Multi-Institutional RCT Results

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, starts_with("Ethnicity"), int_student, starts_with("SES"), .direction = "downup") %>%
  ungroup()

Set contrasts

# condition
contrasts(merged_data_long$cond) <- cbind(flourish_vs_control=c(-1,1))

# time
merged_data_long <- merged_data_long |> 
  dplyr::mutate(time_f = factor(time),
                treatment_vs_baseline = ifelse(time > 1, 0.33, -1))

contrasts(merged_data_long$time_f) <- cbind(linear=c(-1.5, -0.5, 0.5, 1.5))

Exclusions

3 levels: 1. Intention to treat (no exclusions) 2. Preregistered: “Participants who fail to complete a minimum of 2 timepoints will be excluded from the final analysis. Additionally, students in the treatment condition who do not use the Flourish app, as well as those in the control condition who gain access to the app will also be excluded.” 3. In addition to preregistered exclusions, also exclude students who report unreasonable engagement numbers

Intent to treat

No exclusions

# rename
data_ITT <- merged_data_long

# count number of timepoints participants completed
data_ITT %>%
  dplyr::group_by(unique_ID) %>%
  dplyr::summarise(num_timepoints_completed = sum(Finished == 1, na.rm = TRUE)) %>%  # Count only rows where Finished is 1
  dplyr::count(num_timepoints_completed)  # Count participants by number of completed timepoints

## # A tibble: 4 × 2
##   num_timepoints_completed     n
##                      <int> <int>
## 1                        1    76
## 2                        2    51
## 3                        3    40
## 4                        4   319

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    51
## 2                        3    46
## 3                        4   292

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    50
## 2                        3    38
## 3                        4   269

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	4.04

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	4.22

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	4.3

Sex

Intention to Treat

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

Sex	count
Man	195
Woman	755
NA	3

Excluded Preregistered

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

Sex	count
Man	134
Woman	626
NA	2

Excluded Unreasonable Numbers

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

Sex	count
Man	122
Woman	576
NA	1

Gender

Intention to Treat

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

Gender	count
Female	725
Male	197
Genderqueer/Gender non-conforming	8
Gender non-binary	12
NA	11

Excluded Preregistered

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

Gender	count
Female	600
Male	136
Genderqueer/Gender non-conforming	8
Gender non-binary	8
NA	10

Excluded Unreasonable Numbers

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

Gender	count
Female	550
Male	124
Genderqueer/Gender non-conforming	8
Gender non-binary	8
NA	9

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_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	554	58.13
Hispanic	163	17.10
Black	60	6.30
East_Asian	177	18.57
South_Asian	82	8.60
Native_Hawaiian_Pacific_Islander	18	1.89
Middle_Eastern	35	3.67
American_Indian	12	1.26
Self_Identify	38	3.99

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_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	450	59.06
Hispanic	111	14.57
Black	42	5.51
East_Asian	141	18.50
South_Asian	70	9.19
Native_Hawaiian_Pacific_Islander	16	2.10
Middle_Eastern	27	3.54
American_Indian	12	1.57
Self_Identify	32	4.20

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_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	418	59.80
Hispanic	105	15.02
Black	40	5.72
East_Asian	131	18.74
South_Asian	60	8.58
Native_Hawaiian_Pacific_Islander	16	2.29
Middle_Eastern	17	2.43
American_Indian	10	1.43
Self_Identify	30	4.29

International student

Intention to Treat

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

int_student	count
Yes	66
No	884
NA	3

data_ITT_demog |> 
  dplyr::filter(int_student == "Yes") |> 
  group_by(int_student_country) |> 
  summarise(count = n()) |> 
  kable(digits = 2)

int_student_country	count
Brazil	2
China	10
Colombia	1
Cote d’Ivoire	1
England	1
Hong Kong	1
Hong Kong(China)	1
India	4
Indonesia	1
Japan	1
Philippines	1
South Africa	1
Sweden	1
Taiwan	1
Thailand	1
Turkey	1
Vietnam	1
china	1
mexico	1
NA	34

Excluded Preregistered

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

int_student	count
Yes	56
No	704
NA	2

data_excluded_demog |> 
  dplyr::filter(int_student == "Yes") |> 
  group_by(int_student_country) |> 
  summarise(count = n()) |> 
  kable(digits = 2)

int_student_country	count
Brazil	2
China	10
Colombia	1
Cote d’Ivoire	1
England	1
Hong Kong	1
India	3
Indonesia	1
Japan	1
Philippines	1
South Africa	1
Sweden	1
Taiwan	1
Thailand	1
Vietnam	1
china	1
NA	28

Excluded Unreasonable Numbers

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

int_student	count
Yes	46
No	652
NA	1

data_excluded_unreasonable_demog |> 
  dplyr::filter(int_student == "Yes") |> 
  group_by(int_student_country) |> 
  summarise(count = n()) |> 
  kable(digits = 2)

int_student_country	count
Brazil	2
China	9
Colombia	1
Cote d’Ivoire	1
England	1
India	1
Indonesia	1
Japan	1
Philippines	1
South Africa	1
Sweden	1
Taiwan	1
Thailand	1
Vietnam	1
NA	23

SES

Intention to Treat

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

SES	count
1	73
2	153
3	291
4	279
5	154
NA	3

data_ITT_demog |> 
  dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
                   sd_SES = sd(SES_num, na.rm = TRUE)) |> 
  kable(digits = 2)

mean_SES	sd_SES
3.3	1.15

Excluded Preregistered

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

SES	count
1	57
2	120
3	238
4	219
5	126
NA	2

data_excluded_demog |> 
  dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
                   sd_SES = sd(SES_num, na.rm = TRUE)) |> 
  kable(digits = 2)

mean_SES	sd_SES
3.31	1.15

Excluded Unreasonable Numbers

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

SES	count
1	55
2	110
3	226
4	199
5	108
NA	1

data_excluded_unreasonable_demog |> 
  dplyr::summarise(mean_SES = mean(SES_num, na.rm = TRUE),
                   sd_SES = sd(SES_num, na.rm = TRUE)) |> 
  kable(digits = 2)

mean_SES	sd_SES
3.28	1.14

Current degree program

Intention to Treat

data_ITT_demog %>%
  group_by(univ,Education) %>%
  summarise(count = n()) |> 
  kable(digits = 2)

## `summarise()` has grouped output by 'univ'. You can override using the
## `.groups` argument.

univ	Education	count
Chapman	Associates	8
Chapman	Bachelors	224
Chapman	Masters	1
Chapman	PhD	2
Chapman	Other	1
Chapman	Non-degree student	1
Chapman	NA	245
Foothill	Associates	44
Foothill	Bachelors	7
Foothill	Masters	1
Foothill	Non-degree student	8
Foothill	NA	67
UW	Associates	3
UW	Bachelors	158
UW	Masters	2
UW	Other	2
UW	Non-degree student	4
UW	NA	175

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	182
Foothill	Associates	31
Foothill	Bachelors	5
Foothill	Masters	1
Foothill	Non-degree student	7
Foothill	NA	49
UW	Associates	3
UW	Bachelors	143
UW	Masters	1
UW	Other	2
UW	Non-degree student	4
UW	NA	158

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	167
Foothill	Associates	29
Foothill	Bachelors	4
Foothill	Masters	1
Foothill	Non-degree student	7
Foothill	NA	46
UW	Associates	2
UW	Bachelors	131
UW	Masters	1
UW	Other	2
UW	Non-degree student	4
UW	NA	144

Condition count

Intention to Treat

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

cond	count
control	483
flourish	470

Excluded Preregistered

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

cond	count
control	366
flourish	396

Excluded Unreasonable Numbers

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

cond	count
control	366
flourish	333

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 
## -3.472 -1.472 -1.214 -0.214 32.528 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.3429     0.1314 154.850   <2e-16 ***
## condflourish_vs_control   0.1292     0.1314   0.984    0.326    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.044 on 946 degrees of freedom
##   (5 observations deleted due to missingness)
## Multiple R-squared:  0.001021,   Adjusted R-squared:  -3.454e-05 
## F-statistic: 0.9673 on 1 and 946 DF,  p-value: 0.3256

Excluded Preregistered

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

## 
## Call:
## lm(formula = Age ~ cond, data = data_excluded)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.669 -1.669 -1.043 -0.043 32.331 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.3561     0.1115 182.612  < 2e-16 ***
## condflourish_vs_control   0.3133     0.1115   2.811  0.00501 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.27 on 1466 degrees of freedom
##   (7 observations deleted due to missingness)
## Multiple R-squared:  0.005361,   Adjusted R-squared:  0.004682 
## F-statistic: 7.901 on 1 and 1466 DF,  p-value: 0.005007

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 
## -3.675 -1.675 -1.038 -0.038 32.325 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.3565     0.1627 125.142   <2e-16 ***
## condflourish_vs_control   0.3182     0.1627   1.956   0.0508 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.293 on 696 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.005469,   Adjusted R-squared:  0.00404 
## F-statistic: 3.827 on 1 and 696 DF,  p-value: 0.05083

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
13.81	15.9	14.86	11.88

Excluded Unreasonable Numbers

data_excluded_unreasonable |> 
  dplyr::filter(time == 3) |> 
  dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
                   days_sd = sd(Engagement_1, na.rm = TRUE),
                   activities_mean = mean(Engagement_3, na.rm = TRUE),
                   activities_sd = sd(Engagement_3, na.rm = TRUE)) |> 
  kable(digits = 2)

days_mean	days_sd	activities_mean	activities_sd
9.84	7.95	14.21	11.62

Time 4

Intention to Treat

data_ITT %>%
  dplyr::filter(time == 4) |> 
  dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
                   days_sd = sd(Engagement_1, na.rm = TRUE),
                   activities_mean = mean(Engagement_3, na.rm = TRUE),
                   activities_sd = sd(Engagement_3, na.rm = TRUE)) |> 
  kable(digits = 2)

days_mean	days_sd	activities_mean	activities_sd
22.56	20.8	21.82	15.11

Excluded Preregistered

data_excluded |> 
  dplyr::filter(time == 4) |> 
  dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
                   days_sd = sd(Engagement_1, na.rm = TRUE),
                   activities_mean = mean(Engagement_3, na.rm = TRUE),
                   activities_sd = sd(Engagement_3, na.rm = TRUE)) |> 
  kable(digits = 2)

days_mean	days_sd	activities_mean	activities_sd
22.96	20.77	22.21	14.95

Excluded Unreasonable Numbers

data_excluded_unreasonable |> 
  dplyr::filter(time == 4) |> 
  dplyr::summarise(days_mean = mean(Engagement_1, na.rm = TRUE),
                   days_sd = sd(Engagement_1, na.rm = TRUE),
                   activities_mean = mean(Engagement_3, na.rm = TRUE),
                   activities_sd = sd(Engagement_3, na.rm = TRUE)) |> 
  kable(digits = 2)

days_mean	days_sd	activities_mean	activities_sd
16.48	11.5	21.04	14.76

Histograms

Depression

Intention to Treat

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

## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).

data_ITT %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            81.4            18.6

Excluded Preregistered

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            82.7            17.3

Excluded Unreasonable Numbers

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded_unreasonable %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(depression < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(depression >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            82.6            17.4

Anxiety

Intention to Treat

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

## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).

data_ITT %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            58.9            41.1

Excluded Preregistered

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            59.5            40.5

Excluded Unreasonable Numbers

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded_unreasonable %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(anxiety < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(anxiety >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            57.9            42.1

Loneliness

Intention to Treat

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

## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_density()`).

data_ITT %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(loneliness < 3, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(loneliness >= 3, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1               0             100

Excluded Preregistered

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(loneliness < 5, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(loneliness >= 5, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            28.1            71.9

Excluded Unreasonable Numbers

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

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_density()`).

data_excluded_unreasonable %>%
  dplyr::filter(time == 1) |> 
  dplyr::summarise(under_threshold = round(mean(loneliness < 5, na.rm = TRUE) * 100, 2),
                   above_threshold = round(mean(loneliness >= 5, na.rm = TRUE) * 100, 2))

## # A tibble: 1 × 2
##   under_threshold above_threshold
##             <dbl>           <dbl>
## 1            26.7            73.3

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)	1.51	1.30 – 1.72	<0.001	2.97	1.98 – 3.95	<0.001	2.69	1.68 – 3.71	<0.001
condflourish vs control	-0.04	-0.16 – 0.07	0.454	-0.03	-0.15 – 0.08	0.549	-0.05	-0.17 – 0.06	0.357
time - 2 5	0.02	-0.03 – 0.06	0.449	0.01	-0.03 – 0.06	0.506	0.02	-0.03 – 0.06	0.465
condflourish vs control × time - 2 5	-0.03	-0.07 – 0.01	0.114	-0.03	-0.08 – 0.01	0.102	-0.03	-0.08 – 0.01	0.100
Sex [Woman]				0.10	-0.19 – 0.38	0.499	0.07	-0.21 – 0.36	0.618
Age				-0.03	-0.06 – -0.00	0.028	-0.03	-0.06 – 0.00	0.057
int student [No]				-0.01	-0.46 – 0.44	0.967	0.19	-0.29 – 0.66	0.444
SES num				-0.24	-0.34 – -0.14	<0.001	-0.22	-0.32 – -0.12	<0.001
Ethnicity White							-0.25	-0.57 – 0.07	0.119
Ethnicity Hispanic							-0.16	-0.52 – 0.20	0.380
Ethnicity Black							0.47	-0.05 – 0.99	0.074
Ethnicity East Asian							0.04	-0.32 – 0.39	0.843
Ethnicity South Asian							0.52	0.07 – 0.96	0.023
Ethnicity Native Hawaiian Pacific Islander							0.22	-0.63 – 1.06	0.616
Ethnicity Middle Eastern							0.04	-0.58 – 0.66	0.889
Ethnicity American Indian							-0.07	-1.07 – 0.93	0.886
Random Effects
σ2	0.82			0.82			0.82
τ00	1.35 unique_ID			1.27 unique_ID			1.25 unique_ID
	0.02 univ			0.05 univ			0.02 univ
ICC	0.63			0.62			0.61
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.001 / 0.628			0.044 / 0.635			0.071 / 0.636

Excluded Preregistered

m0 <- lmer(depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	depression			depression			depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.45	1.23 – 1.68	<0.001	3.02	1.98 – 4.07	<0.001	2.86	1.76 – 3.96	<0.001
condflourish vs control	-0.07	-0.19 – 0.06	0.280	-0.05	-0.17 – 0.07	0.448	-0.06	-0.18 – 0.06	0.325
time - 2 5	0.01	-0.03 – 0.06	0.562	0.01	-0.03 – 0.06	0.590	0.01	-0.03 – 0.06	0.571
condflourish vs control × time - 2 5	-0.03	-0.07 – 0.02	0.246	-0.03	-0.07 – 0.02	0.244	-0.03	-0.07 – 0.02	0.243
Sex [Woman]				0.08	-0.24 – 0.40	0.618	0.06	-0.26 – 0.38	0.714
Age				-0.04	-0.07 – -0.01	0.022	-0.03	-0.06 – -0.00	0.035
int student [No]				0.03	-0.44 – 0.50	0.888	0.16	-0.33 – 0.66	0.520
SES num				-0.27	-0.38 – -0.17	<0.001	-0.26	-0.37 – -0.16	<0.001
Ethnicity White							-0.14	-0.49 – 0.21	0.428
Ethnicity Hispanic							-0.19	-0.59 – 0.20	0.340
Ethnicity Black							0.32	-0.26 – 0.90	0.279
Ethnicity East Asian							0.08	-0.30 – 0.46	0.672
Ethnicity South Asian							0.37	-0.09 – 0.83	0.115
Ethnicity Native Hawaiian Pacific Islander							0.26	-0.60 – 1.13	0.552
Ethnicity Middle Eastern							0.18	-0.50 – 0.85	0.608
Ethnicity American Indian							0.04	-0.95 – 1.03	0.935
Random Effects
σ2	0.82			0.82			0.83
τ00	1.28 unique_ID			1.18 unique_ID			1.17 unique_ID
	0.03 univ			0.06 univ			0.04 univ
ICC	0.61			0.60			0.59
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.002 / 0.614			0.056 / 0.623			0.070 / 0.624

Excluded Unreasonable Numbers

m0 <- lmer(depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	depression			depression			depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.45	1.21 – 1.69	<0.001	3.05	1.96 – 4.14	<0.001	2.84	1.70 – 3.98	<0.001
condflourish vs control	-0.07	-0.20 – 0.05	0.250	-0.06	-0.19 – 0.06	0.318	-0.08	-0.20 – 0.05	0.233
time - 2 5	0.00	-0.04 – 0.05	0.909	0.00	-0.04 – 0.05	0.914	0.00	-0.04 – 0.05	0.888
condflourish vs control × time - 2 5	-0.04	-0.08 – 0.01	0.121	-0.04	-0.08 – 0.01	0.128	-0.03	-0.08 – 0.01	0.130
Sex [Woman]				0.07	-0.26 – 0.40	0.679	0.05	-0.28 – 0.38	0.753
Age				-0.04	-0.07 – -0.00	0.024	-0.03	-0.06 – -0.00	0.041
int student [No]				0.07	-0.45 – 0.58	0.793	0.17	-0.36 – 0.71	0.527
SES num				-0.29	-0.40 – -0.18	<0.001	-0.28	-0.40 – -0.17	<0.001
Ethnicity White							-0.06	-0.41 – 0.29	0.722
Ethnicity Hispanic							-0.19	-0.59 – 0.21	0.346
Ethnicity Black							0.38	-0.20 – 0.97	0.199
Ethnicity East Asian							0.16	-0.23 – 0.55	0.421
Ethnicity South Asian							0.54	0.06 – 1.02	0.028
Ethnicity Native Hawaiian Pacific Islander							0.27	-0.59 – 1.12	0.539
Ethnicity Middle Eastern							0.00	-0.81 – 0.81	0.999
Ethnicity American Indian							0.17	-0.89 – 1.24	0.753
Random Effects
σ2	0.81			0.81			0.82
τ00	1.26 unique_ID			1.15 unique_ID			1.14 unique_ID
	0.03 univ			0.07 univ			0.05 univ
ICC	0.61			0.60			0.59
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.003 / 0.615			0.062 / 0.625			0.081 / 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)	2.34	2.21 – 2.48	<0.001	3.37	2.32 – 4.41	<0.001	3.28	2.15 – 4.41	<0.001
condflourish vs control	-0.12	-0.25 – 0.01	0.076	-0.10	-0.22 – 0.03	0.136	-0.10	-0.23 – 0.03	0.121
time - 2 5	-0.05	-0.10 – -0.00	0.050	-0.05	-0.10 – -0.00	0.035	-0.05	-0.10 – -0.00	0.036
condflourish vs control × time - 2 5	-0.02	-0.07 – 0.03	0.499	-0.02	-0.07 – 0.03	0.428	-0.02	-0.07 – 0.03	0.420
Sex [Woman]				0.51	0.19 – 0.84	0.002	0.50	0.18 – 0.83	0.003
Age				-0.03	-0.07 – -0.00	0.034	-0.03	-0.07 – -0.00	0.041
int student [No]				0.35	-0.16 – 0.85	0.178	0.41	-0.13 – 0.96	0.136
SES num				-0.32	-0.43 – -0.21	<0.001	-0.30	-0.41 – -0.18	<0.001
Ethnicity White							-0.11	-0.47 – 0.26	0.569
Ethnicity Hispanic							0.07	-0.33 – 0.48	0.732
Ethnicity Black							0.16	-0.43 – 0.75	0.599
Ethnicity East Asian							-0.18	-0.58 – 0.22	0.382
Ethnicity South Asian							0.26	-0.25 – 0.76	0.318
Ethnicity Native Hawaiian Pacific Islander							0.01	-0.95 – 0.97	0.983
Ethnicity Middle Eastern							0.22	-0.49 – 0.92	0.547
Ethnicity American Indian							0.05	-1.09 – 1.20	0.925
Random Effects
σ2	1.14			1.14			1.14
τ00	1.79 unique_ID			1.59 unique_ID			1.61 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC				0.58
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.015 / NA			0.078 / 0.616			0.181 / NA

Excluded Preregistered

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

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

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

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

m2 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)

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

tab_model(m0, m1, m2)

	anxiety			anxiety			anxiety
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.33	2.19 – 2.47	<0.001	3.47	2.36 – 4.58	<0.001	3.45	2.24 – 4.66	<0.001
condflourish vs control	-0.12	-0.26 – 0.02	0.100	-0.09	-0.22 – 0.05	0.201	-0.09	-0.23 – 0.04	0.180
time - 2 5	-0.06	-0.11 – -0.01	0.018	-0.06	-0.12 – -0.01	0.015	-0.06	-0.12 – -0.01	0.015
condflourish vs control × time - 2 5	-0.01	-0.06 – 0.04	0.626	-0.01	-0.07 – 0.04	0.579	-0.01	-0.07 – 0.04	0.574
Sex [Woman]				0.52	0.16 – 0.88	0.005	0.50	0.14 – 0.87	0.007
Age				-0.04	-0.07 – -0.01	0.022	-0.04	-0.07 – -0.00	0.027
int student [No]				0.41	-0.11 – 0.94	0.123	0.41	-0.16 – 0.98	0.160
SES num				-0.35	-0.47 – -0.23	<0.001	-0.34	-0.47 – -0.22	<0.001
Ethnicity White							-0.05	-0.45 – 0.34	0.789
Ethnicity Hispanic							0.09	-0.35 – 0.54	0.678
Ethnicity Black							0.15	-0.52 – 0.81	0.666
Ethnicity East Asian							-0.14	-0.57 – 0.30	0.537
Ethnicity South Asian							0.12	-0.41 – 0.65	0.651
Ethnicity Native Hawaiian Pacific Islander							0.13	-0.86 – 1.12	0.797
Ethnicity Middle Eastern							0.49	-0.28 – 1.26	0.211
Ethnicity American Indian							0.11	-1.02 – 1.24	0.850
Random Effects
σ2	1.16			1.16			1.16
τ00	1.74 unique_ID			1.50 unique_ID			1.52 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.016 / NA			0.194 / NA			0.204 / NA

Excluded Unreasonable Numbers

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

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

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

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

m2 <- lmer(anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)

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

tab_model(m0, m1, m2)

	anxiety			anxiety			anxiety
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.33	2.18 – 2.48	<0.001	3.55	2.38 – 4.72	<0.001	3.44	2.17 – 4.71	<0.001
condflourish vs control	-0.12	-0.27 – 0.03	0.122	-0.10	-0.24 – 0.04	0.166	-0.10	-0.25 – 0.04	0.169
time - 2 5	-0.09	-0.14 – -0.03	0.002	-0.08	-0.14 – -0.03	0.002	-0.08	-0.14 – -0.03	0.002
condflourish vs control × time - 2 5	-0.04	-0.09 – 0.02	0.193	-0.04	-0.09 – 0.02	0.194	-0.04	-0.09 – 0.02	0.196
Sex [Woman]				0.50	0.13 – 0.88	0.009	0.50	0.12 – 0.88	0.010
Age				-0.04	-0.07 – -0.00	0.025	-0.04	-0.07 – -0.00	0.035
int student [No]				0.38	-0.20 – 0.96	0.199	0.38	-0.24 – 1.00	0.232
SES num				-0.37	-0.49 – -0.24	<0.001	-0.35	-0.48 – -0.22	<0.001
Ethnicity White							0.02	-0.39 – 0.43	0.921
Ethnicity Hispanic							0.09	-0.37 – 0.55	0.696
Ethnicity Black							0.20	-0.48 – 0.88	0.563
Ethnicity East Asian							-0.10	-0.55 – 0.35	0.655
Ethnicity South Asian							0.28	-0.28 – 0.83	0.332
Ethnicity Native Hawaiian Pacific Islander							0.13	-0.87 – 1.12	0.802
Ethnicity Middle Eastern							0.11	-0.83 – 1.04	0.823
Ethnicity American Indian							0.20	-1.04 – 1.45	0.748
Random Effects
σ2	1.15			1.15			1.15
τ00	1.74 unique_ID			1.51 unique_ID			1.54 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.020 / NA			0.198 / NA			0.205 / 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)
tab_model(m0, m1, m2)

	loneliness			loneliness			loneliness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.29	5.16 – 5.42	<0.001	6.02	4.93 – 7.11	<0.001	5.62	4.47 – 6.77	<0.001
condflourish vs control	-0.09	-0.22 – 0.04	0.187	-0.08	-0.21 – 0.05	0.250	-0.09	-0.22 – 0.04	0.192
time - 2 5	-0.12	-0.16 – -0.07	<0.001	-0.12	-0.16 – -0.07	<0.001	-0.12	-0.16 – -0.07	<0.001
condflourish vs control × time - 2 5	-0.05	-0.09 – -0.00	0.038	-0.05	-0.09 – -0.00	0.038	-0.05	-0.09 – -0.00	0.037
Sex [Woman]				0.16	-0.17 – 0.48	0.349	0.13	-0.20 – 0.46	0.450
Age				-0.03	-0.06 – 0.01	0.139	-0.02	-0.05 – 0.01	0.272
int student [No]				0.45	-0.07 – 0.97	0.090	0.65	0.09 – 1.20	0.022
SES num				-0.22	-0.33 – -0.10	<0.001	-0.21	-0.33 – -0.09	0.001
Ethnicity White							-0.10	-0.46 – 0.27	0.606
Ethnicity Hispanic							0.19	-0.22 – 0.60	0.370
Ethnicity Black							-0.04	-0.64 – 0.56	0.890
Ethnicity East Asian							0.25	-0.15 – 0.66	0.218
Ethnicity South Asian							0.53	0.02 – 1.04	0.042
Ethnicity Native Hawaiian Pacific Islander							-0.53	-1.50 – 0.45	0.289
Ethnicity Middle Eastern							0.09	-0.62 – 0.80	0.800
Ethnicity American Indian							0.03	-1.13 – 1.18	0.963
Random Effects
σ2	0.97			0.97			0.97
τ00	1.79 unique_ID			1.71 unique_ID			1.71 unique_ID
	0.00 univ			0.02 univ			0.01 univ
ICC				0.64			0.64
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.028 / NA			0.043 / 0.656			0.056 / 0.659

Excluded Preregistered

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

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

m1 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(loneliness ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	loneliness			loneliness			loneliness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.27	5.12 – 5.41	<0.001	6.06	4.87 – 7.26	<0.001	5.75	4.47 – 7.03	<0.001
condflourish vs control	-0.10	-0.24 – 0.05	0.194	-0.08	-0.22 – 0.06	0.271	-0.09	-0.24 – 0.05	0.210
time - 2 5	-0.12	-0.17 – -0.07	<0.001	-0.12	-0.16 – -0.07	<0.001	-0.12	-0.16 – -0.07	<0.001
condflourish vs control × time - 2 5	-0.05	-0.10 – -0.00	0.039	-0.05	-0.10 – -0.00	0.046	-0.05	-0.10 – -0.00	0.045
Sex [Woman]				0.05	-0.33 – 0.43	0.799	0.01	-0.37 – 0.39	0.961
Age				-0.03	-0.06 – 0.01	0.126	-0.02	-0.06 – 0.01	0.195
int student [No]				0.56	0.00 – 1.12	0.049	0.73	0.13 – 1.32	0.017
SES num				-0.23	-0.36 – -0.11	<0.001	-0.23	-0.36 – -0.09	0.001
Ethnicity White							-0.07	-0.49 – 0.34	0.727
Ethnicity Hispanic							0.16	-0.31 – 0.63	0.494
Ethnicity Black							0.04	-0.66 – 0.74	0.910
Ethnicity East Asian							0.34	-0.12 – 0.80	0.144
Ethnicity South Asian							0.37	-0.18 – 0.92	0.189
Ethnicity Native Hawaiian Pacific Islander							-0.59	-1.62 – 0.44	0.262
Ethnicity Middle Eastern							0.26	-0.55 – 1.07	0.529
Ethnicity American Indian							0.14	-1.04 – 1.32	0.813
Random Effects
σ2	0.97			0.97			0.97
τ00	1.83 unique_ID			1.75 unique_ID			1.75 unique_ID
	0.00 univ			0.02 univ			0.01 univ
ICC	0.65			0.64			0.64
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.010 / 0.657			0.049 / 0.662			0.060 / 0.665

Excluded Unreasonable Numbers

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

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

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

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues

## Warning: Model failed to converge with 1 negative eigenvalue: -8.2e+01

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)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0118602 (tol = 0.002, component 1)

tab_model(m0, m1, m2)

	loneliness			loneliness			loneliness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.27	5.12 – 5.42	<0.001	6.02	4.79 – 7.25	<0.001	5.64	4.32 – 6.96	<0.001
condflourish vs control	-0.09	-0.24 – 0.06	0.245	-0.09	-0.24 – 0.06	0.240	-0.10	-0.25 – 0.05	0.196
time - 2 5	-0.13	-0.18 – -0.08	<0.001	-0.13	-0.18 – -0.08	<0.001	-0.13	-0.18 – -0.08	<0.001
condflourish vs control × time - 2 5	-0.06	-0.11 – -0.01	0.011	-0.06	-0.11 – -0.02	0.010	-0.06	-0.11 – -0.02	0.010
Sex [Woman]				0.00	-0.39 – 0.40	0.983	-0.02	-0.42 – 0.37	0.909
Age				-0.02	-0.06 – 0.01	0.204	-0.02	-0.05 – 0.02	0.309
int student [No]				0.47	-0.14 – 1.08	0.130	0.64	-0.00 – 1.29	0.051
SES num				-0.22	-0.35 – -0.09	0.001	-0.22	-0.35 – -0.08	0.002
Ethnicity White							-0.01	-0.43 – 0.41	0.964
Ethnicity Hispanic							0.20	-0.28 – 0.68	0.422
Ethnicity Black							0.07	-0.64 – 0.78	0.843
Ethnicity East Asian							0.37	-0.10 – 0.84	0.120
Ethnicity South Asian							0.51	-0.06 – 1.09	0.081
Ethnicity Native Hawaiian Pacific Islander							-0.56	-1.59 – 0.47	0.286
Ethnicity Middle Eastern							0.17	-0.80 – 1.14	0.728
Ethnicity American Indian							0.48	-0.81 – 1.76	0.466
Random Effects
σ2	0.97			0.97			0.97
τ00	1.80 unique_ID			1.74 unique_ID			1.73 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC				0.64			0.64
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.033 / NA			0.043 / 0.657			0.058 / 0.662

Perceived Stress

Intention to Treat

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

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

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

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

m2 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)

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

tab_model(m0, m1, m2)

	perceived stress			perceived stress			perceived stress
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.63	6.41 – 6.85	<0.001	8.90	7.16 – 10.65	<0.001	8.31	6.45 – 10.18	<0.001
condflourish vs control	-0.08	-0.30 – 0.14	0.482	-0.06	-0.28 – 0.15	0.552	-0.09	-0.30 – 0.13	0.435
time - 2 5	-0.08	-0.17 – 0.00	0.061	-0.08	-0.16 – 0.01	0.067	-0.08	-0.16 – 0.01	0.074
condflourish vs control × time - 2 5	-0.03	-0.12 – 0.05	0.461	-0.03	-0.11 – 0.06	0.509	-0.03	-0.11 – 0.06	0.503
Sex [Woman]				0.72	0.18 – 1.26	0.009	0.67	0.13 – 1.21	0.016
Age				-0.05	-0.10 – 0.01	0.086	-0.04	-0.10 – 0.01	0.127
int student [No]				0.07	-0.77 – 0.91	0.874	0.28	-0.62 – 1.18	0.543
SES num				-0.60	-0.78 – -0.41	<0.001	-0.53	-0.73 – -0.34	<0.001
Ethnicity White							-0.12	-0.72 – 0.48	0.694
Ethnicity Hispanic							0.33	-0.34 – 1.00	0.332
Ethnicity Black							0.86	-0.12 – 1.84	0.087
Ethnicity East Asian							0.03	-0.63 – 0.70	0.924
Ethnicity South Asian							0.85	0.01 – 1.68	0.048
Ethnicity Native Hawaiian Pacific Islander							0.90	-0.70 – 2.49	0.271
Ethnicity Middle Eastern							0.36	-0.80 – 1.52	0.548
Ethnicity American Indian							-0.71	-2.60 – 1.17	0.458
Random Effects
σ2	3.54			3.52			3.52
τ00	4.83 unique_ID			4.29 unique_ID			4.26 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC	0.58			0.55
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.002 / 0.578			0.074 / 0.583			0.181 / NA

Excluded Preregistered

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

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

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

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

m2 <- lmer(perceived_stress ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)

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

tab_model(m0, m1, m2)

	perceived stress			perceived stress			perceived stress
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.57	6.33 – 6.81	<0.001	8.95	7.13 – 10.78	<0.001	8.60	6.63 – 10.58	<0.001
condflourish vs control	-0.09	-0.33 – 0.14	0.436	-0.06	-0.28 – 0.17	0.626	-0.08	-0.30 – 0.15	0.508
time - 2 5	-0.10	-0.19 – -0.01	0.025	-0.10	-0.19 – -0.01	0.033	-0.10	-0.19 – -0.01	0.034
condflourish vs control × time - 2 5	-0.02	-0.11 – 0.06	0.582	-0.02	-0.11 – 0.07	0.662	-0.02	-0.11 – 0.07	0.658
Sex [Woman]				0.66	0.07 – 1.26	0.028	0.60	0.00 – 1.20	0.048
Age				-0.04	-0.10 – 0.01	0.103	-0.04	-0.10 – 0.01	0.136
int student [No]				0.17	-0.69 – 1.04	0.695	0.27	-0.66 – 1.20	0.566
SES num				-0.66	-0.85 – -0.46	<0.001	-0.61	-0.81 – -0.41	<0.001
Ethnicity White							-0.10	-0.75 – 0.54	0.754
Ethnicity Hispanic							0.26	-0.47 – 0.99	0.487
Ethnicity Black							0.69	-0.40 – 1.78	0.215
Ethnicity East Asian							-0.02	-0.74 – 0.69	0.956
Ethnicity South Asian							0.49	-0.37 – 1.35	0.266
Ethnicity Native Hawaiian Pacific Islander							0.88	-0.74 – 2.50	0.289
Ethnicity Middle Eastern							0.74	-0.51 – 1.99	0.248
Ethnicity American Indian							-0.71	-2.57 – 1.14	0.450
Random Effects
σ2	3.48			3.47			3.47
τ00	4.59 unique_ID			3.96 unique_ID			3.95 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC	0.57
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.003 / 0.570			0.173 / NA			0.192 / 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)	6.54	6.30 – 6.78	<0.001	9.14	7.25 – 11.02	<0.001	8.73	6.70 – 10.76	<0.001
condflourish vs control	-0.12	-0.37 – 0.12	0.324	-0.10	-0.33 – 0.13	0.411	-0.12	-0.35 – 0.12	0.330
time - 2 5	-0.11	-0.20 – -0.01	0.024	-0.11	-0.20 – -0.01	0.028	-0.11	-0.20 – -0.01	0.028
condflourish vs control × time - 2 5	-0.03	-0.13 – 0.06	0.510	-0.03	-0.12 – 0.07	0.550	-0.03	-0.12 – 0.07	0.555
Sex [Woman]				0.66	0.06 – 1.27	0.032	0.64	0.03 – 1.25	0.041
Age				-0.05	-0.10 – 0.01	0.090	-0.05	-0.10 – 0.01	0.109
int student [No]				0.11	-0.83 – 1.04	0.824	0.21	-0.78 – 1.20	0.681
SES num				-0.69	-0.90 – -0.49	<0.001	-0.65	-0.86 – -0.44	<0.001
Ethnicity White							0.01	-0.64 – 0.66	0.986
Ethnicity Hispanic							0.26	-0.48 – 1.00	0.498
Ethnicity Black							0.78	-0.31 – 1.87	0.161
Ethnicity East Asian							0.11	-0.61 – 0.84	0.760
Ethnicity South Asian							0.64	-0.25 – 1.54	0.157
Ethnicity Native Hawaiian Pacific Islander							0.89	-0.71 – 2.49	0.273
Ethnicity Middle Eastern							-0.17	-1.67 – 1.32	0.819
Ethnicity American Indian							-0.85	-2.85 – 1.14	0.402
Random Effects
σ2	3.53			3.52			3.52
τ00	4.45 unique_ID			3.76 unique_ID			3.77 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC				0.52
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.008 / NA			0.097 / 0.563			0.199 / NA

SAS: Calm

Intention to Treat

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

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

m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)

	SAS calm			SAS calm			SAS calm
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.74	5.55 – 5.94	<0.001	4.92	3.37 – 6.46	<0.001	4.92	3.25 – 6.58	<0.001
condflourish vs control	0.22	0.03 – 0.41	0.026	0.19	0.01 – 0.38	0.042	0.19	0.00 – 0.38	0.047
time - 2 5	0.11	0.03 – 0.19	0.006	0.11	0.03 – 0.19	0.006	0.11	0.03 – 0.19	0.006
condflourish vs control × time - 2 5	0.10	0.02 – 0.18	0.015	0.10	0.02 – 0.18	0.014	0.10	0.02 – 0.18	0.014
Sex [Woman]				-0.67	-1.15 – -0.20	0.005	-0.63	-1.11 – -0.15	0.010
Age				0.02	-0.03 – 0.07	0.387	0.02	-0.03 – 0.07	0.446
int student [No]				-0.61	-1.35 – 0.13	0.107	-0.67	-1.47 – 0.13	0.099
SES num				0.45	0.29 – 0.61	<0.001	0.44	0.27 – 0.61	<0.001
Ethnicity White							0.12	-0.41 – 0.66	0.648
Ethnicity Hispanic							-0.13	-0.73 – 0.47	0.669
Ethnicity Black							0.09	-0.78 – 0.96	0.847
Ethnicity East Asian							0.12	-0.47 – 0.71	0.685
Ethnicity South Asian							0.00	-0.74 – 0.75	0.993
Ethnicity Native Hawaiian Pacific Islander							0.09	-1.33 – 1.50	0.903
Ethnicity Middle Eastern							-0.17	-1.21 – 0.86	0.741
Ethnicity American Indian							1.52	-0.15 – 3.20	0.074
Random Effects
σ2	3.06			3.05			3.05
τ00	3.58 unique_ID			3.22 unique_ID			3.25 unique_ID
	0.00 univ			0.01 univ			0.01 univ
ICC				0.51			0.52
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.023 / NA			0.068 / 0.547			0.073 / 0.552

Excluded Preregistered

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

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00220683 (tol = 0.002, component 1)

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

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00208136 (tol = 0.002, component 1)

m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	SAS calm			SAS calm			SAS calm
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.73	5.53 – 5.94	<0.001	5.15	3.48 – 6.82	<0.001	5.28	3.45 – 7.10	<0.001
condflourish vs control	0.27	0.07 – 0.48	0.010	0.25	0.05 – 0.45	0.016	0.25	0.04 – 0.45	0.018
time - 2 5	0.15	0.07 – 0.23	<0.001	0.15	0.07 – 0.23	<0.001	0.15	0.07 – 0.23	<0.001
condflourish vs control × time - 2 5	0.09	0.01 – 0.17	0.036	0.09	0.00 – 0.17	0.041	0.09	0.00 – 0.17	0.040
Sex [Woman]				-0.61	-1.15 – -0.08	0.024	-0.54	-1.08 – -0.00	0.049
Age				0.01	-0.04 – 0.06	0.695	0.00	-0.05 – 0.05	0.883
int student [No]				-0.72	-1.50 – 0.07	0.073	-0.70	-1.53 – 0.14	0.104
SES num				0.46	0.29 – 0.64	<0.001	0.45	0.26 – 0.63	<0.001
Ethnicity White							0.00	-0.58 – 0.58	0.999
Ethnicity Hispanic							-0.42	-1.08 – 0.25	0.217
Ethnicity Black							0.18	-0.80 – 1.16	0.720
Ethnicity East Asian							0.09	-0.56 – 0.73	0.795
Ethnicity South Asian							0.08	-0.70 – 0.86	0.843
Ethnicity Native Hawaiian Pacific Islander							-0.06	-1.53 – 1.40	0.932
Ethnicity Middle Eastern							-0.71	-1.85 – 0.43	0.220
Ethnicity American Indian							1.56	-0.12 – 3.24	0.068
Random Effects
σ2	3.03			3.03			3.03
τ00	3.50 unique_ID			3.15 unique_ID			3.16 unique_ID
	0.00 univ			0.03 univ			0.05 univ
ICC	0.54			0.51			0.51
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.017 / 0.544			0.075 / 0.548			0.084 / 0.555

Excluded Unreasonable Numbers

m0 <- lmer(SAS_calm ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_calm ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS calm			SAS calm			SAS calm
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.72	5.47 – 5.96	<0.001	5.24	3.50 – 6.99	<0.001	5.29	3.40 – 7.18	<0.001
condflourish vs control	0.26	0.04 – 0.47	0.018	0.26	0.05 – 0.47	0.015	0.26	0.05 – 0.47	0.016
time - 2 5	0.14	0.06 – 0.23	0.001	0.14	0.06 – 0.23	0.001	0.14	0.06 – 0.23	0.001
condflourish vs control × time - 2 5	0.08	-0.00 – 0.17	0.064	0.08	-0.01 – 0.17	0.074	0.08	-0.01 – 0.17	0.073
Sex [Woman]				-0.58	-1.13 – -0.03	0.038	-0.54	-1.10 – 0.01	0.056
Age				0.00	-0.05 – 0.05	0.883	0.00	-0.05 – 0.05	0.930
int student [No]				-0.60	-1.45 – 0.26	0.171	-0.64	-1.54 – 0.27	0.168
SES num				0.43	0.25 – 0.62	<0.001	0.42	0.23 – 0.61	<0.001
Ethnicity White							0.07	-0.53 – 0.66	0.822
Ethnicity Hispanic							-0.34	-1.01 – 0.34	0.329
Ethnicity Black							0.19	-0.81 – 1.18	0.715
Ethnicity East Asian							0.12	-0.54 – 0.78	0.725
Ethnicity South Asian							-0.00	-0.82 – 0.81	0.996
Ethnicity Native Hawaiian Pacific Islander							-0.07	-1.53 – 1.39	0.926
Ethnicity Middle Eastern							0.10	-1.27 – 1.47	0.885
Ethnicity American Indian							1.43	-0.39 – 3.25	0.123
Random Effects
σ2	3.00			2.99			2.99
τ00	3.34 unique_ID			3.06 unique_ID			3.11 unique_ID
	0.01 univ			0.04 univ			0.04 univ
ICC	0.53			0.51			0.51
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.015 / 0.535			0.065 / 0.540			0.070 / 0.547

SAS: Well-Being

Intention to Treat

m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
m1 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)

	SAS well being			SAS well being			SAS well being
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.86	6.46 – 7.27	<0.001	4.56	2.90 – 6.22	<0.001	4.99	3.25 – 6.72	<0.001
condflourish vs control	0.19	-0.01 – 0.38	0.057	0.17	-0.01 – 0.36	0.067	0.21	0.02 – 0.39	0.033
time - 2 5	-0.06	-0.13 – 0.01	0.112	-0.06	-0.14 – 0.01	0.090	-0.07	-0.14 – 0.01	0.077
condflourish vs control × time - 2 5	0.08	0.01 – 0.16	0.022	0.08	0.01 – 0.16	0.025	0.08	0.01 – 0.16	0.025
Sex [Woman]				0.21	-0.27 – 0.68	0.387	0.24	-0.24 – 0.71	0.330
Age				0.05	-0.00 – 0.10	0.058	0.04	-0.01 – 0.09	0.103
int student [No]				-0.44	-1.19 – 0.31	0.251	-0.62	-1.42 – 0.18	0.126
SES num				0.45	0.29 – 0.61	<0.001	0.42	0.25 – 0.59	<0.001
Ethnicity White							0.24	-0.29 – 0.77	0.379
Ethnicity Hispanic							0.05	-0.55 – 0.64	0.879
Ethnicity Black							-0.56	-1.43 – 0.30	0.202
Ethnicity East Asian							-0.32	-0.90 – 0.27	0.287
Ethnicity South Asian							-0.41	-1.15 – 0.33	0.279
Ethnicity Native Hawaiian Pacific Islander							-1.18	-2.59 – 0.22	0.099
Ethnicity Middle Eastern							-0.12	-1.15 – 0.92	0.827
Ethnicity American Indian							0.13	-1.53 – 1.79	0.880
Random Effects
σ2	2.57			2.57			2.57
τ00	3.66 unique_ID			3.37 unique_ID			3.35 unique_ID
	0.09 univ			0.21 univ			0.12 univ
ICC	0.59			0.58			0.58
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1578			1569			1569
Marginal R2 / Conditional R2	0.007 / 0.596			0.054 / 0.604			0.068 / 0.604

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)	6.89	6.47 – 7.30	<0.001	4.53	2.78 – 6.28	<0.001	5.16	3.31 – 7.02	<0.001
condflourish vs control	0.21	0.01 – 0.42	0.044	0.19	-0.01 – 0.39	0.068	0.22	0.02 – 0.42	0.033
time - 2 5	-0.06	-0.13 – 0.02	0.154	-0.06	-0.13 – 0.02	0.132	-0.06	-0.14 – 0.02	0.126
condflourish vs control × time - 2 5	0.07	-0.01 – 0.14	0.092	0.06	-0.02 – 0.14	0.119	0.06	-0.02 – 0.14	0.117
Sex [Woman]				0.24	-0.29 – 0.77	0.370	0.28	-0.26 – 0.81	0.310
Age				0.05	-0.00 – 0.10	0.075	0.04	-0.02 – 0.09	0.173
int student [No]				-0.48	-1.26 – 0.31	0.235	-0.64	-1.47 – 0.20	0.133
SES num				0.48	0.31 – 0.66	<0.001	0.43	0.25 – 0.62	<0.001
Ethnicity White							0.13	-0.45 – 0.71	0.660
Ethnicity Hispanic							-0.31	-0.97 – 0.35	0.350
Ethnicity Black							-0.20	-1.18 – 0.78	0.687
Ethnicity East Asian							-0.39	-1.03 – 0.26	0.238
Ethnicity South Asian							-0.36	-1.13 – 0.42	0.367
Ethnicity Native Hawaiian Pacific Islander							-1.20	-2.66 – 0.25	0.104
Ethnicity Middle Eastern							-0.26	-1.39 – 0.88	0.656
Ethnicity American Indian							0.22	-1.44 – 1.88	0.797
Random Effects
σ2	2.54			2.54			2.54
τ00	3.53 unique_ID			3.22 unique_ID			3.23 unique_ID
	0.09 univ			0.18 univ			0.12 univ
ICC	0.59			0.57			0.57
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1411			1405			1405
Marginal R2 / Conditional R2	0.008 / 0.591			0.062 / 0.600			0.073 / 0.600

Excluded Unreasonable Numbers

m0 <- lmer(SAS_well_being ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_well_being ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS well being			SAS well being			SAS well being
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.86	6.45 – 7.27	<0.001	4.56	2.74 – 6.38	<0.001	5.26	3.35 – 7.17	<0.001
condflourish vs control	0.18	-0.03 – 0.40	0.095	0.18	-0.03 – 0.39	0.095	0.22	0.01 – 0.43	0.040
time - 2 5	-0.06	-0.14 – 0.02	0.164	-0.06	-0.14 – 0.02	0.147	-0.06	-0.14 – 0.02	0.139
condflourish vs control × time - 2 5	0.06	-0.01 – 0.14	0.112	0.06	-0.02 – 0.14	0.135	0.06	-0.02 – 0.14	0.137
Sex [Woman]				0.28	-0.27 – 0.83	0.324	0.29	-0.26 – 0.85	0.299
Age				0.04	-0.01 – 0.09	0.131	0.03	-0.02 – 0.08	0.281
int student [No]				-0.37	-1.23 – 0.50	0.408	-0.57	-1.47 – 0.33	0.217
SES num				0.47	0.29 – 0.66	<0.001	0.43	0.24 – 0.62	<0.001
Ethnicity White							0.12	-0.48 – 0.71	0.704
Ethnicity Hispanic							-0.29	-0.97 – 0.38	0.391
Ethnicity Black							-0.18	-1.17 – 0.81	0.717
Ethnicity East Asian							-0.39	-1.04 – 0.27	0.249
Ethnicity South Asian							-0.68	-1.49 – 0.12	0.097
Ethnicity Native Hawaiian Pacific Islander							-1.19	-2.64 – 0.25	0.106
Ethnicity Middle Eastern							0.51	-0.85 – 1.88	0.461
Ethnicity American Indian							-0.10	-1.90 – 1.70	0.912
Random Effects
σ2	2.51			2.51			2.51
τ00	3.49 unique_ID			3.21 unique_ID			3.20 unique_ID
	0.09 univ			0.16 univ			0.10 univ
ICC	0.59			0.57			0.57
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.007 / 0.591			0.057 / 0.598			0.072 / 0.599

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)	5.72	5.33 – 6.11	<0.001	3.89	2.09 – 5.70	<0.001	4.60	2.71 – 6.50	<0.001
condflourish vs control	0.14	-0.07 – 0.36	0.188	0.13	-0.08 – 0.34	0.235	0.15	-0.07 – 0.36	0.176
time - 2 5	-0.07	-0.15 – 0.00	0.057	-0.08	-0.15 – 0.00	0.055	-0.08	-0.16 – -0.00	0.046
condflourish vs control × time - 2 5	0.05	-0.03 – 0.13	0.205	0.05	-0.03 – 0.13	0.199	0.05	-0.03 – 0.13	0.202
Sex [Woman]				-0.04	-0.58 – 0.49	0.869	-0.04	-0.57 – 0.50	0.894
Age				0.05	-0.01 – 0.10	0.082	0.04	-0.01 – 0.10	0.113
int student [No]				-0.49	-1.33 – 0.35	0.255	-0.56	-1.45 – 0.33	0.216
SES num				0.38	0.20 – 0.56	<0.001	0.34	0.15 – 0.53	<0.001
Ethnicity White							-0.19	-0.79 – 0.40	0.520
Ethnicity Hispanic							-0.19	-0.85 – 0.47	0.574
Ethnicity Black							-1.12	-2.09 – -0.16	0.022
Ethnicity East Asian							-0.61	-1.26 – 0.05	0.069
Ethnicity South Asian							-0.73	-1.55 – 0.10	0.083
Ethnicity Native Hawaiian Pacific Islander							-1.23	-2.80 – 0.34	0.124
Ethnicity Middle Eastern							0.60	-0.55 – 1.75	0.308
Ethnicity American Indian							-0.55	-2.41 – 1.31	0.563
Random Effects
σ2	2.87			2.87			2.87
τ00	4.58 unique_ID			4.35 unique_ID			4.30 unique_ID
	0.08 univ			0.13 univ			0.07 univ
ICC	0.62			0.61			0.60
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1578			1569			1569
Marginal R2 / Conditional R2	0.004 / 0.621			0.036 / 0.624			0.055 / 0.626

Excluded Preregistered

m0 <- lmer(SAS_vigour ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	SAS vigour			SAS vigour			SAS vigour
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.69	5.31 – 6.06	<0.001	3.79	1.90 – 5.68	<0.001	4.79	2.77 – 6.82	<0.001
condflourish vs control	0.25	0.03 – 0.48	0.027	0.23	0.01 – 0.46	0.038	0.26	0.03 – 0.48	0.024
time - 2 5	-0.07	-0.15 – 0.01	0.100	-0.07	-0.15 – 0.01	0.094	-0.07	-0.15 – 0.01	0.090
condflourish vs control × time - 2 5	0.04	-0.04 – 0.12	0.307	0.04	-0.04 – 0.12	0.336	0.04	-0.04 – 0.12	0.338
Sex [Woman]				0.21	-0.38 – 0.80	0.484	0.21	-0.39 – 0.80	0.496
Age				0.05	-0.01 – 0.10	0.097	0.04	-0.02 – 0.10	0.181
int student [No]				-0.68	-1.55 – 0.19	0.123	-0.70	-1.62 – 0.22	0.134
SES num				0.41	0.21 – 0.60	<0.001	0.35	0.15 – 0.56	0.001
Ethnicity White							-0.44	-1.08 – 0.20	0.182
Ethnicity Hispanic							-0.61	-1.33 – 0.12	0.103
Ethnicity Black							-0.80	-1.88 – 0.28	0.144
Ethnicity East Asian							-0.81	-1.52 – -0.11	0.024
Ethnicity South Asian							-0.67	-1.52 – 0.18	0.122
Ethnicity Native Hawaiian Pacific Islander							-1.19	-2.79 – 0.41	0.146
Ethnicity Middle Eastern							0.28	-0.97 – 1.54	0.656
Ethnicity American Indian							-0.38	-2.21 – 1.45	0.685
Random Effects
σ2	2.87			2.87			2.87
τ00	4.27 unique_ID			4.04 unique_ID			4.01 unique_ID
	0.06 univ			0.11 univ			0.09 univ
ICC	0.60			0.59			0.59
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1411			1405			1405
Marginal R2 / Conditional R2	0.010 / 0.606			0.049 / 0.611			0.064 / 0.614

Excluded Unreasonable Numbers

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

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00303228 (tol = 0.002, component 1)

m2 <- lmer(SAS_vigour ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS vigour			SAS vigour			SAS vigour
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.60	5.24 – 5.97	<0.001	3.98	2.00 – 5.96	<0.001	5.10	3.00 – 7.19	<0.001
condflourish vs control	0.17	-0.06 – 0.41	0.153	0.18	-0.06 – 0.41	0.138	0.22	-0.02 – 0.45	0.068
time - 2 5	-0.09	-0.17 – -0.00	0.040	-0.09	-0.18 – -0.01	0.035	-0.09	-0.18 – -0.01	0.033
condflourish vs control × time - 2 5	0.02	-0.06 – 0.11	0.616	0.02	-0.07 – 0.10	0.671	0.02	-0.07 – 0.10	0.677
Sex [Woman]				0.28	-0.34 – 0.89	0.374	0.27	-0.35 – 0.88	0.390
Age				0.04	-0.02 – 0.10	0.164	0.03	-0.03 – 0.09	0.329
int student [No]				-0.66	-1.63 – 0.30	0.176	-0.73	-1.73 – 0.27	0.154
SES num				0.35	0.15 – 0.56	0.001	0.31	0.10 – 0.52	0.004
Ethnicity White							-0.41	-1.07 – 0.25	0.219
Ethnicity Hispanic							-0.62	-1.36 – 0.13	0.107
Ethnicity Black							-0.83	-1.93 – 0.27	0.139
Ethnicity East Asian							-0.84	-1.57 – -0.11	0.024
Ethnicity South Asian							-1.00	-1.90 – -0.10	0.029
Ethnicity Native Hawaiian Pacific Islander							-1.15	-2.76 – 0.45	0.159
Ethnicity Middle Eastern							0.62	-0.90 – 2.13	0.424
Ethnicity American Indian							-0.90	-2.90 – 1.10	0.376
Random Effects
σ2	2.85			2.85			2.85
τ00	4.24 unique_ID			4.09 unique_ID			4.02 unique_ID
	0.05 univ			0.10 univ			0.07 univ
ICC	0.60			0.60			0.59
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1292			1289			1289
Marginal R2 / Conditional R2	0.005 / 0.604			0.036 / 0.610			0.058 / 0.613

SAS: Depression

Intention to Treat

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

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

m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)

	SAS depression			SAS depression			SAS depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.06	3.83 – 4.28	<0.001	5.58	3.67 – 7.48	<0.001	5.15	3.14 – 7.16	<0.001
condflourish vs control	-0.14	-0.37 – 0.08	0.216	-0.12	-0.35 – 0.10	0.283	-0.15	-0.38 – 0.08	0.204
time - 2 5	-0.05	-0.13 – 0.04	0.279	-0.05	-0.13 – 0.04	0.263	-0.05	-0.13 – 0.04	0.277
condflourish vs control × time - 2 5	0.03	-0.06 – 0.11	0.535	0.03	-0.06 – 0.11	0.511	0.03	-0.06 – 0.11	0.523
Sex [Woman]				0.44	-0.13 – 1.02	0.130	0.39	-0.18 – 0.97	0.182
Age				-0.06	-0.12 – 0.00	0.053	-0.05	-0.10 – 0.01	0.123
int student [No]				0.69	-0.21 – 1.60	0.133	0.96	-0.00 – 1.92	0.051
SES num				-0.39	-0.58 – -0.19	<0.001	-0.38	-0.59 – -0.18	<0.001
Ethnicity White							-0.26	-0.91 – 0.38	0.418
Ethnicity Hispanic							-0.24	-0.95 – 0.48	0.517
Ethnicity Black							0.19	-0.86 – 1.23	0.723
Ethnicity East Asian							-0.04	-0.75 – 0.67	0.915
Ethnicity South Asian							0.88	-0.02 – 1.77	0.054
Ethnicity Native Hawaiian Pacific Islander							0.14	-1.56 – 1.84	0.872
Ethnicity Middle Eastern							0.77	-0.47 – 2.02	0.224
Ethnicity American Indian							-0.39	-2.41 – 1.62	0.702
Random Effects
σ2	3.54			3.54			3.53
τ00	5.24 unique_ID			5.00 unique_ID			5.01 unique_ID
	0.00 univ			0.07 univ			0.02 univ
ICC				0.59			0.59
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1578			1569			1569
Marginal R2 / Conditional R2	0.007 / NA			0.040 / 0.605			0.052 / 0.609

Excluded Preregistered

m0 <- lmer(SAS_depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	SAS depression			SAS depression			SAS depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.92	3.63 – 4.21	<0.001	5.36	3.35 – 7.37	<0.001	5.24	3.08 – 7.40	<0.001
condflourish vs control	-0.18	-0.42 – 0.07	0.157	-0.14	-0.38 – 0.09	0.235	-0.16	-0.40 – 0.08	0.178
time - 2 5	-0.04	-0.13 – 0.05	0.337	-0.04	-0.13 – 0.05	0.358	-0.04	-0.13 – 0.05	0.362
condflourish vs control × time - 2 5	0.02	-0.07 – 0.11	0.679	0.02	-0.07 – 0.11	0.634	0.02	-0.07 – 0.11	0.636
Sex [Woman]				0.45	-0.18 – 1.08	0.160	0.41	-0.23 – 1.05	0.208
Age				-0.05	-0.11 – 0.01	0.085	-0.04	-0.11 – 0.02	0.150
int student [No]				0.79	-0.14 – 1.72	0.097	0.96	-0.03 – 1.96	0.058
SES num				-0.43	-0.64 – -0.22	<0.001	-0.44	-0.66 – -0.23	<0.001
Ethnicity White							-0.21	-0.90 – 0.48	0.551
Ethnicity Hispanic							-0.36	-1.14 – 0.43	0.369
Ethnicity Black							-0.37	-1.53 – 0.80	0.535
Ethnicity East Asian							-0.13	-0.90 – 0.63	0.732
Ethnicity South Asian							0.51	-0.41 – 1.43	0.277
Ethnicity Native Hawaiian Pacific Islander							0.53	-1.20 – 2.26	0.552
Ethnicity Middle Eastern							0.82	-0.53 – 2.17	0.235
Ethnicity American Indian							-0.22	-2.20 – 1.76	0.829
Random Effects
σ2	3.42			3.42			3.42
τ00	4.96 unique_ID			4.63 unique_ID			4.66 unique_ID
	0.02 univ			0.08 univ			0.06 univ
ICC	0.59			0.58			0.58
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1411			1405			1405
Marginal R2 / Conditional R2	0.004 / 0.594			0.050 / 0.601			0.058 / 0.605

Excluded Unreasonable Numbers

m0 <- lmer(SAS_depression ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_depression ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS depression			SAS depression			SAS depression
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.91	3.63 – 4.20	<0.001	5.32	3.21 – 7.43	<0.001	5.06	2.80 – 7.32	<0.001
condflourish vs control	-0.18	-0.43 – 0.07	0.162	-0.16	-0.41 – 0.09	0.205	-0.17	-0.42 – 0.08	0.185
time - 2 5	-0.06	-0.15 – 0.03	0.215	-0.06	-0.15 – 0.03	0.213	-0.06	-0.15 – 0.03	0.217
condflourish vs control × time - 2 5	0.00	-0.09 – 0.10	0.947	0.00	-0.09 – 0.10	0.930	0.00	-0.09 – 0.10	0.922
Sex [Woman]				0.52	-0.13 – 1.18	0.117	0.50	-0.17 – 1.16	0.141
Age				-0.05	-0.11 – 0.01	0.120	-0.04	-0.10 – 0.02	0.223
int student [No]				0.74	-0.29 – 1.76	0.160	0.84	-0.24 – 1.92	0.129
SES num				-0.45	-0.67 – -0.23	<0.001	-0.46	-0.68 – -0.23	<0.001
Ethnicity White							-0.03	-0.74 – 0.68	0.932
Ethnicity Hispanic							-0.28	-1.09 – 0.53	0.496
Ethnicity Black							-0.35	-1.54 – 0.84	0.562
Ethnicity East Asian							-0.09	-0.88 – 0.70	0.821
Ethnicity South Asian							0.80	-0.17 – 1.77	0.106
Ethnicity Native Hawaiian Pacific Islander							0.57	-1.17 – 2.30	0.521
Ethnicity Middle Eastern							0.45	-1.19 – 2.08	0.593
Ethnicity American Indian							0.10	-2.06 – 2.26	0.926
Random Effects
σ2	3.44			3.44			3.44
τ00	4.96 unique_ID			4.61 unique_ID			4.66 unique_ID
	0.01 univ			0.09 univ			0.07 univ
ICC	0.59			0.58			0.58
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.004 / 0.593			0.051 / 0.599			0.059 / 0.604

SAS: Anxiety

Intention to Treat

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

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

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

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

m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)

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

tab_model(m0, m1, m2)

	SAS anxiety			SAS anxiety			SAS anxiety
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.02	5.80 – 6.24	<0.001	6.31	4.62 – 8.01	<0.001	6.19	4.35 – 8.02	<0.001
condflourish vs control	-0.17	-0.39 – 0.05	0.126	-0.15	-0.35 – 0.06	0.169	-0.15	-0.36 – 0.06	0.167
time - 2 5	-0.12	-0.21 – -0.03	0.006	-0.12	-0.21 – -0.04	0.006	-0.12	-0.21 – -0.04	0.006
condflourish vs control × time - 2 5	-0.02	-0.11 – 0.06	0.580	-0.03	-0.11 – 0.06	0.575	-0.03	-0.11 – 0.06	0.562
Sex [Woman]				1.12	0.59 – 1.65	<0.001	1.10	0.57 – 1.63	<0.001
Age				-0.03	-0.08 – 0.02	0.284	-0.03	-0.08 – 0.02	0.296
int student [No]				0.98	0.16 – 1.80	0.019	1.10	0.22 – 1.99	0.014
SES num				-0.46	-0.65 – -0.28	<0.001	-0.44	-0.63 – -0.25	<0.001
Ethnicity White							-0.15	-0.74 – 0.44	0.624
Ethnicity Hispanic							0.17	-0.49 – 0.83	0.615
Ethnicity Black							0.21	-0.75 – 1.17	0.669
Ethnicity East Asian							-0.16	-0.81 – 0.49	0.627
Ethnicity South Asian							0.38	-0.44 – 1.20	0.364
Ethnicity Native Hawaiian Pacific Islander							-0.69	-2.26 – 0.87	0.386
Ethnicity Middle Eastern							0.27	-0.87 – 1.41	0.641
Ethnicity American Indian							0.22	-1.63 – 2.07	0.812
Random Effects
σ2	3.79			3.76			3.76
τ00	4.47 unique_ID			3.92 unique_ID			3.98 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC							0.51
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.013 / NA			0.146 / NA			0.081 / 0.554

Excluded Preregistered

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

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

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

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

m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)

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

tab_model(m0, m1, m2)

	SAS anxiety			SAS anxiety			SAS anxiety
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.97	5.73 – 6.20	<0.001	5.91	4.11 – 7.72	<0.001	5.98	4.01 – 7.95	<0.001
condflourish vs control	-0.19	-0.42 – 0.05	0.118	-0.15	-0.37 – 0.07	0.184	-0.15	-0.38 – 0.07	0.186
time - 2 5	-0.14	-0.23 – -0.05	0.003	-0.14	-0.23 – -0.04	0.004	-0.14	-0.23 – -0.04	0.004
condflourish vs control × time - 2 5	-0.03	-0.13 – 0.06	0.491	-0.03	-0.13 – 0.06	0.492	-0.03	-0.13 – 0.06	0.486
Sex [Woman]				1.15	0.56 – 1.74	<0.001	1.13	0.53 – 1.73	<0.001
Age				-0.02	-0.07 – 0.03	0.485	-0.02	-0.08 – 0.03	0.470
int student [No]				1.26	0.41 – 2.12	0.004	1.28	0.35 – 2.20	0.007
SES num				-0.51	-0.70 – -0.31	<0.001	-0.49	-0.70 – -0.29	<0.001
Ethnicity White							-0.14	-0.78 – 0.50	0.671
Ethnicity Hispanic							0.21	-0.52 – 0.94	0.565
Ethnicity Black							0.00	-1.08 – 1.09	0.994
Ethnicity East Asian							-0.23	-0.95 – 0.48	0.524
Ethnicity South Asian							0.06	-0.80 – 0.92	0.893
Ethnicity Native Hawaiian Pacific Islander							-0.52	-2.14 – 1.10	0.531
Ethnicity Middle Eastern							0.45	-0.80 – 1.70	0.480
Ethnicity American Indian							0.31	-1.54 – 2.17	0.740
Random Effects
σ2	3.78			3.77			3.77
τ00	4.42 unique_ID			3.79 unique_ID			3.86 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.016 / NA			0.166 / NA			0.172 / NA

Excluded Unreasonable Numbers

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

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

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

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

m2 <- lmer(SAS_anxiety ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)

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

tab_model(m0, m1, m2)

	SAS anxiety			SAS anxiety			SAS anxiety
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	6.01	5.76 – 6.25	<0.001	5.89	3.97 – 7.82	<0.001	5.87	3.80 – 7.95	<0.001
condflourish vs control	-0.15	-0.39 – 0.10	0.237	-0.13	-0.37 – 0.10	0.268	-0.13	-0.37 – 0.11	0.286
time - 2 5	-0.15	-0.25 – -0.05	0.002	-0.15	-0.25 – -0.06	0.002	-0.15	-0.25 – -0.06	0.002
condflourish vs control × time - 2 5	-0.05	-0.14 – 0.05	0.358	-0.05	-0.15 – 0.05	0.326	-0.05	-0.15 – 0.05	0.329
Sex [Woman]				1.17	0.55 – 1.78	<0.001	1.18	0.56 – 1.80	<0.001
Age				-0.02	-0.07 – 0.04	0.575	-0.02	-0.07 – 0.04	0.588
int student [No]				1.17	0.21 – 2.12	0.016	1.17	0.16 – 2.18	0.023
SES num				-0.49	-0.70 – -0.29	<0.001	-0.48	-0.69 – -0.26	<0.001
Ethnicity White							-0.06	-0.73 – 0.60	0.857
Ethnicity Hispanic							0.19	-0.56 – 0.95	0.619
Ethnicity Black							0.05	-1.06 – 1.17	0.925
Ethnicity East Asian							-0.27	-1.01 – 0.47	0.475
Ethnicity South Asian							0.20	-0.71 – 1.11	0.666
Ethnicity Native Hawaiian Pacific Islander							-0.53	-2.17 – 1.11	0.524
Ethnicity Middle Eastern							-0.23	-1.76 – 1.30	0.765
Ethnicity American Indian							0.96	-1.08 – 3.00	0.354
Random Effects
σ2	3.77			3.77			3.77
τ00	4.43 unique_ID			3.85 unique_ID			3.92 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC	0.54
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.006 / 0.543			0.155 / NA			0.162 / NA

SAS: Anger

Intention to Treat

m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT)
tab_model(m0, m1, m2)

	SAS anger			SAS anger			SAS anger
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.73	2.18 – 3.28	<0.001	3.20	1.51 – 4.89	<0.001	3.14	1.38 – 4.91	<0.001
condflourish vs control	0.07	-0.12 – 0.26	0.475	0.07	-0.12 – 0.26	0.488	0.05	-0.14 – 0.24	0.628
time - 2 5	0.05	-0.03 – 0.12	0.198	0.05	-0.02 – 0.13	0.169	0.05	-0.02 – 0.13	0.171
condflourish vs control × time - 2 5	0.00	-0.07 – 0.07	0.999	0.00	-0.07 – 0.08	0.912	0.00	-0.07 – 0.08	0.930
Sex [Woman]				0.14	-0.35 – 0.62	0.583	0.11	-0.37 – 0.60	0.653
Age				-0.02	-0.07 – 0.03	0.468	-0.01	-0.06 – 0.04	0.620
int student [No]				0.48	-0.28 – 1.25	0.218	0.71	-0.10 – 1.52	0.085
SES num				-0.20	-0.37 – -0.03	0.018	-0.19	-0.36 – -0.02	0.031
Ethnicity White							-0.49	-1.03 – 0.05	0.077
Ethnicity Hispanic							-0.07	-0.67 – 0.54	0.828
Ethnicity Black							-0.27	-1.15 – 0.61	0.544
Ethnicity East Asian							-0.34	-0.93 – 0.26	0.267
Ethnicity South Asian							0.29	-0.46 – 1.05	0.443
Ethnicity Native Hawaiian Pacific Islander							-0.62	-2.05 – 0.81	0.396
Ethnicity Middle Eastern							0.88	-0.18 – 1.93	0.102
Ethnicity American Indian							0.40	-1.30 – 2.09	0.647
Random Effects
σ2	2.73			2.71			2.71
τ00	3.59 unique_ID			3.47 unique_ID			3.45 unique_ID
	0.20 univ			0.22 univ			0.14 univ
ICC	0.58			0.58			0.57
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1579			1570			1570
Marginal R2 / Conditional R2	0.001 / 0.582			0.013 / 0.582			0.029 / 0.583

Excluded Preregistered

m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	SAS anger			SAS anger			SAS anger
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.65	2.16 – 3.13	<0.001	3.37	1.55 – 5.19	<0.001	3.66	1.74 – 5.58	<0.001
condflourish vs control	0.12	-0.09 – 0.32	0.270	0.12	-0.08 – 0.33	0.242	0.11	-0.10 – 0.31	0.319
time - 2 5	0.05	-0.03 – 0.12	0.262	0.05	-0.03 – 0.13	0.245	0.05	-0.03 – 0.13	0.251
condflourish vs control × time - 2 5	-0.01	-0.09 – 0.06	0.725	-0.01	-0.09 – 0.07	0.763	-0.01	-0.09 – 0.07	0.753
Sex [Woman]				0.10	-0.45 – 0.65	0.728	0.06	-0.49 – 0.62	0.828
Age				-0.02	-0.07 – 0.04	0.569	-0.01	-0.07 – 0.04	0.629
int student [No]				0.37	-0.44 – 1.19	0.369	0.65	-0.21 – 1.51	0.141
SES num				-0.24	-0.43 – -0.06	0.009	-0.24	-0.43 – -0.06	0.011
Ethnicity White							-0.74	-1.34 – -0.14	0.016
Ethnicity Hispanic							-0.22	-0.90 – 0.46	0.523
Ethnicity Black							-0.68	-1.69 – 0.33	0.186
Ethnicity East Asian							-0.56	-1.22 – 0.11	0.100
Ethnicity South Asian							0.01	-0.79 – 0.80	0.989
Ethnicity Native Hawaiian Pacific Islander							-0.63	-2.13 – 0.87	0.412
Ethnicity Middle Eastern							0.59	-0.59 – 1.76	0.328
Ethnicity American Indian							0.44	-1.28 – 2.15	0.618
Random Effects
σ2	2.74			2.74			2.74
τ00	3.55 unique_ID			3.49 unique_ID			3.45 unique_ID
	0.14 univ			0.20 univ			0.13 univ
ICC	0.57			0.57			0.57
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1412			1406			1406
Marginal R2 / Conditional R2	0.003 / 0.575			0.017 / 0.581			0.035 / 0.582

Excluded Unreasonable Numbers

m0 <- lmer(SAS_anger ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_anger ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS anger			SAS anger			SAS anger
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	2.61	2.04 – 3.17	<0.001	3.06	1.16 – 4.96	0.002	3.20	1.19 – 5.21	0.002
condflourish vs control	0.06	-0.16 – 0.27	0.601	0.06	-0.16 – 0.27	0.594	0.05	-0.16 – 0.27	0.636
time - 2 5	0.03	-0.05 – 0.11	0.474	0.03	-0.05 – 0.11	0.482	0.03	-0.05 – 0.11	0.484
condflourish vs control × time - 2 5	-0.03	-0.11 – 0.05	0.455	-0.03	-0.11 – 0.05	0.454	-0.03	-0.11 – 0.05	0.454
Sex [Woman]				0.21	-0.35 – 0.78	0.461	0.20	-0.37 – 0.77	0.493
Age				-0.01	-0.06 – 0.05	0.753	-0.00	-0.06 – 0.05	0.869
int student [No]				0.59	-0.30 – 1.48	0.196	0.80	-0.14 – 1.73	0.094
SES num				-0.30	-0.49 – -0.11	0.002	-0.29	-0.48 – -0.09	0.004
Ethnicity White							-0.55	-1.16 – 0.06	0.078
Ethnicity Hispanic							-0.13	-0.83 – 0.56	0.707
Ethnicity Black							-0.72	-1.74 – 0.31	0.169
Ethnicity East Asian							-0.43	-1.11 – 0.24	0.210
Ethnicity South Asian							0.08	-0.75 – 0.92	0.843
Ethnicity Native Hawaiian Pacific Islander							-0.55	-2.05 – 0.94	0.470
Ethnicity Middle Eastern							0.26	-1.15 – 1.68	0.716
Ethnicity American Indian							1.06	-0.80 – 2.92	0.265
Random Effects
σ2	2.61			2.61			2.62
τ00	3.52 unique_ID			3.42 unique_ID			3.43 unique_ID
	0.20 univ			0.25 univ			0.20 univ
ICC	0.59			0.58			0.58
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.001 / 0.588			0.024 / 0.594			0.036 / 0.596

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)
tab_model(m0, m1, m2)

	SAS positive			SAS positive			SAS positive
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	18.36	17.49 – 19.22	<0.001	13.42	8.95 – 17.89	<0.001	14.57	9.83 – 19.30	<0.001
condflourish vs control	0.53	-0.00 – 1.07	0.051	0.48	-0.04 – 1.00	0.069	0.53	0.00 – 1.05	0.050
time - 2 5	-0.02	-0.21 – 0.16	0.796	-0.03	-0.22 – 0.16	0.764	-0.03	-0.22 – 0.15	0.718
condflourish vs control × time - 2 5	0.22	0.04 – 0.41	0.020	0.22	0.03 – 0.41	0.020	0.22	0.03 – 0.41	0.020
Sex [Woman]				-0.49	-1.81 – 0.82	0.463	-0.41	-1.74 – 0.91	0.541
Age				0.12	-0.02 – 0.25	0.096	0.10	-0.04 – 0.24	0.148
int student [No]				-1.57	-3.66 – 0.52	0.140	-1.86	-4.08 – 0.36	0.101
SES num				1.29	0.83 – 1.74	<0.001	1.20	0.73 – 1.67	<0.001
Ethnicity White							0.16	-1.32 – 1.64	0.832
Ethnicity Hispanic							-0.25	-1.91 – 1.40	0.763
Ethnicity Black							-1.60	-4.01 – 0.81	0.193
Ethnicity East Asian							-0.78	-2.41 – 0.86	0.352
Ethnicity South Asian							-1.14	-3.20 – 0.92	0.279
Ethnicity Native Hawaiian Pacific Islander							-2.32	-6.23 – 1.59	0.244
Ethnicity Middle Eastern							0.27	-2.61 – 3.15	0.854
Ethnicity American Indian							1.17	-3.47 – 5.81	0.622
Random Effects
σ2	17.05			17.01			17.00
τ00	29.49 unique_ID			26.89 unique_ID			27.08 unique_ID
	0.32 univ			0.77 univ			0.46 univ
ICC	0.64			0.62			0.62
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1577			1568			1568
Marginal R2 / Conditional R2	0.007 / 0.639			0.061 / 0.643			0.069 / 0.645

Excluded Preregistered

m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded)
m1 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded)
m2 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)
tab_model(m0, m1, m2)

	SAS positive			SAS positive			SAS positive
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	18.31	17.37 – 19.26	<0.001	13.54	8.78 – 18.29	<0.001	15.29	10.17 – 20.40	<0.001
condflourish vs control	0.73	0.16 – 1.30	0.012	0.66	0.11 – 1.21	0.019	0.71	0.15 – 1.27	0.013
time - 2 5	0.02	-0.17 – 0.22	0.806	0.02	-0.18 – 0.21	0.858	0.02	-0.18 – 0.21	0.861
condflourish vs control × time - 2 5	0.19	-0.01 – 0.39	0.059	0.18	-0.02 – 0.38	0.073	0.18	-0.02 – 0.38	0.072
Sex [Woman]				-0.14	-1.61 – 1.33	0.856	-0.03	-1.52 – 1.46	0.968
Age				0.10	-0.04 – 0.24	0.156	0.08	-0.07 – 0.22	0.299
int student [No]				-1.91	-4.09 – 0.26	0.085	-2.05	-4.37 – 0.27	0.084
SES num				1.35	0.86 – 1.83	<0.001	1.23	0.73 – 1.74	<0.001
Ethnicity White							-0.32	-1.93 – 1.29	0.695
Ethnicity Hispanic							-1.35	-3.18 – 0.48	0.148
Ethnicity Black							-0.80	-3.50 – 1.91	0.565
Ethnicity East Asian							-1.09	-2.87 – 0.68	0.228
Ethnicity South Asian							-0.93	-3.08 – 1.21	0.393
Ethnicity Native Hawaiian Pacific Islander							-2.45	-6.47 – 1.58	0.233
Ethnicity Middle Eastern							-0.70	-3.85 – 2.45	0.662
Ethnicity American Indian							1.46	-3.15 – 6.06	0.535
Random Effects
σ2	16.93			16.94			16.95
τ00	27.99 unique_ID			25.48 unique_ID			25.72 unique_ID
	0.39 univ			0.84 univ			0.70 univ
ICC	0.63			0.61			0.61
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1410			1404			1404
Marginal R2 / Conditional R2	0.012 / 0.631			0.072 / 0.637			0.079 / 0.640

Excluded Unreasonable Numbers

m0 <- lmer(SAS_positive ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m1 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
m2 <- lmer(SAS_positive ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)
tab_model(m0, m1, m2)

	SAS positive			SAS positive			SAS positive
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	18.19	17.22 – 19.16	<0.001	13.84	8.89 – 18.80	<0.001	15.69	10.42 – 20.97	<0.001
condflourish vs control	0.60	0.02 – 1.19	0.044	0.61	0.03 – 1.18	0.039	0.69	0.11 – 1.27	0.020
time - 2 5	-0.00	-0.21 – 0.20	0.975	-0.01	-0.21 – 0.19	0.919	-0.01	-0.22 – 0.19	0.911
condflourish vs control × time - 2 5	0.16	-0.04 – 0.37	0.122	0.15	-0.05 – 0.36	0.146	0.15	-0.05 – 0.36	0.147
Sex [Woman]				-0.01	-1.53 – 1.51	0.988	0.03	-1.50 – 1.57	0.965
Age				0.08	-0.06 – 0.23	0.259	0.06	-0.09 – 0.20	0.436
int student [No]				-1.66	-4.05 – 0.73	0.173	-1.95	-4.45 – 0.56	0.127
SES num				1.26	0.75 – 1.76	<0.001	1.16	0.63 – 1.68	<0.001
Ethnicity White							-0.25	-1.89 – 1.40	0.768
Ethnicity Hispanic							-1.26	-3.13 – 0.61	0.187
Ethnicity Black							-0.80	-3.55 – 1.95	0.568
Ethnicity East Asian							-1.09	-2.91 – 0.73	0.241
Ethnicity South Asian							-1.67	-3.92 – 0.57	0.144
Ethnicity Native Hawaiian Pacific Islander							-2.41	-6.42 – 1.60	0.239
Ethnicity Middle Eastern							1.22	-2.58 – 5.01	0.529
Ethnicity American Indian							0.47	-4.51 – 5.46	0.852
Random Effects
σ2	16.66			16.64			16.64
τ00	27.22 unique_ID			25.29 unique_ID			25.44 unique_ID
	0.42 univ			0.80 univ			0.60 univ
ICC	0.62			0.61			0.61
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1292			1289			1289
Marginal R2 / Conditional R2	0.009 / 0.627			0.059 / 0.634			0.070 / 0.637

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)

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

tab_model(m0, m1, m2)

	SAS negative			SAS negative			SAS negative
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.69	12.16 – 13.23	<0.001	14.41	10.12 – 18.70	<0.001	13.98	9.37 – 18.59	<0.001
condflourish vs control	-0.25	-0.78 – 0.29	0.366	-0.21	-0.73 – 0.31	0.431	-0.26	-0.78 – 0.27	0.339
time - 2 5	-0.12	-0.31 – 0.07	0.223	-0.12	-0.31 – 0.07	0.228	-0.12	-0.31 – 0.07	0.230
condflourish vs control × time - 2 5	0.00	-0.19 – 0.19	0.987	0.01	-0.19 – 0.20	0.956	0.00	-0.19 – 0.19	0.973
Sex [Woman]				1.70	0.38 – 3.03	0.012	1.61	0.28 – 2.95	0.018
Age				-0.07	-0.20 – 0.06	0.269	-0.07	-0.20 – 0.07	0.318
int student [No]				2.03	-0.05 – 4.10	0.056	2.74	0.51 – 4.96	0.016
SES num				-1.05	-1.51 – -0.60	<0.001	-1.01	-1.48 – -0.53	<0.001
Ethnicity White							-0.93	-2.41 – 0.56	0.221
Ethnicity Hispanic							-0.04	-1.69 – 1.61	0.965
Ethnicity Black							0.21	-2.20 – 2.63	0.863
Ethnicity East Asian							-0.57	-2.21 – 1.07	0.496
Ethnicity South Asian							1.61	-0.46 – 3.67	0.128
Ethnicity Native Hawaiian Pacific Islander							-1.09	-5.01 – 2.84	0.588
Ethnicity Middle Eastern							2.08	-0.79 – 4.94	0.155
Ethnicity American Indian							0.12	-4.54 – 4.78	0.960
Random Effects
σ2	17.75			17.59			17.57
τ00	29.49 unique_ID			27.29 unique_ID			27.26 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	486 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	1578			1569			1569
Marginal R2 / Conditional R2	0.005 / NA			0.124 / NA			0.155 / NA

Excluded Preregistered

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

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

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

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

m2 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded)

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

tab_model(m0, m1, m2)

	SAS negative			SAS negative			SAS negative
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.43	11.86 – 13.01	<0.001	14.04	9.48 – 18.60	<0.001	14.39	9.45 – 19.34	<0.001
condflourish vs control	-0.25	-0.82 – 0.33	0.402	-0.18	-0.74 – 0.38	0.530	-0.22	-0.78 – 0.34	0.442
time - 2 5	-0.14	-0.34 – 0.06	0.180	-0.13	-0.33 – 0.07	0.196	-0.13	-0.33 – 0.07	0.193
condflourish vs control × time - 2 5	-0.03	-0.23 – 0.17	0.772	-0.03	-0.23 – 0.17	0.803	-0.03	-0.23 – 0.17	0.796
Sex [Woman]				1.70	0.22 – 3.18	0.025	1.59	0.10 – 3.09	0.037
Age				-0.06	-0.20 – 0.07	0.378	-0.06	-0.20 – 0.08	0.409
int student [No]				2.32	0.16 – 4.48	0.036	2.83	0.51 – 5.16	0.017
SES num				-1.18	-1.67 – -0.69	<0.001	-1.17	-1.68 – -0.66	<0.001
Ethnicity White							-1.13	-2.74 – 0.47	0.167
Ethnicity Hispanic							-0.30	-2.13 – 1.52	0.744
Ethnicity Black							-1.04	-3.76 – 1.69	0.455
Ethnicity East Asian							-0.94	-2.73 – 0.85	0.304
Ethnicity South Asian							0.60	-1.55 – 2.76	0.582
Ethnicity Native Hawaiian Pacific Islander							-0.49	-4.54 – 3.55	0.811
Ethnicity Middle Eastern							1.94	-1.19 – 5.08	0.225
Ethnicity American Indian							0.40	-4.23 – 5.02	0.866
Random Effects
σ2	17.52			17.45			17.45
τ00	28.46 unique_ID			25.90 unique_ID			25.99 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	1411			1405			1405
Marginal R2 / Conditional R2	0.005 / NA			0.145 / NA			0.166 / NA

Excluded Unreasonable Numbers

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

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

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

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

m2 <- lmer(SAS_negative ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable)

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

tab_model(m0, m1, m2)

	SAS negative			SAS negative			SAS negative
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	12.41	11.81 – 13.01	<0.001	13.63	8.84 – 18.43	<0.001	13.54	8.36 – 18.71	<0.001
condflourish vs control	-0.27	-0.87 – 0.33	0.382	-0.24	-0.82 – 0.34	0.420	-0.25	-0.84 – 0.34	0.399
time - 2 5	-0.18	-0.39 – 0.03	0.088	-0.18	-0.39 – 0.02	0.085	-0.18	-0.39 – 0.02	0.085
condflourish vs control × time - 2 5	-0.07	-0.28 – 0.13	0.488	-0.08	-0.28 – 0.13	0.473	-0.07	-0.28 – 0.13	0.477
Sex [Woman]				1.88	0.34 – 3.41	0.017	1.85	0.30 – 3.40	0.020
Age				-0.04	-0.18 – 0.09	0.535	-0.03	-0.18 – 0.11	0.639
int student [No]				2.35	-0.03 – 4.73	0.053	2.73	0.20 – 5.25	0.035
SES num				-1.24	-1.75 – -0.73	<0.001	-1.21	-1.74 – -0.68	<0.001
Ethnicity White							-0.69	-2.35 – 0.96	0.410
Ethnicity Hispanic							-0.15	-2.04 – 1.73	0.874
Ethnicity Black							-1.02	-3.80 – 1.76	0.471
Ethnicity East Asian							-0.83	-2.67 – 1.01	0.378
Ethnicity South Asian							1.12	-1.15 – 3.39	0.334
Ethnicity Native Hawaiian Pacific Islander							-0.36	-4.41 – 3.70	0.863
Ethnicity Middle Eastern							0.66	-3.15 – 4.47	0.735
Ethnicity American Indian							2.01	-3.04 – 7.06	0.435
Random Effects
σ2	17.07			17.08			17.08
τ00	28.49 unique_ID			25.76 unique_ID			26.09 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	1293			1290			1290
Marginal R2 / Conditional R2	0.007 / NA			0.156 / NA			0.170 / 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)	44.44	43.01 – 45.88	<0.001	38.14	33.33 – 42.95	<0.001	38.52	33.50 – 43.54	<0.001
condflourish vs control	0.13	-0.42 – 0.68	0.648	0.08	-0.46 – 0.62	0.763	0.18	-0.37 – 0.72	0.525
time - 2 5	-0.03	-0.21 – 0.14	0.716	-0.03	-0.21 – 0.14	0.720	-0.04	-0.21 – 0.14	0.669
condflourish vs control × time - 2 5	0.19	0.02 – 0.36	0.032	0.19	0.02 – 0.36	0.031	0.19	0.02 – 0.36	0.032
Sex [Woman]				0.40	-0.96 – 1.75	0.564	0.47	-0.89 – 1.83	0.494
Age				0.11	-0.03 – 0.25	0.129	0.10	-0.04 – 0.25	0.162
int student [No]				-0.81	-2.98 – 1.36	0.464	-1.71	-4.01 – 0.59	0.145
SES num				1.32	0.85 – 1.79	<0.001	1.27	0.78 – 1.76	<0.001
Ethnicity White							1.51	-0.02 – 3.04	0.053
Ethnicity Hispanic							0.56	-1.15 – 2.26	0.521
Ethnicity Black							-0.20	-2.67 – 2.27	0.875
Ethnicity East Asian							-0.59	-2.28 – 1.10	0.494
Ethnicity South Asian							-0.68	-2.81 – 1.46	0.535
Ethnicity Native Hawaiian Pacific Islander							-1.08	-5.14 – 2.98	0.602
Ethnicity Middle Eastern							0.39	-2.61 – 3.38	0.801
Ethnicity American Indian							0.74	-4.14 – 5.63	0.765
Random Effects
σ2	12.77			12.77			12.79
τ00	29.89 unique_ID			27.66 unique_ID			27.51 unique_ID
	1.30 univ			1.89 univ			1.22 univ
ICC	0.71			0.70			0.69
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	832			829			829
Marginal R2 / Conditional R2	0.002 / 0.710			0.055 / 0.715			0.072 / 0.714

Excluded Preregistered

m0 <- lmer(flourishing ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)

	flourishing			flourishing			flourishing
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	44.61	43.13 – 46.08	<0.001	38.17	32.96 – 43.38	<0.001	38.54	33.01 – 44.08	<0.001
condflourish vs control	0.15	-0.45 – 0.76	0.621	0.10	-0.49 – 0.69	0.743	0.18	-0.42 – 0.78	0.552
time - 2 5	-0.05	-0.23 – 0.14	0.612	-0.05	-0.23 – 0.14	0.607	-0.05	-0.24 – 0.13	0.592
condflourish vs control × time - 2 5	0.18	-0.01 – 0.36	0.060	0.17	-0.01 – 0.36	0.068	0.17	-0.01 – 0.36	0.065
Sex [Woman]				0.97	-0.60 – 2.55	0.225	1.09	-0.50 – 2.68	0.178
Age				0.11	-0.04 – 0.26	0.158	0.09	-0.06 – 0.25	0.232
int student [No]				-0.74	-3.07 – 1.58	0.531	-1.62	-4.09 – 0.85	0.199
SES num				1.21	0.69 – 1.73	<0.001	1.18	0.64 – 1.72	<0.001
Ethnicity White							1.42	-0.30 – 3.15	0.105
Ethnicity Hispanic							0.37	-1.59 – 2.33	0.711
Ethnicity Black							1.12	-1.77 – 4.01	0.447
Ethnicity East Asian							-0.58	-2.48 – 1.33	0.553
Ethnicity South Asian							-0.23	-2.52 – 2.06	0.844
Ethnicity Native Hawaiian Pacific Islander							-0.90	-5.21 – 3.41	0.681
Ethnicity Middle Eastern							-0.57	-3.96 – 2.82	0.742
Ethnicity American Indian							1.01	-3.90 – 5.92	0.686
Random Effects
σ2	13.12			13.17			13.21
τ00	29.04 unique_ID			27.22 unique_ID			27.26 unique_ID
	1.34 univ			1.73 univ			1.26 univ
ICC	0.70			0.69			0.68
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	711			709			709
Marginal R2 / Conditional R2	0.002 / 0.699			0.049 / 0.703			0.063 / 0.703

Excluded Unreasonable Numbers

m0 <- lmer(flourishing ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(flourishing ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)

	flourishing			flourishing			flourishing
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	44.61	43.25 – 45.97	<0.001	38.23	32.94 – 43.51	<0.001	39.02	33.44 – 44.60	<0.001
condflourish vs control	0.15	-0.47 – 0.76	0.641	0.13	-0.47 – 0.74	0.666	0.23	-0.38 – 0.84	0.459
time - 2 5	0.02	-0.17 – 0.21	0.811	0.02	-0.17 – 0.21	0.816	0.02	-0.17 – 0.21	0.834
condflourish vs control × time - 2 5	0.25	0.06 – 0.44	0.011	0.24	0.05 – 0.44	0.013	0.24	0.05 – 0.44	0.013
Sex [Woman]				0.83	-0.78 – 2.43	0.311	0.90	-0.72 – 2.51	0.276
Age				0.09	-0.06 – 0.24	0.243	0.07	-0.08 – 0.23	0.358
int student [No]				-0.20	-2.72 – 2.31	0.875	-1.12	-3.74 – 1.51	0.405
SES num				1.22	0.68 – 1.75	<0.001	1.19	0.64 – 1.74	<0.001
Ethnicity White							1.11	-0.62 – 2.84	0.209
Ethnicity Hispanic							0.23	-1.74 – 2.20	0.816
Ethnicity Black							1.09	-1.79 – 3.98	0.457
Ethnicity East Asian							-0.96	-2.87 – 0.96	0.326
Ethnicity South Asian							-1.09	-3.44 – 1.27	0.365
Ethnicity Native Hawaiian Pacific Islander							-1.01	-5.23 – 3.22	0.640
Ethnicity Middle Eastern							1.50	-2.54 – 5.54	0.467
Ethnicity American Indian							0.16	-5.11 – 5.42	0.954
Random Effects
σ2	12.93			12.97			13.01
τ00	27.58 unique_ID			25.88 unique_ID			25.87 unique_ID
	1.08 univ			1.36 univ			0.89 univ
ICC	0.69			0.68			0.67
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.004 / 0.690			0.050 / 0.693			0.067 / 0.695

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)	6.92	6.65 – 7.20	<0.001	6.75	5.97 – 7.53	<0.001	6.75	5.92 – 7.58	<0.001
condflourish vs control	0.05	-0.04 – 0.15	0.273	0.06	-0.03 – 0.15	0.182	0.07	-0.02 – 0.17	0.116
time - 2 5	0.03	-0.01 – 0.08	0.111	0.03	-0.01 – 0.08	0.116	0.03	-0.01 – 0.08	0.117
condflourish vs control × time - 2 5	-0.00	-0.04 – 0.04	0.950	0.00	-0.04 – 0.04	0.989	0.00	-0.04 – 0.04	0.951
Sex [Woman]				0.41	0.18 – 0.65	<0.001	0.41	0.17 – 0.64	0.001
Age				-0.03	-0.05 – -0.01	0.015	-0.03	-0.05 – -0.01	0.014
int student [No]				0.19	-0.18 – 0.55	0.312	0.14	-0.25 – 0.52	0.487
SES num				0.09	0.01 – 0.17	0.028	0.09	0.01 – 0.17	0.030
Ethnicity White							0.13	-0.13 – 0.39	0.328
Ethnicity Hispanic							0.11	-0.18 – 0.41	0.438
Ethnicity Black							0.01	-0.41 – 0.43	0.974
Ethnicity East Asian							0.04	-0.24 – 0.33	0.763
Ethnicity South Asian							-0.07	-0.43 – 0.29	0.705
Ethnicity Native Hawaiian Pacific Islander							-0.07	-0.77 – 0.63	0.838
Ethnicity Middle Eastern							-0.49	-1.00 – 0.02	0.057
Ethnicity American Indian							-0.61	-1.44 – 0.23	0.153
Random Effects
σ2	0.81			0.81			0.81
τ00	0.59 unique_ID			0.53 unique_ID			0.53 unique_ID
	0.05 univ			0.02 univ			0.01 univ
ICC	0.44			0.40			0.40
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.004 / 0.441			0.046 / 0.432			0.060 / 0.437

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)	6.91	6.59 – 7.23	<0.001	6.66	5.83 – 7.50	<0.001	6.61	5.71 – 7.51	<0.001
condflourish vs control	0.03	-0.07 – 0.13	0.555	0.04	-0.06 – 0.14	0.392	0.06	-0.05 – 0.16	0.282
time - 2 5	0.03	-0.02 – 0.07	0.246	0.03	-0.02 – 0.07	0.224	0.03	-0.02 – 0.07	0.239
condflourish vs control × time - 2 5	0.01	-0.04 – 0.05	0.810	0.00	-0.04 – 0.05	0.842	0.01	-0.04 – 0.05	0.806
Sex [Woman]				0.43	0.16 – 0.69	0.002	0.42	0.15 – 0.68	0.002
Age				-0.03	-0.06 – -0.01	0.010	-0.03	-0.06 – -0.01	0.010
int student [No]				0.30	-0.09 – 0.68	0.133	0.24	-0.17 – 0.65	0.252
SES num				0.10	0.02 – 0.19	0.020	0.11	0.02 – 0.21	0.014
Ethnicity White							0.14	-0.15 – 0.42	0.361
Ethnicity Hispanic							0.24	-0.09 – 0.57	0.154
Ethnicity Black							0.19	-0.30 – 0.67	0.451
Ethnicity East Asian							0.04	-0.28 – 0.36	0.820
Ethnicity South Asian							-0.03	-0.42 – 0.36	0.882
Ethnicity Native Hawaiian Pacific Islander							-0.20	-0.93 – 0.54	0.599
Ethnicity Middle Eastern							-0.50	-1.07 – 0.07	0.085
Ethnicity American Indian							-0.61	-1.45 – 0.23	0.153
Random Effects
σ2	0.83			0.83			0.83
τ00	0.57 unique_ID			0.52 unique_ID			0.51 unique_ID
	0.07 univ			0.01 univ			0.01 univ
ICC	0.43			0.39			0.39
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.002 / 0.434			0.053 / 0.422			0.070 / 0.430

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)	6.91	6.54 – 7.28	<0.001	6.59	5.72 – 7.46	<0.001	6.62	5.68 – 7.56	<0.001
condflourish vs control	0.04	-0.06 – 0.15	0.416	0.05	-0.05 – 0.16	0.317	0.06	-0.05 – 0.16	0.265
time - 2 5	0.03	-0.02 – 0.08	0.189	0.03	-0.01 – 0.08	0.175	0.03	-0.02 – 0.08	0.188
condflourish vs control × time - 2 5	0.01	-0.04 – 0.06	0.673	0.01	-0.04 – 0.06	0.712	0.01	-0.04 – 0.06	0.698
Sex [Woman]				0.40	0.13 – 0.67	0.004	0.40	0.12 – 0.67	0.004
Age				-0.03	-0.05 – -0.00	0.022	-0.03	-0.06 – -0.01	0.014
int student [No]				0.33	-0.09 – 0.75	0.122	0.26	-0.19 – 0.70	0.258
SES num				0.11	0.01 – 0.20	0.023	0.10	0.01 – 0.20	0.031
Ethnicity White							0.15	-0.14 – 0.45	0.317
Ethnicity Hispanic							0.18	-0.16 – 0.51	0.300
Ethnicity Black							0.20	-0.29 – 0.69	0.419
Ethnicity East Asian							0.05	-0.28 – 0.37	0.781
Ethnicity South Asian							-0.12	-0.52 – 0.28	0.561
Ethnicity Native Hawaiian Pacific Islander							-0.19	-0.92 – 0.54	0.601
Ethnicity Middle Eastern							-0.30	-0.99 – 0.40	0.402
Ethnicity American Indian							-0.46	-1.37 – 0.46	0.327
Random Effects
σ2	0.80			0.80			0.80
τ00	0.54 unique_ID			0.50 unique_ID			0.51 unique_ID
	0.10 univ			0.02 univ			0.02 univ
ICC	0.44			0.39			0.40
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.003 / 0.443			0.051 / 0.425			0.062 / 0.433

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)	5.68	5.33 – 6.04	<0.001	4.37	2.84 – 5.90	<0.001	4.19	2.58 – 5.80	<0.001
condflourish vs control	0.11	-0.08 – 0.29	0.252	0.12	-0.06 – 0.30	0.186	0.16	-0.02 – 0.34	0.080
time - 2 5	0.08	0.03 – 0.14	0.001	0.08	0.03 – 0.14	0.002	0.08	0.03 – 0.13	0.002
condflourish vs control × time - 2 5	0.06	0.01 – 0.11	0.029	0.06	0.01 – 0.11	0.027	0.06	0.01 – 0.11	0.027
Sex [Woman]				0.78	0.33 – 1.23	0.001	0.76	0.30 – 1.21	0.001
Age				-0.02	-0.07 – 0.03	0.440	-0.01	-0.06 – 0.03	0.581
int student [No]				0.29	-0.44 – 1.01	0.439	0.18	-0.59 – 0.94	0.648
SES num				0.25	0.09 – 0.40	0.002	0.21	0.05 – 0.38	0.010
Ethnicity White							0.56	0.05 – 1.07	0.031
Ethnicity Hispanic							0.19	-0.38 – 0.75	0.516
Ethnicity Black							-0.25	-1.08 – 0.57	0.546
Ethnicity East Asian							-0.04	-0.61 – 0.52	0.879
Ethnicity South Asian							0.39	-0.33 – 1.10	0.288
Ethnicity Native Hawaiian Pacific Islander							-1.39	-2.74 – -0.05	0.043
Ethnicity Middle Eastern							-0.07	-1.07 – 0.92	0.883
Ethnicity American Indian							-1.07	-2.70 – 0.55	0.195
Random Effects
σ2	1.13			1.14			1.13
τ00	3.46 unique_ID			3.29 unique_ID			3.23 unique_ID
	0.07 univ			0.05 univ			0.03 univ
ICC	0.76			0.75			0.74
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.007 / 0.758			0.046 / 0.758			0.075 / 0.761

Excluded Preregistered

m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)

	cohesion			cohesion			cohesion
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.60	5.14 – 6.07	<0.001	4.00	2.29 – 5.71	<0.001	3.97	2.17 – 5.78	<0.001
condflourish vs control	0.17	-0.03 – 0.38	0.093	0.19	-0.01 – 0.40	0.059	0.23	0.03 – 0.43	0.025
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	0.008
condflourish vs control × time - 2 5	0.05	-0.00 – 0.11	0.066	0.05	-0.00 – 0.11	0.073	0.05	-0.00 – 0.11	0.066
Sex [Woman]				0.97	0.43 – 1.51	<0.001	0.95	0.41 – 1.48	0.001
Age				-0.01	-0.06 – 0.04	0.747	-0.01	-0.06 – 0.04	0.763
int student [No]				0.31	-0.48 – 1.10	0.440	0.21	-0.63 – 1.05	0.621
SES num				0.22	0.04 – 0.40	0.014	0.17	-0.01 – 0.36	0.064
Ethnicity White							0.58	-0.01 – 1.16	0.052
Ethnicity Hispanic							-0.08	-0.74 – 0.58	0.813
Ethnicity Black							-0.05	-1.02 – 0.93	0.925
Ethnicity East Asian							0.03	-0.61 – 0.67	0.928
Ethnicity South Asian							0.52	-0.25 – 1.30	0.187
Ethnicity Native Hawaiian Pacific Islander							-1.41	-2.86 – 0.04	0.057
Ethnicity Middle Eastern							-0.35	-1.49 – 0.79	0.552
Ethnicity American Indian							-0.95	-2.61 – 0.71	0.260
Random Effects
σ2	1.15			1.16			1.16
τ00	3.50 unique_ID			3.38 unique_ID			3.32 unique_ID
	0.13 univ			0.06 univ			0.03 univ
ICC	0.76			0.75			0.74
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.010 / 0.761			0.050 / 0.761			0.081 / 0.764

Excluded Unreasonable Numbers

m0 <- lmer(cohesion ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(cohesion ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)

	cohesion			cohesion			cohesion
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	5.57	5.06 – 6.09	<0.001	3.63	1.79 – 5.46	<0.001	3.78	1.85 – 5.72	<0.001
condflourish vs control	0.16	-0.06 – 0.37	0.158	0.17	-0.04 – 0.39	0.118	0.21	-0.01 – 0.43	0.059
time - 2 5	0.08	0.03 – 0.14	0.003	0.08	0.03 – 0.14	0.003	0.08	0.03 – 0.14	0.004
condflourish vs control × time - 2 5	0.06	0.00 – 0.12	0.036	0.06	0.00 – 0.12	0.039	0.06	0.00 – 0.12	0.037
Sex [Woman]				0.96	0.39 – 1.53	0.001	0.95	0.37 – 1.52	0.001
Age				-0.00	-0.06 – 0.05	0.913	-0.01	-0.06 – 0.05	0.810
int student [No]				0.58	-0.32 – 1.47	0.205	0.44	-0.49 – 1.37	0.352
SES num				0.22	0.03 – 0.41	0.022	0.16	-0.03 – 0.36	0.105
Ethnicity White							0.53	-0.08 – 1.14	0.089
Ethnicity Hispanic							-0.13	-0.83 – 0.56	0.705
Ethnicity Black							-0.05	-1.07 – 0.97	0.918
Ethnicity East Asian							0.06	-0.62 – 0.74	0.865
Ethnicity South Asian							0.36	-0.48 – 1.19	0.401
Ethnicity Native Hawaiian Pacific Islander							-1.39	-2.88 – 0.09	0.066
Ethnicity Middle Eastern							-0.05	-1.47 – 1.37	0.946
Ethnicity American Indian							-0.91	-2.76 – 0.94	0.335
Random Effects
σ2	1.10			1.10			1.10
τ00	3.69 unique_ID			3.57 unique_ID			3.55 unique_ID
	0.16 univ			0.08 univ			0.03 univ
ICC	0.78			0.77			0.77
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.009 / 0.780			0.049 / 0.779			0.076 / 0.783

Mindfulness

Intention to Treat

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

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

m1 <- lmer(mindfulness ~ 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 ~ 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			mindfulness			mindfulness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	20.19	19.70 – 20.69	<0.001	23.67	19.76 – 27.58	<0.001	23.55	19.32 – 27.78	<0.001
condflourish vs control	-0.20	-0.70 – 0.30	0.430	-0.11	-0.59 – 0.37	0.655	-0.14	-0.63 – 0.35	0.574
time - 2 5	0.34	0.16 – 0.53	<0.001	0.34	0.16 – 0.52	<0.001	0.34	0.16 – 0.52	<0.001
condflourish vs control × time - 2 5	-0.19	-0.37 – -0.00	0.044	-0.19	-0.38 – -0.01	0.039	-0.19	-0.38 – -0.01	0.040
Sex [Woman]				1.79	0.58 – 2.99	0.004	1.78	0.56 – 3.00	0.004
Age				-0.22	-0.34 – -0.10	<0.001	-0.22	-0.34 – -0.10	<0.001
int student [No]				2.45	0.56 – 4.34	0.011	2.70	0.66 – 4.73	0.010
SES num				-0.81	-1.23 – -0.40	<0.001	-0.74	-1.18 – -0.31	0.001
Ethnicity White							-0.49	-1.85 – 0.87	0.484
Ethnicity Hispanic							0.04	-1.48 – 1.55	0.963
Ethnicity Black							0.07	-2.14 – 2.28	0.952
Ethnicity East Asian							-0.60	-2.11 – 0.91	0.435
Ethnicity South Asian							0.44	-1.46 – 2.35	0.647
Ethnicity Native Hawaiian Pacific Islander							1.49	-2.16 – 5.14	0.423
Ethnicity Middle Eastern							-0.57	-3.22 – 2.08	0.674
Ethnicity American Indian							-0.27	-4.64 – 4.09	0.902
Random Effects
σ2	14.63			14.59			14.57
τ00	21.51 unique_ID			19.05 unique_ID			19.41 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.024 / NA			0.182 / NA			0.189 / NA

Excluded Preregistered

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

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

m1 <- lmer(mindfulness ~ 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 ~ 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			mindfulness			mindfulness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	20.06	19.52 – 20.60	<0.001	23.58	19.41 – 27.75	<0.001	23.92	19.38 – 28.47	<0.001
condflourish vs control	-0.30	-0.84 – 0.23	0.268	-0.19	-0.71 – 0.32	0.459	-0.21	-0.73 – 0.31	0.424
time - 2 5	0.37	0.17 – 0.56	<0.001	0.37	0.17 – 0.56	<0.001	0.37	0.18 – 0.56	<0.001
condflourish vs control × time - 2 5	-0.23	-0.42 – -0.03	0.022	-0.23	-0.42 – -0.04	0.019	-0.23	-0.42 – -0.04	0.019
Sex [Woman]				1.54	0.18 – 2.89	0.026	1.53	0.15 – 2.90	0.030
Age				-0.21	-0.33 – -0.09	0.001	-0.21	-0.34 – -0.09	0.001
int student [No]				2.80	0.83 – 4.78	0.005	2.70	0.57 – 4.83	0.013
SES num				-0.94	-1.39 – -0.49	<0.001	-0.89	-1.36 – -0.42	<0.001
Ethnicity White							-0.38	-1.86 – 1.11	0.617
Ethnicity Hispanic							-0.05	-1.74 – 1.64	0.953
Ethnicity Black							0.42	-2.09 – 2.92	0.745
Ethnicity East Asian							-0.91	-2.56 – 0.74	0.279
Ethnicity South Asian							-0.15	-2.14 – 1.84	0.882
Ethnicity Native Hawaiian Pacific Islander							1.80	-1.96 – 5.55	0.349
Ethnicity Middle Eastern							0.88	-2.03 – 3.78	0.555
Ethnicity American Indian							-0.24	-4.52 – 4.04	0.912
Random Effects
σ2	14.51			14.48			14.48
τ00	20.50 unique_ID			17.72 unique_ID			18.02 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC	0.59
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.013 / 0.591			0.200 / NA			0.210 / NA

Excluded Unreasonable Numbers

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

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

m1 <- lmer(mindfulness ~ 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 ~ 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			mindfulness			mindfulness
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	20.10	19.55 – 20.65	<0.001	24.26	19.91 – 28.60	<0.001	23.85	19.16 – 28.54	<0.001
condflourish vs control	-0.26	-0.81 – 0.29	0.354	-0.20	-0.73 – 0.33	0.449	-0.22	-0.76 – 0.32	0.423
time - 2 5	0.37	0.17 – 0.57	<0.001	0.38	0.18 – 0.58	<0.001	0.38	0.18 – 0.58	<0.001
condflourish vs control × time - 2 5	-0.22	-0.42 – -0.02	0.032	-0.22	-0.42 – -0.02	0.033	-0.21	-0.41 – -0.01	0.037
Sex [Woman]				1.39	-0.01 – 2.78	0.051	1.50	0.09 – 2.91	0.037
Age				-0.20	-0.32 – -0.07	0.002	-0.19	-0.32 – -0.06	0.004
int student [No]				2.32	0.17 – 4.47	0.035	2.06	-0.22 – 4.34	0.076
SES num				-1.06	-1.52 – -0.59	<0.001	-1.00	-1.48 – -0.51	<0.001
Ethnicity White							0.36	-1.15 – 1.86	0.640
Ethnicity Hispanic							0.36	-1.35 – 2.07	0.681
Ethnicity Black							0.64	-1.89 – 3.16	0.621
Ethnicity East Asian							-0.71	-2.38 – 0.97	0.408
Ethnicity South Asian							0.71	-1.35 – 2.77	0.499
Ethnicity Native Hawaiian Pacific Islander							1.92	-1.80 – 5.63	0.312
Ethnicity Middle Eastern							-1.78	-5.30 – 1.74	0.320
Ethnicity American Indian							1.71	-2.93 – 6.35	0.469
Random Effects
σ2	14.21			14.23			14.21
τ00	19.85 unique_ID			17.17 unique_ID			17.40 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.033 / NA			0.196 / NA			0.213 / NA

Emotional Resilience

Intention to Treat

m0 <- lmer(emo_res ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_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_ITT_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_ITT_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)	18.17	18.02 – 18.32	<0.001	18.70	17.51 – 19.88	<0.001	18.36	17.09 – 19.63	<0.001
condflourish vs control	0.02	-0.13 – 0.17	0.802	0.00	-0.14 – 0.15	0.965	-0.02	-0.17 – 0.12	0.742
time - 2 5	0.00	-0.08 – 0.08	0.973	0.00	-0.07 – 0.08	0.916	0.01	-0.07 – 0.08	0.885
condflourish vs control × time - 2 5	0.03	-0.04 – 0.11	0.401	0.04	-0.04 – 0.11	0.327	0.04	-0.04 – 0.11	0.331
Sex [Woman]				0.08	-0.29 – 0.45	0.681	0.07	-0.30 – 0.44	0.728
Age				-0.02	-0.06 – 0.02	0.264	-0.01	-0.05 – 0.02	0.420
int student [No]				-0.16	-0.73 – 0.41	0.580	0.10	-0.51 – 0.70	0.750
SES num				-0.01	-0.14 – 0.11	0.834	0.01	-0.12 – 0.14	0.852
Ethnicity White							-0.26	-0.67 – 0.15	0.217
Ethnicity Hispanic							0.20	-0.26 – 0.66	0.391
Ethnicity Black							-0.51	-1.18 – 0.16	0.135
Ethnicity East Asian							0.06	-0.39 – 0.52	0.790
Ethnicity South Asian							0.42	-0.15 – 1.00	0.147
Ethnicity Native Hawaiian Pacific Islander							0.55	-0.57 – 1.67	0.333
Ethnicity Middle Eastern							-0.13	-0.93 – 0.67	0.749
Ethnicity American Indian							0.17	-1.16 – 1.49	0.807
Random Effects
σ2	2.70			2.62			2.63
τ00	1.11 unique_ID			1.02 unique_ID			1.00 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	832			829			829
Marginal R2 / Conditional R2	0.001 / NA			0.005 / NA			0.026 / NA

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	17.96 – 18.27	<0.001	18.92	17.67 – 20.18	<0.001	18.83	17.48 – 20.18	<0.001
condflourish vs control	-0.00	-0.16 – 0.15	0.965	0.00	-0.15 – 0.16	0.975	-0.03	-0.18 – 0.13	0.726
time - 2 5	0.02	-0.06 – 0.10	0.607	0.02	-0.06 – 0.10	0.643	0.02	-0.06 – 0.10	0.636
condflourish vs control × time - 2 5	0.05	-0.03 – 0.13	0.251	0.05	-0.03 – 0.12	0.268	0.05	-0.03 – 0.12	0.267
Sex [Woman]				0.05	-0.36 – 0.46	0.825	0.04	-0.37 – 0.45	0.835
Age				-0.02	-0.06 – 0.01	0.237	-0.02	-0.05 – 0.02	0.409
int student [No]				-0.23	-0.82 – 0.36	0.438	0.15	-0.48 – 0.78	0.636
SES num				-0.06	-0.19 – 0.08	0.426	-0.05	-0.19 – 0.09	0.503
Ethnicity White							-0.55	-0.99 – -0.11	0.015
Ethnicity Hispanic							-0.25	-0.75 – 0.25	0.333
Ethnicity Black							-1.05	-1.80 – -0.31	0.006
Ethnicity East Asian							-0.18	-0.68 – 0.31	0.470
Ethnicity South Asian							0.34	-0.25 – 0.94	0.261
Ethnicity Native Hawaiian Pacific Islander							0.64	-0.50 – 1.78	0.273
Ethnicity Middle Eastern							-0.27	-1.14 – 0.60	0.547
Ethnicity American Indian							0.16	-1.13 – 1.46	0.804
Random Effects
σ2	2.54			2.54			2.53
τ00	0.94 unique_ID			0.96 unique_ID			0.91 unique_ID
	0.00 univ			0.00 univ			0.00 univ
ICC							0.27
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	711			709			709
Marginal R2 / Conditional R2	0.002 / NA			0.008 / NA			0.034 / 0.291

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	19.16	17.86 – 20.47	<0.001	19.11	17.72 – 20.50	<0.001
condflourish vs control	-0.01	-0.16 – 0.15	0.948	0.00	-0.16 – 0.16	0.964	-0.03	-0.19 – 0.13	0.748
time - 2 5	0.04	-0.05 – 0.12	0.406	0.03	-0.05 – 0.12	0.432	0.03	-0.05 – 0.12	0.439
condflourish vs control × time - 2 5	0.06	-0.02 – 0.14	0.151	0.06	-0.02 – 0.14	0.156	0.06	-0.02 – 0.15	0.143
Sex [Woman]				-0.03	-0.45 – 0.39	0.878	-0.02	-0.44 – 0.40	0.931
Age				-0.02	-0.06 – 0.01	0.215	-0.02	-0.05 – 0.02	0.378
int student [No]				-0.33	-0.97 – 0.32	0.322	0.03	-0.64 – 0.70	0.935
SES num				-0.07	-0.22 – 0.07	0.299	-0.08	-0.22 – 0.07	0.307
Ethnicity White							-0.51	-0.96 – -0.06	0.026
Ethnicity Hispanic							-0.30	-0.81 – 0.21	0.253
Ethnicity Black							-1.09	-1.84 – -0.34	0.005
Ethnicity East Asian							-0.22	-0.72 – 0.28	0.392
Ethnicity South Asian							0.42	-0.19 – 1.04	0.178
Ethnicity Native Hawaiian Pacific Islander							0.61	-0.52 – 1.73	0.291
Ethnicity Middle Eastern							-1.05	-2.11 – 0.01	0.052
Ethnicity American Indian							0.22	-1.19 – 1.62	0.763
Random Effects
σ2	2.55			2.55			2.54
τ00	0.88 unique_ID			0.89 unique_ID			0.83 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.004 / NA			0.012 / NA			0.056 / NA

School Satisfaction

Intention to Treat

m0 <- lmer(school_satis ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m1 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)

	school satis			school satis			school satis
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.54	4.35 – 4.72	<0.001	3.31	2.67 – 3.94	<0.001	3.45	2.81 – 4.10	<0.001
condflourish vs control	0.03	-0.04 – 0.11	0.387	0.03	-0.04 – 0.10	0.393	0.04	-0.03 – 0.12	0.222
time - 2 5	0.03	0.01 – 0.05	0.018	0.03	0.00 – 0.05	0.026	0.03	0.00 – 0.05	0.036
condflourish vs control × time - 2 5	0.01	-0.02 – 0.03	0.612	0.00	-0.02 – 0.03	0.702	0.00	-0.02 – 0.03	0.718
Sex [Woman]				0.20	0.02 – 0.38	0.032	0.22	0.04 – 0.40	0.016
Age				0.02	0.00 – 0.04	0.045	0.02	0.00 – 0.04	0.050
int student [No]				0.07	-0.22 – 0.37	0.618	-0.09	-0.39 – 0.21	0.570
SES num				0.18	0.12 – 0.24	<0.001	0.16	0.10 – 0.23	<0.001
Ethnicity White							0.22	0.02 – 0.43	0.028
Ethnicity Hispanic							0.10	-0.12 – 0.33	0.366
Ethnicity Black							-0.42	-0.74 – -0.09	0.012
Ethnicity East Asian							-0.12	-0.34 – 0.10	0.287
Ethnicity South Asian							-0.29	-0.57 – -0.01	0.042
Ethnicity Native Hawaiian Pacific Islander							-0.16	-0.69 – 0.38	0.567
Ethnicity Middle Eastern							0.22	-0.17 – 0.61	0.273
Ethnicity American Indian							0.24	-0.40 – 0.88	0.467
Random Effects
σ2	0.26			0.26			0.26
τ00	0.52 unique_ID			0.48 unique_ID			0.45 unique_ID
	0.02 univ			0.03 univ			0.01 univ
ICC	0.68			0.66			0.64
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.004 / 0.679			0.062 / 0.683			0.115 / 0.684

Excluded Preregistered

m0 <- lmer(school_satis ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(school_satis ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
tab_model(m0, m1, m2)

	school satis			school satis			school satis
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.56	4.36 – 4.76	<0.001	3.35	2.66 – 4.04	<0.001	3.39	2.67 – 4.11	<0.001
condflourish vs control	0.06	-0.02 – 0.14	0.152	0.05	-0.03 – 0.13	0.221	0.06	-0.02 – 0.14	0.140
time - 2 5	0.02	-0.01 – 0.05	0.126	0.02	-0.01 – 0.04	0.146	0.02	-0.01 – 0.04	0.162
condflourish vs control × time - 2 5	0.02	-0.01 – 0.04	0.251	0.01	-0.01 – 0.04	0.308	0.01	-0.01 – 0.04	0.312
Sex [Woman]				0.19	-0.02 – 0.40	0.070	0.22	0.01 – 0.42	0.045
Age				0.02	-0.00 – 0.04	0.063	0.02	-0.00 – 0.04	0.054
int student [No]				0.07	-0.24 – 0.38	0.650	-0.08	-0.41 – 0.24	0.626
SES num				0.18	0.11 – 0.24	<0.001	0.16	0.09 – 0.23	<0.001
Ethnicity White							0.25	0.02 – 0.47	0.034
Ethnicity Hispanic							0.09	-0.17 – 0.35	0.481
Ethnicity Black							-0.25	-0.63 – 0.13	0.203
Ethnicity East Asian							-0.10	-0.35 – 0.15	0.435
Ethnicity South Asian							-0.12	-0.42 – 0.19	0.449
Ethnicity Native Hawaiian Pacific Islander							-0.03	-0.60 – 0.54	0.913
Ethnicity Middle Eastern							0.25	-0.19 – 0.70	0.266
Ethnicity American Indian							0.25	-0.40 – 0.90	0.455
Random Effects
σ2	0.26			0.26			0.26
τ00	0.51 unique_ID			0.47 unique_ID			0.46 unique_ID
	0.02 univ			0.03 univ			0.01 univ
ICC	0.67			0.66			0.65
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.006 / 0.677			0.060 / 0.682			0.096 / 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)	4.54	4.34 – 4.74	<0.001	3.37	2.65 – 4.09	<0.001	3.41	2.66 – 4.16	<0.001
condflourish vs control	0.05	-0.04 – 0.13	0.279	0.04	-0.04 – 0.12	0.315	0.05	-0.03 – 0.14	0.200
time - 2 5	0.03	-0.00 – 0.05	0.057	0.03	-0.00 – 0.05	0.057	0.03	-0.00 – 0.05	0.064
condflourish vs control × time - 2 5	0.02	-0.01 – 0.05	0.124	0.02	-0.01 – 0.05	0.136	0.02	-0.01 – 0.05	0.141
Sex [Woman]				0.19	-0.03 – 0.40	0.094	0.20	-0.02 – 0.42	0.075
Age				0.02	-0.00 – 0.04	0.106	0.02	-0.00 – 0.04	0.096
int student [No]				0.14	-0.20 – 0.49	0.409	0.02	-0.33 – 0.37	0.912
SES num				0.16	0.09 – 0.23	<0.001	0.15	0.08 – 0.23	<0.001
Ethnicity White							0.20	-0.04 – 0.43	0.098
Ethnicity Hispanic							0.11	-0.16 – 0.38	0.419
Ethnicity Black							-0.25	-0.64 – 0.14	0.215
Ethnicity East Asian							-0.10	-0.35 – 0.16	0.468
Ethnicity South Asian							-0.20	-0.51 – 0.12	0.227
Ethnicity Native Hawaiian Pacific Islander							-0.05	-0.62 – 0.52	0.869
Ethnicity Middle Eastern							0.31	-0.23 – 0.86	0.263
Ethnicity American Indian							0.08	-0.63 – 0.79	0.825
Random Effects
σ2	0.25			0.25			0.25
τ00	0.50 unique_ID			0.47 unique_ID			0.46 unique_ID
	0.02 univ			0.03 univ			0.01 univ
ICC	0.67			0.66			0.65
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.005 / 0.676			0.053 / 0.681			0.084 / 0.682

School Prioritizes Well-Being

“At my school, I feel that students’ mental and emotional well-being is a priority.”

Intention to Treat

m0 <- lmer(wellbeing_priority ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m1 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
m2 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)
tab_model(m0, m1, m2)

	wellbeing priority			wellbeing priority			wellbeing priority
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.57	4.17 – 4.97	<0.001	4.06	3.17 – 4.95	<0.001	4.22	3.26 – 5.18	<0.001
condflourish vs control	0.03	-0.07 – 0.13	0.499	0.03	-0.07 – 0.13	0.559	0.04	-0.06 – 0.14	0.454
time - 2 5	0.03	-0.01 – 0.07	0.142	0.03	-0.01 – 0.07	0.165	0.03	-0.01 – 0.07	0.179
condflourish vs control × time - 2 5	0.02	-0.02 – 0.06	0.407	0.02	-0.03 – 0.06	0.426	0.02	-0.03 – 0.06	0.438
Sex [Woman]				0.03	-0.22 – 0.28	0.805	0.04	-0.21 – 0.29	0.747
Age				0.02	-0.01 – 0.05	0.121	0.02	-0.01 – 0.04	0.197
int student [No]				-0.41	-0.81 – -0.02	0.040	-0.44	-0.86 – -0.02	0.042
SES num				0.13	0.04 – 0.21	0.004	0.12	0.03 – 0.21	0.011
Ethnicity White							0.01	-0.27 – 0.30	0.929
Ethnicity Hispanic							-0.06	-0.37 – 0.26	0.724
Ethnicity Black							-0.03	-0.49 – 0.43	0.901
Ethnicity East Asian							-0.11	-0.42 – 0.20	0.500
Ethnicity South Asian							-0.06	-0.46 – 0.33	0.755
Ethnicity Native Hawaiian Pacific Islander							-0.71	-1.47 – 0.05	0.065
Ethnicity Middle Eastern							0.04	-0.52 – 0.59	0.891
Ethnicity American Indian							0.35	-0.56 – 1.25	0.451
Random Effects
σ2	0.78			0.79			0.78
τ00	0.75 unique_ID			0.72 unique_ID			0.73 unique_ID
	0.12 univ			0.08 univ			0.09 univ
ICC	0.52			0.50			0.51
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.002 / 0.526			0.027 / 0.517			0.034 / 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)	4.53	4.22 – 4.84	<0.001	4.08	3.14 – 5.02	<0.001	4.33	3.32 – 5.35	<0.001
condflourish vs control	0.04	-0.07 – 0.15	0.451	0.03	-0.08 – 0.14	0.551	0.04	-0.07 – 0.15	0.476
time - 2 5	0.03	-0.02 – 0.07	0.246	0.02	-0.02 – 0.07	0.270	0.02	-0.02 – 0.07	0.281
condflourish vs control × time - 2 5	0.02	-0.03 – 0.06	0.494	0.01	-0.03 – 0.06	0.514	0.01	-0.03 – 0.06	0.512
Sex [Woman]				-0.03	-0.33 – 0.26	0.815	-0.02	-0.32 – 0.27	0.870
Age				0.02	-0.00 – 0.05	0.091	0.02	-0.01 – 0.05	0.145
int student [No]				-0.55	-0.98 – -0.12	0.011	-0.55	-1.00 – -0.09	0.019
SES num				0.14	0.04 – 0.24	0.004	0.13	0.03 – 0.23	0.011
Ethnicity White							-0.12	-0.44 – 0.20	0.452
Ethnicity Hispanic							-0.07	-0.43 – 0.29	0.701
Ethnicity Black							-0.28	-0.82 – 0.25	0.301
Ethnicity East Asian							-0.29	-0.65 – 0.06	0.106
Ethnicity South Asian							-0.11	-0.53 – 0.32	0.624
Ethnicity Native Hawaiian Pacific Islander							-0.53	-1.33 – 0.28	0.202
Ethnicity Middle Eastern							-0.19	-0.82 – 0.44	0.550
Ethnicity American Indian							0.25	-0.67 – 1.16	0.598
Random Effects
σ2	0.74			0.74			0.74
τ00	0.80 unique_ID			0.77 unique_ID			0.78 unique_ID
	0.06 univ			0.03 univ			0.03 univ
ICC	0.54			0.52			0.52
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.002 / 0.540			0.040 / 0.537			0.048 / 0.545

Excluded Unreasonable Numbers

m0 <- lmer(wellbeing_priority ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m1 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
m2 <- lmer(wellbeing_priority ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)
tab_model(m0, m1, m2)

	wellbeing priority			wellbeing priority			wellbeing priority
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	4.50	4.21 – 4.79	<0.001	4.09	3.11 – 5.06	<0.001	4.35	3.30 – 5.40	<0.001
condflourish vs control	0.02	-0.09 – 0.14	0.678	0.02	-0.09 – 0.14	0.703	0.03	-0.09 – 0.15	0.628
time - 2 5	0.03	-0.02 – 0.07	0.252	0.02	-0.02 – 0.07	0.275	0.02	-0.02 – 0.07	0.288
condflourish vs control × time - 2 5	0.02	-0.03 – 0.06	0.493	0.02	-0.03 – 0.06	0.507	0.02	-0.03 – 0.06	0.507
Sex [Woman]				-0.10	-0.40 – 0.20	0.523	-0.09	-0.40 – 0.21	0.552
Age				0.02	-0.00 – 0.05	0.083	0.02	-0.01 – 0.05	0.124
int student [No]				-0.48	-0.95 – -0.01	0.046	-0.45	-0.95 – 0.05	0.075
SES num				0.12	0.02 – 0.22	0.019	0.11	0.01 – 0.22	0.033
Ethnicity White							-0.19	-0.52 – 0.14	0.259
Ethnicity Hispanic							-0.06	-0.44 – 0.31	0.746
Ethnicity Black							-0.33	-0.87 – 0.22	0.242
Ethnicity East Asian							-0.31	-0.67 – 0.06	0.097
Ethnicity South Asian							-0.16	-0.61 – 0.29	0.476
Ethnicity Native Hawaiian Pacific Islander							-0.54	-1.35 – 0.27	0.189
Ethnicity Middle Eastern							-0.23	-1.00 – 0.54	0.555
Ethnicity American Indian							0.15	-0.86 – 1.16	0.770
Random Effects
σ2	0.72			0.72			0.72
τ00	0.81 unique_ID			0.78 unique_ID			0.79 unique_ID
	0.05 univ			0.03 univ			0.02 univ
ICC	0.54			0.53			0.53
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.002 / 0.545			0.034 / 0.544			0.044 / 0.552

Academic Self-Efficacy

Intention to Treat

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

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

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

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

m2 <- lmer(acad_selfefficacy  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_ITT_factor)

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

tab_model(m0, m1, m2)

	acad selfefficacy			acad selfefficacy			acad selfefficacy
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	24.00	23.66 – 24.33	<0.001	21.18	18.45 – 23.92	<0.001	20.87	17.94 – 23.80	<0.001
condflourish vs control	-0.00	-0.34 – 0.33	0.995	-0.02	-0.36 – 0.31	0.905	0.03	-0.30 – 0.37	0.847
time - 2 5	0.15	0.02 – 0.28	0.021	0.15	0.02 – 0.28	0.023	0.15	0.02 – 0.28	0.023
condflourish vs control × time - 2 5	0.05	-0.08 – 0.18	0.436	0.05	-0.08 – 0.18	0.474	0.05	-0.08 – 0.17	0.491
Sex [Woman]				-0.31	-1.15 – 0.54	0.475	-0.28	-1.12 – 0.57	0.518
Age				0.07	-0.01 – 0.15	0.103	0.08	-0.01 – 0.16	0.079
int student [No]				0.15	-1.18 – 1.47	0.827	-0.65	-2.07 – 0.76	0.365
SES num				0.46	0.17 – 0.75	0.002	0.44	0.14 – 0.74	0.004
Ethnicity White							1.31	0.37 – 2.26	0.006
Ethnicity Hispanic							0.67	-0.38 – 1.72	0.211
Ethnicity Black							0.42	-1.11 – 1.95	0.591
Ethnicity East Asian							0.12	-0.92 – 1.17	0.815
Ethnicity South Asian							-0.18	-1.50 – 1.14	0.786
Ethnicity Native Hawaiian Pacific Islander							0.39	-2.14 – 2.92	0.762
Ethnicity Middle Eastern							0.95	-0.89 – 2.79	0.310
Ethnicity American Indian							0.73	-2.30 – 3.75	0.638
Random Effects
σ2	7.13			7.13			7.12
τ00	9.52 unique_ID			9.33 unique_ID			9.24 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	831			828			828
Marginal R2 / Conditional R2	0.008 / NA			0.056 / NA			0.096 / NA

Excluded Preregistered

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

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

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

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

m2 <- lmer(acad_selfefficacy  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)

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

tab_model(m0, m1, m2)

	acad selfefficacy			acad selfefficacy			acad selfefficacy
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	24.07	23.71 – 24.44	<0.001	20.56	17.56 – 23.56	<0.001	20.28	17.03 – 23.52	<0.001
condflourish vs control	-0.00	-0.37 – 0.36	0.981	-0.05	-0.42 – 0.32	0.797	-0.02	-0.39 – 0.35	0.930
time - 2 5	0.15	0.01 – 0.29	0.031	0.15	0.01 – 0.28	0.033	0.15	0.01 – 0.29	0.033
condflourish vs control × time - 2 5	0.07	-0.06 – 0.21	0.298	0.07	-0.07 – 0.21	0.323	0.07	-0.07 – 0.21	0.318
Sex [Woman]				0.01	-0.96 – 0.99	0.982	0.06	-0.93 – 1.04	0.912
Age				0.06	-0.02 – 0.15	0.152	0.07	-0.02 – 0.16	0.133
int student [No]				0.55	-0.87 – 1.96	0.450	-0.15	-1.67 – 1.38	0.851
SES num				0.51	0.18 – 0.83	0.002	0.52	0.18 – 0.86	0.002
Ethnicity White							1.06	-0.00 – 2.12	0.051
Ethnicity Hispanic							0.86	-0.35 – 2.06	0.163
Ethnicity Black							0.28	-1.51 – 2.08	0.755
Ethnicity East Asian							-0.14	-1.32 – 1.04	0.812
Ethnicity South Asian							-0.09	-1.51 – 1.33	0.905
Ethnicity Native Hawaiian Pacific Islander							0.43	-2.26 – 3.11	0.756
Ethnicity Middle Eastern							0.06	-2.02 – 2.14	0.953
Ethnicity American Indian							0.68	-2.37 – 3.74	0.662
Random Effects
σ2	7.24			7.24			7.25
τ00	9.52 unique_ID			9.30 unique_ID			9.27 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	711			709			709
Marginal R2 / Conditional R2	0.009 / NA			0.059 / NA			0.093 / NA

Excluded Unreasonable Numbers

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

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

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

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

m2 <- lmer(acad_selfefficacy  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)

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

tab_model(m0, m1, m2)

	acad selfefficacy			acad selfefficacy			acad selfefficacy
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	24.12	23.74 – 24.49	<0.001	20.44	17.34 – 23.54	<0.001	20.26	16.92 – 23.60	<0.001
condflourish vs control	0.04	-0.34 – 0.41	0.851	-0.01	-0.38 – 0.37	0.978	0.02	-0.36 – 0.41	0.900
time - 2 5	0.15	0.01 – 0.29	0.033	0.15	0.01 – 0.29	0.033	0.15	0.01 – 0.29	0.032
condflourish vs control × time - 2 5	0.07	-0.07 – 0.21	0.298	0.07	-0.07 – 0.21	0.308	0.07	-0.07 – 0.21	0.312
Sex [Woman]				-0.29	-1.29 – 0.70	0.563	-0.25	-1.25 – 0.75	0.625
Age				0.07	-0.02 – 0.16	0.143	0.07	-0.02 – 0.16	0.140
int student [No]				0.89	-0.64 – 2.43	0.253	0.28	-1.35 – 1.90	0.738
SES num				0.53	0.19 – 0.86	0.002	0.54	0.20 – 0.89	0.002
Ethnicity White							0.86	-0.21 – 1.93	0.115
Ethnicity Hispanic							0.66	-0.56 – 1.88	0.285
Ethnicity Black							0.52	-1.27 – 2.31	0.570
Ethnicity East Asian							-0.12	-1.31 – 1.07	0.840
Ethnicity South Asian							-0.43	-1.89 – 1.04	0.567
Ethnicity Native Hawaiian Pacific Islander							0.29	-2.36 – 2.93	0.832
Ethnicity Middle Eastern							0.76	-1.74 – 3.26	0.551
Ethnicity American Indian							1.09	-2.21 – 4.39	0.517
Random Effects
σ2	6.99			6.99			7.00
τ00	9.18 unique_ID			8.89 unique_ID			8.94 unique_ID
	0.00 univ			0.00 univ			0.00 univ
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.009 / NA			0.071 / NA			0.098 / 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)

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

tab_model(m0, m1, m2)

	ios			ios			ios
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.29	3.13 – 3.46	<0.001	2.82	1.86 – 3.77	<0.001	2.51	1.49 – 3.52	<0.001
condflourish vs control	0.07	-0.05 – 0.18	0.253	0.08	-0.04 – 0.20	0.182	0.10	-0.02 – 0.21	0.104
time - 2 5	0.05	0.00 – 0.09	0.034	0.05	0.00 – 0.09	0.039	0.04	0.00 – 0.09	0.045
condflourish vs control × time - 2 5	0.04	-0.00 – 0.08	0.069	0.04	-0.00 – 0.08	0.063	0.04	-0.00 – 0.08	0.066
Sex [Woman]				0.47	0.18 – 0.77	0.002	0.46	0.17 – 0.75	0.002
Age				-0.02	-0.05 – 0.01	0.275	-0.01	-0.04 – 0.02	0.466
int student [No]				0.13	-0.33 – 0.59	0.591	0.02	-0.47 – 0.51	0.936
SES num				0.10	-0.00 – 0.20	0.057	0.08	-0.02 – 0.19	0.119
Ethnicity White							0.45	0.12 – 0.78	0.007
Ethnicity Hispanic							0.10	-0.26 – 0.47	0.578
Ethnicity Black							0.27	-0.26 – 0.80	0.323
Ethnicity East Asian							0.10	-0.27 – 0.46	0.602
Ethnicity South Asian							0.44	-0.01 – 0.90	0.057
Ethnicity Native Hawaiian Pacific Islander							-0.32	-1.19 – 0.56	0.475
Ethnicity Middle Eastern							0.47	-0.16 – 1.11	0.144
Ethnicity American Indian							-0.20	-1.25 – 0.84	0.702
Random Effects
σ2	0.80			0.80			0.80
τ00	1.18 unique_ID			1.16 unique_ID			1.14 unique_ID
	0.01 univ			0.00 univ			0.00 univ
ICC	0.60			0.59
N	485 unique_ID			482 unique_ID			482 unique_ID
	3 univ			3 univ			3 univ
Observations	833			829			829
Marginal R2 / Conditional R2	0.006 / 0.602			0.033 / 0.606			0.122 / NA

Excluded Preregistered

m0 <- lmer(ios  ~ cond * I(time - 2.5)+ (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m1 <- lmer(ios  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)
m2 <- lmer(ios  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_factor)

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

tab_model(m0, m1, m2)

	ios			ios			ios
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.34	3.16 – 3.52	<0.001	3.03	1.97 – 4.10	<0.001	2.67	1.53 – 3.81	<0.001
condflourish vs control	0.09	-0.03 – 0.22	0.150	0.10	-0.02 – 0.23	0.113	0.12	-0.01 – 0.25	0.080
time - 2 5	0.04	-0.01 – 0.08	0.116	0.04	-0.01 – 0.08	0.107	0.04	-0.01 – 0.08	0.112
condflourish vs control × time - 2 5	0.04	-0.00 – 0.09	0.066	0.04	-0.00 – 0.09	0.063	0.04	-0.00 – 0.09	0.060
Sex [Woman]				0.42	0.08 – 0.77	0.016	0.40	0.05 – 0.74	0.024
Age				-0.02	-0.05 – 0.02	0.334	-0.01	-0.04 – 0.02	0.434
int student [No]				0.14	-0.36 – 0.64	0.574	-0.01	-0.55 – 0.52	0.965
SES num				0.05	-0.07 – 0.16	0.435	0.05	-0.07 – 0.17	0.417
Ethnicity White							0.51	0.14 – 0.88	0.007
Ethnicity Hispanic							0.25	-0.17 – 0.68	0.239
Ethnicity Black							0.71	0.08 – 1.34	0.027
Ethnicity East Asian							0.20	-0.21 – 0.61	0.340
Ethnicity South Asian							0.48	-0.02 – 0.98	0.060
Ethnicity Native Hawaiian Pacific Islander							-0.23	-1.17 – 0.71	0.633
Ethnicity Middle Eastern							0.39	-0.34 – 1.12	0.296
Ethnicity American Indian							-0.25	-1.32 – 0.82	0.651
Random Effects
σ2	0.80			0.81			0.81
τ00	1.21 unique_ID			1.20 unique_ID			1.19 unique_ID
	0.01 univ			0.00 univ			0.00 univ
ICC	0.60			0.60
N	389 unique_ID			387 unique_ID			387 unique_ID
	3 univ			3 univ			3 univ
Observations	712			709			709
Marginal R2 / Conditional R2	0.007 / 0.606			0.025 / 0.610			0.111 / NA

Excluded Unreasonable Numbers

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

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00662956 (tol = 0.002, component 1)

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

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

m2 <- lmer(ios  ~ cond * I(time - 2.5) + Sex + Age + int_student + SES_num + + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian + (1 | unique_ID) + (1 | univ), data = data_excluded_unreasonable_factor)

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

tab_model(m0, m1, m2)

	ios			ios			ios
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	3.31	3.15 – 3.48	<0.001	2.99	1.90 – 4.09	<0.001	2.81	1.64 – 3.98	<0.001
condflourish vs control	0.06	-0.07 – 0.19	0.357	0.07	-0.06 – 0.20	0.293	0.09	-0.04 – 0.22	0.188
time - 2 5	0.03	-0.02 – 0.08	0.227	0.03	-0.02 – 0.08	0.230	0.03	-0.02 – 0.07	0.235
condflourish vs control × time - 2 5	0.04	-0.01 – 0.08	0.138	0.03	-0.01 – 0.08	0.146	0.03	-0.01 – 0.08	0.142
Sex [Woman]				0.39	0.04 – 0.74	0.029	0.37	0.02 – 0.72	0.041
Age				-0.02	-0.05 – 0.02	0.316	-0.02	-0.05 – 0.02	0.326
int student [No]				0.22	-0.32 – 0.76	0.431	0.01	-0.56 – 0.58	0.962
SES num				0.04	-0.08 – 0.16	0.497	0.04	-0.08 – 0.16	0.549
Ethnicity White							0.48	0.10 – 0.85	0.013
Ethnicity Hispanic							0.18	-0.25 – 0.61	0.404
Ethnicity Black							0.71	0.08 – 1.34	0.027
Ethnicity East Asian							0.16	-0.26 – 0.57	0.464
Ethnicity South Asian							0.29	-0.22 – 0.80	0.269
Ethnicity Native Hawaiian Pacific Islander							-0.22	-1.15 – 0.70	0.637
Ethnicity Middle Eastern							0.42	-0.46 – 1.29	0.352
Ethnicity American Indian							-0.51	-1.67 – 0.64	0.384
Random Effects
σ2	0.77			0.77			0.78
τ00	1.17 unique_ID			1.16 unique_ID			1.15 unique_ID
	0.01 univ			0.00 univ			0.00 univ
ICC	0.60
N	357 unique_ID			356 unique_ID			356 unique_ID
	3 univ			3 univ			3 univ
Observations	652			651			651
Marginal R2 / Conditional R2	0.004 / 0.606			0.052 / NA			0.103 / NA

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  2.909e-05,  Adjusted R-squared:  -0.002046 
## F-statistic: 0.01402 on 1 and 482 DF,  p-value: 0.9058

# Time 2
lm(loneliness ~ cond, data = subset(data_ITT, time == 2)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time == 
##     2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2727 -1.2727 -0.0777  0.9223  3.9223 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.17522    0.08065  64.170   <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
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.003743,   Adjusted R-squared:  0.001182 
## F-statistic: 1.462 on 1 and 389 DF,  p-value: 0.2274

# Time 3
lm(loneliness ~ cond, data = subset(data_ITT, time == 3)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_ITT, time == 
##     3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1582 -1.1582 -0.1582  1.0225  4.0225 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.06786    0.08931  56.746   <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
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.00289,    Adjusted R-squared:  6.519e-05 
## F-statistic: 1.023 on 1 and 353 DF,  p-value: 0.3125

# 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
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.007644,   Adjusted R-squared:  0.004784 
## F-statistic: 2.673 on 1 and 347 DF,  p-value: 0.103

# 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: 2657.5
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.07365 -0.52507 -0.01295  0.47543  3.02493 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.7635   1.328   
##  Residual              0.9565   0.978   
## Number of obs: 787, groups:  unique_ID, 239
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.20327    0.09425 238.11892  55.207  < 2e-16 ***
## I(time - 2.5)  -0.16696    0.03192 577.31120  -5.231 2.37e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.083

# 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: 2700.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0586 -0.5271 -0.0342  0.5360  3.2079 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.8152   1.3473  
##  Residual              0.9839   0.9919  
## Number of obs: 792, groups:  unique_ID, 247
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.37957    0.09475 246.16356  56.775   <2e-16 ***
## I(time - 2.5)  -0.07197    0.03273 581.21600  -2.199   0.0283 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.106

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 
## -2.5775 -1.5721  0.4225  1.4225  3.4279 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.57484    0.08469  65.825   <2e-16 ***
## condflourish_vs_control -0.00270    0.08469  -0.032    0.975    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.667 on 386 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  2.634e-06,  Adjusted R-squared:  -0.002588 
## F-statistic: 0.001017 on 1 and 386 DF,  p-value: 0.9746

# Time 2
lm(loneliness ~ cond, data = subset(data_excluded, time == 2)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded, 
##     time == 2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2732 -1.2732 -0.1087  0.8913  3.8913 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.19096    0.08324  62.361   <2e-16 ***
## condflourish_vs_control -0.08226    0.08324  -0.988    0.324    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.595 on 365 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.002669,   Adjusted R-squared:  -6.372e-05 
## F-statistic: 0.9767 on 1 and 365 DF,  p-value: 0.3237

# Time 3
lm(loneliness ~ cond, data = subset(data_excluded, time == 3)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded, 
##     time == 3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1677 -1.1677 -0.1677  1.0291  4.0291 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.06932    0.09266  54.709   <2e-16 ***
## condflourish_vs_control -0.09839    0.09266  -1.062    0.289    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.69 on 331 degrees of freedom
##   (26 observations deleted due to missingness)
## Multiple R-squared:  0.003395,   Adjusted R-squared:  0.0003836 
## F-statistic: 1.127 on 1 and 331 DF,  p-value: 0.2891

# Time 4
lm(loneliness ~ cond, data = subset(data_excluded, time == 4)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded, 
##     time == 4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3613 -1.3613 -0.1124  0.8876  3.8876 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.23686    0.09494  55.159   <2e-16 ***
## condflourish_vs_control -0.12443    0.09494  -1.311    0.191    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.707 on 322 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.005306,   Adjusted R-squared:  0.002217 
## F-statistic: 1.718 on 1 and 322 DF,  p-value: 0.1909

# Flourish cond: over time
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: 2445.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.06036 -0.54573 -0.00642  0.50502  3.02017 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.7842   1.3357  
##  Residual              0.9618   0.9807  
## Number of obs: 726, groups:  unique_ID, 202
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)     5.1713     0.1012 200.5307  51.116  < 2e-16 ***
## I(time - 2.5)  -0.1677     0.0331 533.0931  -5.066 5.59e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.037

# 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: 2326.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0572 -0.5323 -0.0300  0.5393  3.2051 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.8881   1.374   
##  Residual              0.9821   0.991   
## Number of obs: 686, groups:  unique_ID, 187
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.36317    0.10778 184.93939  49.761   <2e-16 ***
## I(time - 2.5)  -0.06868    0.03474 506.52395  -1.977   0.0486 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.043

Excluded Unreasonable Numbers

# Time 1
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6272 -1.5775  0.3728  1.3728  3.4225 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.60238    0.08772  63.870   <2e-16 ***
## condflourish_vs_control  0.02484    0.08772   0.283    0.777    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.653 on 354 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0002265,  Adjusted R-squared:  -0.002598 
## F-statistic: 0.08019 on 1 and 354 DF,  p-value: 0.7772

# Time 2
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 2)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2732 -1.2732 -0.1474  0.8526  3.8526 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.21033    0.08734   59.66   <2e-16 ***
## condflourish_vs_control -0.06289    0.08734   -0.72    0.472    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.603 on 337 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.001536,   Adjusted R-squared:  -0.001426 
## F-statistic: 0.5186 on 1 and 337 DF,  p-value: 0.472

# Time 3
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 3)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1677 -1.1677 -0.1677  1.0142  4.0142 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.07676    0.09687  52.408   <2e-16 ***
## condflourish_vs_control -0.09094    0.09687  -0.939    0.349    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.68 on 300 degrees of freedom
##   (26 observations deleted due to missingness)
## Multiple R-squared:  0.002929,   Adjusted R-squared:  -0.0003943 
## F-statistic: 0.8814 on 1 and 300 DF,  p-value: 0.3486

# Time 4
lm(loneliness ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()

## 
## Call:
## lm(formula = loneliness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.3613 -1.3613 -0.0993  0.9007  3.9007 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              5.23029    0.09767  53.548   <2e-16 ***
## condflourish_vs_control -0.13100    0.09767  -1.341    0.181    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.679 on 294 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.006081,   Adjusted R-squared:  0.0027 
## F-statistic: 1.799 on 1 and 294 DF,  p-value: 0.1809

# Flourish cond: over time
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: 2035.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8654 -0.5787  0.0022  0.5025  2.8698 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.6937   1.3014  
##  Residual              0.9554   0.9775  
## Number of obs: 607, groups:  unique_ID, 170
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.18538    0.10788 168.92218  48.064  < 2e-16 ***
## I(time - 2.5)  -0.19706    0.03615 446.18570  -5.451 8.31e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.042

# 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: 2326.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0572 -0.5323 -0.0300  0.5393  3.2051 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.8881   1.374   
##  Residual              0.9821   0.991   
## Number of obs: 686, groups:  unique_ID, 187
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.36317    0.10778 184.93939  49.761   <2e-16 ***
## I(time - 2.5)  -0.06868    0.03474 506.52395  -1.977   0.0486 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.043

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.00115,    Adjusted R-squared:  -0.0009225 
## F-statistic: 0.5549 on 1 and 482 DF,  p-value: 0.4567

# Time 2
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 2)) |> summary()

## 
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time == 
##     2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7876 -1.7876  0.2124  2.2124  6.6364 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5756     0.1306  42.706   <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
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.00673,    Adjusted R-squared:  0.004177 
## F-statistic: 2.636 on 1 and 389 DF,  p-value: 0.1053

# Time 3
lm(SAS_calm ~ cond, data = subset(data_ITT, time == 3)) |> summary()

## 
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_ITT, time == 
##     3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0506 -2.0506 -0.0506  1.9494  6.5085 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.7710     0.1387  41.608   <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
##   (131 observations deleted due to missingness)
## Multiple R-squared:  0.01137,    Adjusted R-squared:  0.008574 
## F-statistic: 4.061 on 1 and 353 DF,  p-value: 0.04464

# 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
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.0267, Adjusted R-squared:  0.02389 
## F-statistic: 9.519 on 1 and 347 DF,  p-value: 0.002197

# 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: 3512.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3044 -0.6100  0.0433  0.5270  3.3115 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.722    1.929   
##  Residual              3.176    1.782   
## Number of obs: 787, groups:  unique_ID, 239
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.96423    0.14293 229.59720  41.727  < 2e-16 ***
## I(time - 2.5)   0.20984    0.05786 580.02738   3.627 0.000312 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.093

# Control cond: over time
lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 3477.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6351 -0.5626  0.0430  0.6064  3.3250 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.445    1.856   
##  Residual              2.947    1.717   
## Number of obs: 792, groups:  unique_ID, 247
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.52396    0.13660 242.93591  40.439   <2e-16 ***
## I(time - 2.5)   0.01313    0.05628 591.80043   0.233    0.816    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.118

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 
## -5.7562 -1.7562  0.2438  1.6043  6.6043 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5760     0.1286  43.358   <2e-16 ***
## condflourish_vs_control   0.1802     0.1286   1.402    0.162    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.532 on 386 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.005063,   Adjusted R-squared:  0.002486 
## F-statistic: 1.964 on 1 and 386 DF,  p-value: 0.1618

# Time 2
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 2)) |> summary()

## 
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time == 
##     2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.7228 -1.7228  0.2772  1.9637  6.6503 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5363     0.1341  41.271   <2e-16 ***
## condflourish_vs_control   0.1865     0.1341   1.391    0.165    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.57 on 365 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.005271,   Adjusted R-squared:  0.002545 
## F-statistic: 1.934 on 1 and 365 DF,  p-value: 0.1652

# Time 3
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 3)) |> summary()

## 
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time == 
##     3))
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -6.093 -2.093 -0.093  1.907  6.472 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.8105     0.1438  40.408   <2e-16 ***
## condflourish_vs_control   0.2825     0.1438   1.965   0.0503 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.623 on 331 degrees of freedom
##   (26 observations deleted due to missingness)
## Multiple R-squared:  0.01153,    Adjusted R-squared:  0.008543 
## F-statistic: 3.861 on 1 and 331 DF,  p-value: 0.05027

# Time 4
lm(SAS_calm ~ cond, data = subset(data_excluded, time == 4)) |> summary()

## 
## Call:
## lm(formula = SAS_calm ~ cond, data = subset(data_excluded, time == 
##     4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.3609 -1.4774 -0.3609  1.6391  6.5226 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.9192     0.1386  42.715  < 2e-16 ***
## condflourish_vs_control   0.4418     0.1386   3.188  0.00157 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.492 on 322 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.0306, Adjusted R-squared:  0.02759 
## F-statistic: 10.16 on 1 and 322 DF,  p-value: 0.001574

# 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: 3227.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3070 -0.6101  0.0399  0.5344  3.3053 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.629    1.905   
##  Residual              3.163    1.779   
## Number of obs: 726, groups:  unique_ID, 202
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     6.00844    0.15018 197.22369  40.009  < 2e-16 ***
## I(time - 2.5)   0.23938    0.05989 534.46481   3.997 7.32e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.044

# Control cond: over time
lmer(SAS_calm ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "control")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "control")
## 
## REML criterion at convergence: 2986.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6976 -0.5777  0.0299  0.6084  3.3787 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.367    1.835   
##  Residual              2.890    1.700   
## Number of obs: 686, groups:  unique_ID, 187
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.45986    0.14983 189.33385  36.440   <2e-16 ***
## I(time - 2.5)   0.06216    0.05946 515.03617   1.045    0.296    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.051

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.001794,   Adjusted R-squared:  -0.0002772 
## F-statistic: 0.8661 on 1 and 482 DF,  p-value: 0.3525

# Time 2
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 2)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time == 
##     2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.7071 -1.7071  0.1503  2.1503  5.2929 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              6.77841    0.12200  55.558   <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
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.0008781,  Adjusted R-squared:  -0.00169 
## F-statistic: 0.3419 on 1 and 389 DF,  p-value: 0.5591

# Time 3
lm(SAS_well_being ~ cond, data = subset(data_ITT, time == 3)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_ITT, time == 
##     3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1638 -1.5819 -0.1638  1.8362  5.4181 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.8729     0.1374  50.033   <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
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01259,    Adjusted R-squared:  0.00978 
## F-statistic: 4.486 on 1 and 352 DF,  p-value: 0.03486

# 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
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.02296,    Adjusted R-squared:  0.02015 
## F-statistic: 8.156 on 1 and 347 DF,  p-value: 0.004551

# 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: 3384
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2113 -0.5509  0.0342  0.5666  3.5552 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.356    1.832   
##  Residual              2.663    1.632   
## Number of obs: 786, groups:  unique_ID, 239
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     7.12678    0.13469 228.83359  52.913   <2e-16 ***
## I(time - 2.5)   0.02255    0.05304 576.41091   0.425    0.671    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.091

# Control cond: over time
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: 3405.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1816 -0.5645  0.0224  0.5232  3.3843 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.056    2.014   
##  Residual              2.479    1.574   
## Number of obs: 792, groups:  unique_ID, 247
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     6.76501    0.14314 243.78085  47.262  < 2e-16 ***
## I(time - 2.5)  -0.14733    0.05186 582.40708  -2.841  0.00466 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.110

Excluded Preregistered

# Time 1
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 1)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded, 
##     time == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.3134 -1.4824  0.0107  1.6866  5.0107 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.1514     0.1248  57.307   <2e-16 ***
## condflourish_vs_control   0.1621     0.1248   1.299    0.195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.456 on 386 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.00435,    Adjusted R-squared:  0.001771 
## F-statistic: 1.687 on 1 and 386 DF,  p-value: 0.1948

# Time 2
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 2)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded, 
##     time == 2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.6885 -1.6885  0.1793  2.1793  5.3115 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              6.75459    0.12546  53.840   <2e-16 ***
## condflourish_vs_control  0.06606    0.12546   0.527    0.599    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.403 on 365 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.0007591,  Adjusted R-squared:  -0.001979 
## F-statistic: 0.2773 on 1 and 365 DF,  p-value: 0.5988

# Time 3
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 3)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded, 
##     time == 3))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1871 -1.6335 -0.1871  1.8129  5.3665 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.9103     0.1400  49.360   <2e-16 ***
## condflourish_vs_control   0.2768     0.1400   1.977   0.0489 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.55 on 330 degrees of freedom
##   (27 observations deleted due to missingness)
## Multiple R-squared:  0.01171,    Adjusted R-squared:  0.008712 
## F-statistic: 3.909 on 1 and 330 DF,  p-value: 0.04886

# Time 4
lm(SAS_well_being ~ cond, data = subset(data_excluded, time == 4)) |> summary()

## 
## Call:
## lm(formula = SAS_well_being ~ cond, data = subset(data_excluded, 
##     time == 4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -7.1893 -1.5355  0.4645  1.8107  5.4645 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               6.8624     0.1368   50.17   <2e-16 ***
## condflourish_vs_control   0.3269     0.1368    2.39   0.0174 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.46 on 322 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.01743,    Adjusted R-squared:  0.01438 
## F-statistic: 5.712 on 1 and 322 DF,  p-value: 0.01742

# Flourish cond: over time
lmer(SAS_well_being ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded, cond == "flourish")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "flourish")
## 
## REML criterion at convergence: 3098.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2377 -0.5391  0.0348  0.5702  3.5855 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.248    1.802   
##  Residual              2.618    1.618   
## Number of obs: 725, groups:  unique_ID, 202
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)   7.160e+00  1.410e-01 1.985e+02  50.779   <2e-16 ***
## I(time - 2.5) 7.556e-03  5.452e-02 5.338e+02   0.139     0.89    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.043

# Control cond: over time
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: 2924
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9241 -0.6007  0.0398  0.5342  3.4260 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.936    1.984   
##  Residual              2.453    1.566   
## Number of obs: 686, groups:  unique_ID, 187
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     6.73698    0.15760 186.99796  42.747   <2e-16 ***
## I(time - 2.5)  -0.12206    0.05487 509.95645  -2.225   0.0265 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.046

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.001113,   Adjusted R-squared:  -0.0009594 
## F-statistic: 0.5371 on 1 and 482 DF,  p-value: 0.464

# Time 2
lm(SAS_positive ~ cond, data = subset(data_ITT, time == 2)) |> summary()

## 
## Call:
## lm(formula = SAS_positive ~ cond, data = subset(data_ITT, time == 
##     2))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.513  -5.374   0.487   4.626  18.626 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              17.9433     0.3416  52.523   <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
##   (95 observations deleted due to missingness)
## Multiple R-squared:  0.007096,   Adjusted R-squared:  0.004543 
## F-statistic:  2.78 on 1 and 389 DF,  p-value: 0.09625

# 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
##   (132 observations deleted due to missingness)
## Multiple R-squared:  0.01362,    Adjusted R-squared:  0.01082 
## F-statistic: 4.861 on 1 and 352 DF,  p-value: 0.02812

# 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
##   (138 observations deleted due to missingness)
## Multiple R-squared:  0.0265, Adjusted R-squared:  0.02369 
## F-statistic: 9.419 on 1 and 346 DF,  p-value: 0.002317

# 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: 4912
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6582 -0.5314  0.0175  0.5229  4.1828 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 27.22    5.217   
##  Residual              18.04    4.248   
## Number of obs: 785, groups:  unique_ID, 239
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    18.9590     0.3768 231.4955  50.311   <2e-16 ***
## I(time - 2.5)   0.1921     0.1386 573.6030   1.386    0.166    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.089

# Control cond: over time
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: 4923
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9763 -0.5577 -0.0234  0.5574  4.3450 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 32.17    5.671   
##  Residual              16.06    4.007   
## Number of obs: 792, groups:  unique_ID, 247
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    17.9208     0.3962 243.3704   45.23   <2e-16 ***
## I(time - 2.5)  -0.2514     0.1324 576.4465   -1.90    0.058 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.104

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.0008492,  Adjusted R-squared:  -0.001224 
## F-statistic: 0.4097 on 1 and 482 DF,  p-value: 0.5224

# Time 4
lm(flourishing ~ cond, data = subset(data_ITT, time == 4)) |> summary()

## 
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_ITT, time == 
##     4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -21.345  -3.921   1.079   4.079  11.655 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              44.6330     0.3545 125.894   <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
##   (138 observations deleted due to missingness)
## Multiple R-squared:  0.001903,   Adjusted R-squared:  -0.0009819 
## F-statistic: 0.6596 on 1 and 346 DF,  p-value: 0.4173

# Flourish cond: over time
lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "flourish")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "flourish")
## 
## REML criterion at convergence: 2592.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.58345 -0.38205  0.07545  0.42343  1.85076 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 31.89    5.647   
##  Residual              10.80    3.286   
## Number of obs: 415, groups:  unique_ID, 239
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.8152     0.4037 239.3478 111.004   <2e-16 ***
## I(time - 2.5)   0.1430     0.1148 185.9160   1.246    0.214    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.098

# Control cond: over time
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: 2665.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5743 -0.4046  0.0814  0.4465  2.7338 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 29.70    5.450   
##  Residual              14.81    3.848   
## Number of obs: 417, groups:  unique_ID, 246
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.6044     0.4023 243.8148 110.877   <2e-16 ***
## I(time - 2.5)  -0.2420     0.1352 182.5680  -1.791    0.075 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.135

Excluded Unreasonable Numbers

# Time 1
lm(flourishing ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()

## 
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 1))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -22.503  -3.616   1.043   4.043  11.497 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              44.7301     0.3355 133.338   <2e-16 ***
## condflourish_vs_control  -0.2271     0.3355  -0.677    0.499    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.321 on 354 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.001293,   Adjusted R-squared:  -0.001528 
## F-statistic: 0.4584 on 1 and 354 DF,  p-value: 0.4988

# Time 4
lm(flourishing ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()

## 
## Call:
## lm(formula = flourishing ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -21.3742  -4.0851   0.9149   3.6981  11.6258 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              44.7296     0.3806 117.536   <2e-16 ***
## condflourish_vs_control   0.3555     0.3806   0.934    0.351    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.54 on 294 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.002959,   Adjusted R-squared:  -0.0004327 
## F-statistic: 0.8724 on 1 and 294 DF,  p-value: 0.3511

# Flourish cond: over time
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: 1903.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.62936 -0.37597  0.05827  0.41930  1.87235 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 28.88    5.374   
##  Residual              10.09    3.176   
## Number of obs: 310, groups:  unique_ID, 170
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.9202     0.4527 169.1751  99.223   <2e-16 ***
## I(time - 2.5)   0.2674     0.1250 145.1821   2.139   0.0341 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.061

# Control cond: over time
lmer(flourishing ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "control")
## 
## REML criterion at convergence: 2177.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.5327 -0.4104  0.0600  0.4705  2.6399 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 27.98    5.290   
##  Residual              15.49    3.936   
## Number of obs: 342, groups:  unique_ID, 187
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    44.6042     0.4451 185.7692 100.222   <2e-16 ***
## I(time - 2.5)  -0.2354     0.1467 162.7959  -1.604    0.111    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.072

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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  5.911e-05,  Adjusted R-squared:  -0.002015 
## F-statistic: 0.02849 on 1 and 482 DF,  p-value: 0.866

# Time 4
lm(cohesion ~ cond, data = subset(data_ITT, time == 4)) |> summary()

## 
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_ITT, time == 
##     4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1067 -1.1067  0.3275  1.3275  4.3275 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.8896     0.1157  50.902   <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
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.01005,    Adjusted R-squared:  0.007192 
## F-statistic: 3.521 on 1 and 347 DF,  p-value: 0.06143

# 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: 1696.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.24918 -0.43582 -0.03829  0.47162  1.89841 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.624    1.904   
##  Residual              1.221    1.105   
## Number of obs: 416, groups:  unique_ID, 239
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.84424    0.13596 238.17755  42.985  < 2e-16 ***
## I(time - 2.5)   0.13926    0.03849 185.66322   3.618 0.000382 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.096

# Control cond: over time
lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_ITT, cond == "control")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 1659.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.4673 -0.3786  0.0809  0.5052  2.5259 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.391    1.841   
##  Residual              1.043    1.021   
## Number of obs: 417, groups:  unique_ID, 246
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.64038    0.12933 251.67813   43.61   <2e-16 ***
## I(time - 2.5)   0.02313    0.03615 185.08337    0.64    0.523    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.117

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 
## -5.6331 -1.4973  0.3669  1.5027  4.5027 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.5652     0.1168  47.632   <2e-16 ***
## condflourish_vs_control   0.0679     0.1168   0.581    0.561    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.202 on 354 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0009533,  Adjusted R-squared:  -0.001869 
## F-statistic: 0.3378 on 1 and 354 DF,  p-value: 0.5615

# Time 4
lm(cohesion ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()

## 
## Call:
## lm(formula = cohesion ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 4))
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.0496 -1.6194  0.3806  1.3806  4.3806 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               5.8345     0.1290  45.224   <2e-16 ***
## condflourish_vs_control   0.2151     0.1290   1.668   0.0965 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.217 on 294 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.009371,   Adjusted R-squared:  0.006001 
## F-statistic: 2.781 on 1 and 294 DF,  p-value: 0.09645

# Flourish cond: over time
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: 1258.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.18777 -0.41409  0.01838  0.46319  1.99315 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.014    2.004   
##  Residual              1.121    1.059   
## Number of obs: 310, groups:  unique_ID, 170
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.84112    0.16591 168.50397  35.206  < 2e-16 ***
## I(time - 2.5)   0.14190    0.04176 143.45334   3.398 0.000878 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.056

# Control cond: over time
lmer(cohesion ~ I(time - 2.5) + (1 | unique_ID), data = subset(data_excluded_unreasonable, cond == "control")) |> summary()

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "control")
## 
## REML criterion at convergence: 1361.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.40666 -0.38214  0.02661  0.52749  2.49832 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.560    1.887   
##  Residual              1.078    1.038   
## Number of obs: 342, groups:  unique_ID, 187
## 
## Fixed effects:
##                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)     5.53131    0.14981 188.08233  36.923   <2e-16 ***
## I(time - 2.5)   0.02266    0.03891 161.57126   0.582    0.561    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.060

Mindfulness

Intention to Treat

# Time 1
lm(mindfulness ~ cond, data = subset(data_ITT, time == 1)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_ITT, time == 
##     1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.7689  -3.5935   0.4065   4.2311  15.4065 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             19.68120    0.27130  72.545   <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
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.0002168,  Adjusted R-squared:  -0.001857 
## F-statistic: 0.1045 on 1 and 482 DF,  p-value: 0.7466

# Time 4
lm(mindfulness ~ cond, data = subset(data_ITT, time == 4)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_ITT, time == 
##     4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15.0056  -4.0056  -0.0056   3.9944  14.9944 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.6344     0.3243  63.625   <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
##   (137 observations deleted due to missingness)
## Multiple R-squared:  0.01072,    Adjusted R-squared:  0.007866 
## F-statistic: 3.759 on 1 and 347 DF,  p-value: 0.05334

# Flourish cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "flourish")
## 
## REML criterion at convergence: 2628.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.26629 -0.50981 -0.00727  0.52118  2.45922 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 20.51    4.529   
##  Residual              17.21    4.148   
## Number of obs: 416, groups:  unique_ID, 239
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.9852     0.3625 233.9249  55.131   <2e-16 ***
## I(time - 2.5)   0.1491     0.1425 192.8186   1.046    0.297    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.121

# Control cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_ITT, cond == "control")
## 
## REML criterion at convergence: 2566.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.21617 -0.48029  0.02245  0.42423  2.35469 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 22.40    4.733   
##  Residual              12.07    3.474   
## Number of obs: 417, groups:  unique_ID, 246
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.3913     0.3528 252.1561  57.801  < 2e-16 ***
## I(time - 2.5)   0.5318     0.1218 192.2772   4.365 2.08e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.138

Excluded Preregistered

# Time 1
lm(mindfulness ~ cond, data = subset(data_excluded, time == 1)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_excluded, 
##     time == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.5572  -3.5572   0.5241   3.5241  15.5241 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             19.51657    0.29737  65.631   <2e-16 ***
## condflourish_vs_control  0.04064    0.29737   0.137    0.891    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.854 on 386 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  4.838e-05,  Adjusted R-squared:  -0.002542 
## F-statistic: 0.01868 on 1 and 386 DF,  p-value: 0.8914

# Time 4
lm(mindfulness ~ cond, data = subset(data_excluded, time == 4)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_excluded, 
##     time == 4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.9053  -4.2774   0.0947   3.7226  15.0947 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.5914     0.3343  61.596   <2e-16 ***
## condflourish_vs_control  -0.6860     0.3343  -2.052    0.041 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6.012 on 322 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.01291,    Adjusted R-squared:  0.009845 
## F-statistic: 4.211 on 1 and 322 DF,  p-value: 0.04096

# Flourish cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "flourish")
## 
## REML criterion at convergence: 2326.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.2521 -0.5175  0.0235  0.5073  2.4968 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 19.19    4.380   
##  Residual              17.32    4.162   
## Number of obs: 370, groups:  unique_ID, 202
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.7552     0.3800 200.8598  51.990   <2e-16 ***
## I(time - 2.5)   0.1371     0.1483 180.7662   0.925    0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.074

# Control cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded, cond == "control")
## 
## REML criterion at convergence: 2083.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.18221 -0.50623  0.02933  0.42930  2.38364 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 21.87    4.677   
##  Residual              11.49    3.390   
## Number of obs: 342, groups:  unique_ID, 187
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.3614     0.3911 187.8765  52.066  < 2e-16 ***
## I(time - 2.5)   0.5903     0.1264 164.5608   4.669 6.26e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.071

Excluded Unreasonable Numbers

# Time 1
lm(mindfulness ~ cond, data = subset(data_excluded_unreasonable, time == 1)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 1))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.6213  -3.4759   0.5241   3.5241  15.5241 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             19.54862    0.30501  64.091   <2e-16 ***
## condflourish_vs_control  0.07268    0.30501   0.238    0.812    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.748 on 354 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.0001604,  Adjusted R-squared:  -0.002664 
## F-statistic: 0.05678 on 1 and 354 DF,  p-value: 0.8118

# Time 4
lm(mindfulness ~ cond, data = subset(data_excluded_unreasonable, time == 4)) |> summary()

## 
## Call:
## lm(formula = mindfulness ~ cond, data = subset(data_excluded_unreasonable, 
##     time == 4))
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -14.2774  -4.0587   0.0142   3.7226  15.0142 
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)    
## (Intercept)              20.6316     0.3467  59.503   <2e-16 ***
## condflourish_vs_control  -0.6458     0.3467  -1.863   0.0635 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.959 on 294 degrees of freedom
##   (32 observations deleted due to missingness)
## Multiple R-squared:  0.01166,    Adjusted R-squared:  0.0083 
## F-statistic: 3.469 on 1 and 294 DF,  p-value: 0.06353

# Flourish cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "flourish")
## 
## REML criterion at convergence: 1938.9
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.28421 -0.49768  0.04009  0.52291  2.52078 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 17.53    4.187   
##  Residual              17.26    4.155   
## Number of obs: 310, groups:  unique_ID, 170
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    19.8370     0.4024 168.4224  49.298   <2e-16 ***
## I(time - 2.5)   0.1499     0.1618 151.2869   0.926    0.356    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.078

# Control cond: over time
lmer(mindfulness ~ 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 ~ I(time - 2.5) + (1 | unique_ID)
##    Data: subset(data_excluded_unreasonable, cond == "control")
## 
## REML criterion at convergence: 2083.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.18221 -0.50623  0.02933  0.42930  2.38364 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 21.87    4.677   
##  Residual              11.49    3.390   
## Number of obs: 342, groups:  unique_ID, 187
## 
## Fixed effects:
##               Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)    20.3614     0.3911 187.8765  52.066  < 2e-16 ***
## I(time - 2.5)   0.5903     0.1264 164.5608   4.669 6.26e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## I(time-2.5) 0.071

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, 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_4 - mindfulness_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, 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_4 - mindfulness_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, 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_4 - mindfulness_1,
                emo_res_diff = emo_res_4 - emo_res_1,
                school_satis_diff = school_satis_4 - school_satis_1,
                wellbeing_priority_diff = wellbeing_priority_4 - wellbeing_priority_1,
                acad_selfefficacy_diff = acad_selfefficacy_4 - acad_selfefficacy_1,
                ios_diff = ios_4 - ios_1)

Depression

Intention to Treat

m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)

	depression diff			depression diff			depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.15	-0.54 – 0.23	0.434	-0.94	-2.58 – 0.70	0.259	-1.33	-3.18 – 0.53	0.161
ActiveDays	-0.00	-0.01 – 0.01	0.918	0.00	-0.01 – 0.01	0.867	-0.00	-0.01 – 0.01	0.933
Reports	-0.00	-0.04 – 0.03	0.889	-0.00	-0.04 – 0.04	0.966	-0.01	-0.05 – 0.03	0.713
Activities	0.01	-0.01 – 0.02	0.341	0.01	-0.01 – 0.03	0.316	0.01	-0.01 – 0.03	0.235
univ [Foothill]				0.68	-0.06 – 1.43	0.073	0.55	-0.24 – 1.33	0.170
univ [UW]				0.22	-0.26 – 0.70	0.370	0.36	-0.16 – 0.88	0.170
Sex [Woman]				0.15	-0.40 – 0.70	0.600	0.12	-0.44 – 0.68	0.684
Age				-0.01	-0.05 – 0.04	0.739	0.01	-0.04 – 0.05	0.829
int student [No]				0.53	-0.30 – 1.37	0.209	0.33	-0.60 – 1.25	0.486
SES num				0.02	-0.17 – 0.21	0.838	0.03	-0.17 – 0.23	0.757
Ethnicity White							0.24	-0.38 – 0.87	0.443
Ethnicity Hispanic							0.38	-0.36 – 1.11	0.309
Ethnicity Black							-0.40	-1.51 – 0.71	0.474
Ethnicity East Asian							0.21	-0.52 – 0.95	0.566
Ethnicity South Asian							-0.07	-0.90 – 0.76	0.865
Ethnicity Native Hawaiian Pacific Islander							0.83	-0.71 – 2.36	0.290
Ethnicity Middle Eastern							1.28	0.11 – 2.44	0.032
Ethnicity American Indian							0.24	-1.50 – 1.99	0.782
Observations	171			170			170
R2 / R2 adjusted	0.006 / -0.012			0.032 / -0.023			0.078 / -0.026

Excluded Preregistered

m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)

	depression diff			depression diff			depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.12	-0.52 – 0.28	0.551	-0.81	-2.56 – 0.95	0.365	-1.20	-3.18 – 0.79	0.235
ActiveDays	-0.00	-0.01 – 0.01	0.902	0.00	-0.01 – 0.01	0.906	-0.00	-0.01 – 0.01	0.916
Reports	-0.00	-0.04 – 0.03	0.870	-0.00	-0.04 – 0.04	0.942	-0.01	-0.05 – 0.03	0.691
Activities	0.01	-0.01 – 0.02	0.397	0.01	-0.01 – 0.03	0.350	0.01	-0.01 – 0.03	0.259
univ [Foothill]				0.78	0.01 – 1.55	0.047	0.61	-0.20 – 1.43	0.137
univ [UW]				0.21	-0.28 – 0.70	0.390	0.35	-0.17 – 0.88	0.188
Sex [Woman]				0.13	-0.44 – 0.69	0.662	0.11	-0.47 – 0.69	0.717
Age				-0.01	-0.06 – 0.04	0.649	0.00	-0.05 – 0.05	0.911
int student [No]				0.48	-0.39 – 1.36	0.277	0.32	-0.64 – 1.29	0.508
SES num				0.02	-0.17 – 0.22	0.812	0.03	-0.17 – 0.24	0.756
Ethnicity White							0.20	-0.45 – 0.84	0.546
Ethnicity Hispanic							0.32	-0.43 – 1.08	0.397
Ethnicity Black							-0.45	-1.57 – 0.68	0.434
Ethnicity East Asian							0.17	-0.59 – 0.92	0.661
Ethnicity South Asian							-0.08	-0.93 – 0.77	0.851
Ethnicity Native Hawaiian Pacific Islander							0.76	-0.80 – 2.33	0.337
Ethnicity Middle Eastern							1.22	0.04 – 2.41	0.042
Ethnicity American Indian							0.25	-1.51 – 2.01	0.778
Observations	168			167			167
R2 / R2 adjusted	0.005 / -0.013			0.033 / -0.022			0.075 / -0.030

Excluded Unreasonable Numbers

m0_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_depression_diff <- lm(depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_depression_diff, m1_depression_diff, m2_depression_diff)

	depression diff			depression diff			depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.12	-0.34 – 0.58	0.606	-1.05	-2.97 – 0.87	0.281	-1.85	-3.96 – 0.27	0.086
ActiveDays	-0.01	-0.04 – 0.02	0.446	-0.00	-0.03 – 0.02	0.763	-0.01	-0.04 – 0.02	0.666
Reports	-0.01	-0.06 – 0.04	0.681	-0.01	-0.06 – 0.04	0.632	-0.02	-0.07 – 0.04	0.543
Activities	0.00	-0.02 – 0.02	0.744	0.00	-0.02 – 0.02	0.992	0.00	-0.02 – 0.02	0.849
univ [Foothill]				0.88	0.03 – 1.73	0.044	0.70	-0.20 – 1.60	0.125
univ [UW]				0.36	-0.19 – 0.90	0.195	0.53	-0.05 – 1.11	0.072
Sex [Woman]				0.15	-0.47 – 0.77	0.628	0.10	-0.53 – 0.72	0.756
Age				-0.00	-0.05 – 0.04	0.851	0.02	-0.03 – 0.07	0.484
int student [No]				1.17	0.08 – 2.25	0.035	1.03	-0.10 – 2.17	0.074
SES num				-0.08	-0.30 – 0.13	0.444	-0.04	-0.26 – 0.19	0.751
Ethnicity White							0.33	-0.37 – 1.02	0.355
Ethnicity Hispanic							0.61	-0.19 – 1.40	0.135
Ethnicity Black							-0.89	-2.07 – 0.29	0.137
Ethnicity East Asian							0.09	-0.70 – 0.89	0.815
Ethnicity South Asian							-0.08	-1.00 – 0.83	0.860
Ethnicity Native Hawaiian Pacific Islander							0.66	-0.90 – 2.22	0.404
Ethnicity Middle Eastern							1.40	-0.38 – 3.17	0.121
Ethnicity American Indian							0.63	-1.57 – 2.83	0.571
Observations	140			140			140
R2 / R2 adjusted	0.011 / -0.011			0.070 / 0.005			0.139 / 0.019

Anxiety

Intention to Treat

m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)

	anxiety diff			anxiety diff			anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.17	-0.60 – 0.26	0.440	0.47	-1.33 – 2.26	0.608	-0.34	-2.40 – 1.71	0.742
ActiveDays	0.01	-0.00 – 0.02	0.196	0.01	-0.00 – 0.03	0.185	0.01	-0.00 – 0.03	0.169
Reports	-0.01	-0.05 – 0.03	0.759	-0.01	-0.05 – 0.04	0.761	-0.00	-0.05 – 0.04	0.878
Activities	-0.01	-0.02 – 0.01	0.469	-0.00	-0.02 – 0.02	0.724	-0.00	-0.02 – 0.02	0.798
univ [Foothill]				1.11	0.29 – 1.93	0.008	1.02	0.15 – 1.88	0.022
univ [UW]				0.26	-0.27 – 0.78	0.339	0.16	-0.41 – 0.74	0.571
Sex [Woman]				-0.16	-0.76 – 0.45	0.606	-0.18	-0.80 – 0.44	0.575
Age				-0.04	-0.09 – 0.01	0.133	-0.03	-0.08 – 0.02	0.262
int student [No]				0.05	-0.86 – 0.96	0.910	0.20	-0.82 – 1.22	0.698
SES num				-0.04	-0.25 – 0.17	0.699	0.00	-0.22 – 0.23	0.978
Ethnicity White							0.16	-0.53 – 0.85	0.652
Ethnicity Hispanic							0.44	-0.37 – 1.25	0.281
Ethnicity Black							0.37	-0.86 – 1.60	0.552
Ethnicity East Asian							0.60	-0.21 – 1.41	0.147
Ethnicity South Asian							0.53	-0.38 – 1.44	0.255
Ethnicity Native Hawaiian Pacific Islander							1.19	-0.51 – 2.89	0.168
Ethnicity Middle Eastern							0.18	-1.11 – 1.46	0.783
Ethnicity American Indian							-0.27	-2.20 – 1.66	0.786
Observations	171			170			170
R2 / R2 adjusted	0.011 / -0.007			0.059 / 0.006			0.087 / -0.015

Excluded Preregistered

m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)

	anxiety diff			anxiety diff			anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.26	-0.70 – 0.18	0.244	0.17	-1.75 – 2.09	0.861	-0.81	-2.99 – 1.37	0.466
ActiveDays	0.01	-0.00 – 0.02	0.178	0.01	-0.00 – 0.03	0.168	0.01	-0.00 – 0.03	0.146
Reports	-0.00	-0.05 – 0.04	0.811	-0.01	-0.05 – 0.04	0.799	-0.00	-0.05 – 0.04	0.963
Activities	-0.00	-0.02 – 0.01	0.631	-0.00	-0.02 – 0.02	0.834	-0.00	-0.02 – 0.02	0.949
univ [Foothill]				1.04	0.20 – 1.88	0.015	0.91	0.02 – 1.81	0.045
univ [UW]				0.25	-0.28 – 0.78	0.358	0.15	-0.43 – 0.73	0.603
Sex [Woman]				-0.11	-0.73 – 0.51	0.724	-0.12	-0.76 – 0.51	0.699
Age				-0.03	-0.08 – 0.02	0.177	-0.02	-0.08 – 0.03	0.358
int student [No]				0.16	-0.80 – 1.12	0.744	0.30	-0.76 – 1.36	0.572
SES num				-0.03	-0.24 – 0.19	0.789	0.02	-0.20 – 0.25	0.843
Ethnicity White							0.22	-0.49 – 0.92	0.540
Ethnicity Hispanic							0.55	-0.28 – 1.38	0.191
Ethnicity Black							0.45	-0.78 – 1.69	0.469
Ethnicity East Asian							0.71	-0.12 – 1.54	0.091
Ethnicity South Asian							0.52	-0.41 – 1.45	0.270
Ethnicity Native Hawaiian Pacific Islander							1.35	-0.36 – 3.07	0.122
Ethnicity Middle Eastern							0.24	-1.06 – 1.53	0.717
Ethnicity American Indian							-0.29	-2.22 – 1.65	0.769
Observations	168			167			167
R2 / R2 adjusted	0.012 / -0.006			0.051 / -0.003			0.086 / -0.018

Excluded Unreasonable Numbers

m0_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_anxiety_diff <- lm(anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_anxiety_diff, m1_anxiety_diff, m2_anxiety_diff)

	anxiety diff			anxiety diff			anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.02	-0.48 – 0.52	0.935	-0.45	-2.53 – 1.63	0.669	-1.55	-3.87 – 0.77	0.188
ActiveDays	-0.01	-0.04 – 0.02	0.633	-0.00	-0.04 – 0.03	0.815	-0.00	-0.04 – 0.03	0.812
Reports	0.00	-0.05 – 0.05	0.994	-0.00	-0.05 – 0.05	0.986	0.00	-0.06 – 0.06	0.996
Activities	-0.01	-0.03 – 0.01	0.430	-0.01	-0.03 – 0.01	0.425	-0.01	-0.03 – 0.02	0.570
univ [Foothill]				1.27	0.35 – 2.19	0.007	1.08	0.10 – 2.07	0.031
univ [UW]				0.28	-0.31 – 0.87	0.349	0.28	-0.36 – 0.91	0.389
Sex [Woman]				-0.27	-0.94 – 0.40	0.428	-0.29	-0.98 – 0.40	0.407
Age				-0.03	-0.08 – 0.03	0.328	-0.01	-0.07 – 0.05	0.724
int student [No]				1.14	-0.04 – 2.31	0.057	1.11	-0.14 – 2.35	0.080
SES num				-0.06	-0.29 – 0.17	0.626	0.01	-0.24 – 0.25	0.965
Ethnicity White							0.37	-0.39 – 1.13	0.336
Ethnicity Hispanic							0.80	-0.07 – 1.68	0.072
Ethnicity Black							0.10	-1.20 – 1.39	0.882
Ethnicity East Asian							0.70	-0.18 – 1.57	0.118
Ethnicity South Asian							0.20	-0.81 – 1.21	0.694
Ethnicity Native Hawaiian Pacific Islander							1.29	-0.42 – 3.00	0.136
Ethnicity Middle Eastern							1.02	-0.93 – 2.97	0.301
Ethnicity American Indian							0.33	-2.09 – 2.74	0.790
Observations	140			140			140
R2 / R2 adjusted	0.013 / -0.008			0.094 / 0.032			0.137 / 0.017

Loneliness

Intention to Treat

m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)

	loneliness diff			loneliness diff			loneliness diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.34	-0.72 – 0.05	0.086	-1.53	-3.17 – 0.10	0.066	-2.43	-4.28 – -0.57	0.011
ActiveDays	0.01	-0.00 – 0.03	0.060	0.02	0.00 – 0.03	0.030	0.02	0.00 – 0.03	0.030
Reports	-0.02	-0.05 – 0.02	0.411	-0.01	-0.05 – 0.03	0.581	-0.01	-0.05 – 0.03	0.654
Activities	-0.02	-0.03 – 0.00	0.066	-0.02	-0.04 – -0.00	0.039	-0.02	-0.03 – 0.00	0.067
univ [Foothill]				0.13	-0.61 – 0.87	0.734	0.04	-0.75 – 0.82	0.926
univ [UW]				0.22	-0.26 – 0.70	0.369	0.13	-0.39 – 0.65	0.619
Sex [Woman]				0.07	-0.48 – 0.62	0.810	0.01	-0.55 – 0.57	0.967
Age				0.02	-0.03 – 0.06	0.390	0.03	-0.02 – 0.08	0.180
int student [No]				0.58	-0.25 – 1.41	0.167	0.80	-0.13 – 1.73	0.090
SES num				0.02	-0.17 – 0.21	0.826	0.04	-0.16 – 0.25	0.678
Ethnicity White							0.22	-0.40 – 0.85	0.485
Ethnicity Hispanic							0.38	-0.36 – 1.11	0.313
Ethnicity Black							-0.07	-1.18 – 1.04	0.898
Ethnicity East Asian							0.69	-0.04 – 1.43	0.065
Ethnicity South Asian							0.62	-0.20 – 1.45	0.138
Ethnicity Native Hawaiian Pacific Islander							0.67	-0.87 – 2.21	0.389
Ethnicity Middle Eastern							0.45	-0.71 – 1.61	0.444
Ethnicity American Indian							-0.55	-2.30 – 1.20	0.538
Observations	171			170			170
R2 / R2 adjusted	0.029 / 0.012			0.048 / -0.005			0.086 / -0.016

Excluded Preregistered

m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)

	loneliness diff			loneliness diff			loneliness diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.34	-0.73 – 0.06	0.096	-2.01	-3.74 – -0.28	0.023	-2.90	-4.87 – -0.94	0.004
ActiveDays	0.01	-0.00 – 0.03	0.060	0.02	0.00 – 0.03	0.023	0.02	0.00 – 0.03	0.022
Reports	-0.02	-0.05 – 0.02	0.412	-0.01	-0.05 – 0.03	0.609	-0.01	-0.05 – 0.03	0.729
Activities	-0.02	-0.03 – 0.00	0.069	-0.02	-0.04 – -0.00	0.041	-0.02	-0.03 – 0.00	0.075
univ [Foothill]				0.15	-0.61 – 0.91	0.697	0.04	-0.76 – 0.85	0.921
univ [UW]				0.24	-0.24 – 0.73	0.318	0.16	-0.37 – 0.68	0.557
Sex [Woman]				0.18	-0.38 – 0.74	0.535	0.13	-0.45 – 0.70	0.662
Age				0.02	-0.02 – 0.07	0.307	0.04	-0.01 – 0.08	0.138
int student [No]				0.80	-0.06 – 1.67	0.069	1.02	0.07 – 1.98	0.036
SES num				0.04	-0.15 – 0.24	0.670	0.06	-0.14 – 0.27	0.544
Ethnicity White							0.19	-0.45 – 0.83	0.557
Ethnicity Hispanic							0.37	-0.38 – 1.12	0.329
Ethnicity Black							-0.10	-1.21 – 1.02	0.864
Ethnicity East Asian							0.70	-0.04 – 1.45	0.065
Ethnicity South Asian							0.50	-0.34 – 1.33	0.245
Ethnicity Native Hawaiian Pacific Islander							0.70	-0.84 – 2.25	0.369
Ethnicity Middle Eastern							0.41	-0.76 – 1.58	0.493
Ethnicity American Indian							-0.58	-2.32 – 1.17	0.516
Observations	168			167			167
R2 / R2 adjusted	0.030 / 0.012			0.059 / 0.005			0.096 / -0.007

Excluded Unreasonable Numbers

m0_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_loneliness_diff <- lm(loneliness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_loneliness_diff, m1_loneliness_diff, m2_loneliness_diff)

	loneliness diff			loneliness diff			loneliness diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.26	-0.71 – 0.20	0.267	-1.70	-3.65 – 0.25	0.086	-2.90	-5.06 – -0.75	0.009
ActiveDays	-0.00	-0.03 – 0.02	0.782	-0.00	-0.03 – 0.03	0.905	-0.00	-0.03 – 0.03	0.824
Reports	0.01	-0.04 – 0.06	0.737	0.01	-0.04 – 0.06	0.690	0.02	-0.04 – 0.08	0.488
Activities	-0.01	-0.03 – 0.00	0.151	-0.02	-0.04 – 0.00	0.098	-0.01	-0.03 – 0.01	0.223
univ [Foothill]				0.03	-0.83 – 0.90	0.942	-0.03	-0.94 – 0.88	0.947
univ [UW]				0.12	-0.43 – 0.67	0.673	0.06	-0.53 – 0.65	0.840
Sex [Woman]				0.13	-0.50 – 0.76	0.686	0.06	-0.58 – 0.70	0.849
Age				0.03	-0.02 – 0.08	0.271	0.04	-0.01 – 0.10	0.097
int student [No]				0.73	-0.37 – 1.83	0.190	0.82	-0.34 – 1.98	0.164
SES num				0.01	-0.20 – 0.23	0.905	0.06	-0.17 – 0.28	0.625
Ethnicity White							0.47	-0.24 – 1.17	0.194
Ethnicity Hispanic							0.68	-0.14 – 1.49	0.103
Ethnicity Black							-0.27	-1.47 – 0.93	0.658
Ethnicity East Asian							0.79	-0.03 – 1.60	0.058
Ethnicity South Asian							0.81	-0.12 – 1.75	0.088
Ethnicity Native Hawaiian Pacific Islander							0.88	-0.71 – 2.47	0.275
Ethnicity Middle Eastern							0.19	-1.62 – 2.00	0.836
Ethnicity American Indian							-0.60	-2.84 – 1.65	0.600
Observations	140			140			140
R2 / R2 adjusted	0.024 / 0.002			0.043 / -0.023			0.105 / -0.019

Perceived Stress

Intention to Treat

m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)

	perceived stress diff			perceived stress diff			perceived stress diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.15	-0.91 – 0.62	0.703	-1.87	-5.03 – 1.28	0.242	-2.61	-6.22 – 1.00	0.156
ActiveDays	0.01	-0.01 – 0.04	0.321	0.02	-0.01 – 0.04	0.166	0.02	-0.01 – 0.04	0.209
Reports	0.02	-0.05 – 0.10	0.538	0.04	-0.03 – 0.11	0.292	0.04	-0.04 – 0.12	0.356
Activities	-0.02	-0.06 – 0.01	0.135	-0.03	-0.07 – -0.00	0.046	-0.03	-0.07 – 0.00	0.050
univ [Foothill]				1.08	-0.35 – 2.52	0.137	0.88	-0.65 – 2.41	0.257
univ [UW]				0.64	-0.28 – 1.57	0.173	0.74	-0.27 – 1.75	0.151
Sex [Woman]				-0.81	-1.87 – 0.25	0.131	-0.85	-1.94 – 0.24	0.128
Age				0.07	-0.01 – 0.16	0.089	0.09	-0.00 – 0.18	0.064
int student [No]				0.44	-1.16 – 2.04	0.589	0.03	-1.77 – 1.83	0.972
SES num				-0.00	-0.38 – 0.37	0.986	0.05	-0.34 – 0.45	0.797
Ethnicity White							0.68	-0.54 – 1.89	0.272
Ethnicity Hispanic							0.69	-0.74 – 2.12	0.342
Ethnicity Black							1.21	-0.95 – 3.36	0.271
Ethnicity East Asian							0.20	-1.23 – 1.63	0.783
Ethnicity South Asian							0.67	-0.94 – 2.28	0.411
Ethnicity Native Hawaiian Pacific Islander							1.59	-1.40 – 4.58	0.296
Ethnicity Middle Eastern							1.23	-1.02 – 3.49	0.282
Ethnicity American Indian							-0.54	-3.94 – 2.86	0.754
Observations	171			170			170
R2 / R2 adjusted	0.018 / 0.001			0.091 / 0.040			0.114 / 0.015

Excluded Preregistered

m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)

	perceived stress diff			perceived stress diff			perceived stress diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.24	-1.02 – 0.54	0.547	-2.85	-6.20 – 0.49	0.094	-3.72	-7.54 – 0.10	0.056
ActiveDays	0.01	-0.01 – 0.04	0.305	0.02	-0.01 – 0.05	0.129	0.02	-0.01 – 0.05	0.161
Reports	0.02	-0.05 – 0.10	0.513	0.04	-0.03 – 0.11	0.266	0.04	-0.04 – 0.12	0.293
Activities	-0.02	-0.05 – 0.01	0.175	-0.03	-0.07 – -0.00	0.048	-0.03	-0.07 – 0.00	0.053
univ [Foothill]				0.88	-0.58 – 2.35	0.237	0.63	-0.94 – 2.19	0.429
univ [UW]				0.73	-0.20 – 1.66	0.125	0.86	-0.16 – 1.87	0.098
Sex [Woman]				-0.59	-1.68 – 0.49	0.281	-0.62	-1.74 – 0.50	0.274
Age				0.09	0.00 – 0.18	0.048	0.10	0.01 – 0.20	0.031
int student [No]				0.88	-0.79 – 2.56	0.298	0.38	-1.48 – 2.23	0.687
SES num				0.01	-0.37 – 0.38	0.976	0.06	-0.34 – 0.46	0.761
Ethnicity White							0.81	-0.43 – 2.04	0.198
Ethnicity Hispanic							0.85	-0.60 – 2.30	0.248
Ethnicity Black							1.30	-0.86 – 3.47	0.236
Ethnicity East Asian							0.34	-1.11 – 1.80	0.642
Ethnicity South Asian							0.47	-1.16 – 2.10	0.567
Ethnicity Native Hawaiian Pacific Islander							1.82	-1.19 – 4.82	0.234
Ethnicity Middle Eastern							1.37	-0.90 – 3.64	0.234
Ethnicity American Indian							-0.61	-4.00 – 2.78	0.722
Observations	168			167			167
R2 / R2 adjusted	0.018 / 0.000			0.089 / 0.037			0.115 / 0.014

Excluded Unreasonable Numbers

m0_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_perceived_stress_diff <- lm(perceived_stress_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_perceived_stress_diff, m1_perceived_stress_diff, m2_perceived_stress_diff)

	perceived stress diff			perceived stress diff			perceived stress diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.18	-1.10 – 0.74	0.699	-2.87	-6.69 – 0.96	0.140	-3.49	-7.77 – 0.78	0.108
ActiveDays	0.02	-0.03 – 0.08	0.449	0.03	-0.03 – 0.09	0.269	0.03	-0.03 – 0.09	0.272
Reports	0.02	-0.08 – 0.12	0.683	0.03	-0.07 – 0.13	0.518	0.05	-0.06 – 0.16	0.358
Activities	-0.03	-0.06 – 0.01	0.156	-0.04	-0.08 – -0.00	0.036	-0.04	-0.08 – -0.00	0.035
univ [Foothill]				1.18	-0.52 – 2.87	0.173	0.93	-0.89 – 2.74	0.314
univ [UW]				0.90	-0.19 – 1.98	0.104	1.08	-0.09 – 2.25	0.071
Sex [Woman]				-0.53	-1.77 – 0.71	0.400	-0.56	-1.83 – 0.70	0.380
Age				0.07	-0.02 – 0.17	0.132	0.09	-0.02 – 0.19	0.105
int student [No]				1.30	-0.85 – 3.46	0.234	0.67	-1.63 – 2.97	0.565
SES num				-0.05	-0.47 – 0.38	0.826	-0.00	-0.45 – 0.44	0.986
Ethnicity White							0.83	-0.58 – 2.23	0.246
Ethnicity Hispanic							0.74	-0.87 – 2.36	0.365
Ethnicity Black							1.36	-1.03 – 3.75	0.261
Ethnicity East Asian							0.18	-1.43 – 1.80	0.823
Ethnicity South Asian							0.08	-1.77 – 1.94	0.929
Ethnicity Native Hawaiian Pacific Islander							1.58	-1.57 – 4.73	0.323
Ethnicity Middle Eastern							1.96	-1.63 – 5.56	0.283
Ethnicity American Indian							-2.36	-6.81 – 2.08	0.295
Observations	140			140			140
R2 / R2 adjusted	0.018 / -0.003			0.085 / 0.022			0.125 / 0.004

SAS: Calm

Intention to Treat

m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)

	SAS calm diff			SAS calm diff			SAS calm diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.21	-0.91 – 0.50	0.565	2.17	-0.78 – 5.11	0.148	2.08	-1.28 – 5.44	0.223
ActiveDays	-0.01	-0.03 – 0.02	0.527	-0.01	-0.04 – 0.01	0.336	-0.01	-0.04 – 0.01	0.284
Reports	0.01	-0.06 – 0.08	0.783	-0.00	-0.07 – 0.06	0.918	-0.01	-0.08 – 0.06	0.818
Activities	0.05	0.02 – 0.07	0.003	0.05	0.02 – 0.08	0.001	0.05	0.02 – 0.08	0.001
univ [Foothill]				-1.23	-2.57 – 0.11	0.071	-0.94	-2.36 – 0.48	0.195
univ [UW]				-0.82	-1.69 – 0.04	0.063	-0.72	-1.66 – 0.22	0.132
Sex [Woman]				0.37	-0.62 – 1.36	0.466	0.50	-0.51 – 1.51	0.330
Age				-0.05	-0.13 – 0.03	0.200	-0.06	-0.15 – 0.03	0.173
int student [No]				-0.79	-2.28 – 0.71	0.300	-1.11	-2.78 – 0.56	0.192
SES num				-0.08	-0.43 – 0.26	0.636	-0.08	-0.45 – 0.29	0.663
Ethnicity White							0.67	-0.46 – 1.80	0.243
Ethnicity Hispanic							-0.43	-1.76 – 0.90	0.523
Ethnicity Black							-0.07	-2.08 – 1.93	0.942
Ethnicity East Asian							0.04	-1.29 – 1.37	0.953
Ethnicity South Asian							0.09	-1.40 – 1.59	0.900
Ethnicity Native Hawaiian Pacific Islander							0.58	-2.20 – 3.35	0.683
Ethnicity Middle Eastern							0.31	-1.79 – 2.41	0.769
Ethnicity American Indian							2.06	-1.10 – 5.22	0.199
Observations	171			170			170
R2 / R2 adjusted	0.059 / 0.042			0.115 / 0.065			0.147 / 0.052

Excluded Preregistered

m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)

	SAS calm diff			SAS calm diff			SAS calm diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.09	-0.82 – 0.63	0.799	2.33	-0.81 – 5.47	0.145	2.53	-1.04 – 6.10	0.164
ActiveDays	-0.01	-0.03 – 0.02	0.502	-0.01	-0.04 – 0.01	0.320	-0.01	-0.04 – 0.01	0.269
Reports	0.01	-0.06 – 0.08	0.822	-0.00	-0.07 – 0.06	0.892	-0.01	-0.09 – 0.06	0.766
Activities	0.04	0.01 – 0.07	0.006	0.05	0.02 – 0.08	0.002	0.05	0.02 – 0.08	0.002
univ [Foothill]				-1.05	-2.43 – 0.33	0.135	-0.73	-2.19 – 0.73	0.327
univ [UW]				-0.81	-1.69 – 0.06	0.069	-0.71	-1.66 – 0.24	0.142
Sex [Woman]				0.36	-0.66 – 1.38	0.488	0.48	-0.56 – 1.53	0.361
Age				-0.06	-0.14 – 0.03	0.173	-0.07	-0.15 – 0.02	0.135
int student [No]				-0.82	-2.39 – 0.75	0.304	-1.12	-2.86 – 0.61	0.203
SES num				-0.08	-0.43 – 0.27	0.660	-0.09	-0.46 – 0.28	0.633
Ethnicity White							0.52	-0.63 – 1.68	0.372
Ethnicity Hispanic							-0.62	-1.98 – 0.74	0.369
Ethnicity Black							-0.23	-2.26 – 1.79	0.820
Ethnicity East Asian							-0.14	-1.50 – 1.22	0.841
Ethnicity South Asian							0.03	-1.49 – 1.56	0.964
Ethnicity Native Hawaiian Pacific Islander							0.33	-2.48 – 3.15	0.815
Ethnicity Middle Eastern							0.15	-1.97 – 2.28	0.887
Ethnicity American Indian							2.08	-1.09 – 5.25	0.197
Observations	168			167			167
R2 / R2 adjusted	0.050 / 0.033			0.103 / 0.052			0.137 / 0.039

Excluded Unreasonable Numbers

m0_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_calm_diff <- lm(SAS_calm_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_calm_diff, m1_SAS_calm_diff, m2_SAS_calm_diff)

	SAS calm diff			SAS calm diff			SAS calm diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.10	-0.77 – 0.97	0.821	3.39	-0.22 – 7.00	0.065	3.21	-0.83 – 7.24	0.118
ActiveDays	-0.02	-0.08 – 0.03	0.379	-0.04	-0.09 – 0.02	0.188	-0.04	-0.09 – 0.02	0.183
Reports	0.01	-0.08 – 0.11	0.758	0.00	-0.09 – 0.09	0.999	-0.01	-0.12 – 0.09	0.799
Activities	0.04	0.01 – 0.08	0.019	0.06	0.02 – 0.10	0.002	0.06	0.02 – 0.10	0.003
univ [Foothill]				-1.05	-2.65 – 0.55	0.197	-0.78	-2.49 – 0.93	0.369
univ [UW]				-1.06	-2.08 – -0.03	0.043	-1.08	-2.19 – 0.02	0.055
Sex [Woman]				0.38	-0.79 – 1.55	0.522	0.44	-0.75 – 1.64	0.463
Age				-0.08	-0.17 – 0.02	0.102	-0.07	-0.17 – 0.02	0.133
int student [No]				-1.21	-3.24 – 0.83	0.243	-1.36	-3.53 – 0.81	0.217
SES num				-0.10	-0.50 – 0.31	0.638	-0.10	-0.52 – 0.32	0.634
Ethnicity White							0.65	-0.67 – 1.98	0.331
Ethnicity Hispanic							-0.65	-2.18 – 0.87	0.399
Ethnicity Black							-0.45	-2.70 – 1.81	0.695
Ethnicity East Asian							0.12	-1.40 – 1.65	0.872
Ethnicity South Asian							0.32	-1.44 – 2.07	0.722
Ethnicity Native Hawaiian Pacific Islander							0.29	-2.68 – 3.27	0.846
Ethnicity Middle Eastern							0.04	-3.36 – 3.43	0.983
Ethnicity American Indian							2.40	-1.79 – 6.60	0.259
Observations	140			140			140
R2 / R2 adjusted	0.041 / 0.020			0.111 / 0.050			0.151 / 0.032

SAS: Well-Being

Intention to Treat

m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)

	SAS well being diff			SAS well being diff			SAS well being diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.45	-1.12 – 0.22	0.184	3.88	1.13 – 6.63	0.006	3.87	0.74 – 7.01	0.016
ActiveDays	-0.00	-0.03 – 0.02	0.758	-0.01	-0.03 – 0.01	0.348	-0.01	-0.03 – 0.02	0.483
Reports	0.01	-0.05 – 0.07	0.755	-0.01	-0.07 – 0.06	0.852	-0.00	-0.07 – 0.07	0.930
Activities	0.02	-0.01 – 0.05	0.192	0.03	-0.00 – 0.06	0.054	0.03	-0.00 – 0.06	0.063
univ [Foothill]				-0.97	-2.22 – 0.28	0.129	-0.68	-2.01 – 0.64	0.309
univ [UW]				-0.79	-1.60 – 0.02	0.056	-1.09	-1.96 – -0.21	0.015
Sex [Woman]				-0.10	-1.02 – 0.83	0.835	-0.01	-0.95 – 0.94	0.991
Age				-0.10	-0.17 – -0.02	0.013	-0.11	-0.19 – -0.03	0.006
int student [No]				-1.49	-2.89 – -0.09	0.037	-1.09	-2.65 – 0.47	0.171
SES num				-0.11	-0.43 – 0.22	0.506	-0.15	-0.49 – 0.19	0.395
Ethnicity White							0.01	-1.05 – 1.06	0.987
Ethnicity Hispanic							-0.71	-1.95 – 0.52	0.256
Ethnicity Black							0.31	-1.56 – 2.18	0.742
Ethnicity East Asian							0.84	-0.40 – 2.08	0.183
Ethnicity South Asian							0.09	-1.31 – 1.48	0.903
Ethnicity Native Hawaiian Pacific Islander							-0.46	-3.05 – 2.13	0.727
Ethnicity Middle Eastern							-0.59	-2.55 – 1.37	0.552
Ethnicity American Indian							1.02	-1.93 – 3.97	0.495
Observations	171			170			170
R2 / R2 adjusted	0.013 / -0.005			0.101 / 0.050			0.135 / 0.038

Excluded Preregistered

m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)

	SAS well being diff			SAS well being diff			SAS well being diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.38	-1.06 – 0.31	0.280	4.48	1.58 – 7.37	0.003	4.71	1.43 – 7.99	0.005
ActiveDays	-0.00	-0.03 – 0.02	0.735	-0.01	-0.04 – 0.01	0.287	-0.01	-0.03 – 0.01	0.412
Reports	0.01	-0.05 – 0.07	0.781	-0.01	-0.07 – 0.06	0.808	-0.01	-0.08 – 0.06	0.833
Activities	0.02	-0.01 – 0.05	0.240	0.03	-0.00 – 0.06	0.054	0.03	-0.00 – 0.06	0.063
univ [Foothill]				-0.64	-1.92 – 0.63	0.319	-0.33	-1.67 – 1.02	0.632
univ [UW]				-0.86	-1.67 – -0.05	0.037	-1.20	-2.08 – -0.33	0.007
Sex [Woman]				-0.22	-1.16 – 0.72	0.641	-0.14	-1.10 – 0.82	0.778
Age				-0.11	-0.18 – -0.03	0.005	-0.13	-0.21 – -0.05	0.002
int student [No]				-1.75	-3.20 – -0.30	0.018	-1.23	-2.82 – 0.36	0.129
SES num				-0.09	-0.42 – 0.23	0.582	-0.13	-0.47 – 0.21	0.437
Ethnicity White							-0.22	-1.28 – 0.84	0.680
Ethnicity Hispanic							-0.96	-2.21 – 0.29	0.131
Ethnicity Black							0.13	-1.73 – 1.99	0.888
Ethnicity East Asian							0.65	-0.60 – 1.90	0.307
Ethnicity South Asian							0.16	-1.24 – 1.56	0.826
Ethnicity Native Hawaiian Pacific Islander							-0.75	-3.33 – 1.83	0.567
Ethnicity Middle Eastern							-0.85	-2.80 – 1.10	0.389
Ethnicity American Indian							1.07	-1.84 – 3.99	0.467
Observations	168			167			167
R2 / R2 adjusted	0.010 / -0.008			0.105 / 0.053			0.145 / 0.048

Excluded Unreasonable Numbers

m0_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_well_being_diff <- lm(SAS_well_being_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_well_being_diff, m1_SAS_well_being_diff, m2_SAS_well_being_diff)

	SAS well being diff			SAS well being diff			SAS well being diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.42	-1.22 – 0.38	0.302	4.90	1.67 – 8.14	0.003	5.14	1.56 – 8.72	0.005
ActiveDays	0.02	-0.03 – 0.07	0.494	0.00	-0.05 – 0.05	0.984	0.00	-0.05 – 0.05	0.917
Reports	-0.02	-0.11 – 0.06	0.591	-0.03	-0.12 – 0.05	0.413	-0.05	-0.14 – 0.05	0.310
Activities	0.01	-0.02 – 0.04	0.505	0.03	-0.00 – 0.06	0.078	0.03	-0.00 – 0.07	0.066
univ [Foothill]				-0.96	-2.40 – 0.48	0.188	-0.67	-2.19 – 0.85	0.384
univ [UW]				-1.01	-1.93 – -0.09	0.031	-1.42	-2.40 – -0.44	0.005
Sex [Woman]				-0.20	-1.25 – 0.85	0.709	-0.15	-1.21 – 0.91	0.774
Age				-0.11	-0.20 – -0.03	0.007	-0.13	-0.22 – -0.04	0.004
int student [No]				-2.30	-4.12 – -0.47	0.014	-1.72	-3.65 – 0.20	0.078
SES num				-0.03	-0.39 – 0.33	0.865	-0.07	-0.45 – 0.30	0.695
Ethnicity White							-0.30	-1.47 – 0.88	0.615
Ethnicity Hispanic							-1.17	-2.52 – 0.18	0.088
Ethnicity Black							-0.32	-2.32 – 1.68	0.751
Ethnicity East Asian							0.59	-0.76 – 1.94	0.387
Ethnicity South Asian							0.61	-0.94 – 2.16	0.437
Ethnicity Native Hawaiian Pacific Islander							-0.70	-3.34 – 1.93	0.598
Ethnicity Middle Eastern							-1.24	-4.25 – 1.77	0.417
Ethnicity American Indian							1.63	-2.09 – 5.35	0.388
Observations	140			140			140
R2 / R2 adjusted	0.013 / -0.009			0.130 / 0.070			0.186 / 0.073

SAS: Vigour

Intention to Treat

m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)

	SAS vigour diff			SAS vigour diff			SAS vigour diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.81	-1.47 – -0.14	0.017	2.41	-0.36 – 5.18	0.088	2.43	-0.76 – 5.62	0.135
ActiveDays	0.01	-0.02 – 0.03	0.552	0.00	-0.02 – 0.02	0.913	0.00	-0.02 – 0.02	0.981
Reports	0.04	-0.02 – 0.10	0.213	0.03	-0.03 – 0.09	0.350	0.02	-0.05 – 0.09	0.570
Activities	0.01	-0.02 – 0.04	0.433	0.02	-0.01 – 0.05	0.252	0.02	-0.01 – 0.05	0.216
univ [Foothill]				-0.79	-2.05 – 0.47	0.219	-0.91	-2.27 – 0.44	0.185
univ [UW]				-0.75	-1.58 – 0.07	0.073	-0.82	-1.72 – 0.08	0.074
Sex [Woman]				-0.15	-1.10 – 0.79	0.747	-0.24	-1.21 – 0.74	0.635
Age				-0.04	-0.11 – 0.04	0.360	-0.03	-0.11 – 0.05	0.481
int student [No]				-1.59	-2.99 – -0.18	0.027	-1.34	-2.93 – 0.25	0.098
SES num				-0.12	-0.45 – 0.21	0.474	-0.15	-0.50 – 0.19	0.384
Ethnicity White							-0.18	-1.26 – 0.89	0.736
Ethnicity Hispanic							-0.12	-1.38 – 1.14	0.849
Ethnicity Black							-0.55	-2.46 – 1.36	0.572
Ethnicity East Asian							-0.02	-1.28 – 1.25	0.978
Ethnicity South Asian							0.49	-0.93 – 1.91	0.494
Ethnicity Native Hawaiian Pacific Islander							-1.17	-3.81 – 1.47	0.383
Ethnicity Middle Eastern							0.48	-1.52 – 2.48	0.636
Ethnicity American Indian							-0.15	-3.16 – 2.86	0.923
Observations	170			169			169
R2 / R2 adjusted	0.037 / 0.019			0.087 / 0.035			0.100 / -0.002

Excluded Preregistered

m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)

	SAS vigour diff			SAS vigour diff			SAS vigour diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.73	-1.42 – -0.04	0.037	2.58	-0.38 – 5.54	0.087	2.89	-0.51 – 6.29	0.095
ActiveDays	0.01	-0.02 – 0.03	0.571	0.00	-0.02 – 0.02	0.933	-0.00	-0.02 – 0.02	0.982
Reports	0.04	-0.02 – 0.10	0.227	0.03	-0.04 – 0.09	0.368	0.02	-0.05 – 0.09	0.621
Activities	0.01	-0.02 – 0.04	0.516	0.02	-0.02 – 0.05	0.324	0.02	-0.01 – 0.05	0.289
univ [Foothill]				-0.69	-1.99 – 0.62	0.299	-0.77	-2.17 – 0.63	0.277
univ [UW]				-0.72	-1.55 – 0.11	0.090	-0.79	-1.71 – 0.12	0.089
Sex [Woman]				-0.16	-1.13 – 0.81	0.749	-0.26	-1.27 – 0.75	0.608
Age				-0.04	-0.12 – 0.04	0.338	-0.03	-0.12 – 0.05	0.412
int student [No]				-1.62	-3.10 – -0.14	0.032	-1.40	-3.05 – 0.25	0.095
SES num				-0.13	-0.46 – 0.20	0.446	-0.17	-0.53 – 0.18	0.333
Ethnicity White							-0.28	-1.38 – 0.82	0.621
Ethnicity Hispanic							-0.27	-1.56 – 1.03	0.686
Ethnicity Black							-0.66	-2.60 – 1.27	0.498
Ethnicity East Asian							-0.17	-1.46 – 1.13	0.799
Ethnicity South Asian							0.46	-0.99 – 1.91	0.531
Ethnicity Native Hawaiian Pacific Islander							-1.37	-4.05 – 1.31	0.313
Ethnicity Middle Eastern							0.39	-1.64 – 2.41	0.707
Ethnicity American Indian							-0.12	-3.15 – 2.90	0.935
Observations	167			166			166
R2 / R2 adjusted	0.032 / 0.014			0.079 / 0.026			0.094 / -0.010

Excluded Unreasonable Numbers

m0_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_vigour_diff <- lm(SAS_vigour_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_vigour_diff, m1_SAS_vigour_diff, m2_SAS_vigour_diff)

	SAS vigour diff			SAS vigour diff			SAS vigour diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.64	-1.44 – 0.16	0.116	2.26	-1.09 – 5.61	0.184	2.10	-1.67 – 5.88	0.272
ActiveDays	0.01	-0.04 – 0.05	0.831	-0.01	-0.06 – 0.04	0.759	-0.01	-0.06 – 0.04	0.738
Reports	0.06	-0.02 – 0.15	0.146	0.06	-0.03 – 0.14	0.199	0.05	-0.04 – 0.15	0.274
Activities	0.00	-0.03 – 0.03	0.909	0.01	-0.02 – 0.05	0.419	0.02	-0.02 – 0.06	0.333
univ [Foothill]				-0.78	-2.27 – 0.71	0.304	-0.80	-2.41 – 0.81	0.326
univ [UW]				-0.90	-1.87 – 0.06	0.066	-0.91	-1.96 – 0.13	0.086
Sex [Woman]				-0.33	-1.42 – 0.77	0.557	-0.47	-1.60 – 0.67	0.417
Age				-0.04	-0.12 – 0.05	0.405	-0.02	-0.12 – 0.07	0.596
int student [No]				-1.29	-3.18 – 0.60	0.178	-1.17	-3.19 – 0.86	0.257
SES num				-0.05	-0.42 – 0.32	0.782	-0.07	-0.47 – 0.32	0.714
Ethnicity White							0.01	-1.23 – 1.24	0.992
Ethnicity Hispanic							-0.01	-1.43 – 1.42	0.992
Ethnicity Black							-1.13	-3.24 – 0.98	0.291
Ethnicity East Asian							-0.12	-1.54 – 1.30	0.868
Ethnicity South Asian							0.91	-0.73 – 2.55	0.274
Ethnicity Native Hawaiian Pacific Islander							-1.12	-3.90 – 1.66	0.425
Ethnicity Middle Eastern							-0.06	-3.23 – 3.11	0.972
Ethnicity American Indian							-0.71	-4.64 – 3.22	0.721
Observations	139			139			139
R2 / R2 adjusted	0.028 / 0.006			0.067 / 0.002			0.094 / -0.034

SAS: Depression

Intention to Treat

m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)

	SAS depression diff			SAS depression diff			SAS depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.10	-0.84 – 0.63	0.780	-2.64	-5.72 – 0.44	0.092	-2.68	-6.17 – 0.82	0.133
ActiveDays	-0.00	-0.03 – 0.02	0.964	0.01	-0.02 – 0.03	0.622	0.00	-0.02 – 0.03	0.857
Reports	0.03	-0.04 – 0.11	0.332	0.04	-0.03 – 0.12	0.222	0.04	-0.04 – 0.11	0.356
Activities	-0.00	-0.04 – 0.03	0.759	-0.01	-0.04 – 0.02	0.628	-0.01	-0.04 – 0.03	0.685
univ [Foothill]				1.23	-0.17 – 2.63	0.086	0.93	-0.55 – 2.41	0.215
univ [UW]				0.23	-0.68 – 1.14	0.614	0.60	-0.38 – 1.59	0.226
Sex [Woman]				0.06	-0.99 – 1.11	0.909	-0.07	-1.14 – 1.00	0.898
Age				0.05	-0.03 – 0.14	0.224	0.07	-0.02 – 0.16	0.111
int student [No]				1.15	-0.42 – 2.71	0.150	0.61	-1.14 – 2.35	0.492
SES num				-0.01	-0.38 – 0.35	0.937	-0.00	-0.38 – 0.38	0.997
Ethnicity White							0.27	-0.91 – 1.44	0.652
Ethnicity Hispanic							1.00	-0.38 – 2.38	0.154
Ethnicity Black							-0.48	-2.57 – 1.61	0.651
Ethnicity East Asian							-0.74	-2.12 – 0.65	0.296
Ethnicity South Asian							0.00	-1.55 – 1.56	0.996
Ethnicity Native Hawaiian Pacific Islander							-0.74	-3.63 – 2.15	0.613
Ethnicity Middle Eastern							1.21	-0.98 – 3.39	0.278
Ethnicity American Indian							-0.65	-3.94 – 2.64	0.699
Observations	170			169			169
R2 / R2 adjusted	0.007 / -0.011			0.055 / 0.002			0.098 / -0.003

Excluded Preregistered

m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)

	SAS depression diff			SAS depression diff			SAS depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.19	-0.96 – 0.57	0.614	-2.72	-6.01 – 0.57	0.104	-2.96	-6.68 – 0.75	0.117
ActiveDays	-0.00	-0.03 – 0.03	0.987	0.01	-0.02 – 0.03	0.606	0.00	-0.02 – 0.03	0.846
Reports	0.04	-0.03 – 0.11	0.314	0.05	-0.03 – 0.12	0.215	0.04	-0.04 – 0.11	0.335
Activities	-0.00	-0.03 – 0.03	0.866	-0.01	-0.04 – 0.03	0.686	-0.01	-0.04 – 0.03	0.749
univ [Foothill]				1.04	-0.40 – 2.49	0.155	0.70	-0.82 – 2.22	0.365
univ [UW]				0.23	-0.69 – 1.15	0.628	0.61	-0.38 – 1.61	0.225
Sex [Woman]				0.06	-1.02 – 1.13	0.919	-0.08	-1.18 – 1.02	0.886
Age				0.06	-0.03 – 0.14	0.201	0.08	-0.01 – 0.17	0.087
int student [No]				1.15	-0.50 – 2.80	0.169	0.54	-1.26 – 2.35	0.553
SES num				-0.03	-0.40 – 0.34	0.893	-0.01	-0.39 – 0.38	0.970
Ethnicity White							0.44	-0.76 – 1.64	0.469
Ethnicity Hispanic							1.20	-0.22 – 2.61	0.097
Ethnicity Black							-0.31	-2.42 – 1.80	0.770
Ethnicity East Asian							-0.57	-1.99 – 0.85	0.428
Ethnicity South Asian							0.09	-1.50 – 1.68	0.911
Ethnicity Native Hawaiian Pacific Islander							-0.52	-3.44 – 2.41	0.726
Ethnicity Middle Eastern							1.40	-0.81 – 3.61	0.212
Ethnicity American Indian							-0.66	-3.96 – 2.64	0.694
Observations	167			166			166
R2 / R2 adjusted	0.008 / -0.011			0.051 / -0.004			0.099 / -0.005

Excluded Unreasonable Numbers

m0_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_depression_diff <- lm(SAS_depression_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_depression_diff, m1_SAS_depression_diff, m2_SAS_depression_diff)

	SAS depression diff			SAS depression diff			SAS depression diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.03	-0.91 – 0.86	0.956	-3.40	-7.10 – 0.30	0.071	-3.85	-7.90 – 0.20	0.062
ActiveDays	-0.04	-0.09 – 0.02	0.172	-0.03	-0.09 – 0.03	0.294	-0.03	-0.09 – 0.02	0.232
Reports	0.10	0.00 – 0.19	0.046	0.10	0.00 – 0.20	0.042	0.10	-0.01 – 0.20	0.076
Activities	0.00	-0.03 – 0.04	0.929	-0.01	-0.04 – 0.03	0.722	-0.01	-0.05 – 0.03	0.712
univ [Foothill]				1.08	-0.56 – 2.72	0.196	0.72	-0.99 – 2.44	0.405
univ [UW]				0.24	-0.81 – 1.29	0.653	0.76	-0.35 – 1.87	0.176
Sex [Woman]				-0.02	-1.22 – 1.18	0.971	-0.15	-1.35 – 1.05	0.803
Age				0.07	-0.02 – 0.17	0.120	0.11	0.01 – 0.20	0.034
int student [No]				2.15	0.07 – 4.24	0.043	1.53	-0.65 – 3.70	0.167
SES num				-0.12	-0.53 – 0.29	0.556	-0.09	-0.52 – 0.33	0.659
Ethnicity White							0.53	-0.80 – 1.86	0.430
Ethnicity Hispanic							1.39	-0.14 – 2.92	0.074
Ethnicity Black							-0.92	-3.18 – 1.35	0.425
Ethnicity East Asian							-0.79	-2.31 – 0.74	0.309
Ethnicity South Asian							-0.22	-1.97 – 1.54	0.808
Ethnicity Native Hawaiian Pacific Islander							-0.70	-3.69 – 2.28	0.643
Ethnicity Middle Eastern							2.17	-1.24 – 5.57	0.210
Ethnicity American Indian							-0.32	-4.53 – 3.89	0.880
Observations	140			140			140
R2 / R2 adjusted	0.031 / 0.009			0.097 / 0.035			0.173 / 0.058

SAS: Anxiety

Intention to Treat

m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)

	SAS anxiety diff			SAS anxiety diff			SAS anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.36	-1.22 – 0.51	0.418	-3.55	-7.20 – 0.11	0.057	-4.06	-8.27 – 0.15	0.059
ActiveDays	0.00	-0.03 – 0.03	0.810	0.01	-0.02 – 0.04	0.677	0.01	-0.02 – 0.04	0.641
Reports	0.03	-0.05 – 0.12	0.410	0.05	-0.03 – 0.14	0.210	0.06	-0.03 – 0.16	0.174
Activities	-0.02	-0.05 – 0.02	0.411	-0.02	-0.06 – 0.01	0.221	-0.02	-0.06 – 0.01	0.217
univ [Foothill]				0.53	-1.13 – 2.19	0.528	0.40	-1.38 – 2.18	0.658
univ [UW]				0.64	-0.43 – 1.72	0.240	0.59	-0.59 – 1.76	0.325
Sex [Woman]				0.17	-1.06 – 1.39	0.790	0.07	-1.20 – 1.34	0.918
Age				0.08	-0.02 – 0.18	0.103	0.09	-0.02 – 0.20	0.093
int student [No]				0.31	-1.55 – 2.17	0.743	0.25	-1.85 – 2.35	0.812
SES num				0.21	-0.22 – 0.65	0.327	0.21	-0.25 – 0.67	0.362
Ethnicity White							0.37	-1.05 – 1.78	0.608
Ethnicity Hispanic							0.68	-0.98 – 2.35	0.418
Ethnicity Black							0.93	-1.58 – 3.45	0.464
Ethnicity East Asian							0.53	-1.14 – 2.20	0.529
Ethnicity South Asian							0.27	-1.61 – 2.14	0.779
Ethnicity Native Hawaiian Pacific Islander							-0.40	-3.89 – 3.08	0.819
Ethnicity Middle Eastern							0.35	-2.29 – 2.98	0.794
Ethnicity American Indian							-1.94	-5.90 – 2.02	0.335
Observations	171			170			170
R2 / R2 adjusted	0.008 / -0.010			0.044 / -0.010			0.060 / -0.046

Excluded Preregistered

m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)

	SAS anxiety diff			SAS anxiety diff			SAS anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.40	-1.30 – 0.50	0.382	-3.99	-7.91 – -0.08	0.046	-4.69	-9.19 – -0.20	0.041
ActiveDays	0.00	-0.03 – 0.03	0.802	0.01	-0.02 – 0.04	0.642	0.01	-0.02 – 0.04	0.598
Reports	0.04	-0.05 – 0.12	0.406	0.06	-0.03 – 0.14	0.205	0.07	-0.03 – 0.16	0.158
Activities	-0.01	-0.05 – 0.02	0.450	-0.02	-0.06 – 0.02	0.235	-0.02	-0.06 – 0.02	0.238
univ [Foothill]				0.41	-1.31 – 2.13	0.639	0.23	-1.61 – 2.07	0.808
univ [UW]				0.68	-0.42 – 1.77	0.223	0.64	-0.56 – 1.83	0.294
Sex [Woman]				0.26	-1.01 – 1.53	0.686	0.17	-1.14 – 1.49	0.796
Age				0.09	-0.01 – 0.19	0.085	0.10	-0.01 – 0.21	0.071
int student [No]				0.50	-1.46 – 2.46	0.613	0.41	-1.77 – 2.60	0.709
SES num				0.22	-0.22 – 0.66	0.329	0.22	-0.25 – 0.69	0.353
Ethnicity White							0.47	-0.99 – 1.92	0.526
Ethnicity Hispanic							0.81	-0.90 – 2.52	0.350
Ethnicity Black							1.02	-1.52 – 3.57	0.428
Ethnicity East Asian							0.65	-1.06 – 2.36	0.455
Ethnicity South Asian							0.19	-1.72 – 2.11	0.842
Ethnicity Native Hawaiian Pacific Islander							-0.23	-3.77 – 3.31	0.899
Ethnicity Middle Eastern							0.46	-2.22 – 3.13	0.737
Ethnicity American Indian							-1.97	-5.96 – 2.02	0.330
Observations	168			167			167
R2 / R2 adjusted	0.008 / -0.010			0.045 / -0.010			0.063 / -0.044

Excluded Unreasonable Numbers

m0_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_anxiety_diff <- lm(SAS_anxiety_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_anxiety_diff, m1_SAS_anxiety_diff, m2_SAS_anxiety_diff)

	SAS anxiety diff			SAS anxiety diff			SAS anxiety diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.28	-1.33 – 0.78	0.606	-3.47	-7.93 – 1.00	0.127	-4.43	-9.46 – 0.60	0.084
ActiveDays	-0.02	-0.09 – 0.04	0.491	-0.02	-0.08 – 0.05	0.656	-0.02	-0.09 – 0.05	0.568
Reports	0.08	-0.04 – 0.19	0.186	0.09	-0.02 – 0.21	0.122	0.12	-0.01 – 0.25	0.080
Activities	-0.01	-0.05 – 0.03	0.648	-0.02	-0.07 – 0.02	0.314	-0.02	-0.07 – 0.03	0.380
univ [Foothill]				-0.17	-2.15 – 1.81	0.868	-0.33	-2.47 – 1.80	0.756
univ [UW]				0.52	-0.74 – 1.79	0.415	0.64	-0.73 – 2.02	0.358
Sex [Woman]				-0.13	-1.58 – 1.31	0.856	-0.22	-1.71 – 1.26	0.766
Age				0.11	-0.01 – 0.22	0.066	0.12	-0.00 – 0.24	0.053
int student [No]				0.34	-2.17 – 2.86	0.787	0.07	-2.63 – 2.77	0.960
SES num				0.20	-0.30 – 0.70	0.432	0.21	-0.31 – 0.74	0.428
Ethnicity White							0.77	-0.88 – 2.42	0.357
Ethnicity Hispanic							1.41	-0.49 – 3.31	0.144
Ethnicity Black							0.74	-2.06 – 3.55	0.601
Ethnicity East Asian							0.62	-1.27 – 2.52	0.517
Ethnicity South Asian							0.26	-1.92 – 2.44	0.816
Ethnicity Native Hawaiian Pacific Islander							0.00	-3.71 – 3.71	1.000
Ethnicity Middle Eastern							0.72	-3.51 – 4.94	0.737
Ethnicity American Indian							-2.36	-7.59 – 2.87	0.374
Observations	140			140			140
R2 / R2 adjusted	0.016 / -0.006			0.052 / -0.014			0.080 / -0.048

SAS: Anger

Intention to Treat

m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)

	SAS anger diff			SAS anger diff			SAS anger diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.21	-0.90 – 0.49	0.559	-2.93	-5.89 – 0.02	0.052	-2.20	-5.51 – 1.11	0.191
ActiveDays	-0.01	-0.03 – 0.01	0.467	-0.01	-0.03 – 0.02	0.580	-0.01	-0.04 – 0.01	0.387
Reports	0.04	-0.03 – 0.11	0.225	0.05	-0.02 – 0.12	0.148	0.03	-0.04 – 0.10	0.413
Activities	0.01	-0.02 – 0.04	0.395	0.01	-0.02 – 0.04	0.526	0.01	-0.02 – 0.04	0.426
univ [Foothill]				0.03	-1.31 – 1.38	0.963	-0.31	-1.71 – 1.09	0.663
univ [UW]				0.10	-0.77 – 0.97	0.817	0.32	-0.60 – 1.25	0.490
Sex [Woman]				0.39	-0.60 – 1.38	0.440	0.21	-0.79 – 1.21	0.677
Age				0.04	-0.04 – 0.13	0.283	0.06	-0.03 – 0.14	0.168
int student [No]				0.71	-0.79 – 2.22	0.349	0.65	-1.00 – 2.30	0.440
SES num				0.23	-0.12 – 0.58	0.190	0.15	-0.21 – 0.51	0.413
Ethnicity White							-0.47	-1.59 – 0.64	0.401
Ethnicity Hispanic							0.38	-0.93 – 1.68	0.571
Ethnicity Black							-1.60	-3.57 – 0.38	0.112
Ethnicity East Asian							-0.69	-2.00 – 0.62	0.301
Ethnicity South Asian							-0.35	-1.82 – 1.12	0.637
Ethnicity Native Hawaiian Pacific Islander							-3.09	-5.83 – -0.35	0.027
Ethnicity Middle Eastern							1.44	-0.63 – 3.51	0.171
Ethnicity American Indian							-0.36	-3.48 – 2.75	0.818
Observations	171			170			170
R2 / R2 adjusted	0.014 / -0.003			0.040 / -0.014			0.109 / 0.009

Excluded Preregistered

m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)

	SAS anger diff			SAS anger diff			SAS anger diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.20	-0.92 – 0.53	0.589	-3.32	-6.48 – -0.15	0.040	-2.50	-6.04 – 1.03	0.164
ActiveDays	-0.01	-0.03 – 0.02	0.469	-0.01	-0.03 – 0.02	0.614	-0.01	-0.04 – 0.01	0.420
Reports	0.04	-0.03 – 0.11	0.230	0.05	-0.02 – 0.12	0.145	0.03	-0.04 – 0.10	0.399
Activities	0.01	-0.02 – 0.04	0.410	0.01	-0.02 – 0.04	0.519	0.01	-0.02 – 0.04	0.438
univ [Foothill]				0.03	-1.36 – 1.42	0.962	-0.29	-1.74 – 1.16	0.695
univ [UW]				0.12	-0.76 – 1.00	0.791	0.35	-0.59 – 1.29	0.465
Sex [Woman]				0.47	-0.55 – 1.50	0.364	0.30	-0.74 – 1.33	0.572
Age				0.05	-0.04 – 0.13	0.259	0.06	-0.02 – 0.15	0.161
int student [No]				0.88	-0.70 – 2.47	0.272	0.81	-0.91 – 2.53	0.353
SES num				0.25	-0.11 – 0.60	0.168	0.16	-0.21 – 0.53	0.385
Ethnicity White							-0.51	-1.66 – 0.63	0.378
Ethnicity Hispanic							0.35	-1.00 – 1.69	0.609
Ethnicity Black							-1.64	-3.64 – 0.37	0.109
Ethnicity East Asian							-0.70	-2.05 – 0.64	0.304
Ethnicity South Asian							-0.46	-1.97 – 1.05	0.550
Ethnicity Native Hawaiian Pacific Islander							-3.09	-5.88 – -0.31	0.030
Ethnicity Middle Eastern							1.39	-0.71 – 3.49	0.193
Ethnicity American Indian							-0.38	-3.52 – 2.76	0.810
Observations	168			167			167
R2 / R2 adjusted	0.014 / -0.004			0.043 / -0.012			0.111 / 0.010

Excluded Unreasonable Numbers

m0_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_anger_diff <- lm(SAS_anger_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_anger_diff, m1_SAS_anger_diff, m2_SAS_anger_diff)

	SAS anger diff			SAS anger diff			SAS anger diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.36	-1.14 – 0.41	0.357	-2.26	-5.57 – 1.04	0.178	-2.40	-5.91 – 1.12	0.180
ActiveDays	-0.02	-0.07 – 0.03	0.382	-0.02	-0.07 – 0.03	0.479	-0.02	-0.07 – 0.03	0.340
Reports	0.07	-0.02 – 0.15	0.113	0.07	-0.01 – 0.16	0.101	0.07	-0.03 – 0.16	0.157
Activities	0.02	-0.01 – 0.05	0.183	0.02	-0.02 – 0.05	0.366	0.02	-0.01 – 0.05	0.275
univ [Foothill]				-0.71	-2.17 – 0.76	0.342	-1.00	-2.49 – 0.49	0.186
univ [UW]				0.08	-0.86 – 1.01	0.873	0.42	-0.54 – 1.39	0.385
Sex [Woman]				0.41	-0.66 – 1.48	0.446	0.20	-0.84 – 1.24	0.705
Age				0.06	-0.02 – 0.15	0.139	0.09	0.01 – 0.18	0.037
int student [No]				0.06	-1.81 – 1.93	0.949	0.09	-1.80 – 1.98	0.926
SES num				0.09	-0.27 – 0.46	0.611	0.07	-0.29 – 0.44	0.692
Ethnicity White							-0.27	-1.42 – 0.88	0.644
Ethnicity Hispanic							1.27	-0.06 – 2.60	0.060
Ethnicity Black							-2.03	-4.00 – -0.07	0.043
Ethnicity East Asian							-0.74	-2.07 – 0.58	0.269
Ethnicity South Asian							0.11	-1.41 – 1.64	0.884
Ethnicity Native Hawaiian Pacific Islander							-2.73	-5.32 – -0.14	0.039
Ethnicity Middle Eastern							0.64	-2.32 – 3.59	0.669
Ethnicity American Indian							-0.72	-4.38 – 2.94	0.697
Observations	140			140			140
R2 / R2 adjusted	0.031 / 0.010			0.056 / -0.010			0.184 / 0.070

SAS: Positive

Intention to Treat

m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)

	SAS positive diff			SAS positive diff			SAS positive diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-1.35	-3.01 – 0.31	0.111	8.31	1.50 – 15.13	0.017	8.28	0.44 – 16.12	0.039
ActiveDays	-0.00	-0.06 – 0.05	0.939	-0.02	-0.08 – 0.04	0.507	-0.02	-0.08 – 0.04	0.520
Reports	0.06	-0.09 – 0.22	0.426	0.03	-0.13 – 0.19	0.731	0.01	-0.16 – 0.19	0.867
Activities	0.06	-0.01 – 0.14	0.079	0.09	0.01 – 0.16	0.022	0.09	0.01 – 0.16	0.023
univ [Foothill]				-2.84	-5.95 – 0.26	0.073	-2.39	-5.71 – 0.94	0.158
univ [UW]				-2.19	-4.21 – -0.16	0.035	-2.45	-4.66 – -0.24	0.030
Sex [Woman]				0.32	-1.99 – 2.64	0.783	0.49	-1.91 – 2.89	0.687
Age				-0.19	-0.38 – -0.00	0.049	-0.21	-0.41 – -0.01	0.043
int student [No]				-3.84	-7.30 – -0.38	0.030	-3.54	-7.45 – 0.37	0.075
SES num				-0.30	-1.10 – 0.50	0.461	-0.37	-1.22 – 0.49	0.399
Ethnicity White							0.48	-2.16 – 3.11	0.721
Ethnicity Hispanic							-1.22	-4.31 – 1.88	0.438
Ethnicity Black							-0.13	-4.82 – 4.56	0.957
Ethnicity East Asian							0.89	-2.22 – 3.99	0.573
Ethnicity South Asian							0.63	-2.85 – 4.12	0.721
Ethnicity Native Hawaiian Pacific Islander							-0.98	-7.46 – 5.51	0.767
Ethnicity Middle Eastern							0.20	-4.70 – 5.11	0.934
Ethnicity American Indian							3.19	-4.19 – 10.58	0.394
Observations	170			169			169
R2 / R2 adjusted	0.035 / 0.018			0.121 / 0.071			0.137 / 0.039

Excluded Preregistered

m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)

	SAS positive diff			SAS positive diff			SAS positive diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-1.08	-2.79 – 0.64	0.216	9.24	2.00 – 16.48	0.013	10.02	1.73 – 18.32	0.018
ActiveDays	-0.00	-0.06 – 0.05	0.909	-0.02	-0.08 – 0.04	0.462	-0.02	-0.08 – 0.04	0.471
Reports	0.06	-0.10 – 0.22	0.455	0.02	-0.13 – 0.18	0.767	0.01	-0.17 – 0.18	0.950
Activities	0.06	-0.01 – 0.13	0.118	0.08	0.01 – 0.16	0.031	0.08	0.01 – 0.16	0.034
univ [Foothill]				-2.22	-5.41 – 0.97	0.171	-1.67	-5.08 – 1.74	0.334
univ [UW]				-2.20	-4.24 – -0.16	0.034	-2.53	-4.76 – -0.29	0.027
Sex [Woman]				0.20	-2.18 – 2.58	0.869	0.33	-2.14 – 2.79	0.794
Age				-0.21	-0.40 – -0.02	0.032	-0.24	-0.44 – -0.03	0.023
int student [No]				-4.16	-7.78 – -0.54	0.025	-3.74	-7.77 – 0.28	0.068
SES num				-0.29	-1.10 – 0.52	0.486	-0.38	-1.24 – 0.48	0.383
Ethnicity White							-0.00	-2.69 – 2.68	0.999
Ethnicity Hispanic							-1.81	-4.96 – 1.35	0.260
Ethnicity Black							-0.59	-5.31 – 4.12	0.804
Ethnicity East Asian							0.36	-2.80 – 3.51	0.824
Ethnicity South Asian							0.60	-2.94 – 4.14	0.739
Ethnicity Native Hawaiian Pacific Islander							-1.72	-8.25 – 4.81	0.603
Ethnicity Middle Eastern							-0.32	-5.25 – 4.61	0.898
Ethnicity American Indian							3.29	-4.08 – 10.67	0.379
Observations	167			166			166
R2 / R2 adjusted	0.028 / 0.010			0.113 / 0.062			0.132 / 0.033

Excluded Unreasonable Numbers

m0_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_positive_diff <- lm(SAS_positive_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_positive_diff, m1_SAS_positive_diff, m2_SAS_positive_diff)

	SAS positive diff			SAS positive diff			SAS positive diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.78	-2.82 – 1.26	0.448	10.36	2.09 – 18.64	0.014	10.32	1.02 – 19.63	0.030
ActiveDays	-0.00	-0.13 – 0.12	0.972	-0.04	-0.17 – 0.09	0.527	-0.04	-0.17 – 0.09	0.538
Reports	0.06	-0.16 – 0.28	0.578	0.03	-0.19 – 0.24	0.799	-0.00	-0.25 – 0.24	0.981
Activities	0.04	-0.04 – 0.13	0.313	0.09	0.00 – 0.18	0.044	0.10	0.01 – 0.19	0.038
univ [Foothill]				-2.58	-6.27 – 1.11	0.169	-2.06	-6.02 – 1.91	0.306
univ [UW]				-2.74	-5.13 – -0.36	0.025	-3.20	-5.78 – -0.62	0.015
Sex [Woman]				0.09	-2.62 – 2.80	0.947	0.07	-2.73 – 2.86	0.962
Age				-0.23	-0.44 – -0.02	0.031	-0.23	-0.46 – -0.01	0.041
int student [No]				-4.71	-9.38 – -0.04	0.048	-4.18	-9.18 – 0.82	0.100
SES num				-0.17	-1.10 – 0.75	0.709	-0.24	-1.22 – 0.73	0.622
Ethnicity White							0.33	-2.72 – 3.38	0.831
Ethnicity Hispanic							-1.80	-5.31 – 1.71	0.313
Ethnicity Black							-1.72	-6.93 – 3.49	0.514
Ethnicity East Asian							0.58	-2.92 – 4.09	0.742
Ethnicity South Asian							1.77	-2.27 – 5.81	0.387
Ethnicity Native Hawaiian Pacific Islander							-1.50	-8.36 – 5.36	0.666
Ethnicity Middle Eastern							-1.31	-9.13 – 6.51	0.740
Ethnicity American Indian							3.51	-6.17 – 13.20	0.474
Observations	139			139			139
R2 / R2 adjusted	0.015 / -0.007			0.112 / 0.050			0.142 / 0.021

SAS: Negative

Intention to Treat

m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)

	SAS negative diff			SAS negative diff			SAS negative diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.65	-2.45 – 1.15	0.475	-9.12	-16.63 – -1.61	0.018	-8.92	-17.46 – -0.38	0.041
ActiveDays	-0.00	-0.07 – 0.06	0.879	0.01	-0.06 – 0.07	0.817	0.00	-0.06 – 0.06	0.990
Reports	0.11	-0.06 – 0.28	0.210	0.15	-0.03 – 0.32	0.096	0.13	-0.06 – 0.32	0.174
Activities	-0.01	-0.08 – 0.07	0.827	-0.02	-0.10 – 0.06	0.555	-0.02	-0.10 – 0.06	0.608
univ [Foothill]				1.79	-1.62 – 5.21	0.301	1.03	-2.58 – 4.65	0.573
univ [UW]				1.02	-1.20 – 3.24	0.366	1.57	-0.83 – 3.97	0.199
Sex [Woman]				0.54	-2.01 – 3.10	0.674	0.13	-2.47 – 2.74	0.919
Age				0.18	-0.03 – 0.39	0.087	0.22	0.01 – 0.44	0.044
int student [No]				2.19	-1.63 – 6.01	0.260	1.51	-2.75 – 5.76	0.486
SES num				0.45	-0.44 – 1.34	0.322	0.38	-0.56 – 1.31	0.427
Ethnicity White							0.18	-2.70 – 3.05	0.904
Ethnicity Hispanic							2.05	-1.32 – 5.42	0.231
Ethnicity Black							-1.16	-6.26 – 3.94	0.653
Ethnicity East Asian							-0.92	-4.31 – 2.46	0.591
Ethnicity South Asian							-0.11	-3.91 – 3.69	0.955
Ethnicity Native Hawaiian Pacific Islander							-4.23	-11.30 – 2.84	0.239
Ethnicity Middle Eastern							2.97	-2.37 – 8.32	0.273
Ethnicity American Indian							-3.01	-11.05 – 5.04	0.461
Observations	170			169			169
R2 / R2 adjusted	0.011 / -0.007			0.056 / 0.003			0.095 / -0.006

Excluded Preregistered

m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)

	SAS negative diff			SAS negative diff			SAS negative diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.78	-2.64 – 1.09	0.413	-10.02	-18.06 – -1.98	0.015	-10.13	-19.25 – -1.01	0.030
ActiveDays	-0.00	-0.07 – 0.06	0.892	0.01	-0.05 – 0.07	0.779	0.00	-0.06 – 0.07	0.946
Reports	0.11	-0.06 – 0.29	0.206	0.15	-0.03 – 0.33	0.092	0.14	-0.05 – 0.32	0.159
Activities	-0.01	-0.08 – 0.07	0.889	-0.02	-0.10 – 0.06	0.593	-0.02	-0.10 – 0.06	0.648
univ [Foothill]				1.49	-2.04 – 5.02	0.405	0.65	-3.08 – 4.38	0.731
univ [UW]				1.06	-1.19 – 3.31	0.354	1.65	-0.79 – 4.10	0.184
Sex [Woman]				0.72	-1.92 – 3.36	0.592	0.32	-2.38 – 3.01	0.817
Age				0.19	-0.02 – 0.40	0.072	0.24	0.02 – 0.47	0.034
int student [No]				2.55	-1.47 – 6.58	0.212	1.76	-2.66 – 6.19	0.432
SES num				0.46	-0.45 – 1.36	0.322	0.39	-0.56 – 1.34	0.420
Ethnicity White							0.41	-2.54 – 3.36	0.784
Ethnicity Hispanic							2.35	-1.12 – 5.82	0.183
Ethnicity Black							-0.95	-6.12 – 4.23	0.718
Ethnicity East Asian							-0.66	-4.14 – 2.82	0.709
Ethnicity South Asian							-0.20	-4.09 – 3.69	0.920
Ethnicity Native Hawaiian Pacific Islander							-3.84	-11.02 – 3.33	0.292
Ethnicity Middle Eastern							3.23	-2.19 – 8.65	0.241
Ethnicity American Indian							-3.07	-11.17 – 5.03	0.455
Observations	167			166			166
R2 / R2 adjusted	0.011 / -0.007			0.058 / 0.003			0.098 / -0.005

Excluded Unreasonable Numbers

m0_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_SAS_negative_diff <- lm(SAS_negative_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_SAS_negative_diff, m1_SAS_negative_diff, m2_SAS_negative_diff)

	SAS negative diff			SAS negative diff			SAS negative diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.66	-2.79 – 1.46	0.538	-9.13	-18.09 – -0.18	0.046	-10.68	-20.45 – -0.90	0.033
ActiveDays	-0.08	-0.21 – 0.05	0.218	-0.06	-0.20 – 0.07	0.359	-0.08	-0.22 – 0.06	0.258
Reports	0.24	0.01 – 0.47	0.039	0.26	0.03 – 0.49	0.027	0.28	0.02 – 0.53	0.033
Activities	0.01	-0.07 – 0.10	0.765	-0.01	-0.11 – 0.08	0.753	-0.01	-0.10 – 0.08	0.833
univ [Foothill]				0.20	-3.77 – 4.18	0.919	-0.61	-4.76 – 3.53	0.770
univ [UW]				0.84	-1.70 – 3.38	0.515	1.83	-0.85 – 4.50	0.179
Sex [Woman]				0.26	-2.64 – 3.16	0.860	-0.18	-3.07 – 2.72	0.904
Age				0.24	0.02 – 0.47	0.036	0.32	0.08 – 0.55	0.009
int student [No]				2.56	-2.50 – 7.61	0.319	1.68	-3.57 – 6.94	0.527
SES num				0.17	-0.83 – 1.17	0.736	0.19	-0.83 – 1.21	0.713
Ethnicity White							1.03	-2.18 – 4.24	0.525
Ethnicity Hispanic							4.08	0.38 – 7.77	0.031
Ethnicity Black							-2.20	-7.66 – 3.26	0.426
Ethnicity East Asian							-0.91	-4.60 – 2.78	0.626
Ethnicity South Asian							0.15	-4.09 – 4.40	0.943
Ethnicity Native Hawaiian Pacific Islander							-3.43	-10.64 – 3.78	0.348
Ethnicity Middle Eastern							3.53	-4.69 – 11.74	0.397
Ethnicity American Indian							-3.40	-13.57 – 6.77	0.509
Observations	140			140			140
R2 / R2 adjusted	0.031 / 0.010			0.073 / 0.009			0.156 / 0.038

Flourishing Scale

Intention to Treat

m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)

	flourishing diff			flourishing diff			flourishing diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.10	-1.34 – 1.14	0.878	1.79	-3.50 – 7.09	0.504	2.27	-3.79 – 8.34	0.460
ActiveDays	-0.07	-0.11 – -0.03	0.001	-0.07	-0.12 – -0.03	0.002	-0.07	-0.12 – -0.03	0.001
Reports	0.04	-0.08 – 0.16	0.544	0.03	-0.09 – 0.16	0.601	0.03	-0.11 – 0.16	0.687
Activities	0.09	0.03 – 0.14	0.001	0.10	0.04 – 0.15	0.001	0.10	0.04 – 0.15	0.001
univ [Foothill]				0.99	-1.42 – 3.40	0.419	1.19	-1.38 – 3.75	0.361
univ [UW]				-0.10	-1.65 – 1.46	0.903	0.12	-1.57 – 1.82	0.886
Sex [Woman]				0.49	-1.29 – 2.27	0.589	0.55	-1.28 – 2.37	0.557
Age				-0.09	-0.24 – 0.06	0.223	-0.10	-0.25 – 0.06	0.214
int student [No]				-0.40	-3.09 – 2.29	0.771	-0.88	-3.90 – 2.15	0.568
SES num				-0.09	-0.71 – 0.54	0.782	-0.16	-0.82 – 0.50	0.638
Ethnicity White							0.43	-1.61 – 2.47	0.678
Ethnicity Hispanic							0.53	-1.86 – 2.93	0.662
Ethnicity Black							-0.61	-4.23 – 3.01	0.739
Ethnicity East Asian							0.42	-1.99 – 2.82	0.733
Ethnicity South Asian							-1.28	-3.98 – 1.42	0.349
Ethnicity Native Hawaiian Pacific Islander							-2.56	-7.58 – 2.46	0.315
Ethnicity Middle Eastern							0.87	-2.92 – 4.67	0.650
Ethnicity American Indian							2.21	-3.50 – 7.91	0.446
Observations	170			170			170
R2 / R2 adjusted	0.085 / 0.068			0.098 / 0.047			0.123 / 0.025

Excluded Preregistered

m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)

	flourishing diff			flourishing diff			flourishing diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.10	-1.38 – 1.18	0.875	3.08	-2.54 – 8.70	0.281	3.39	-3.05 – 9.83	0.300
ActiveDays	-0.07	-0.11 – -0.03	0.001	-0.07	-0.12 – -0.03	0.001	-0.08	-0.12 – -0.03	0.001
Reports	0.04	-0.08 – 0.16	0.544	0.03	-0.09 – 0.15	0.626	0.02	-0.11 – 0.16	0.740
Activities	0.09	0.03 – 0.14	0.001	0.10	0.04 – 0.15	0.001	0.10	0.04 – 0.16	0.001
univ [Foothill]				1.27	-1.20 – 3.73	0.312	1.41	-1.22 – 4.05	0.292
univ [UW]				-0.26	-1.82 – 1.31	0.748	-0.08	-1.79 – 1.64	0.931
Sex [Woman]				0.17	-1.65 – 2.00	0.853	0.25	-1.63 – 2.14	0.790
Age				-0.11	-0.26 – 0.04	0.144	-0.12	-0.27 – 0.04	0.150
int student [No]				-1.02	-3.83 – 1.79	0.475	-1.31	-4.43 – 1.82	0.410
SES num				-0.08	-0.71 – 0.55	0.795	-0.15	-0.82 – 0.52	0.658
Ethnicity White							0.34	-1.75 – 2.42	0.751
Ethnicity Hispanic							0.44	-2.00 – 2.89	0.720
Ethnicity Black							-0.63	-4.28 – 3.02	0.735
Ethnicity East Asian							0.37	-2.08 – 2.82	0.768
Ethnicity South Asian							-0.96	-3.71 – 1.78	0.489
Ethnicity Native Hawaiian Pacific Islander							-2.68	-7.75 – 2.38	0.297
Ethnicity Middle Eastern							0.77	-3.06 – 4.60	0.692
Ethnicity American Indian							2.29	-3.43 – 8.00	0.430
Observations	167			167			167
R2 / R2 adjusted	0.086 / 0.069			0.105 / 0.054			0.126 / 0.026

Excluded Unreasonable Numbers

m0_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_flourishing_diff <- lm(flourishing_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_flourishing_diff, m1_flourishing_diff, m2_flourishing_diff)

	flourishing diff			flourishing diff			flourishing diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.47	-1.89 – 0.96	0.518	2.64	-3.31 – 8.60	0.381	3.60	-3.12 – 10.32	0.291
ActiveDays	-0.02	-0.11 – 0.06	0.577	-0.01	-0.10 – 0.08	0.891	-0.00	-0.10 – 0.09	0.971
Reports	-0.01	-0.16 – 0.14	0.906	-0.03	-0.18 – 0.12	0.704	-0.06	-0.24 – 0.11	0.490
Activities	0.08	0.03 – 0.14	0.004	0.10	0.04 – 0.16	0.002	0.10	0.03 – 0.16	0.003
univ [Foothill]				2.69	0.04 – 5.33	0.046	2.32	-0.53 – 5.17	0.109
univ [UW]				0.35	-1.34 – 2.04	0.681	0.12	-1.72 – 1.96	0.895
Sex [Woman]				0.79	-1.14 – 2.72	0.421	0.72	-1.27 – 2.71	0.476
Age				-0.16	-0.31 – -0.01	0.034	-0.17	-0.33 – -0.01	0.040
int student [No]				-1.56	-4.93 – 1.80	0.359	-1.15	-4.76 – 2.46	0.531
SES num				0.04	-0.62 – 0.70	0.905	-0.04	-0.74 – 0.67	0.920
Ethnicity White							-0.90	-3.11 – 1.30	0.419
Ethnicity Hispanic							-0.42	-2.96 – 2.12	0.745
Ethnicity Black							0.03	-3.72 – 3.78	0.988
Ethnicity East Asian							0.10	-2.44 – 2.63	0.941
Ethnicity South Asian							-0.69	-3.61 – 2.23	0.640
Ethnicity Native Hawaiian Pacific Islander							-3.06	-8.02 – 1.89	0.223
Ethnicity Middle Eastern							2.50	-3.15 – 8.15	0.383
Ethnicity American Indian							0.97	-6.02 – 7.96	0.784
Observations	140			140			140
R2 / R2 adjusted	0.064 / 0.044			0.118 / 0.057			0.142 / 0.022

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)	0.11	-0.25 – 0.47	0.547	0.29	-1.23 – 1.81	0.707	0.51	-1.24 – 2.26	0.567
ActiveDays	0.00	-0.01 – 0.01	0.900	-0.00	-0.01 – 0.01	0.893	-0.00	-0.02 – 0.01	0.751
Reports	-0.01	-0.05 – 0.02	0.408	-0.01	-0.04 – 0.03	0.585	-0.01	-0.05 – 0.03	0.521
Activities	0.00	-0.01 – 0.02	0.719	0.00	-0.01 – 0.02	0.587	0.00	-0.01 – 0.02	0.618
univ [Foothill]				0.61	-0.08 – 1.31	0.082	0.58	-0.16 – 1.32	0.124
univ [UW]				0.25	-0.20 – 0.69	0.278	0.37	-0.12 – 0.86	0.135
Sex [Woman]				0.27	-0.24 – 0.78	0.295	0.27	-0.26 – 0.79	0.322
Age				-0.02	-0.06 – 0.02	0.395	-0.02	-0.06 – 0.03	0.458
int student [No]				-0.52	-1.30 – 0.25	0.185	-0.77	-1.64 – 0.10	0.083
SES num				0.07	-0.11 – 0.25	0.446	0.07	-0.12 – 0.26	0.488
Ethnicity White							0.10	-0.49 – 0.69	0.732
Ethnicity Hispanic							0.12	-0.57 – 0.81	0.728
Ethnicity Black							0.01	-1.04 – 1.05	0.988
Ethnicity East Asian							-0.36	-1.06 – 0.33	0.303
Ethnicity South Asian							-0.14	-0.92 – 0.64	0.724
Ethnicity Native Hawaiian Pacific Islander							-0.27	-1.72 – 1.18	0.715
Ethnicity Middle Eastern							0.28	-0.82 – 1.37	0.616
Ethnicity American Indian							0.06	-1.59 – 1.70	0.947
Observations	171			170			170
R2 / R2 adjusted	0.005 / -0.013			0.053 / -0.000			0.071 / -0.033

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)	0.12	-0.26 – 0.49	0.539	0.45	-1.18 – 2.08	0.587	0.74	-1.13 – 2.61	0.434
ActiveDays	0.00	-0.01 – 0.01	0.905	-0.00	-0.01 – 0.01	0.858	-0.00	-0.02 – 0.01	0.712
Reports	-0.01	-0.05 – 0.02	0.410	-0.01	-0.05 – 0.03	0.569	-0.01	-0.05 – 0.03	0.488
Activities	0.00	-0.01 – 0.02	0.739	0.00	-0.01 – 0.02	0.627	0.00	-0.01 – 0.02	0.672
univ [Foothill]				0.67	-0.05 – 1.38	0.067	0.64	-0.13 – 1.40	0.102
univ [UW]				0.24	-0.21 – 0.69	0.297	0.36	-0.13 – 0.86	0.150
Sex [Woman]				0.24	-0.29 – 0.77	0.365	0.23	-0.32 – 0.78	0.404
Age				-0.02	-0.06 – 0.02	0.347	-0.02	-0.07 – 0.03	0.394
int student [No]				-0.58	-1.40 – 0.23	0.159	-0.83	-1.74 – 0.08	0.074
SES num				0.07	-0.11 – 0.25	0.465	0.06	-0.13 – 0.26	0.536
Ethnicity White							0.07	-0.54 – 0.67	0.821
Ethnicity Hispanic							0.07	-0.64 – 0.78	0.840
Ethnicity Black							-0.03	-1.09 – 1.03	0.959
Ethnicity East Asian							-0.41	-1.12 – 0.30	0.255
Ethnicity South Asian							-0.12	-0.92 – 0.68	0.763
Ethnicity Native Hawaiian Pacific Islander							-0.34	-1.81 – 1.13	0.650
Ethnicity Middle Eastern							0.24	-0.87 – 1.36	0.664
Ethnicity American Indian							0.07	-1.59 – 1.73	0.935
Observations	168			167			167
R2 / R2 adjusted	0.005 / -0.013			0.056 / 0.002			0.074 / -0.031

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)	0.27	-0.15 – 0.68	0.203	1.10	-0.64 – 2.84	0.214	1.54	-0.43 – 3.51	0.124
ActiveDays	-0.02	-0.04 – 0.01	0.147	-0.01	-0.04 – 0.01	0.283	-0.02	-0.04 – 0.01	0.262
Reports	0.00	-0.04 – 0.05	0.865	0.00	-0.04 – 0.05	0.965	0.01	-0.05 – 0.06	0.812
Activities	0.01	-0.01 – 0.02	0.412	0.01	-0.01 – 0.03	0.323	0.01	-0.01 – 0.03	0.436
univ [Foothill]				0.43	-0.34 – 1.20	0.273	0.49	-0.35 – 1.33	0.248
univ [UW]				0.10	-0.40 – 0.59	0.698	0.18	-0.36 – 0.72	0.516
Sex [Woman]				0.12	-0.44 – 0.69	0.674	0.13	-0.45 – 0.71	0.660
Age				-0.03	-0.07 – 0.02	0.230	-0.03	-0.08 – 0.01	0.173
int student [No]				-0.73	-1.72 – 0.25	0.143	-0.90	-1.96 – 0.15	0.094
SES num				0.04	-0.16 – 0.23	0.707	0.01	-0.20 – 0.21	0.935
Ethnicity White							0.08	-0.57 – 0.72	0.814
Ethnicity Hispanic							-0.12	-0.87 – 0.62	0.745
Ethnicity Black							0.27	-0.83 – 1.38	0.623
Ethnicity East Asian							-0.33	-1.07 – 0.42	0.388
Ethnicity South Asian							-0.29	-1.14 – 0.57	0.506
Ethnicity Native Hawaiian Pacific Islander							-0.52	-1.97 – 0.93	0.481
Ethnicity Middle Eastern							-0.10	-1.76 – 1.55	0.903
Ethnicity American Indian							-0.53	-2.58 – 1.52	0.608
Observations	140			140			140
R2 / R2 adjusted	0.019 / -0.003			0.053 / -0.012			0.075 / -0.053

Cohesion

Intention to Treat

m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)

	cohesion diff			cohesion diff			cohesion diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.41	-0.02 – 0.84	0.062	-0.24	-2.08 – 1.61	0.801	-0.34	-2.40 – 1.72	0.742
ActiveDays	-0.01	-0.02 – 0.01	0.477	-0.00	-0.02 – 0.01	0.703	-0.00	-0.02 – 0.01	0.572
Reports	-0.01	-0.05 – 0.04	0.784	-0.01	-0.05 – 0.04	0.792	-0.02	-0.06 – 0.03	0.482
Activities	0.01	-0.01 – 0.02	0.507	0.00	-0.02 – 0.02	0.703	0.00	-0.01 – 0.02	0.635
univ [Foothill]				0.01	-0.83 – 0.85	0.983	0.15	-0.73 – 1.02	0.742
univ [UW]				0.03	-0.52 – 0.57	0.924	-0.05	-0.63 – 0.53	0.865
Sex [Woman]				0.19	-0.43 – 0.81	0.551	0.24	-0.38 – 0.86	0.442
Age				0.03	-0.02 – 0.08	0.245	0.02	-0.03 – 0.08	0.397
int student [No]				0.22	-0.72 – 1.15	0.650	0.20	-0.82 – 1.23	0.695
SES num				-0.10	-0.32 – 0.11	0.351	-0.14	-0.36 – 0.09	0.233
Ethnicity White							0.42	-0.27 – 1.12	0.229
Ethnicity Hispanic							0.06	-0.75 – 0.87	0.885
Ethnicity Black							0.22	-1.01 – 1.45	0.728
Ethnicity East Asian							0.55	-0.26 – 1.37	0.181
Ethnicity South Asian							0.41	-0.51 – 1.33	0.377
Ethnicity Native Hawaiian Pacific Islander							-1.57	-3.27 – 0.14	0.072
Ethnicity Middle Eastern							0.53	-0.76 – 1.82	0.421
Ethnicity American Indian							1.97	0.04 – 3.91	0.046
Observations	171			170			170
R2 / R2 adjusted	0.006 / -0.012			0.023 / -0.032			0.096 / -0.005

Excluded Preregistered

m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)

	cohesion diff			cohesion diff			cohesion diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.35	-0.09 – 0.80	0.121	-0.45	-2.42 – 1.52	0.651	-0.60	-2.80 – 1.59	0.588
ActiveDays	-0.01	-0.02 – 0.01	0.500	-0.00	-0.02 – 0.01	0.738	-0.00	-0.02 – 0.01	0.605
Reports	-0.00	-0.05 – 0.04	0.817	-0.00	-0.05 – 0.04	0.824	-0.01	-0.06 – 0.03	0.526
Activities	0.01	-0.01 – 0.03	0.425	0.01	-0.01 – 0.02	0.606	0.01	-0.01 – 0.03	0.539
univ [Foothill]				-0.04	-0.91 – 0.82	0.918	0.08	-0.82 – 0.98	0.864
univ [UW]				0.01	-0.54 – 0.56	0.967	-0.07	-0.65 – 0.52	0.816
Sex [Woman]				0.22	-0.42 – 0.85	0.507	0.26	-0.38 – 0.90	0.427
Age				0.03	-0.02 – 0.08	0.221	0.03	-0.03 – 0.08	0.354
int student [No]				0.28	-0.70 – 1.27	0.570	0.24	-0.82 – 1.31	0.653
SES num				-0.09	-0.31 – 0.13	0.413	-0.12	-0.35 – 0.11	0.289
Ethnicity White							0.47	-0.24 – 1.18	0.195
Ethnicity Hispanic							0.14	-0.70 – 0.97	0.748
Ethnicity Black							0.28	-0.97 – 1.53	0.657
Ethnicity East Asian							0.64	-0.20 – 1.47	0.134
Ethnicity South Asian							0.43	-0.51 – 1.37	0.367
Ethnicity Native Hawaiian Pacific Islander							-1.45	-3.18 – 0.27	0.099
Ethnicity Middle Eastern							0.57	-0.74 – 1.88	0.390
Ethnicity American Indian							1.96	0.01 – 3.91	0.048
Observations	168			167			167
R2 / R2 adjusted	0.006 / -0.012			0.023 / -0.033			0.097 / -0.006

Excluded Unreasonable Numbers

m0_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_cohesion_diff <- lm(cohesion_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_cohesion_diff, m1_cohesion_diff, m2_cohesion_diff)

	cohesion diff			cohesion diff			cohesion diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.16	-0.32 – 0.65	0.508	0.12	-1.95 – 2.19	0.911	0.24	-2.06 – 2.53	0.837
ActiveDays	0.00	-0.03 – 0.03	0.854	0.01	-0.02 – 0.04	0.637	0.01	-0.02 – 0.04	0.608
Reports	0.00	-0.05 – 0.06	0.923	0.00	-0.05 – 0.05	0.984	-0.01	-0.07 – 0.05	0.816
Activities	0.01	-0.01 – 0.03	0.325	0.01	-0.01 – 0.03	0.509	0.01	-0.01 – 0.03	0.480
univ [Foothill]				-0.21	-1.13 – 0.71	0.650	-0.26	-1.23 – 0.71	0.601
univ [UW]				0.17	-0.42 – 0.76	0.568	-0.03	-0.66 – 0.60	0.922
Sex [Woman]				0.40	-0.27 – 1.07	0.238	0.35	-0.33 – 1.03	0.313
Age				0.01	-0.04 – 0.07	0.594	0.01	-0.04 – 0.07	0.700
int student [No]				-0.43	-1.59 – 0.74	0.471	-0.23	-1.46 – 1.00	0.711
SES num				-0.07	-0.30 – 0.16	0.553	-0.12	-0.36 – 0.12	0.332
Ethnicity White							-0.01	-0.77 – 0.74	0.973
Ethnicity Hispanic							-0.17	-1.03 – 0.70	0.705
Ethnicity Black							0.22	-1.06 – 1.51	0.729
Ethnicity East Asian							0.43	-0.43 – 1.30	0.326
Ethnicity South Asian							0.42	-0.58 – 1.42	0.406
Ethnicity Native Hawaiian Pacific Islander							-1.60	-3.29 – 0.09	0.064
Ethnicity Middle Eastern							0.64	-1.29 – 2.57	0.515
Ethnicity American Indian							-0.01	-2.40 – 2.37	0.992
Observations	140			140			140
R2 / R2 adjusted	0.013 / -0.009			0.039 / -0.028			0.097 / -0.029

Mindfulness

Intention to Treat

m0_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_mindfulness_diff <- lm(mindfulness_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_mindfulness_diff, m1_mindfulness_diff, m2_mindfulness_diff)

	mindfulness diff			mindfulness diff			mindfulness diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.54	-0.06 – 3.14	0.060	-3.42	-10.14 – 3.31	0.317	-6.09	-13.71 – 1.54	0.117
ActiveDays	0.02	-0.04 – 0.07	0.584	0.02	-0.04 – 0.07	0.557	0.01	-0.04 – 0.07	0.619
Reports	0.08	-0.07 – 0.24	0.289	0.11	-0.04 – 0.27	0.161	0.11	-0.06 – 0.27	0.211
Activities	-0.09	-0.15 – -0.02	0.014	-0.10	-0.18 – -0.03	0.004	-0.10	-0.17 – -0.03	0.005
univ [Foothill]				0.55	-2.51 – 3.61	0.724	0.02	-3.20 – 3.25	0.988
univ [UW]				2.00	0.02 – 3.98	0.047	1.94	-0.19 – 4.07	0.074
Sex [Woman]				1.08	-1.18 – 3.34	0.345	1.14	-1.16 – 3.44	0.329
Age				0.14	-0.04 – 0.33	0.132	0.16	-0.04 – 0.35	0.108
int student [No]				-0.31	-3.73 – 3.11	0.859	-0.57	-4.37 – 3.23	0.768
SES num				0.19	-0.60 – 0.99	0.632	0.48	-0.35 – 1.31	0.255
Ethnicity White							0.86	-1.70 – 3.42	0.509
Ethnicity Hispanic							2.81	-0.20 – 5.82	0.067
Ethnicity Black							3.24	-1.31 – 7.80	0.161
Ethnicity East Asian							0.90	-2.12 – 3.92	0.555
Ethnicity South Asian							2.66	-0.73 – 6.06	0.123
Ethnicity Native Hawaiian Pacific Islander							4.70	-1.61 – 11.01	0.143
Ethnicity Middle Eastern							0.43	-4.34 – 5.20	0.858
Ethnicity American Indian							1.93	-5.24 – 9.11	0.595
Observations	171			170			170
R2 / R2 adjusted	0.040 / 0.023			0.080 / 0.028			0.123 / 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)	1.53	-0.13 – 3.19	0.070	-4.94	-12.10 – 2.22	0.175	-7.60	-15.71 – 0.51	0.066
ActiveDays	0.02	-0.04 – 0.07	0.585	0.02	-0.04 – 0.08	0.499	0.02	-0.04 – 0.07	0.553
Reports	0.08	-0.07 – 0.24	0.291	0.11	-0.04 – 0.27	0.154	0.11	-0.06 – 0.28	0.187
Activities	-0.08	-0.15 – -0.02	0.015	-0.11	-0.18 – -0.04	0.004	-0.10	-0.18 – -0.03	0.005
univ [Foothill]				0.36	-2.78 – 3.51	0.820	-0.23	-3.55 – 3.09	0.891
univ [UW]				2.17	0.17 – 4.16	0.033	2.13	-0.03 – 4.29	0.053
Sex [Woman]				1.46	-0.86 – 3.78	0.217	1.50	-0.87 – 3.87	0.214
Age				0.16	-0.03 – 0.35	0.092	0.18	-0.02 – 0.38	0.075
int student [No]				0.43	-3.16 – 4.01	0.815	0.00	-3.94 – 3.94	0.999
SES num				0.21	-0.59 – 1.01	0.610	0.49	-0.35 – 1.34	0.249
Ethnicity White							0.96	-1.67 – 3.58	0.472
Ethnicity Hispanic							2.94	-0.15 – 6.03	0.062
Ethnicity Black							3.28	-1.32 – 7.88	0.161
Ethnicity East Asian							1.01	-2.08 – 4.10	0.519
Ethnicity South Asian							2.30	-1.17 – 5.76	0.192
Ethnicity Native Hawaiian Pacific Islander							4.91	-1.48 – 11.29	0.131
Ethnicity Middle Eastern							0.53	-4.29 – 5.35	0.828
Ethnicity American Indian							1.83	-5.37 – 9.03	0.616
Observations	168			167			167
R2 / R2 adjusted	0.040 / 0.023			0.086 / 0.034			0.127 / 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)	1.45	-0.40 – 3.30	0.124	-7.57	-15.23 – 0.08	0.052	-10.28	-18.63 – -1.94	0.016
ActiveDays	0.01	-0.11 – 0.12	0.927	0.05	-0.07 – 0.16	0.431	0.06	-0.06 – 0.18	0.316
Reports	0.20	-0.00 – 0.40	0.051	0.22	0.02 – 0.42	0.032	0.19	-0.03 – 0.41	0.085
Activities	-0.09	-0.17 – -0.02	0.014	-0.14	-0.22 – -0.06	0.001	-0.14	-0.22 – -0.06	<0.001
univ [Foothill]				0.09	-3.31 – 3.49	0.960	-0.87	-4.40 – 2.67	0.629
univ [UW]				2.72	0.54 – 4.89	0.015	3.08	0.80 – 5.37	0.009
Sex [Woman]				1.72	-0.76 – 4.20	0.172	1.90	-0.57 – 4.37	0.130
Age				0.21	0.02 – 0.41	0.032	0.24	0.04 – 0.44	0.018
int student [No]				2.35	-1.97 – 6.67	0.284	0.57	-3.91 – 5.05	0.802
SES num				-0.03	-0.88 – 0.83	0.950	0.25	-0.62 – 1.12	0.573
Ethnicity White							1.97	-0.77 – 4.71	0.157
Ethnicity Hispanic							4.13	0.98 – 7.28	0.011
Ethnicity Black							5.49	0.83 – 10.15	0.021
Ethnicity East Asian							0.78	-2.37 – 3.92	0.626
Ethnicity South Asian							1.67	-1.95 – 5.29	0.362
Ethnicity Native Hawaiian Pacific Islander							5.58	-0.57 – 11.73	0.075
Ethnicity Middle Eastern							3.71	-3.30 – 10.72	0.297
Ethnicity American Indian							2.42	-6.26 – 11.09	0.582
Observations	140			140			140
R2 / R2 adjusted	0.074 / 0.054			0.149 / 0.090			0.229 / 0.121

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)	0.07	-0.53 – 0.68	0.809	0.31	-2.26 – 2.88	0.812	-0.56	-3.46 – 2.35	0.704
ActiveDays	-0.01	-0.03 – 0.01	0.329	-0.01	-0.03 – 0.01	0.433	-0.01	-0.03 – 0.01	0.322
Reports	0.06	-0.00 – 0.11	0.059	0.05	-0.01 – 0.11	0.102	0.04	-0.02 – 0.11	0.189
Activities	0.00	-0.02 – 0.03	0.784	0.00	-0.02 – 0.03	0.808	0.01	-0.02 – 0.03	0.704
univ [Foothill]				-0.51	-1.68 – 0.66	0.390	-0.89	-2.12 – 0.34	0.154
univ [UW]				-0.45	-1.21 – 0.30	0.236	-0.35	-1.16 – 0.46	0.400
Sex [Woman]				0.14	-0.72 – 1.00	0.751	0.06	-0.82 – 0.94	0.892
Age				0.02	-0.05 – 0.09	0.495	0.04	-0.03 – 0.12	0.258
int student [No]				0.02	-1.28 – 1.33	0.972	-0.12	-1.57 – 1.32	0.866
SES num				-0.18	-0.48 – 0.12	0.242	-0.10	-0.42 – 0.21	0.519
Ethnicity White							0.13	-0.85 – 1.10	0.796
Ethnicity Hispanic							1.48	0.33 – 2.62	0.012
Ethnicity Black							0.32	-1.41 – 2.06	0.712
Ethnicity East Asian							0.26	-0.89 – 1.41	0.659
Ethnicity South Asian							0.56	-0.73 – 1.86	0.390
Ethnicity Native Hawaiian Pacific Islander							0.72	-1.68 – 3.12	0.554
Ethnicity Middle Eastern							0.97	-0.85 – 2.78	0.294
Ethnicity American Indian							0.19	-2.54 – 2.92	0.892
Observations	170			170			170
R2 / R2 adjusted	0.022 / 0.005			0.043 / -0.011			0.092 / -0.009

Excluded Preregistered

m0_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_emo_res_diff <- lm(emo_res_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_emo_res_diff, m1_emo_res_diff, m2_emo_res_diff)

	emo res diff			emo res diff			emo res diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.04	-0.58 – 0.65	0.906	0.91	-1.78 – 3.61	0.504	-0.12	-3.15 – 2.91	0.938
ActiveDays	-0.01	-0.03 – 0.01	0.331	-0.01	-0.03 – 0.01	0.365	-0.01	-0.03 – 0.01	0.268
Reports	0.06	-0.00 – 0.11	0.054	0.05	-0.01 – 0.11	0.101	0.04	-0.02 – 0.10	0.197
Activities	0.00	-0.02 – 0.03	0.732	0.01	-0.02 – 0.03	0.660	0.01	-0.02 – 0.04	0.521
univ [Foothill]				-0.38	-1.57 – 0.80	0.523	-0.83	-2.07 – 0.41	0.186
univ [UW]				-0.57	-1.32 – 0.18	0.133	-0.51	-1.32 – 0.29	0.210
Sex [Woman]				-0.04	-0.91 – 0.84	0.935	-0.13	-1.01 – 0.76	0.778
Age				0.01	-0.06 – 0.08	0.693	0.03	-0.04 – 0.11	0.366
int student [No]				-0.31	-1.65 – 1.04	0.654	-0.38	-1.85 – 1.09	0.606
SES num				-0.16	-0.47 – 0.14	0.283	-0.08	-0.39 – 0.23	0.612
Ethnicity White							0.13	-0.85 – 1.11	0.796
Ethnicity Hispanic							1.53	0.38 – 2.68	0.010
Ethnicity Black							0.41	-1.31 – 2.12	0.640
Ethnicity East Asian							0.34	-0.81 – 1.49	0.560
Ethnicity South Asian							0.82	-0.47 – 2.11	0.212
Ethnicity Native Hawaiian Pacific Islander							0.79	-1.59 – 3.17	0.513
Ethnicity Middle Eastern							0.96	-0.84 – 2.76	0.293
Ethnicity American Indian							0.23	-2.45 – 2.92	0.864
Observations	167			167			167
R2 / R2 adjusted	0.024 / 0.006			0.047 / -0.008			0.103 / 0.001

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)	0.09	-0.62 – 0.80	0.802	0.40	-2.60 – 3.41	0.791	-0.59	-3.93 – 2.75	0.726
ActiveDays	-0.01	-0.05 – 0.03	0.631	-0.02	-0.07 – 0.03	0.392	-0.02	-0.06 – 0.03	0.506
Reports	0.07	-0.01 – 0.14	0.085	0.07	-0.01 – 0.15	0.090	0.05	-0.04 – 0.14	0.258
Activities	0.00	-0.03 – 0.03	0.908	0.00	-0.03 – 0.03	0.833	0.00	-0.03 – 0.04	0.783
univ [Foothill]				-0.85	-2.19 – 0.48	0.208	-1.31	-2.73 – 0.11	0.069
univ [UW]				-0.57	-1.42 – 0.28	0.189	-0.45	-1.36 – 0.46	0.332
Sex [Woman]				-0.26	-1.24 – 0.71	0.593	-0.32	-1.31 – 0.67	0.519
Age				0.03	-0.04 – 0.11	0.371	0.06	-0.02 – 0.14	0.154
int student [No]				0.00	-1.70 – 1.70	0.998	-0.19	-1.99 – 1.60	0.833
SES num				-0.10	-0.44 – 0.23	0.543	-0.02	-0.37 – 0.33	0.927
Ethnicity White							0.10	-1.00 – 1.19	0.862
Ethnicity Hispanic							1.40	0.14 – 2.67	0.029
Ethnicity Black							0.16	-1.71 – 2.02	0.868
Ethnicity East Asian							0.13	-1.13 – 1.39	0.842
Ethnicity South Asian							0.71	-0.74 – 2.16	0.336
Ethnicity Native Hawaiian Pacific Islander							0.81	-1.65 – 3.28	0.514
Ethnicity Middle Eastern							1.55	-1.26 – 4.35	0.278
Ethnicity American Indian							0.74	-2.73 – 4.22	0.674
Observations	140			140			140
R2 / R2 adjusted	0.024 / 0.002			0.049 / -0.017			0.104 / -0.021

School Satisfaction

Intention to Treat

m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)

	school satis diff			school satis diff			school satis diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.01	-0.19 – 0.21	0.934	-0.13	-0.99 – 0.73	0.762	-0.10	-1.09 – 0.88	0.836
ActiveDays	-0.01	-0.01 – 0.00	0.055	-0.01	-0.01 – 0.00	0.054	-0.01	-0.01 – 0.00	0.081
Reports	0.01	-0.01 – 0.03	0.355	0.01	-0.01 – 0.03	0.545	0.01	-0.01 – 0.03	0.344
Activities	0.01	0.00 – 0.02	0.046	0.01	0.00 – 0.02	0.036	0.01	0.00 – 0.02	0.050
univ [Foothill]				-0.24	-0.63 – 0.15	0.229	-0.14	-0.55 – 0.28	0.512
univ [UW]				-0.12	-0.38 – 0.13	0.330	-0.13	-0.40 – 0.15	0.352
Sex [Woman]				0.04	-0.25 – 0.33	0.776	0.06	-0.24 – 0.35	0.714
Age				-0.00	-0.03 – 0.02	0.753	-0.01	-0.03 – 0.02	0.644
int student [No]				0.21	-0.23 – 0.64	0.348	0.18	-0.32 – 0.67	0.482
SES num				0.02	-0.08 – 0.13	0.634	0.01	-0.10 – 0.12	0.834
Ethnicity White							0.14	-0.19 – 0.47	0.395
Ethnicity Hispanic							-0.17	-0.56 – 0.22	0.386
Ethnicity Black							0.03	-0.56 – 0.62	0.915
Ethnicity East Asian							0.07	-0.32 – 0.46	0.714
Ethnicity South Asian							-0.12	-0.56 – 0.31	0.578
Ethnicity Native Hawaiian Pacific Islander							-0.09	-0.91 – 0.72	0.824
Ethnicity Middle Eastern							-0.15	-0.76 – 0.47	0.634
Ethnicity American Indian							-0.21	-1.14 – 0.71	0.651
Observations	171			170			170
R2 / R2 adjusted	0.033 / 0.016			0.063 / 0.011			0.086 / -0.017

Excluded Preregistered

m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)

	school satis diff			school satis diff			school satis diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.03	-0.18 – 0.23	0.774	0.23	-0.67 – 1.12	0.619	0.25	-0.78 – 1.28	0.630
ActiveDays	-0.01	-0.01 – -0.00	0.048	-0.01	-0.01 – -0.00	0.033	-0.01	-0.01 – 0.00	0.052
Reports	0.01	-0.01 – 0.03	0.364	0.01	-0.01 – 0.03	0.584	0.01	-0.01 – 0.03	0.416
Activities	0.01	-0.00 – 0.02	0.058	0.01	0.00 – 0.02	0.029	0.01	0.00 – 0.02	0.040
univ [Foothill]				-0.18	-0.57 – 0.22	0.373	-0.08	-0.50 – 0.34	0.695
univ [UW]				-0.16	-0.41 – 0.09	0.201	-0.18	-0.45 – 0.09	0.198
Sex [Woman]				-0.04	-0.34 – 0.25	0.766	-0.03	-0.33 – 0.27	0.829
Age				-0.01	-0.03 – 0.01	0.468	-0.01	-0.04 – 0.01	0.400
int student [No]				0.04	-0.41 – 0.49	0.866	0.03	-0.46 – 0.53	0.891
SES num				0.02	-0.08 – 0.12	0.665	0.01	-0.10 – 0.12	0.867
Ethnicity White							0.12	-0.21 – 0.46	0.462
Ethnicity Hispanic							-0.19	-0.59 – 0.20	0.328
Ethnicity Black							0.03	-0.55 – 0.61	0.921
Ethnicity East Asian							0.05	-0.34 – 0.45	0.785
Ethnicity South Asian							-0.03	-0.47 – 0.41	0.896
Ethnicity Native Hawaiian Pacific Islander							-0.13	-0.94 – 0.68	0.748
Ethnicity Middle Eastern							-0.17	-0.78 – 0.44	0.589
Ethnicity American Indian							-0.19	-1.10 – 0.73	0.685
Observations	168			167			167
R2 / R2 adjusted	0.033 / 0.015			0.055 / 0.001			0.076 / -0.029

Excluded Unreasonable Numbers

m0_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_school_satis_diff <- lm(school_satis_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_school_satis_diff, m1_school_satis_diff, m2_school_satis_diff)

	school satis diff			school satis diff			school satis diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	-0.10	-0.33 – 0.12	0.368	-0.13	-1.11 – 0.85	0.792	0.03	-1.07 – 1.14	0.952
ActiveDays	0.01	-0.01 – 0.02	0.251	0.01	-0.01 – 0.02	0.286	0.01	-0.01 – 0.02	0.247
Reports	-0.00	-0.03 – 0.02	0.946	-0.00	-0.03 – 0.02	0.855	-0.00	-0.03 – 0.03	0.928
Activities	0.01	-0.00 – 0.01	0.233	0.01	-0.00 – 0.02	0.132	0.01	-0.00 – 0.02	0.160
univ [Foothill]				0.07	-0.36 – 0.51	0.746	0.09	-0.38 – 0.56	0.709
univ [UW]				-0.07	-0.34 – 0.21	0.635	-0.11	-0.41 – 0.19	0.480
Sex [Woman]				0.11	-0.21 – 0.42	0.506	0.10	-0.23 – 0.42	0.557
Age				-0.01	-0.04 – 0.01	0.295	-0.02	-0.04 – 0.01	0.233
int student [No]				0.04	-0.52 – 0.59	0.896	0.05	-0.54 – 0.64	0.868
SES num				0.05	-0.06 – 0.16	0.359	0.04	-0.08 – 0.15	0.508
Ethnicity White							-0.04	-0.40 – 0.32	0.831
Ethnicity Hispanic							-0.27	-0.69 – 0.15	0.201
Ethnicity Black							0.08	-0.54 – 0.69	0.805
Ethnicity East Asian							-0.04	-0.45 – 0.38	0.867
Ethnicity South Asian							0.12	-0.36 – 0.60	0.617
Ethnicity Native Hawaiian Pacific Islander							-0.11	-0.93 – 0.70	0.782
Ethnicity Middle Eastern							0.04	-0.88 – 0.97	0.925
Ethnicity American Indian							-0.42	-1.56 – 0.73	0.474
Observations	140			140			140
R2 / R2 adjusted	0.044 / 0.023			0.064 / -0.000			0.090 / -0.037

School Prioritizes Well-Being

Intention to Treat

m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)

	wellbeing priority diff			wellbeing priority diff			wellbeing priority diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.15	-0.17 – 0.47	0.348	-0.62	-1.97 – 0.74	0.370	-0.24	-1.78 – 1.30	0.759
ActiveDays	-0.00	-0.01 – 0.01	0.980	0.00	-0.01 – 0.01	0.841	0.00	-0.01 – 0.01	0.709
Reports	-0.00	-0.03 – 0.03	0.952	-0.00	-0.03 – 0.03	0.984	0.00	-0.03 – 0.04	0.935
Activities	-0.00	-0.02 – 0.01	0.682	-0.00	-0.02 – 0.01	0.612	-0.00	-0.02 – 0.01	0.542
univ [Foothill]				-0.10	-0.72 – 0.52	0.749	-0.00	-0.65 – 0.65	1.000
univ [UW]				0.03	-0.37 – 0.42	0.896	-0.02	-0.45 – 0.41	0.924
Sex [Woman]				0.35	-0.10 – 0.81	0.127	0.36	-0.11 – 0.82	0.132
Age				0.01	-0.03 – 0.05	0.569	0.01	-0.03 – 0.05	0.772
int student [No]				0.30	-0.38 – 0.99	0.384	0.36	-0.41 – 1.13	0.361
SES num				-0.01	-0.17 – 0.15	0.910	-0.05	-0.22 – 0.12	0.538
Ethnicity White							-0.05	-0.57 – 0.47	0.854
Ethnicity Hispanic							-0.64	-1.25 – -0.03	0.040
Ethnicity Black							-0.09	-1.01 – 0.83	0.847
Ethnicity East Asian							-0.06	-0.68 – 0.55	0.835
Ethnicity South Asian							-0.24	-0.92 – 0.45	0.495
Ethnicity Native Hawaiian Pacific Islander							-0.33	-1.61 – 0.95	0.609
Ethnicity Middle Eastern							-0.05	-1.01 – 0.92	0.926
Ethnicity American Indian							-0.49	-1.95 – 0.96	0.502
Observations	171			170			170
R2 / R2 adjusted	0.002 / -0.016			0.021 / -0.034			0.057 / -0.048

Excluded Preregistered

m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)

	wellbeing priority diff			wellbeing priority diff			wellbeing priority diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.22	-0.10 – 0.55	0.178	-0.74	-2.17 – 0.69	0.309	-0.17	-1.79 – 1.44	0.832
ActiveDays	-0.00	-0.01 – 0.01	0.935	0.00	-0.01 – 0.01	0.828	0.00	-0.01 – 0.01	0.689
Reports	-0.00	-0.03 – 0.03	0.896	-0.00	-0.03 – 0.03	0.965	0.00	-0.03 – 0.03	0.971
Activities	-0.00	-0.02 – 0.01	0.512	-0.01	-0.02 – 0.01	0.484	-0.01	-0.02 – 0.01	0.388
univ [Foothill]				-0.01	-0.64 – 0.61	0.967	0.12	-0.54 – 0.78	0.716
univ [UW]				0.06	-0.34 – 0.46	0.765	0.02	-0.41 – 0.45	0.926
Sex [Woman]				0.41	-0.06 – 0.87	0.086	0.41	-0.06 – 0.89	0.087
Age				0.01	-0.03 – 0.05	0.561	0.00	-0.04 – 0.04	0.845
int student [No]				0.39	-0.32 – 1.11	0.280	0.46	-0.33 – 1.25	0.249
SES num				-0.00	-0.17 – 0.16	0.951	-0.06	-0.22 – 0.11	0.509
Ethnicity White							-0.15	-0.67 – 0.37	0.570
Ethnicity Hispanic							-0.77	-1.39 – -0.16	0.014
Ethnicity Black							-0.21	-1.13 – 0.70	0.647
Ethnicity East Asian							-0.19	-0.80 – 0.43	0.548
Ethnicity South Asian							-0.36	-1.05 – 0.33	0.309
Ethnicity Native Hawaiian Pacific Islander							-0.49	-1.76 – 0.79	0.451
Ethnicity Middle Eastern							-0.16	-1.12 – 0.80	0.745
Ethnicity American Indian							-0.50	-1.94 – 0.94	0.494
Observations	168			167			167
R2 / R2 adjusted	0.005 / -0.014			0.027 / -0.028			0.074 / -0.032

Excluded Unreasonable Numbers

m0_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_wellbeing_priority_diff <- lm(wellbeing_priority_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_wellbeing_priority_diff, m1_wellbeing_priority_diff, m2_wellbeing_priority_diff)

	wellbeing priority diff			wellbeing priority diff			wellbeing priority diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.04	-0.31 – 0.39	0.826	-1.14	-2.63 – 0.35	0.131	-0.60	-2.25 – 1.05	0.474
ActiveDays	0.01	-0.01 – 0.03	0.545	0.01	-0.01 – 0.04	0.237	0.01	-0.01 – 0.04	0.262
Reports	0.01	-0.03 – 0.05	0.579	0.01	-0.03 – 0.05	0.661	0.02	-0.03 – 0.06	0.466
Activities	-0.00	-0.02 – 0.01	0.574	-0.01	-0.02 – 0.01	0.424	-0.01	-0.02 – 0.01	0.417
univ [Foothill]				0.24	-0.42 – 0.90	0.481	0.44	-0.26 – 1.14	0.217
univ [UW]				0.19	-0.23 – 0.61	0.367	0.11	-0.35 – 0.56	0.644
Sex [Woman]				0.51	0.03 – 0.99	0.038	0.52	0.03 – 1.01	0.036
Age				0.01	-0.02 – 0.05	0.487	0.00	-0.04 – 0.04	0.940
int student [No]				0.21	-0.63 – 1.05	0.618	0.40	-0.49 – 1.28	0.375
SES num				0.03	-0.13 – 0.20	0.681	-0.00	-0.18 – 0.17	0.964
Ethnicity White							-0.26	-0.80 – 0.28	0.346
Ethnicity Hispanic							-0.77	-1.40 – -0.15	0.015
Ethnicity Black							-0.24	-1.16 – 0.68	0.603
Ethnicity East Asian							-0.15	-0.78 – 0.47	0.624
Ethnicity South Asian							-0.12	-0.83 – 0.60	0.743
Ethnicity Native Hawaiian Pacific Islander							-0.37	-1.59 – 0.85	0.549
Ethnicity Middle Eastern							-1.01	-2.39 – 0.38	0.153
Ethnicity American Indian							-0.63	-2.34 – 1.09	0.471
Observations	140			140			140
R2 / R2 adjusted	0.010 / -0.012			0.051 / -0.015			0.108 / -0.017

Academic Self-Efficacy

Intention to Treat

m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)

	acad selfefficacy diff			acad selfefficacy diff			acad selfefficacy diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.98	-0.09 – 2.06	0.073	2.21	-2.31 – 6.73	0.337	2.05	-3.16 – 7.26	0.438
ActiveDays	-0.02	-0.06 – 0.01	0.236	-0.03	-0.07 – 0.01	0.126	-0.03	-0.07 – 0.01	0.163
Reports	0.02	-0.08 – 0.12	0.709	0.01	-0.10 – 0.11	0.898	0.02	-0.10 – 0.13	0.762
Activities	0.00	-0.04 – 0.05	0.908	0.01	-0.04 – 0.06	0.683	0.01	-0.04 – 0.06	0.716
univ [Foothill]				-1.73	-3.78 – 0.33	0.099	-1.34	-3.54 – 0.87	0.233
univ [UW]				-0.87	-2.20 – 0.46	0.197	-0.95	-2.40 – 0.51	0.200
Sex [Woman]				-0.21	-1.73 – 1.31	0.782	-0.12	-1.69 – 1.45	0.882
Age				-0.05	-0.17 – 0.07	0.422	-0.06	-0.19 – 0.07	0.371
int student [No]				-0.24	-2.54 – 2.06	0.836	-0.31	-2.90 – 2.29	0.815
SES num				0.28	-0.25 – 0.82	0.299	0.24	-0.33 – 0.81	0.406
Ethnicity White							0.62	-1.13 – 2.37	0.487
Ethnicity Hispanic							-0.53	-2.59 – 1.52	0.608
Ethnicity Black							0.18	-2.93 – 3.29	0.910
Ethnicity East Asian							0.83	-1.23 – 2.90	0.427
Ethnicity South Asian							-0.45	-2.77 – 1.87	0.702
Ethnicity Native Hawaiian Pacific Islander							0.05	-4.26 – 4.36	0.981
Ethnicity Middle Eastern							-0.07	-3.33 – 3.19	0.966
Ethnicity American Indian							0.43	-4.47 – 5.33	0.862
Observations	170			170			170
R2 / R2 adjusted	0.010 / -0.008			0.051 / -0.003			0.066 / -0.039

Excluded Preregistered

m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)

	acad selfefficacy diff			acad selfefficacy diff			acad selfefficacy diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	1.11	-0.00 – 2.21	0.050	3.36	-1.46 – 8.18	0.170	3.17	-2.37 – 8.71	0.260
ActiveDays	-0.02	-0.06 – 0.01	0.226	-0.03	-0.07 – 0.01	0.104	-0.03	-0.07 – 0.01	0.137
Reports	0.02	-0.09 – 0.12	0.737	0.00	-0.10 – 0.11	0.935	0.01	-0.10 – 0.13	0.830
Activities	-0.00	-0.05 – 0.05	0.991	0.01	-0.04 – 0.06	0.695	0.01	-0.04 – 0.06	0.721
univ [Foothill]				-1.53	-3.64 – 0.59	0.156	-1.16	-3.43 – 1.10	0.312
univ [UW]				-0.96	-2.30 – 0.38	0.159	-1.06	-2.54 – 0.41	0.157
Sex [Woman]				-0.47	-2.03 – 1.09	0.554	-0.37	-1.99 – 1.25	0.654
Age				-0.07	-0.19 – 0.06	0.307	-0.07	-0.21 – 0.06	0.279
int student [No]				-0.76	-3.17 – 1.65	0.534	-0.72	-3.42 – 1.97	0.596
SES num				0.26	-0.28 – 0.80	0.335	0.22	-0.35 – 0.80	0.448
Ethnicity White							0.55	-1.24 – 2.35	0.543
Ethnicity Hispanic							-0.63	-2.74 – 1.48	0.554
Ethnicity Black							0.14	-3.00 – 3.29	0.929
Ethnicity East Asian							0.74	-1.37 – 2.85	0.491
Ethnicity South Asian							-0.20	-2.57 – 2.17	0.868
Ethnicity Native Hawaiian Pacific Islander							-0.11	-4.48 – 4.25	0.959
Ethnicity Middle Eastern							-0.13	-3.43 – 3.16	0.938
Ethnicity American Indian							0.50	-4.42 – 5.42	0.840
Observations	167			167			167
R2 / R2 adjusted	0.012 / -0.006			0.051 / -0.003			0.064 / -0.042

Excluded Unreasonable Numbers

m0_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_acad_selfefficacy_diff <- lm(acad_selfefficacy_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_acad_selfefficacy_diff, m1_acad_selfefficacy_diff, m2_acad_selfefficacy_diff)

	acad selfefficacy diff			acad selfefficacy diff			acad selfefficacy diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.84	-0.39 – 2.07	0.181	3.99	-1.14 – 9.12	0.127	3.92	-1.86 – 9.70	0.182
ActiveDays	0.00	-0.07 – 0.08	0.948	-0.01	-0.09 – 0.06	0.729	-0.02	-0.10 – 0.06	0.638
Reports	-0.02	-0.15 – 0.12	0.817	-0.01	-0.14 – 0.12	0.886	0.01	-0.14 – 0.16	0.854
Activities	-0.00	-0.05 – 0.05	0.911	0.01	-0.05 – 0.06	0.774	0.01	-0.04 – 0.07	0.638
univ [Foothill]				-2.03	-4.31 – 0.25	0.081	-1.48	-3.93 – 0.97	0.235
univ [UW]				-0.59	-2.05 – 0.87	0.424	-0.76	-2.34 – 0.83	0.346
Sex [Woman]				-0.31	-1.98 – 1.35	0.708	-0.34	-2.06 – 1.37	0.691
Age				-0.05	-0.18 – 0.08	0.405	-0.06	-0.20 – 0.08	0.387
int student [No]				-2.73	-5.62 – 0.17	0.065	-2.15	-5.25 – 0.95	0.173
SES num				0.43	-0.15 – 1.00	0.144	0.39	-0.22 – 0.99	0.208
Ethnicity White							-0.13	-2.03 – 1.77	0.893
Ethnicity Hispanic							-1.01	-3.19 – 1.17	0.361
Ethnicity Black							-1.76	-4.99 – 1.47	0.282
Ethnicity East Asian							0.19	-1.99 – 2.37	0.866
Ethnicity South Asian							0.60	-1.91 – 3.10	0.639
Ethnicity Native Hawaiian Pacific Islander							-0.05	-4.31 – 4.21	0.980
Ethnicity Middle Eastern							-3.22	-8.08 – 1.64	0.192
Ethnicity American Indian							-1.00	-7.02 – 5.01	0.741
Observations	140			140			140
R2 / R2 adjusted	0.001 / -0.021			0.071 / 0.006			0.100 / -0.025

Closeness to School (IOS)

Intention to Treat

m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_ITT)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_ITT)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_ITT)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)

	ios diff			ios diff			ios diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.05	-0.29 – 0.40	0.759	-1.05	-2.51 – 0.41	0.157	-1.12	-2.73 – 0.49	0.170
ActiveDays	-0.01	-0.02 – 0.01	0.347	-0.00	-0.02 – 0.01	0.453	-0.01	-0.02 – 0.01	0.323
Reports	0.01	-0.03 – 0.04	0.655	0.01	-0.02 – 0.05	0.467	-0.00	-0.04 – 0.03	0.893
Activities	0.01	-0.00 – 0.03	0.081	0.01	-0.00 – 0.03	0.167	0.01	-0.00 – 0.03	0.169
univ [Foothill]				0.02	-0.64 – 0.69	0.941	-0.17	-0.86 – 0.51	0.612
univ [UW]				0.16	-0.27 – 0.59	0.456	0.14	-0.31 – 0.59	0.535
Sex [Woman]				0.33	-0.16 – 0.81	0.192	0.37	-0.11 – 0.86	0.132
Age				0.02	-0.02 – 0.06	0.332	0.01	-0.03 – 0.06	0.478
int student [No]				0.17	-0.57 – 0.91	0.652	-0.02	-0.82 – 0.78	0.964
SES num				0.06	-0.11 – 0.23	0.481	0.09	-0.08 – 0.27	0.300
Ethnicity White							0.05	-0.49 – 0.59	0.863
Ethnicity Hispanic							0.62	-0.01 – 1.26	0.055
Ethnicity Black							1.07	0.11 – 2.03	0.030
Ethnicity East Asian							0.37	-0.26 – 1.01	0.247
Ethnicity South Asian							0.17	-0.55 – 0.89	0.640
Ethnicity Native Hawaiian Pacific Islander							0.30	-1.03 – 1.63	0.657
Ethnicity Middle Eastern							1.10	0.10 – 2.11	0.032
Ethnicity American Indian							1.93	0.41 – 3.44	0.013
Observations	171			170			170
R2 / R2 adjusted	0.020 / 0.002			0.039 / -0.015			0.133 / 0.036

Excluded Preregistered

m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)

	ios diff			ios diff			ios diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.08	-0.28 – 0.43	0.668	-0.94	-2.50 – 0.62	0.236	-1.05	-2.77 – 0.67	0.228
ActiveDays	-0.01	-0.02 – 0.01	0.341	-0.00	-0.02 – 0.01	0.440	-0.01	-0.02 – 0.01	0.318
Reports	0.01	-0.03 – 0.04	0.673	0.01	-0.02 – 0.05	0.482	-0.00	-0.04 – 0.03	0.883
Activities	0.01	-0.00 – 0.03	0.101	0.01	-0.01 – 0.03	0.185	0.01	-0.00 – 0.03	0.173
univ [Foothill]				0.05	-0.64 – 0.74	0.887	-0.18	-0.88 – 0.53	0.620
univ [UW]				0.16	-0.28 – 0.59	0.472	0.13	-0.33 – 0.59	0.568
Sex [Woman]				0.30	-0.20 – 0.81	0.238	0.35	-0.15 – 0.85	0.171
Age				0.02	-0.02 – 0.06	0.378	0.01	-0.03 – 0.06	0.508
int student [No]				0.13	-0.66 – 0.91	0.753	-0.06	-0.89 – 0.78	0.895
SES num				0.06	-0.12 – 0.23	0.509	0.09	-0.09 – 0.27	0.318
Ethnicity White							0.05	-0.50 – 0.61	0.848
Ethnicity Hispanic							0.63	-0.03 – 1.28	0.061
Ethnicity Black							1.07	0.10 – 2.05	0.031
Ethnicity East Asian							0.38	-0.28 – 1.03	0.255
Ethnicity South Asian							0.20	-0.54 – 0.93	0.600
Ethnicity Native Hawaiian Pacific Islander							0.30	-1.05 – 1.66	0.661
Ethnicity Middle Eastern							1.11	0.09 – 2.14	0.033
Ethnicity American Indian							1.93	0.40 – 3.46	0.014
Observations	168			167			167
R2 / R2 adjusted	0.018 / -0.000			0.034 / -0.021			0.128 / 0.029

Excluded Unreasonable Numbers

m0_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities, data = diff_flourish_excluded_unreasonable)
m1_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num, data = diff_flourish_excluded_unreasonable)
m2_ios_diff <- lm(ios_diff ~ ActiveDays + Reports + Activities + univ + Sex + Age + int_student + SES_num + Ethnicity_White + Ethnicity_Hispanic + Ethnicity_Black + Ethnicity_East_Asian + Ethnicity_South_Asian + Ethnicity_Native_Hawaiian_Pacific_Islander + Ethnicity_Middle_Eastern + Ethnicity_American_Indian, data = diff_flourish_excluded_unreasonable)
tab_model(m0_ios_diff, m1_ios_diff, m2_ios_diff)

	ios diff			ios diff			ios diff
Predictors	Estimates	CI	p	Estimates	CI	p	Estimates	CI	p
(Intercept)	0.24	-0.13 – 0.61	0.208	-0.25	-1.83 – 1.33	0.757	-0.37	-2.09 – 1.35	0.672
ActiveDays	-0.04	-0.06 – -0.02	0.001	-0.04	-0.06 – -0.01	0.004	-0.03	-0.05 – -0.01	0.014
Reports	0.04	0.00 – 0.08	0.037	0.04	0.00 – 0.08	0.049	0.03	-0.01 – 0.08	0.134
Activities	0.02	0.01 – 0.04	0.008	0.02	0.00 – 0.04	0.014	0.02	0.00 – 0.04	0.018
univ [Foothill]				0.20	-0.51 – 0.90	0.582	-0.17	-0.90 – 0.56	0.645
univ [UW]				0.15	-0.30 – 0.60	0.504	0.10	-0.37 – 0.57	0.671
Sex [Woman]				0.38	-0.13 – 0.89	0.141	0.33	-0.18 – 0.84	0.202
Age				-0.01	-0.05 – 0.03	0.710	-0.00	-0.04 – 0.04	0.925
int student [No]				-0.16	-1.05 – 0.73	0.724	-0.18	-1.10 – 0.75	0.706
SES num				0.10	-0.08 – 0.27	0.283	0.12	-0.06 – 0.30	0.203
Ethnicity White							-0.22	-0.78 – 0.35	0.448
Ethnicity Hispanic							0.31	-0.33 – 0.96	0.339
Ethnicity Black							0.60	-0.36 – 1.56	0.219
Ethnicity East Asian							0.19	-0.46 – 0.84	0.568
Ethnicity South Asian							0.02	-0.72 – 0.77	0.950
Ethnicity Native Hawaiian Pacific Islander							0.10	-1.17 – 1.37	0.877
Ethnicity Middle Eastern							1.89	0.45 – 3.34	0.011
Ethnicity American Indian							-0.73	-2.52 – 1.06	0.420
Observations	140			140			140
R2 / R2 adjusted	0.094 / 0.074			0.122 / 0.061			0.205 / 0.094

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") +
  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 = 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") +
  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 = 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") +
  theme_minimal() +
  scale_x_continuous(breaks = c(1, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "Week 6"))

Mindfulness

Intention to Treat

ggplot(data_ITT, aes(x = time, y = mindfulness, 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",
       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 = mindfulness, 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",
       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 = mindfulness, 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",
       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"))

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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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, 2, 3, 4), labels = c("Week 0", "Week 2", "Week 4", "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: 5044.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7842 -0.5459 -0.1457  0.4595  3.5544 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.35497  1.1640  
##  univ      (Intercept) 0.02133  0.1460  
##  Residual              0.81505  0.9028  
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      1.51470    0.10575    1.81958
## condflourish_vs_control                         -0.04297    0.05873  471.33418
## treatment_vs_baseline                            0.04251    0.03890 1165.02258
## condflourish_vs_control:treatment_vs_baseline   -0.05032    0.03885 1169.68822
##                                               t value Pr(>|t|)   
## (Intercept)                                    14.324    0.007 **
## condflourish_vs_control                        -0.732    0.465   
## treatment_vs_baseline                           1.093    0.275   
## condflourish_vs_control:treatment_vs_baseline  -1.295    0.195   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.003              
## trtmnt_vs_b  0.056 -0.008       
## cndflr__:__ -0.005  0.105  0.000

# Anxiety
model_anxiety <- lmer(anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)

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

summary(model_anxiety)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 |  
##     univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 5544.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2865 -0.5438 -0.0710  0.4841  4.1262 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.789    1.337   
##  univ      (Intercept) 0.000    0.000   
##  Residual              1.137    1.066   
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                    2.340e+00  6.780e-02  4.783e+02
## condflourish_vs_control                       -1.167e-01  6.780e-02  4.783e+02
## treatment_vs_baseline                         -9.606e-02  4.584e-02  1.177e+03
## condflourish_vs_control:treatment_vs_baseline -4.634e-03  4.584e-02  1.177e+03
##                                               t value Pr(>|t|)    
## (Intercept)                                    34.506   <2e-16 ***
## condflourish_vs_control                        -1.721   0.0859 .  
## treatment_vs_baseline                          -2.096   0.0363 *  
## condflourish_vs_control:treatment_vs_baseline  -0.101   0.9195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.009              
## trtmnt_vs_b  0.107 -0.009       
## cndflr__:__ -0.009  0.107  0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# Loneliness
model_loneliness <- lmer(loneliness ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00607822 (tol = 0.002, component 1)

summary(model_loneliness)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loneliness ~ cond * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 5332.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2396 -0.5381 -0.0525  0.4960  3.2355 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev.
##  unique_ID (Intercept) 1.793e+00 1.338953
##  univ      (Intercept) 3.032e-06 0.001741
##  Residual              9.485e-01 0.973928
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      5.27382    0.06682  102.63973
## condflourish_vs_control                         -0.08741    0.06681  484.86398
## treatment_vs_baseline                           -0.31115    0.04199 1173.16478
## condflourish_vs_control:treatment_vs_baseline   -0.08088    0.04199 1173.29732
##                                               t value Pr(>|t|)    
## (Intercept)                                    78.927  < 2e-16 ***
## condflourish_vs_control                        -1.308   0.1914    
## treatment_vs_baseline                          -7.411  2.4e-13 ***
## condflourish_vs_control:treatment_vs_baseline  -1.926   0.0543 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.009              
## trtmnt_vs_b  0.102 -0.009       
## cndflr__:__ -0.009  0.102  0.000
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00607822 (tol = 0.002, component 1)

# Perceived Stress
model_stress <- lmer(perceived_stress ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)

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

summary(model_stress)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: perceived_stress ~ cond * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 7277.6
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -3.09200 -0.59763 -0.03136  0.54016  3.06803 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  unique_ID (Intercept) 4.821e+00 2.196e+00
##  univ      (Intercept) 8.717e-10 2.952e-05
##  Residual              3.540e+00 1.881e+00
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      6.62665    0.11289  469.97656
## condflourish_vs_control                         -0.07541    0.11289  469.98740
## treatment_vs_baseline                           -0.14549    0.08069 1178.01695
## condflourish_vs_control:treatment_vs_baseline   -0.02625    0.08069 1178.01696
##                                               t value Pr(>|t|)    
## (Intercept)                                    58.701   <2e-16 ***
## condflourish_vs_control                        -0.668   0.5044    
## treatment_vs_baseline                          -1.803   0.0716 .  
## condflourish_vs_control:treatment_vs_baseline  -0.325   0.7450    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.008              
## trtmnt_vs_b  0.110 -0.009       
## cndflr__:__ -0.009  0.110  0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# Affect variables
model_calm <- lmer(SAS_calm ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)

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

summary(model_calm)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_calm ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 |  
##     univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 7000.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.4517 -0.6032  0.0401  0.5837  3.3752 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.56     1.887   
##  univ      (Intercept) 0.00     0.000   
##  Residual              3.09     1.758   
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                   5.731e+00  9.879e-02 4.728e+02
## condflourish_vs_control                       2.167e-01  9.879e-02 4.728e+02
## treatment_vs_baseline                         9.851e-02  7.518e-02 1.190e+03
## condflourish_vs_control:treatment_vs_baseline 1.320e-01  7.518e-02 1.190e+03
##                                               t value Pr(>|t|)    
## (Intercept)                                    58.016   <2e-16 ***
## condflourish_vs_control                         2.193   0.0288 *  
## treatment_vs_baseline                           1.310   0.1903    
## condflourish_vs_control:treatment_vs_baseline   1.756   0.0794 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.007              
## trtmnt_vs_b  0.114 -0.010       
## cndflr__:__ -0.010  0.114  0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

model_well_being <- lmer(SAS_well_being ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_well_being)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_well_being ~ cond * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 6786.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.2100 -0.5604  0.0434  0.5449  3.6885 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.66679  1.9149  
##  univ      (Intercept) 0.08877  0.2979  
##  Residual              2.56327  1.6010  
## Number of obs: 1578, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      6.85023    0.20307    1.88149
## condflourish_vs_control                          0.17425    0.09799  471.09802
## treatment_vs_baseline                           -0.20574    0.06884 1172.04354
## condflourish_vs_control:treatment_vs_baseline    0.06514    0.06874 1176.78345
##                                               t value Pr(>|t|)   
## (Intercept)                                    33.734  0.00124 **
## condflourish_vs_control                         1.778  0.07599 . 
## treatment_vs_baseline                          -2.989  0.00286 **
## condflourish_vs_control:treatment_vs_baseline   0.948  0.34352   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.002              
## trtmnt_vs_b  0.051 -0.008       
## cndflr__:__ -0.004  0.109  0.000

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: 7000.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1242 -0.5531  0.0049  0.5629  4.2960 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 4.58253  2.141   
##  univ      (Intercept) 0.07234  0.269   
##  Residual              2.85468  1.690   
## Number of obs: 1578, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      5.70803    0.19486    2.31559
## condflourish_vs_control                          0.14773    0.10835  472.90286
## treatment_vs_baseline                           -0.20186    0.07277 1166.94493
## condflourish_vs_control:treatment_vs_baseline    0.11466    0.07267 1171.36286
##                                               t value Pr(>|t|)    
## (Intercept)                                    29.293  0.00050 ***
## condflourish_vs_control                         1.363  0.17338    
## treatment_vs_baseline                          -2.774  0.00563 ** 
## condflourish_vs_control:treatment_vs_baseline   1.578  0.11491    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.003              
## trtmnt_vs_b  0.057 -0.008       
## cndflr__:__ -0.005  0.106  0.000

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: 7305.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9133 -0.5559 -0.0962  0.4772  3.5903 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 5.229    2.287   
##  univ      (Intercept) 0.000    0.000   
##  Residual              3.542    1.882   
## Number of obs: 1578, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      4.04924    0.11664  466.45091
## condflourish_vs_control                         -0.14410    0.11664  466.45091
## treatment_vs_baseline                           -0.12495    0.08083 1169.10057
## condflourish_vs_control:treatment_vs_baseline    0.04722    0.08083 1169.10057
##                                               t value Pr(>|t|)    
## (Intercept)                                    34.717   <2e-16 ***
## condflourish_vs_control                        -1.235    0.217    
## treatment_vs_baseline                          -1.546    0.122    
## condflourish_vs_control:treatment_vs_baseline   0.584    0.559    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.009              
## trtmnt_vs_b  0.108 -0.009       
## cndflr__:__ -0.009  0.108  0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

model_SAS_anxiety <- lmer(SAS_anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)

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

summary(model_SAS_anxiety)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_anxiety ~ cond * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 7322.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7329 -0.6180  0.0034  0.5950  3.1698 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  unique_ID (Intercept) 4.475e+00 2.115e+00
##  univ      (Intercept) 2.722e-09 5.217e-05
##  Residual              3.763e+00 1.940e+00
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                      6.00160    0.11036  471.53308
## condflourish_vs_control                         -0.16615    0.11036  471.56565
## treatment_vs_baseline                           -0.33351    0.08301 1187.16585
## condflourish_vs_control:treatment_vs_baseline   -0.02800    0.08301 1187.16586
##                                               t value Pr(>|t|)    
## (Intercept)                                    54.384  < 2e-16 ***
## condflourish_vs_control                        -1.506    0.133    
## treatment_vs_baseline                          -4.018 6.25e-05 ***
## condflourish_vs_control:treatment_vs_baseline  -0.337    0.736    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.007              
## trtmnt_vs_b  0.113 -0.010       
## cndflr__:__ -0.010  0.113  0.002
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

model_SAS_anger <- lmer(SAS_anger ~ cond * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long)
summary(model_SAS_anger)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: SAS_anger ~ cond * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long
## 
## REML criterion at convergence: 6858.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.8905 -0.5224 -0.1795  0.4539  4.5481 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.5798   1.8920  
##  univ      (Intercept) 0.1994   0.4466  
##  Residual              2.7307   1.6525  
## Number of obs: 1579, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                   2.721e+00  2.803e-01 1.713e+00
## condflourish_vs_control                       7.234e-02  9.768e-02 4.739e+02
## treatment_vs_baseline                         3.253e-02  7.096e-02 1.180e+03
## condflourish_vs_control:treatment_vs_baseline 1.026e-02  7.084e-02 1.185e+03
##                                               t value Pr(>|t|)  
## (Intercept)                                     9.707   0.0169 *
## condflourish_vs_control                         0.741   0.4593  
## treatment_vs_baseline                           0.458   0.6467  
## condflourish_vs_control:treatment_vs_baseline   0.145   0.8849  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.001              
## trtmnt_vs_b  0.037 -0.008       
## cndflr__:__ -0.003  0.111  0.000

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: 9836.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7688 -0.5524 -0.0048  0.5479  4.3459 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 29.4895  5.4304  
##  univ      (Intercept)  0.2899  0.5384  
##  Residual              17.0428  4.1283  
## Number of obs: 1577, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                Estimate Std. Error        df
## (Intercept)                                     18.3208     0.4264    2.2595
## condflourish_vs_control                          0.5208     0.2730  474.0945
## treatment_vs_baseline                           -0.3136     0.1780 1162.8401
## condflourish_vs_control:treatment_vs_baseline    0.2953     0.1778 1167.2796
##                                               t value Pr(>|t|)    
## (Intercept)                                    42.970 0.000244 ***
## condflourish_vs_control                         1.907 0.057082 .  
## treatment_vs_baseline                          -1.762 0.078367 .  
## condflourish_vs_control:treatment_vs_baseline   1.661 0.096990 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.004              
## trtmnt_vs_b  0.065 -0.008       
## cndflr__:__ -0.005  0.104  0.000

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: 9884.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9870 -0.5606 -0.0423  0.5036  4.4829 
## 
## Random effects:
##  Groups    Name        Variance  Std.Dev. 
##  unique_ID (Intercept) 2.943e+01 5.425e+00
##  univ      (Intercept) 4.503e-09 6.711e-05
##  Residual              1.770e+01 4.207e+00
## Number of obs: 1578, groups:  unique_ID, 486; univ, 3
## 
## Fixed effects:
##                                                 Estimate Std. Error         df
## (Intercept)                                     12.65757    0.27366  470.61364
## condflourish_vs_control                         -0.24101    0.27366  470.62413
## treatment_vs_baseline                           -0.42151    0.18104 1166.25449
## condflourish_vs_control:treatment_vs_baseline    0.02861    0.18104 1166.25449
##                                               t value Pr(>|t|)    
## (Intercept)                                    46.253   <2e-16 ***
## condflourish_vs_control                        -0.881   0.3789    
## treatment_vs_baseline                          -2.328   0.0201 *  
## condflourish_vs_control:treatment_vs_baseline   0.158   0.8745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cndf__ trtm__
## cndflrsh_v_  0.009              
## trtmnt_vs_b  0.105 -0.009       
## cndflr__:__ -0.009  0.105  0.001
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

## VARIABLES COLLECTED JUST PRE POST (time 1 vs. 4)

merged_data_long_factor <- merged_data_long |> 
  dplyr::filter(time == 1 | time == 4) |> 
  dplyr::mutate(time_factor = as.factor(time)) |> 
  dplyr::mutate(cond_factor = as.factor(cond))

contrasts(merged_data_long_factor$time_factor) <- c(-1,1)

# flourishing score
model_flourishing <- lmer(flourishing ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_flourishing)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: flourishing ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 5253.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.7571 -0.3986  0.0676  0.4461  3.0516 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 29.894   5.468   
##  univ      (Intercept)  1.302   1.141   
##  Residual              12.772   3.574   
## Number of obs: 832, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                      Estimate Std. Error
## (Intercept)                                           44.4189     0.7362
## cond_factorflourish_vs_control                         0.2721     0.2969
## treatment_vs_baseline                                 -0.0728     0.2001
## cond_factorflourish_vs_control:treatment_vs_baseline   0.4284     0.1996
##                                                            df t value Pr(>|t|)
## (Intercept)                                            2.2118  60.333 0.000133
## cond_factorflourish_vs_control                       574.1968   0.916 0.359953
## treatment_vs_baseline                                365.5110  -0.364 0.716140
## cond_factorflourish_vs_control:treatment_vs_baseline 368.1616   2.146 0.032499
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                   
## cond_factorflourish_vs_control:treatment_vs_baseline *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.002              
## trtmnt_vs_b  0.134 -0.013       
## cnd_fc__:__ -0.007  0.336 -0.012

# social fit
model_social_fit <- lmer(social_fit ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_social_fit)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: social_fit ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 2592.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.1956 -0.5363 -0.0131  0.5504  2.5484 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.58557  0.7652  
##  univ      (Intercept) 0.04893  0.2212  
##  Residual              0.81245  0.9014  
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                        Estimate Std. Error
## (Intercept)                                            6.951324   0.140310
## cond_factorflourish_vs_control                         0.051288   0.052755
## treatment_vs_baseline                                  0.078864   0.049424
## cond_factorflourish_vs_control:treatment_vs_baseline  -0.003116   0.049224
##                                                              df t value
## (Intercept)                                            1.829085  49.543
## cond_factorflourish_vs_control                       625.962730   0.972
## treatment_vs_baseline                                394.263998   1.596
## cond_factorflourish_vs_control:treatment_vs_baseline 398.131132  -0.063
##                                                      Pr(>|t|)    
## (Intercept)                                          0.000713 ***
## cond_factorflourish_vs_control                       0.331326    
## treatment_vs_baseline                                0.111362    
## cond_factorflourish_vs_control:treatment_vs_baseline 0.949562    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.006              
## trtmnt_vs_b  0.166 -0.018       
## cnd_fc__:__ -0.009  0.446 -0.012

# cohesion (MARG)
model_cohesion <- lmer(cohesion ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_cohesion)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cohesion ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 3353.7
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.37421 -0.42300  0.04007  0.50388  2.45014 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 3.45707  1.859   
##  univ      (Intercept) 0.06864  0.262   
##  Residual              1.13318  1.065   
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            5.74828    0.18469
## cond_factorflourish_vs_control                         0.15055    0.09748
## treatment_vs_baseline                                  0.19043    0.05971
## cond_factorflourish_vs_control:treatment_vs_baseline   0.13002    0.05961
##                                                             df t value Pr(>|t|)
## (Intercept)                                            2.22992  31.124 0.000548
## cond_factorflourish_vs_control                       566.17055   1.544 0.123032
## treatment_vs_baseline                                368.72018   3.189 0.001549
## cond_factorflourish_vs_control:treatment_vs_baseline 370.88987   2.181 0.029800
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                ** 
## cond_factorflourish_vs_control:treatment_vs_baseline *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.001              
## trtmnt_vs_b  0.160 -0.014       
## cnd_fc__:__ -0.009  0.308 -0.015

# mindfulness
model_mindfulness <- lmer(mindfulness ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)

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

summary(model_mindfulness)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: mindfulness ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 5200.4
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.41784 -0.49969  0.02324  0.45562  2.59292 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 21.51    4.638   
##  univ      (Intercept)  0.00    0.000   
##  Residual              14.63    3.825   
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                      Estimate Std. Error
## (Intercept)                                           20.4551     0.2718
## cond_factorflourish_vs_control                        -0.3428     0.2718
## treatment_vs_baseline                                  0.7779     0.2117
## cond_factorflourish_vs_control:treatment_vs_baseline  -0.4264     0.2117
##                                                            df t value Pr(>|t|)
## (Intercept)                                          605.3701  75.254  < 2e-16
## cond_factorflourish_vs_control                       605.3701  -1.261 0.207800
## treatment_vs_baseline                                386.6873   3.675 0.000271
## cond_factorflourish_vs_control:treatment_vs_baseline 386.6873  -2.015 0.044633
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                ***
## cond_factorflourish_vs_control:treatment_vs_baseline *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.003              
## trtmnt_vs_b  0.383 -0.017       
## cnd_fc__:__ -0.017  0.383 -0.013
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# emotional resilience
model_emo_res <- lmer(emo_res ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)

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

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: 3451.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6336 -0.5173 -0.0796  0.4395  5.1173 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.110    1.053   
##  univ      (Intercept) 0.000    0.000   
##  Residual              2.697    1.642   
## Number of obs: 832, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                          1.817e+01  8.579e-02
## cond_factorflourish_vs_control                       4.408e-02  8.579e-02
## treatment_vs_baseline                                2.974e-03  8.884e-02
## cond_factorflourish_vs_control:treatment_vs_baseline 7.462e-02  8.884e-02
##                                                             df t value Pr(>|t|)
## (Intercept)                                          6.266e+02 211.819   <2e-16
## cond_factorflourish_vs_control                       6.266e+02   0.514    0.608
## treatment_vs_baseline                                3.885e+02   0.033    0.973
## cond_factorflourish_vs_control:treatment_vs_baseline 3.885e+02   0.840    0.401
##                                                         
## (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__ -0.012              
## trtmnt_vs_b  0.484 -0.019       
## cnd_fc__:__ -0.019  0.484 -0.008
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# school satisfaction
model_school_satis <- lmer(school_satis ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_school_satis)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## school_satis ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 1971.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.6788 -0.4027  0.1034  0.3724  1.9676 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.52213  0.7226  
##  univ      (Intercept) 0.02205  0.1485  
##  Residual              0.25920  0.5091  
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            4.55980    0.09650
## cond_factorflourish_vs_control                         0.03757    0.04011
## treatment_vs_baseline                                  0.06718    0.02840
## cond_factorflourish_vs_control:treatment_vs_baseline   0.01438    0.02833
##                                                             df t value Pr(>|t|)
## (Intercept)                                            2.30284  47.253 0.000172
## cond_factorflourish_vs_control                       586.53958   0.936 0.349407
## treatment_vs_baseline                                375.33412   2.365 0.018524
## cond_factorflourish_vs_control:treatment_vs_baseline 378.28188   0.508 0.611982
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                *  
## cond_factorflourish_vs_control:treatment_vs_baseline    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.003              
## trtmnt_vs_b  0.144 -0.015       
## cnd_fc__:__ -0.008  0.351 -0.014

# school prioritizes wellbeing
model_wellbeing_priority <- lmer(wellbeing_priority ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_wellbeing_priority)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: wellbeing_priority ~ cond_factor * treatment_vs_baseline + (1 |  
##     unique_ID) + (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 2639.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0848 -0.4665 -0.1319  0.6356  2.4055 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 0.7458   0.8636  
##  univ      (Intercept) 0.1151   0.3393  
##  Residual              0.7807   0.8836  
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            4.59648    0.20545
## cond_factorflourish_vs_control                         0.04781    0.05547
## treatment_vs_baseline                                  0.07153    0.04870
## cond_factorflourish_vs_control:treatment_vs_baseline   0.04028    0.04851
##                                                             df t value Pr(>|t|)
## (Intercept)                                            1.90111  22.373  0.00254
## cond_factorflourish_vs_control                       621.62599   0.862  0.38909
## treatment_vs_baseline                                393.84638   1.469  0.14270
## cond_factorflourish_vs_control:treatment_vs_baseline 397.72790   0.830  0.40693
##                                                        
## (Intercept)                                          **
## cond_factorflourish_vs_control                         
## treatment_vs_baseline                                  
## cond_factorflourish_vs_control:treatment_vs_baseline   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004              
## trtmnt_vs_b  0.113 -0.017       
## cnd_fc__:__ -0.006  0.423 -0.013

# academic self-efficacy
model_acad_selfefficacy <- lmer(acad_selfefficacy ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)

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

summary(model_acad_selfefficacy)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: acad_selfefficacy ~ cond_factor * treatment_vs_baseline + (1 |  
##     unique_ID) + (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 4562
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.50620 -0.43145  0.07989  0.49916  2.08830 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 9.521    3.086   
##  univ      (Intercept) 0.000    0.000   
##  Residual              7.132    2.671   
## Number of obs: 831, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                           24.11326    0.18426
## cond_factorflourish_vs_control                         0.03749    0.18426
## treatment_vs_baseline                                  0.34149    0.14780
## cond_factorflourish_vs_control:treatment_vs_baseline   0.11531    0.14780
##                                                             df t value Pr(>|t|)
## (Intercept)                                          608.55225 130.864   <2e-16
## cond_factorflourish_vs_control                       608.55225   0.203   0.8388
## treatment_vs_baseline                                387.45747   2.310   0.0214
## cond_factorflourish_vs_control:treatment_vs_baseline 387.45747   0.780   0.4358
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                *  
## cond_factorflourish_vs_control:treatment_vs_baseline    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004              
## trtmnt_vs_b  0.391 -0.018       
## cnd_fc__:__ -0.018  0.391 -0.009
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')

# ios
model_ios <- lmer(ios ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) + (1 | univ), data = merged_data_long_factor)
summary(model_ios)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ios ~ cond_factor * treatment_vs_baseline + (1 | unique_ID) +  
##     (1 | univ)
##    Data: merged_data_long_factor
## 
## REML criterion at convergence: 2792.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.0541 -0.5342 -0.0813  0.4512  3.3759 
## 
## Random effects:
##  Groups    Name        Variance Std.Dev.
##  unique_ID (Intercept) 1.184312 1.08826 
##  univ      (Intercept) 0.008385 0.09157 
##  Residual              0.797026 0.89276 
## Number of obs: 833, groups:  unique_ID, 485; univ, 3
## 
## Fixed effects:
##                                                       Estimate Std. Error
## (Intercept)                                            3.32986    0.08487
## cond_factorflourish_vs_control                         0.09806    0.06366
## treatment_vs_baseline                                  0.10526    0.04950
## cond_factorflourish_vs_control:treatment_vs_baseline   0.08990    0.04942
##                                                             df t value Pr(>|t|)
## (Intercept)                                            2.17979  39.235 0.000379
## cond_factorflourish_vs_control                       606.82385   1.540 0.123996
## treatment_vs_baseline                                388.33945   2.126 0.034100
## cond_factorflourish_vs_control:treatment_vs_baseline 391.37465   1.819 0.069649
##                                                         
## (Intercept)                                          ***
## cond_factorflourish_vs_control                          
## treatment_vs_baseline                                *  
## cond_factorflourish_vs_control:treatment_vs_baseline .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) cnd___ trtm__
## cnd_fctrf__ -0.004              
## trtmnt_vs_b  0.284 -0.016       
## cnd_fc__:__ -0.014  0.382 -0.013