	0.231	0.01	-0.01 – 0.02	0.215	0.01	-0.01 – 0.02	0.234
Reports	0.00	-0.02 – 0.03	0.927	0.00	-0.03 – 0.03	0.984	0.02	-0.02 – 0.05	0.329
Activities	0.00	-0.00 – 0.01	0.294	0.01	-0.00 – 0.02	0.261	0.01	-0.00 – 0.02	0.206
univ [UW]				-0.07	-0.34 – 0.20	0.617	-0.10	-0.40 – 0.20	0.520
Sex [Woman]				0.21	-0.13 – 0.55	0.222	0.25	-0.10 – 0.59	0.158
Age				0.00	-0.05 – 0.06	0.961	0.00	-0.05 – 0.06	0.902
int student [No]				-0.29	-1.01 – 0.43	0.425	-0.24	-1.02 – 0.54	0.544
SES num				0.05	-0.07 – 0.16	0.408	0.03	-0.09 – 0.15	0.636
Ethnicity White							0.04	-0.32 – 0.40	0.838
Ethnicity Hispanic							-0.11	-0.66 – 0.43	0.678
Ethnicity Black							0.02	-0.81 – 0.86	0.954
Ethnicity East Asian							0.09	-0.38 – 0.55	0.714
Ethnicity South Asian							0.16	-0.54 – 0.86	0.645
Ethnicity Native Hawaiian Pacific Islander							-0.97	-2.42 – 0.47	0.182
Ethnicity Middle Eastern							1.04	-0.40 – 2.47	0.154
Ethnicity American Indian							-1.53	-3.41 – 0.34	0.107
Observations	120			120			120
R² / R² adjusted	0.052 / 0.027			0.080 / 0.014			0.141 / 0.008

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)	0.17	-0.18 – 0.52	0.334	-0.08	-2.08 – 1.92	0.937	0.02	-2.18 – 2.22	0.988
ActiveDays	0.00	-0.01 – 0.01	0.851	0.00	-0.01 – 0.02	0.537	0.00	-0.01 – 0.02	0.570
Reports	0.00	-0.03 – 0.03	0.958	-0.00	-0.03 – 0.03	0.968	-0.01	-0.04 – 0.03	0.785
Activities	-0.00	-0.02 – 0.01	0.504	-0.00	-0.02 – 0.01	0.888	-0.00	-0.02 – 0.01	0.846
univ [UW]				0.06	-0.34 – 0.46	0.773	0.00	-0.44 – 0.45	0.989
Sex [Woman]				0.47	-0.05 – 0.98	0.075	0.48	-0.04 – 1.01	0.071
Age				-0.05	-0.12 – 0.03	0.237	-0.04	-0.12 – 0.04	0.285
int student [No]				0.59	-0.29 – 1.47	0.188	0.59	-0.40 – 1.58	0.243
SES num				0.01	-0.16 – 0.19	0.865	-0.02	-0.20 – 0.16	0.824
Ethnicity White							0.10	-0.46 – 0.65	0.730
Ethnicity Hispanic							-0.31	-1.14 – 0.53	0.469
Ethnicity Black							0.00	-1.20 – 1.20	0.996
Ethnicity East Asian							0.14	-0.59 – 0.87	0.707
Ethnicity South Asian							0.00	-0.98 – 0.99	0.999
Ethnicity Native Hawaiian Pacific Islander							-2.21	-4.60 – 0.17	0.069
Ethnicity Middle Eastern							0.20	-1.15 – 1.55	0.773
Ethnicity American Indian							0.89	-1.89 – 3.67	0.527
Observations	147			146			146
R² / R² adjusted	0.003 / -0.018			0.049 / -0.006			0.087 / -0.026

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)	0.20	-0.15 – 0.55	0.267	-0.08	-2.07 – 1.92	0.941	0.03	-2.17 – 2.23	0.980
ActiveDays	0.00	-0.01 – 0.01	0.870	0.00	-0.01 – 0.02	0.539	0.00	-0.01 – 0.02	0.576
Reports	0.00	-0.03 – 0.03	0.977	-0.00	-0.03 – 0.03	0.962	-0.01	-0.05 – 0.03	0.765
Activities	-0.01	-0.02 – 0.01	0.452	-0.00	-0.02 – 0.01	0.794	-0.00	-0.02 – 0.01	0.740
univ [UW]				0.08	-0.32 – 0.48	0.704	0.03	-0.41 – 0.48	0.885
Sex [Woman]				0.48	-0.04 – 0.99	0.069	0.50	-0.03 – 1.02	0.065
Age				-0.04	-0.12 – 0.03	0.259	-0.04	-0.12 – 0.04	0.313
int student [No]				0.59	-0.29 – 1.47	0.189	0.57	-0.42 – 1.56	0.260
SES num				0.01	-0.17 – 0.18	0.948	-0.03	-0.22 – 0.15	0.724
Ethnicity White							0.13	-0.43 – 0.69	0.649
Ethnicity Hispanic							-0.30	-1.14 – 0.53	0.475
Ethnicity Black							0.00	-1.19 – 1.20	0.994
Ethnicity East Asian							0.14	-0.59 – 0.86	0.713
Ethnicity South Asian							0.00	-0.98 – 0.99	0.998
Ethnicity Native Hawaiian Pacific Islander							-2.21	-4.60 – 0.17	0.068
Ethnicity Middle Eastern							0.24	-1.11 – 1.60	0.721
Ethnicity American Indian							0.95	-1.83 – 3.73	0.499
Observations	146			145			145
R² / R² adjusted	0.004 / -0.017			0.050 / -0.005			0.090 / -0.023

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)	-0.00	-0.39 – 0.38	0.985	-0.58	-2.79 – 1.62	0.602	-0.85	-3.29 – 1.59	0.492
ActiveDays	0.00	-0.02 – 0.03	0.769	0.01	-0.01 – 0.03	0.416	0.01	-0.02 – 0.03	0.532
Reports	0.02	-0.02 – 0.06	0.268	0.02	-0.02 – 0.06	0.339	0.03	-0.02 – 0.08	0.242
Activities	-0.00	-0.02 – 0.01	0.662	-0.00	-0.02 – 0.01	0.725	-0.00	-0.02 – 0.01	0.838
univ [UW]				0.19	-0.23 – 0.62	0.374	0.09	-0.38 – 0.55	0.718
Sex [Woman]				0.61	0.08 – 1.15	0.024	0.65	0.11 – 1.19	0.018
Age				-0.01	-0.10 – 0.07	0.729	-0.00	-0.10 – 0.09	0.921
int student [No]				0.07	-1.05 – 1.20	0.897	0.19	-1.04 – 1.41	0.763
SES num				0.03	-0.15 – 0.21	0.735	-0.01	-0.20 – 0.17	0.898
Ethnicity White							0.12	-0.44 – 0.68	0.661
Ethnicity Hispanic							-0.36	-1.21 – 0.50	0.409
Ethnicity Black							-0.10	-1.41 – 1.21	0.876
Ethnicity East Asian							0.26	-0.47 – 0.99	0.482
Ethnicity South Asian							0.57	-0.52 – 1.66	0.301
Ethnicity Native Hawaiian Pacific Islander							-1.91	-4.16 – 0.35	0.096
Ethnicity Middle Eastern							1.11	-1.13 – 3.35	0.328
Ethnicity American Indian							-0.66	-3.59 – 2.27	0.655
Observations	120			120			120
R² / R² adjusted	0.020 / -0.006			0.073 / 0.007			0.134 / -0.000

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)	1.53	0.43 – 2.63	0.007	3.21	-3.09 – 9.51	0.315	3.34	-3.56 – 10.24	0.340
ActiveDays	-0.04	-0.07 – 0.00	0.060	-0.04	-0.08 – -0.00	0.041	-0.04	-0.08 – 0.00	0.071
Reports	0.03	-0.07 – 0.13	0.571	0.02	-0.09 – 0.12	0.751	0.04	-0.08 – 0.17	0.470
Activities	-0.00	-0.04 – 0.04	0.978	0.01	-0.04 – 0.06	0.685	0.00	-0.05 – 0.05	0.904
univ [UW]				-0.91	-2.17 – 0.36	0.159	-1.44	-2.82 – -0.05	0.042
Sex [Woman]				-0.80	-2.42 – 0.82	0.332	-0.60	-2.25 – 1.05	0.474
Age				-0.06	-0.30 – 0.18	0.608	-0.09	-0.33 – 0.16	0.494
int student [No]				-0.26	-3.05 – 2.52	0.852	0.30	-2.80 – 3.41	0.848
SES num				0.26	-0.28 – 0.81	0.337	0.19	-0.38 – 0.76	0.511
Ethnicity White							0.03	-1.71 – 1.77	0.975
Ethnicity Hispanic							-1.52	-4.13 – 1.10	0.254
Ethnicity Black							2.83	-0.93 – 6.59	0.139
Ethnicity East Asian							1.15	-1.14 – 3.43	0.323
Ethnicity South Asian							-0.33	-3.42 – 2.75	0.831
Ethnicity Native Hawaiian Pacific Islander							-2.74	-10.21 – 4.74	0.470
Ethnicity Middle Eastern							-1.72	-5.96 – 2.51	0.422
Ethnicity American Indian							-1.39	-10.12 – 7.33	0.752
Observations	146			146			146
R² / R² adjusted	0.033 / 0.012			0.060 / 0.005			0.108 / -0.003

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)	1.52	0.40 – 2.63	0.008	3.20	-3.11 – 9.52	0.318	3.32	-3.60 – 10.25	0.344
ActiveDays	-0.03	-0.07 – 0.00	0.061	-0.04	-0.08 – -0.00	0.042	-0.04	-0.08 – 0.00	0.072
Reports	0.03	-0.07 – 0.13	0.571	0.02	-0.09 – 0.12	0.749	0.05	-0.08 – 0.17	0.465
Activities	-0.00	-0.04 – 0.04	0.987	0.01	-0.04 – 0.06	0.652	0.00	-0.04 – 0.05	0.859
univ [UW]				-0.93	-2.20 – 0.34	0.151	-1.48	-2.88 – -0.08	0.039
Sex [Woman]				-0.81	-2.44 – 0.82	0.326	-0.61	-2.27 – 1.04	0.464
Age				-0.06	-0.30 – 0.17	0.595	-0.09	-0.34 – 0.16	0.479
int student [No]				-0.26	-3.06 – 2.53	0.854	0.33	-2.79 – 3.45	0.835
SES num				0.28	-0.27 – 0.82	0.321	0.21	-0.37 – 0.79	0.480
Ethnicity White							-0.01	-1.77 – 1.74	0.987
Ethnicity Hispanic							-1.52	-4.15 – 1.10	0.254
Ethnicity Black							2.83	-0.94 – 6.60	0.140
Ethnicity East Asian							1.15	-1.14 – 3.44	0.323
Ethnicity South Asian							-0.33	-3.43 – 2.76	0.831
Ethnicity Native Hawaiian Pacific Islander							-2.73	-10.23 – 4.76	0.472
Ethnicity Middle Eastern							-1.79	-6.04 – 2.47	0.407
Ethnicity American Indian							-1.48	-10.23 – 7.28	0.739
Observations	145			145			145
R² / R² adjusted	0.032 / 0.012			0.060 / 0.005			0.109 / -0.003

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)	1.24	0.02 – 2.47	0.047	2.98	-4.13 – 10.09	0.408	2.05	-6.00 – 10.11	0.614
ActiveDays	-0.00	-0.07 – 0.07	0.960	-0.01	-0.09 – 0.06	0.753	-0.01	-0.09 – 0.06	0.719
Reports	-0.02	-0.15 – 0.11	0.806	-0.01	-0.14 – 0.12	0.903	-0.01	-0.18 – 0.16	0.877
Activities	-0.01	-0.05 – 0.04	0.792	0.00	-0.05 – 0.05	0.964	0.00	-0.05 – 0.06	0.926
univ [UW]				-0.54	-1.91 – 0.83	0.438	-0.81	-2.35 – 0.73	0.301
Sex [Woman]				-0.49	-2.20 – 1.23	0.575	-0.43	-2.21 – 1.35	0.630
Age				-0.02	-0.29 – 0.25	0.877	-0.02	-0.32 – 0.28	0.882
int student [No]				-2.21	-5.84 – 1.42	0.230	-1.46	-5.49 – 2.57	0.475
SES num				0.44	-0.13 – 1.02	0.131	0.34	-0.27 – 0.95	0.275
Ethnicity White							0.73	-1.12 – 2.58	0.436
Ethnicity Hispanic							0.17	-2.64 – 2.98	0.904
Ethnicity Black							1.09	-3.23 – 5.41	0.617
Ethnicity East Asian							1.59	-0.81 – 3.99	0.192
Ethnicity South Asian							1.20	-2.40 – 4.81	0.509
Ethnicity Native Hawaiian Pacific Islander							-1.39	-8.82 – 6.05	0.712
Ethnicity Middle Eastern							0.30	-7.08 – 7.69	0.935
Ethnicity American Indian							1.03	-8.63 – 10.70	0.832
Observations	120			120			120
R² / R² adjusted	0.002 / -0.024			0.042 / -0.028			0.062 / -0.083

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)	0.12	-0.23 – 0.48	0.501	-1.91	-3.93 – 0.11	0.064	-1.37	-3.49 – 0.76	0.205
ActiveDays	-0.00	-0.01 – 0.01	0.880	-0.00	-0.01 – 0.01	0.888	0.00	-0.01 – 0.01	0.864
Reports	-0.01	-0.04 – 0.02	0.514	-0.00	-0.04 – 0.03	0.884	0.01	-0.03 – 0.05	0.679
Activities	0.01	-0.00 – 0.02	0.172	0.00	-0.01 – 0.02	0.573	0.00	-0.01 – 0.02	0.705
univ [UW]				0.14	-0.27 – 0.54	0.504	0.02	-0.41 – 0.44	0.941
Sex [Woman]				0.36	-0.16 – 0.88	0.170	0.40	-0.11 – 0.91	0.121
Age				0.09	0.01 – 0.16	0.025	0.06	-0.02 – 0.14	0.128
int student [No]				-0.09	-0.98 – 0.81	0.847	-0.04	-1.00 – 0.91	0.929
SES num				0.03	-0.15 – 0.20	0.755	0.04	-0.13 – 0.22	0.631
Ethnicity White							-0.32	-0.85 – 0.22	0.246
Ethnicity Hispanic							0.35	-0.46 – 1.15	0.398
Ethnicity Black							1.40	0.24 – 2.56	0.018
Ethnicity East Asian							0.14	-0.56 – 0.85	0.685
Ethnicity South Asian							-0.70	-1.65 – 0.25	0.149
Ethnicity Native Hawaiian Pacific Islander							-1.26	-3.56 – 1.04	0.279
Ethnicity Middle Eastern							-0.52	-1.82 – 0.79	0.435
Ethnicity American Indian							-1.84	-4.53 – 0.84	0.176
Observations	147			146			146
R² / R² adjusted	0.016 / -0.005			0.058 / 0.003			0.176 / 0.073

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)	0.12	-0.24 – 0.48	0.498	-1.91	-3.93 – 0.12	0.065	-1.37	-3.50 – 0.77	0.207
ActiveDays	-0.00	-0.01 – 0.01	0.878	-0.00	-0.01 – 0.01	0.888	0.00	-0.01 – 0.01	0.864
Reports	-0.01	-0.04 – 0.02	0.514	-0.00	-0.04 – 0.03	0.883	0.01	-0.03 – 0.05	0.679
Activities	0.01	-0.00 – 0.02	0.178	0.00	-0.01 – 0.02	0.599	0.00	-0.01 – 0.02	0.705
univ [UW]				0.14	-0.27 – 0.55	0.492	0.01	-0.42 – 0.45	0.945
Sex [Woman]				0.37	-0.16 – 0.89	0.169	0.40	-0.11 – 0.91	0.123
Age				0.09	0.01 – 0.16	0.025	0.06	-0.02 – 0.14	0.130
int student [No]				-0.09	-0.98 – 0.81	0.847	-0.04	-1.00 – 0.92	0.931
SES num				0.03	-0.15 – 0.20	0.777	0.04	-0.14 – 0.22	0.631
Ethnicity White							-0.32	-0.86 – 0.22	0.248
Ethnicity Hispanic							0.34	-0.46 – 1.15	0.400
Ethnicity Black							1.40	0.24 – 2.56	0.019
Ethnicity East Asian							0.14	-0.56 – 0.85	0.686
Ethnicity South Asian							-0.70	-1.65 – 0.26	0.150
Ethnicity Native Hawaiian Pacific Islander							-1.26	-3.57 – 1.05	0.281
Ethnicity Middle Eastern							-0.52	-1.83 – 0.79	0.436
Ethnicity American Indian							-1.85	-4.54 – 0.85	0.178
Observations	146			145			145
R² / R² adjusted	0.015 / -0.005			0.058 / 0.002			0.175 / 0.072

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)	0.28	-0.09 – 0.65	0.135	0.12	-2.01 – 2.26	0.909	0.84	-1.42 – 3.10	0.465
ActiveDays	-0.03	-0.05 – -0.01	0.011	-0.02	-0.05 – -0.00	0.039	-0.02	-0.05 – -0.00	0.039
Reports	0.01	-0.03 – 0.05	0.510	0.01	-0.03 – 0.05	0.529	0.01	-0.04 – 0.06	0.616
Activities	0.02	0.00 – 0.03	0.028	0.01	-0.00 – 0.03	0.070	0.01	-0.00 – 0.03	0.064
univ [UW]				0.14	-0.27 – 0.55	0.496	0.05	-0.39 – 0.48	0.836
Sex [Woman]				0.42	-0.09 – 0.94	0.106	0.47	-0.03 – 0.97	0.064
Age				0.00	-0.08 – 0.09	0.909	-0.05	-0.13 – 0.04	0.289
int student [No]				-0.60	-1.69 – 0.49	0.277	-0.40	-1.54 – 0.73	0.480
SES num				0.05	-0.12 – 0.23	0.546	0.02	-0.15 – 0.20	0.786
Ethnicity White							0.09	-0.42 – 0.61	0.719
Ethnicity Hispanic							0.64	-0.15 – 1.43	0.113
Ethnicity Black							1.92	0.71 – 3.14	0.002
Ethnicity East Asian							0.39	-0.29 – 1.06	0.259
Ethnicity South Asian							-0.31	-1.32 – 0.70	0.546
Ethnicity Native Hawaiian Pacific Islander							-1.27	-3.35 – 0.82	0.231
Ethnicity Middle Eastern							-0.15	-2.22 – 1.93	0.889
Ethnicity American Indian							-0.81	-3.52 – 1.90	0.555
Observations	120			120			120
R² / R² adjusted	0.068 / 0.044			0.111 / 0.047			0.242 / 0.124

Visualizations

Depression

Intention to Treat

ggplot(data_ITT, aes(x = time, y = depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression",
       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"))

Anxiety

Intention to Treat

ggplot(data_ITT, aes(x = time, y = anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety",
       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"))

Loneliness

Intention to Treat

ggplot(data_ITT, aes(x = time, y = loneliness, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Loneliness",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = loneliness, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Loneliness",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = loneliness, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Loneliness",
       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"))

Perceived Stress

Intention to Treat

ggplot(data_ITT, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Stress",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Stress",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = perceived_stress, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Stress",
       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"))

SAS: Calm

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Calm (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Calm (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Calm (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Calm (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_calm, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Calm (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Calm (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"))

SAS: Well-Being

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Well-Being (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Well-Being (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Well-Being (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Well-Being (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_well_being, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Well-Being (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Well-Being (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"))

SAS: Vigour

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Vigour (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Vigour (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Vigour (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Vigour (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_vigour, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Vigour (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Vigour (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"))

SAS: Depression

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Depression (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Depression (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_depression, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Depression (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Depression (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"))

SAS: Anxiety

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anxiety (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anxiety (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_anxiety, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anxiety (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anxiety (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"))

SAS: Anger

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anger (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anger (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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anger (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anger (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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_anger, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Anger (SAS)",
       x = "Time",
       y = "Affect Subcomponent: Anger (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"))

SAS: Positive

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Positive Affect (SAS)",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Positive Affect (SAS)",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_positive, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Positive Affect (SAS)",
       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"))

SAS: Negative

Intention to Treat

ggplot(data_ITT, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Negative Affect (SAS)",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Negative Affect (SAS)",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = SAS_negative, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Negative Affect (SAS)",
       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"))

Flourishing Score

Intention to Treat

ggplot(data_ITT, aes(x = time, y = flourishing, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Flourishing Score",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = flourishing, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Flourishing Score",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = flourishing, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Flourishing Score",
       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"))

Social Fit

Intention to Treat

ggplot(data_ITT, aes(x = time, y = social_fit, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Social Fit",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = social_fit, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Social Fit",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = social_fit, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Social Fit",
       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"))

Cohesion

Intention to Treat

ggplot(data_ITT, aes(x = time, y = cohesion, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Cohesion",
       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"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = cohesion, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Cohesion",
       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"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = cohesion, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Cohesion",
       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"))

Mindfulness

Intention to Treat

ggplot(data_ITT, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Mindfulness",
       x = "Time",
       y = "Mindfulness (5-36)",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Mindfulness",
       x = "Time",
       y = "Mindfulness (5-36)",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = mindfulness_rev, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Mindfulness",
       x = "Time",
       y = "Mindfulness (5-36)",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Emotional Resilience

Intention to Treat

ggplot(data_ITT, aes(x = time, y = emo_res, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Emotional Resilience",
       x = "Time",
       y = "Emotional Resilience",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = emo_res, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Emotional Resilience",
       x = "Time",
       y = "Emotional Resilience",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = emo_res, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Emotional Resilience",
       x = "Time",
       y = "Emotional Resilience",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

School Satisfaction

Intention to Treat

ggplot(data_ITT, aes(x = time, y = school_satis, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Satisfaction",
       x = "Time",
       y = "School Satisfaction",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = school_satis, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Satisfaction",
       x = "Time",
       y = "School Satisfaction",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = school_satis, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Satisfaction",
       x = "Time",
       y = "School Satisfaction",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

School Prioritizes Well-Being

Intention to Treat

ggplot(data_ITT, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Prioritizing Well-Being",
       x = "Time",
       y = "School Prioritizes Well-Being",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Prioritizing Well-Being",
       x = "Time",
       y = "School Prioritizes Well-Being",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = wellbeing_priority, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on School Prioritizing Well-Being",
       x = "Time",
       y = "School Prioritizes Well-Being",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Academic Self-Efficacy

Intention to Treat

ggplot(data_ITT, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Academic Self-Efficacy",
       x = "Time",
       y = "Academic Self-Efficacy",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Academic Self-Efficacy",
       x = "Time",
       y = "Academic Self-Efficacy",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = acad_selfefficacy, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on Academic Self-Efficacy",
       x = "Time",
       y = "Academic Self-Efficacy",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Closeness to School (IOS)

Intention to Treat

ggplot(data_ITT, aes(x = time, y = ios, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on IOS",
       x = "Time",
       y = "IOS",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Preregistered

ggplot(data_excluded, aes(x = time, y = ios, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on IOS",
       x = "Time",
       y = "IOS",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

Excluded Unreasonable Numbers

ggplot(data_excluded_unreasonable, aes(x = time, y = ios, color = cond, group = cond)) +
  # geom_jitter(aes(shape = cond), 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(aes(linetype = cond), stat = "summary", fun = mean, size = 1.2) +  # Plot the mean anxiety per condition over time
  geom_point(aes(shape = cond), stat = "summary", fun = mean, size = 3) +  # Add points for the mean at each time point
  labs(title = "Interaction of Condition and Time on IOS",
       x = "Time",
       y = "IOS",
       color = "Condition",
       linetype = "Condition",
       shape = "Condition") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 4), labels = c("Week 0", "Week 6"))

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: 5079.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6276 -0.5341 -0.1472  0.4482  3.5746 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.41128  1.1880  
##  univ      (Intercept) 0.01954  0.1398  
##  Residual              0.79606  0.8922  
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      1.53792    0.09510    2.73393
## condflourish_vs_control                         -0.04400    0.05719  516.52446
## treatment_vs_baseline                            0.03053    0.03921 1095.13967
## condflourish_vs_control:treatment_vs_baseline   -0.04518    0.03905 1131.67548
##                                               t value Pr(>|t|)    
## (Intercept)                                    16.171 0.000855 ***
## condflourish_vs_control                        -0.769 0.441967    
## treatment_vs_baseline                           0.779 0.436248    
## condflourish_vs_control:treatment_vs_baseline  -1.157 0.247506    
## ---
## 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  0.053 -0.008       
## cndflr__:__ -0.005  0.101  0.001

# 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: 5584.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3052 -0.5349 -0.0868  0.4862  3.3635 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.806    1.344   
##  univ      (Intercept) 0.000    0.000   
##  Residual              1.130    1.063   
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                    2.313e+00  6.533e-02  5.272e+02
## condflourish_vs_control                       -1.086e-01  6.533e-02  5.272e+02
## treatment_vs_baseline                         -1.147e-01  4.643e-02  1.147e+03
## condflourish_vs_control:treatment_vs_baseline  5.757e-03  4.643e-02  1.147e+03
##                                               t value Pr(>|t|)    
## (Intercept)                                    35.406   <2e-16 ***
## condflourish_vs_control                        -1.663   0.0969 .  
## treatment_vs_baseline                          -2.470   0.0136 *  
## condflourish_vs_control:treatment_vs_baseline   0.124   0.9014    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.005              
## trtmnt_vs_b  0.104 -0.009       
## cndflr__:__ -0.009  0.104  0.002
## 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)

## boundary (singular) fit: see help('isSingular')

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: 5383.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2470 -0.5360 -0.0445  0.4907  3.2352 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  unique_ID (Intercept) 1.796e+00 1.3402603
##  univ      (Intercept) 1.299e-10 0.0000114
##  Residual              9.497e-01 0.9745034
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      5.27873    0.06413  537.10147
## condflourish_vs_control                         -0.09094    0.06413  537.12439
## treatment_vs_baseline                           -0.31722    0.04270 1143.48250
## condflourish_vs_control:treatment_vs_baseline   -0.07892    0.04270 1143.48250
##                                               t value Pr(>|t|)    
## (Intercept)                                    82.317  < 2e-16 ***
## condflourish_vs_control                        -1.418   0.1567    
## treatment_vs_baseline                          -7.429 2.14e-13 ***
## condflourish_vs_control:treatment_vs_baseline  -1.848   0.0648 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.005              
## trtmnt_vs_b  0.100 -0.009       
## cndflr__:__ -0.009  0.100  0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# 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: 7290.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.14558 -0.59004 -0.02378  0.53004  3.12419 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 5.039    2.245   
##  univ      (Intercept) 0.000    0.000   
##  Residual              3.411    1.847   
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      6.65009    0.11000  518.08665
## condflourish_vs_control                         -0.08706    0.11000  518.08665
## treatment_vs_baseline                           -0.14955    0.08052 1144.17733
## condflourish_vs_control:treatment_vs_baseline   -0.02587    0.08052 1144.17733
##                                               t value Pr(>|t|)    
## (Intercept)                                    60.454   <2e-16 ***
## condflourish_vs_control                        -0.791   0.4290    
## treatment_vs_baseline                          -1.857   0.0635 .  
## condflourish_vs_control:treatment_vs_baseline  -0.321   0.7480    
## ---
## 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  0.106 -0.009       
## cndflr__:__ -0.009  0.106  0.002
## 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: 7011.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6239 -0.5759  0.0304  0.5843  3.4269 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.720    1.929   
##  univ      (Intercept) 0.000    0.000   
##  Residual              2.987    1.728   
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                   5.725e+00  9.634e-02 5.210e+02
## condflourish_vs_control                       2.392e-01  9.634e-02 5.210e+02
## treatment_vs_baseline                         1.087e-01  7.508e-02 1.160e+03
## condflourish_vs_control:treatment_vs_baseline 1.422e-01  7.508e-02 1.160e+03
##                                               t value Pr(>|t|)    
## (Intercept)                                    59.430   <2e-16 ***
## condflourish_vs_control                         2.483   0.0134 *  
## treatment_vs_baseline                           1.448   0.1478    
## condflourish_vs_control:treatment_vs_baseline   1.894   0.0585 .  
## ---
## 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  0.110 -0.009       
## cndflr__:__ -0.009  0.110  0.003
## 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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00313616 (tol = 0.002, component 1)

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: 6806
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2282 -0.5545  0.0316  0.5419  3.7714 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.91862  1.9796  
##  univ      (Intercept) 0.08618  0.2936  
##  Residual              2.45797  1.5678  
## Number of obs: 1578, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      6.78619    0.18417    2.90310
## condflourish_vs_control                          0.19139    0.09628  513.45901
## treatment_vs_baseline                           -0.19437    0.06884 1098.50013
## condflourish_vs_control:treatment_vs_baseline    0.06804    0.06848 1135.27760
##                                               t value Pr(>|t|)    
## (Intercept)                                    36.847 5.72e-05 ***
## condflourish_vs_control                         1.988  0.04735 *  
## treatment_vs_baseline                          -2.823  0.00484 ** 
## condflourish_vs_control:treatment_vs_baseline   0.994  0.32067    
## ---
## 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  0.046 -0.007       
## cndflr__:__ -0.005  0.104  0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00313616 (tol = 0.002, component 1)

model_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: 7029
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1757 -0.5455  0.0065  0.5517  3.6339 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.729    2.1747  
##  univ      (Intercept) 0.075    0.2739  
##  Residual              2.784    1.6687  
## Number of obs: 1578, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      5.68211    0.18169    3.12947
## condflourish_vs_control                          0.16283    0.10511  519.68607
## treatment_vs_baseline                           -0.17619    0.07330 1102.48177
## condflourish_vs_control:treatment_vs_baseline    0.11781    0.07298 1135.72593
##                                               t value Pr(>|t|)    
## (Intercept)                                    31.274 5.18e-05 ***
## condflourish_vs_control                         1.549   0.1220    
## treatment_vs_baseline                          -2.404   0.0164 *  
## condflourish_vs_control:treatment_vs_baseline   1.614   0.1067    
## ---
## 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  0.051 -0.008       
## cndflr__:__ -0.005  0.102  0.002

model_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: 7311.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7257 -0.5455 -0.0889  0.4969  3.4397 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 5.523    2.350   
##  univ      (Intercept) 0.000    0.000   
##  Residual              3.371    1.836   
## Number of obs: 1578, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      4.05157    0.11396  514.30263
## condflourish_vs_control                         -0.13155    0.11396  514.30263
## treatment_vs_baseline                           -0.16438    0.08023 1131.97421
## condflourish_vs_control:treatment_vs_baseline    0.04436    0.08023 1131.97421
##                                               t value Pr(>|t|)    
## (Intercept)                                    35.552   <2e-16 ***
## condflourish_vs_control                        -1.154   0.2489    
## treatment_vs_baseline                          -2.049   0.0407 *  
## condflourish_vs_control:treatment_vs_baseline   0.553   0.5805    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.005              
## trtmnt_vs_b  0.103 -0.009       
## cndflr__:__ -0.009  0.103  0.002
## 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: 7347.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5076 -0.6184  0.0110  0.5978  3.1874 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.487    2.118   
##  univ      (Intercept) 0.000    0.000   
##  Residual              3.731    1.932   
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      5.97273    0.10625  523.83730
## condflourish_vs_control                         -0.14454    0.10625  523.83730
## treatment_vs_baseline                           -0.34529    0.08384 1164.71696
## condflourish_vs_control:treatment_vs_baseline   -0.03686    0.08384 1164.71696
##                                               t value Pr(>|t|)    
## (Intercept)                                    56.214  < 2e-16 ***
## condflourish_vs_control                        -1.360    0.174    
## treatment_vs_baseline                          -4.118 4.09e-05 ***
## condflourish_vs_control:treatment_vs_baseline  -0.440    0.660    
## ---
## 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  0.110 -0.009       
## cndflr__:__ -0.009  0.110  0.003
## 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: 6884.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6460 -0.5200 -0.1858  0.4596  4.5997 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.7755   1.9431  
##  univ      (Intercept) 0.1491   0.3861  
##  Residual              2.6550   1.6294  
## Number of obs: 1579, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                   2.783e+00  2.240e-01 2.712e+00
## condflourish_vs_control                       9.271e-02  9.563e-02 5.165e+02
## treatment_vs_baseline                         2.823e-02  7.149e-02 1.110e+03
## condflourish_vs_control:treatment_vs_baseline 1.963e-02  7.099e-02 1.148e+03
##                                               t value Pr(>|t|)   
## (Intercept)                                    12.428  0.00181 **
## condflourish_vs_control                         0.969  0.33276   
## treatment_vs_baseline                           0.395  0.69301   
## condflourish_vs_control:treatment_vs_baseline   0.277  0.78220   
## ---
## 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  0.037 -0.008       
## cndflr__:__ -0.004  0.106  0.002

model_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: 9849
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0106 -0.5439 -0.0053  0.5313  4.4560 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 31.2996  5.5946  
##  univ      (Intercept)  0.3171  0.5631  
##  Residual              16.1798  4.0224  
## Number of obs: 1577, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                     18.2268     0.4088    2.9674
## condflourish_vs_control                          0.5787     0.2672  519.9378
## treatment_vs_baseline                           -0.2633     0.1770 1094.9451
## condflourish_vs_control:treatment_vs_baseline    0.3103     0.1764 1125.7934
##                                               t value Pr(>|t|)    
## (Intercept)                                    44.587 2.73e-05 ***
## condflourish_vs_control                         2.166   0.0308 *  
## treatment_vs_baseline                          -1.488   0.1371    
## condflourish_vs_control:treatment_vs_baseline   1.759   0.0788 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.000              
## trtmnt_vs_b  0.058 -0.007       
## cndflr__:__ -0.005  0.099  0.001

model_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: 9906.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4088 -0.5603 -0.0323  0.5007  4.5600 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 30.98    5.566   
##  univ      (Intercept)  0.00    0.000   
##  Residual              17.03    4.127   
## Number of obs: 1578, groups:  unique_ID, 538; univ, 4
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                     12.66337    0.26727  516.94466
## condflourish_vs_control                         -0.18210    0.26727  516.94466
## treatment_vs_baseline                           -0.47885    0.18071 1126.85098
## condflourish_vs_control:treatment_vs_baseline    0.02364    0.18071 1126.85098
##                                               t value Pr(>|t|)    
## (Intercept)                                    47.380  < 2e-16 ***
## condflourish_vs_control                        -0.681  0.49597    
## treatment_vs_baseline                          -2.650  0.00817 ** 
## condflourish_vs_control:treatment_vs_baseline   0.131  0.89594    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.005              
## trtmnt_vs_b  0.101 -0.009       
## cndflr__:__ -0.009  0.101  0.002
## 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: 5264.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.9131 -0.3853  0.0723  0.4183  3.2642 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 31.5907  5.6206  
##  univ      (Intercept)  0.9774  0.9886  
##  Residual              11.5014  3.3914  
## Number of obs: 832, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                           44.32625    0.59860
## cond_factorflourish_vs_control                         0.29351    0.29024
## treatment_vs_baseline                                 -0.09993    0.20249
## cond_factorflourish_vs_control:treatment_vs_baseline   0.39549    0.19955
##                                                             df t value Pr(>|t|)
## (Intercept)                                            3.72827  74.050 4.81e-07
## cond_factorflourish_vs_control                       625.85614   1.011   0.3123
## treatment_vs_baseline                                314.75135  -0.494   0.6220
## cond_factorflourish_vs_control:treatment_vs_baseline 339.60278   1.982   0.0483
##                                                         
## (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__ -0.009              
## trtmnt_vs_b  0.151 -0.007       
## cnd_fc__:__ -0.007  0.338 -0.001

# 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: 2601
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1800 -0.4972  0.0051  0.5420  2.5763 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.59906  0.7740  
##  univ      (Intercept) 0.05129  0.2265  
##  Residual              0.80070  0.8948  
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            6.88841    0.12945
## cond_factorflourish_vs_control                         0.04787    0.05207
## treatment_vs_baseline                                  0.06590    0.05199
## cond_factorflourish_vs_control:treatment_vs_baseline  -0.01008    0.05023
##                                                             df t value Pr(>|t|)
## (Intercept)                                            2.95547  53.215 1.68e-05
## cond_factorflourish_vs_control                       674.71814   0.919    0.358
## treatment_vs_baseline                                332.94756   1.268    0.206
## cond_factorflourish_vs_control:treatment_vs_baseline 397.08949  -0.201    0.841
##                                                         
## (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__ -0.011              
## trtmnt_vs_b  0.172 -0.010       
## cnd_fc__:__ -0.009  0.456 -0.003

# 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: 3388.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.46354 -0.39542  0.03282  0.45264  2.59761 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.78182  1.9447  
##  univ      (Intercept) 0.05977  0.2445  
##  Residual              1.01299  1.0065  
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            5.69083    0.16383
## cond_factorflourish_vs_control                         0.18845    0.09656
## treatment_vs_baseline                                  0.16419    0.06018
## cond_factorflourish_vs_control:treatment_vs_baseline   0.14139    0.05965
##                                                             df t value Pr(>|t|)
## (Intercept)                                            3.54254  34.737 1.29e-05
## cond_factorflourish_vs_control                       607.24512   1.952  0.05143
## treatment_vs_baseline                                310.10159   2.728  0.00673
## cond_factorflourish_vs_control:treatment_vs_baseline 327.63322   2.370  0.01835
##                                                         
## (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__ -0.009              
## trtmnt_vs_b  0.169 -0.009       
## cnd_fc__:__ -0.008  0.305 -0.004

# 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: 5235.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.09075 -0.49465  0.02158  0.47054  2.57528 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 21.75    4.664   
##  univ      (Intercept)  0.00    0.000   
##  Residual              14.96    3.868   
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                      Estimate Std. Error
## (Intercept)                                           20.4188     0.2680
## cond_factorflourish_vs_control                        -0.3182     0.2680
## treatment_vs_baseline                                  0.7544     0.2224
## cond_factorflourish_vs_control:treatment_vs_baseline  -0.4489     0.2224
##                                                            df t value Pr(>|t|)
## (Intercept)                                          654.5269  76.198  < 2e-16
## cond_factorflourish_vs_control                       654.5269  -1.187 0.235472
## treatment_vs_baseline                                361.4709   3.392 0.000772
## cond_factorflourish_vs_control:treatment_vs_baseline 361.4709  -2.018 0.044283
##                                                         
## (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__ -0.009              
## trtmnt_vs_b  0.400 -0.015       
## cnd_fc__:__ -0.015  0.400 -0.005
## 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: 3450
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5904 -0.4974 -0.0658  0.4275  4.9637 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.21420  1.1019  
##  univ      (Intercept) 0.01838  0.1356  
##  Residual              2.58687  1.6084  
## Number of obs: 832, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                           18.19347    0.11511
## cond_factorflourish_vs_control                         0.04031    0.08513
## treatment_vs_baseline                                  0.01554    0.09016
## cond_factorflourish_vs_control:treatment_vs_baseline   0.07091    0.08893
##                                                             df t value Pr(>|t|)
## (Intercept)                                            0.48782 158.056   0.0545
## cond_factorflourish_vs_control                       665.37940   0.474   0.6360
## treatment_vs_baseline                                150.30861   0.172   0.8634
## cond_factorflourish_vs_control:treatment_vs_baseline 395.84033   0.797   0.4257
##                                                       
## (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__ -0.015              
## trtmnt_vs_b  0.354 -0.015       
## cnd_fc__:__ -0.015  0.485 -0.003

# 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: 1998.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.68042 -0.39158  0.08191  0.34974  1.95903 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.53291  0.7300  
##  univ      (Intercept) 0.01781  0.1334  
##  Residual              0.25364  0.5036  
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            4.54579    0.08089
## cond_factorflourish_vs_control                         0.05039    0.03924
## treatment_vs_baseline                                  0.07150    0.02986
## cond_factorflourish_vs_control:treatment_vs_baseline   0.02936    0.02934
##                                                             df t value Pr(>|t|)
## (Intercept)                                            3.75401  56.200 1.24e-06
## cond_factorflourish_vs_control                       642.61789   1.284   0.1995
## treatment_vs_baseline                                324.05101   2.395   0.0172
## cond_factorflourish_vs_control:treatment_vs_baseline 356.08655   1.001   0.3177
##                                                         
## (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__ -0.010              
## trtmnt_vs_b  0.164 -0.009       
## cnd_fc__:__ -0.009  0.364 -0.004

# 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: 2659.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0423 -0.5460 -0.1253  0.6298  2.3347 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.7212   0.8492  
##  univ      (Intercept) 0.1110   0.3332  
##  Residual              0.8054   0.8974  
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            4.68203    0.17943
## cond_factorflourish_vs_control                         0.05583    0.05450
## treatment_vs_baseline                                  0.08902    0.05268
## cond_factorflourish_vs_control:treatment_vs_baseline   0.04220    0.05071
##                                                             df t value Pr(>|t|)
## (Intercept)                                            3.02673  26.093 0.000116
## cond_factorflourish_vs_control                       676.25125   1.024 0.306005
## treatment_vs_baseline                                347.36020   1.690 0.091950
## cond_factorflourish_vs_control:treatment_vs_baseline 398.03173   0.832 0.405755
##                                                         
## (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__ -0.009              
## trtmnt_vs_b  0.123 -0.009       
## cnd_fc__:__ -0.007  0.442 -0.002

# 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: 4575.1
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.47686 -0.42659  0.08612  0.48696  2.00658 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  unique_ID (Intercept) 9.882e+00 3.144e+00
##  univ      (Intercept) 8.614e-10 2.935e-05
##  Residual              6.862e+00 2.620e+00
## Number of obs: 831, groups:  unique_ID, 531; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                           24.13396    0.18105
## cond_factorflourish_vs_control                         0.09736    0.18105
## treatment_vs_baseline                                  0.39066    0.15078
## cond_factorflourish_vs_control:treatment_vs_baseline   0.14983    0.15078
##                                                             df t value Pr(>|t|)
## (Intercept)                                          659.19075 133.298  < 2e-16
## cond_factorflourish_vs_control                       659.19812   0.538  0.59095
## treatment_vs_baseline                                370.84126   2.591  0.00995
## cond_factorflourish_vs_control:treatment_vs_baseline 370.84126   0.994  0.32103
##                                                         
## (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__ -0.008              
## trtmnt_vs_b  0.400 -0.016       
## cnd_fc__:__ -0.016  0.400 -0.003
## 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: 2800.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2952 -0.5053 -0.0996  0.4565  3.5837 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.340281 1.15770 
##  univ      (Intercept) 0.006155 0.07845 
##  Residual              0.699944 0.83663 
## Number of obs: 833, groups:  unique_ID, 532; univ, 4
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            3.33724    0.07743
## cond_factorflourish_vs_control                         0.08412    0.06319
## treatment_vs_baseline                                  0.12600    0.04884
## cond_factorflourish_vs_control:treatment_vs_baseline   0.05819    0.04859
##                                                             df t value Pr(>|t|)
## (Intercept)                                            3.11357  43.099 1.99e-05
## cond_factorflourish_vs_control                       640.30696   1.331   0.1836
## treatment_vs_baseline                                317.92434   2.580   0.0103
## cond_factorflourish_vs_control:treatment_vs_baseline 349.35487   1.198   0.2318
##                                                         
## (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__ -0.010              
## trtmnt_vs_b  0.299 -0.013       
## cnd_fc__:__ -0.012  0.374 -0.005

Multi-Institutional RCT Results

Julie Cachia

2024-10-21

Prep data

Make data long

Set contrasts

Exclusions

Intent to treat

Preregistered exclusions

Additionally exclude unreasonable numbers

Demographics

Age

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Sex

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Gender

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Ethnicity

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

International student

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

SES

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Current degree program

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Condition count

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Condition randomization check

Age

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Gender

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Engagement

Time 2

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Time 3

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Time 4

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Histograms

Depression

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Anxiety

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Loneliness

Intention to Treat

Excluded Preregistered

Excluded Unreasonable Numbers

Analyses

Depression