R Notebook

Setup

Pre Processing:

Preliminary analyses:

Comparisons at baseline

Demographics

# Demographic baseline comparisons
chisq.test(queervibe_data$Sexuality, queervibe_data$Group)

Warning in chisq.test(queervibe_data$Sexuality, queervibe_data$Group) :
  Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  queervibe_data$Sexuality and queervibe_data$Group
X-squared = 4.2526, df = 3, p-value = 0.2354

chisq.test(queervibe_data$Gender, queervibe_data$Group)

Warning in chisq.test(queervibe_data$Gender, queervibe_data$Group) :
  Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  queervibe_data$Gender and queervibe_data$Group
X-squared = 2.2411, df = 2, p-value = 0.3261

chisq.test(queervibe_data$Employment, queervibe_data$Group)

Warning in chisq.test(queervibe_data$Employment, queervibe_data$Group) :
  Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  queervibe_data$Employment and queervibe_data$Group
X-squared = 1.4092, df = 2, p-value = 0.4943

chisq.test(queervibe_data$Disability, queervibe_data$Group)

    Pearson's Chi-squared test with Yates' continuity correction

data:  queervibe_data$Disability and queervibe_data$Group
X-squared = 1.148e-30, df = 1, p-value = 1

table(queervibe_data$Sexuality, queervibe_data$Group)

          
           Intervention Control
  Straight            1       2
  Gay                 3       8
  Bisexual            4       2
  Other              15      10

table(queervibe_data$Gender, queervibe_data$Group)

            
             Intervention Control
  Trans Male           13      12
  Non-Binary            8      10
  Other                 2       0

table(queervibe_data$Employment, queervibe_data$Group)

            
             Intervention Control
  Employed              0       1
  Student              21      18
  Unemployed            2       3

table(queervibe_data$Disability, queervibe_data$Group)

     
      Intervention Control
  Yes            6       5
  No            17      17

Descriptive statistics

# Descriptive statistics by Group and Time

## For the variables in the queervibe_long
queervibe_long %>% group_by(Group, Time) %>% 
                   summarise_at(vars(AICS, CORE, CSE, FS, TPIM, RSES, SAMA), list(mean = mean, sd = sd, min = min, max = max)) %>% 
                   as.tibble()

queervibe_data %>% summarise_at(vars(Age), list(mean = mean, sd = sd, min = min, max = max)) %>% 
                   as.tibble()

# For the variables in the queervibe_SSES
queervibe_SSES %>% group_by(Group, Time) %>% 
                   summarise_at(vars(Score), list(mean = mean, sd = sd, min = min, max = max)) %>% 
                   as.tibble()

Differences in each outcome measure at baseline

baselinefiltered <- queervibe_long %>% filter(Time == "Baseline")

t.test(AICS ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  AICS by Group
t = -1.2737, df = 43, p-value = 0.2096
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -11.574081   2.613607
sample estimates:
mean in group Intervention      mean in group Control 
                  31.56522                   36.04545 

t.test(CORE ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  CORE by Group
t = 0.41837, df = 43, p-value = 0.6778
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -12.09510  18.42711
sample estimates:
mean in group Intervention      mean in group Control 
                  60.34783                   57.18182 

t.test(CSE ~ Group, data = baselinefiltered, var.equal = TRUE) # Yes

    Two Sample t-test

data:  CSE by Group
t = -2.2388, df = 43, p-value = 0.0304
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -9.8570653 -0.5144762
sample estimates:
mean in group Intervention      mean in group Control 
                  51.08696                   56.27273 

t.test(FS ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  FS by Group
t = -0.50995, df = 43, p-value = 0.6127
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -6.403871  3.818890
sample estimates:
mean in group Intervention      mean in group Control 
                  35.43478                   36.72727 

t.test(TPIM ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  TPIM by Group
t = 0.32231, df = 43, p-value = 0.7488
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -8.945261 12.348423
sample estimates:
mean in group Intervention      mean in group Control 
                  121.5652                   119.8636 

t.test(RSES ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  RSES by Group
t = -0.7381, df = 43, p-value = 0.4645
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -4.270736  1.982198
sample estimates:
mean in group Intervention      mean in group Control 
                  21.17391                   22.31818 

t.test(SAMA ~ Group, data = baselinefiltered, var.equal = TRUE) # No

    Two Sample t-test

data:  SAMA by Group
t = 0.085642, df = 43, p-value = 0.9321
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -4.545230  4.948392
sample estimates:
mean in group Intervention      mean in group Control 
                  31.56522                   31.36364 

t.test(Age ~ Group, data = queervibe_data, var.equal = TRUE)

    Two Sample t-test

data:  Age by Group
t = 0.72142, df = 43, p-value = 0.4746
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -0.6457884  1.3651560
sample estimates:
mean in group Intervention      mean in group Control 
                  18.08696                   17.72727 

cohen.d(baselinefiltered$AICS, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$AICS, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,]  -0.2   0.39  0.98

Multivariate (Mahalanobis) distance between groups
[1] 0.39
r equivalent of difference between two means
data 
0.19 

cohen.d(baselinefiltered$CORE, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$CORE, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.71  -0.13  0.46

Multivariate (Mahalanobis) distance between groups
[1] 0.13
r equivalent of difference between two means
 data 
-0.06 

cohen.d(baselinefiltered$CSE, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$CSE, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,]  0.08   0.68  1.28

Multivariate (Mahalanobis) distance between groups
[1] 0.68
r equivalent of difference between two means
data 
0.32 

cohen.d(baselinefiltered$FS, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$FS, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.43   0.16  0.74

Multivariate (Mahalanobis) distance between groups
[1] 0.16
r equivalent of difference between two means
data 
0.08 

cohen.d(baselinefiltered$TPIM, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$TPIM, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.68   -0.1  0.49

Multivariate (Mahalanobis) distance between groups
[1] 0.098
r equivalent of difference between two means
 data 
-0.05 

cohen.d(baselinefiltered$RSES, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$RSES, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.36   0.23  0.81

Multivariate (Mahalanobis) distance between groups
[1] 0.23
r equivalent of difference between two means
data 
0.11 

cohen.d(baselinefiltered$SAMA, baselinefiltered$Group)

Call: cohen.d(x = baselinefiltered$SAMA, group = baselinefiltered$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.61  -0.03  0.56

Multivariate (Mahalanobis) distance between groups
[1] 0.026
r equivalent of difference between two means
 data 
-0.01 

cohen.d(queervibe_data$Age, queervibe_data$Group)

Call: cohen.d(x = queervibe_data$Age, group = queervibe_data$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.81  -0.22  0.37

Multivariate (Mahalanobis) distance between groups
[1] 0.22
r equivalent of difference between two means
 data 
-0.11 

# For SSES
baselinefilteredSSES <- queervibe_SSES %>% filter(Time == "Baseline")

t.test(Score ~ Group, data = baselinefilteredSSES, var.equal = TRUE) # No

    Two Sample t-test

data:  Score by Group
t = 0.027055, df = 43, p-value = 0.9785
alternative hypothesis: true difference in means between group Intervention and group Control is not equal to 0
95 percent confidence interval:
 -8.720233  8.957387
sample estimates:
mean in group Intervention      mean in group Control 
                  53.39130                   53.27273 

cohen.d(baselinefilteredSSES$Score, baselinefilteredSSES$Group)

Call: cohen.d(x = baselinefilteredSSES$Score, group = baselinefilteredSSES$Group)
Cohen d statistic of difference between two means
     lower effect upper
[1,] -0.59  -0.01  0.58

Multivariate (Mahalanobis) distance between groups
[1] 0.0083
r equivalent of difference between two means
data 
   0 

Correlations across time points

Linear mixed effect models

The below analyis relies on the lme4 package (lmer) function. Summaries for the main and interaction effects are gathered using the Anova function (car). Due to the small sample size, REML and Kenward-Roger estimates were used.

SAMA

The results of this suggest that there is a significant Time:Group interaction.Examining the estimated marginal means shows that this difference occured between baseline and post test for the intervention group only. No differences were shown for the intervention group when comparing the baseline - follow up, or post test - follow up. No differences were shown for the control group.

lme_sama <- lme4::lmer(SAMA ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA, pairwise comparisons and effect size
Anova(lme_sama, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: SAMA
                F Df Df.res   Pr(>F)   
Time       0.5365  2     86 0.586735   
Group      1.3903  1     43 0.244844   
Time:Group 6.4748  2     86 0.002402 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emm_sama <- emmeans(lme_sama, pairwise ~ Time | Group, adjust = "holm")
emm_sama

$emmeans
Group = Intervention:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            31.6 1.75 56.6     28.1     35.1
 Post Intervention   34.9 1.75 56.6     31.4     38.4
 Follow Up           33.3 1.75 56.6     29.8     36.9

Group = Control:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            31.4 1.79 56.6     27.8     34.9
 Post Intervention   29.0 1.79 56.6     25.5     32.6
 Follow Up           31.1 1.79 56.6     27.6     34.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention    -3.304 1.11 86  -2.985  0.0111
 Baseline - Follow Up            -1.783 1.11 86  -1.610  0.2221
 Post Intervention - Follow Up    1.522 1.11 86   1.374  0.2221

Group = Control:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention     2.318 1.13 86   2.048  0.1309
 Baseline - Follow Up             0.227 1.13 86   0.201  0.8414
 Post Intervention - Follow Up   -2.091 1.13 86  -1.847  0.1364

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 3 tests 

eff_size(emm_sama, sigma = sigma(lme_sama), edf = df.residual(lme_sama))

Since 'object' is a list, we are using the contrasts already present.

Group = Intervention:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)      -0.8801 0.300 86  -1.4765  -0.2837
 (Baseline - Follow Up)              -0.4748 0.296 86  -1.0640   0.1144
 (Post Intervention - Follow Up)      0.4053 0.296 86  -0.1831   0.9937

Group = Control:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)       0.6174 0.304 86   0.0131   1.2217
 (Baseline - Follow Up)               0.0605 0.302 86  -0.5389   0.6600
 (Post Intervention - Follow Up)     -0.5569 0.304 86  -1.1603   0.0465

sigma used for effect sizes: 3.755 
Degrees-of-freedom method: inherited from kenward-roger when re-gridding 
Confidence level used: 0.95 

# Output as regression
summary(lme_sama)

Linear mixed model fit by REML ['lmerMod']
Formula: SAMA ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 836.3

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-1.9781 -0.5900  0.0329  0.5141  2.4128 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 56.28    7.502   
 Residual             14.10    3.755   
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                         31.5652     1.7492  18.046
TimePost Intervention                3.3043     1.1072   2.985
TimeFollow Up                        1.7826     1.1072   1.610
GroupControl                        -0.2016     2.5017  -0.081
TimePost Intervention:GroupControl  -5.6225     1.5835  -3.551
TimeFollow Up:GroupControl          -2.0099     1.5835  -1.269

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.316                            
TimeFollwUp -0.316  0.500                     
GroupContrl -0.699  0.221  0.221              
TmPIntrv:GC  0.221 -0.699 -0.350 -0.316       
TmFlUp:GrpC  0.221 -0.350 -0.699 -0.316  0.500

tab_model(lme_sama)

# Calculate descriptives
#queervibe_long %>% group_by(Group, Time) %>% summarise_at(vars(SAMA), list(Mean = mean, sd = SD))

AICS

The results of this suggest no significant main, or interaction effects.

lme_aics <- lme4::lmer(AICS ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA, pairwise comparisons and effect size
Anova(lme_aics, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: AICS
                F Df Df.res Pr(>F)
Time       0.1120  2     86 0.8942
Group      0.6574  1     43 0.4219
Time:Group 0.8428  2     86 0.4340

# emmeans(lme_aics, pairwise ~ Time | Group, adjust = "holm") unncessary

# Output as regression
summary(lme_aics)

Linear mixed model fit by REML ['lmerMod']
Formula: AICS ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 943.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-4.8917 -0.3980  0.0144  0.4560  3.0879 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 115.9    10.765  
 Residual              33.8     5.814  
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                          31.565      2.551  12.373
TimePost Intervention                 1.609      1.714   0.938
TimeFollow Up                         1.652      1.714   0.964
GroupControl                          4.480      3.649   1.228
TimePost Intervention:GroupControl   -2.154      2.452  -0.879
TimeFollow Up:GroupControl           -3.107      2.452  -1.267

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.336                            
TimeFollwUp -0.336  0.500                     
GroupContrl -0.699  0.235  0.235              
TmPIntrv:GC  0.235 -0.699 -0.350 -0.336       
TmFlUp:GrpC  0.235 -0.350 -0.699 -0.336  0.500

tab_model(lme_aics)

CORE

The results of this suggest a significant main effect of time and a significant time:group interaction.

lme_core <- lme4::lmer(CORE ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA, pairwise comparisons and effect size
Anova(lme_core, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: CORE
                F Df Df.res   Pr(>F)   
Time       3.2188  2     86 0.044870 * 
Group      1.2424  1     43 0.271206   
Time:Group 6.5818  2     86 0.002189 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emm_core <- emmeans(lme_core, pairwise ~ Time | Group, adjust = "holm")
emm_core

$emmeans
Group = Intervention:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            60.3 4.75 60.9     50.8     69.8
 Post Intervention   46.5 4.75 60.9     37.0     56.0
 Follow Up           50.0 4.75 60.9     40.5     59.5

Group = Control:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            57.2 4.86 60.9     47.5     66.9
 Post Intervention   59.1 4.86 60.9     49.4     68.8
 Follow Up           61.3 4.86 60.9     51.6     71.0

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention     13.83 3.37 86   4.100  0.0003
 Baseline - Follow Up             10.35 3.37 86   3.068  0.0058
 Post Intervention - Follow Up    -3.48 3.37 86  -1.031  0.3052

Group = Control:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention     -1.95 3.45 86  -0.567  1.0000
 Baseline - Follow Up             -4.09 3.45 86  -1.186  0.7162
 Post Intervention - Follow Up    -2.14 3.45 86  -0.620  1.0000

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 3 tests 

eff_size(emm_core, sigma = sigma(lme_core), edf = df.residual(lme_core))

Since 'object' is a list, we are using the contrasts already present.

Group = Intervention:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)        1.209 0.304 86    0.604    1.814
 (Baseline - Follow Up)                0.905 0.300 86    0.308    1.502
 (Post Intervention - Follow Up)      -0.304 0.296 86   -0.892    0.283

Group = Control:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)       -0.171 0.302 86   -0.771    0.429
 (Baseline - Follow Up)               -0.358 0.302 86   -0.959    0.243
 (Post Intervention - Follow Up)      -0.187 0.302 86   -0.787    0.413

sigma used for effect sizes: 11.44 
Degrees-of-freedom method: inherited from kenward-roger when re-gridding 
Confidence level used: 0.95 

# Output as regression
summary(lme_core)

Linear mixed model fit by REML ['lmerMod']
Formula: CORE ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 1112.1

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.3816 -0.5154 -0.1033  0.5232  2.2256 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 388.2    19.70   
 Residual             130.8    11.44   
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                          60.348      4.750  12.704
TimePost Intervention               -13.826      3.372  -4.100
TimeFollow Up                       -10.348      3.372  -3.068
GroupControl                         -3.166      6.794  -0.466
TimePost Intervention:GroupControl   15.781      4.823   3.272
TimeFollow Up:GroupControl           14.439      4.823   2.994

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.355                            
TimeFollwUp -0.355  0.500                     
GroupContrl -0.699  0.248  0.248              
TmPIntrv:GC  0.248 -0.699 -0.350 -0.355       
TmFlUp:GrpC  0.248 -0.350 -0.699 -0.355  0.500

tab_model(lme_core)

# Calculate descriptives
#queervibe_long %>% group_by(Group, Time) %>% summarise_at(vars(CORE), list(Mean = mean, sd = SD))

FS

The results of this suggest a significant interaction time:group interaction. However, examining the adjusted emmeans does not suggest any significant differences. Note: this is common and both the intervention and control show similar increases between baseline and post test.

lme_fs <- lme4::lmer(FS ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA, pairwise comparisons and effect size
Anova(lme_fs, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: FS
                F Df Df.res  Pr(>F)  
Time       0.2744  2     86 0.76066  
Group      0.4830  1     43 0.49082  
Time:Group 4.7585  2     86 0.01096 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

fs_emm <- emmeans(lme_fs, pairwise ~ Time | Group, adjust = "holm")
fs_emm

$emmeans
Group = Intervention:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            35.4 1.88 62.5     31.7     39.2
 Post Intervention   38.4 1.88 62.5     34.7     42.2
 Follow Up           37.5 1.88 62.5     33.7     41.2

Group = Control:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            36.7 1.92 62.5     32.9     40.6
 Post Intervention   33.6 1.92 62.5     29.8     37.5
 Follow Up           35.9 1.92 62.5     32.1     39.8

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention    -3.000 1.38 86  -2.172  0.0979
 Baseline - Follow Up            -2.043 1.38 86  -1.479  0.2854
 Post Intervention - Follow Up    0.957 1.38 86   0.692  0.4905

Group = Control:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention     3.091 1.41 86   2.188  0.0940
 Baseline - Follow Up             0.818 1.41 86   0.579  0.5639
 Post Intervention - Follow Up   -2.273 1.41 86  -1.609  0.2225

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 3 tests 

eff_size(fs_emm, sigma = sigma(lme_fs), edf = df.residual(lme_fs))

Since 'object' is a list, we are using the contrasts already present.

Group = Intervention:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)       -0.640 0.298 86  -1.2321  -0.0488
 (Baseline - Follow Up)               -0.436 0.296 86  -1.0250   0.1525
 (Post Intervention - Follow Up)       0.204 0.295 86  -0.3826   0.7910

Group = Control:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)        0.660 0.304 86   0.0548   1.2648
 (Baseline - Follow Up)                0.175 0.302 86  -0.4251   0.7744
 (Post Intervention - Follow Up)      -0.485 0.303 86  -1.0876   0.1173

sigma used for effect sizes: 4.684 
Degrees-of-freedom method: inherited from kenward-roger when re-gridding 
Confidence level used: 0.95 

# Output as regression
summary(lme_fs)

Linear mixed model fit by REML ['lmerMod']
Formula: FS ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 878.2

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.51715 -0.48242  0.02973  0.63949  2.25402 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 59.31    7.702   
 Residual             21.94    4.684   
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                          35.435      1.880  18.852
TimePost Intervention                 3.000      1.381   2.172
TimeFollow Up                         2.043      1.381   1.479
GroupControl                          1.292      2.688   0.481
TimePost Intervention:GroupControl   -6.091      1.976  -3.083
TimeFollow Up:GroupControl           -2.862      1.976  -1.449

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.367                            
TimeFollwUp -0.367  0.500                     
GroupContrl -0.699  0.257  0.257              
TmPIntrv:GC  0.257 -0.699 -0.350 -0.367       
TmFlUp:GrpC  0.257 -0.350 -0.699 -0.367  0.500

tab_model(lme_fs)

# Calculate descriptives
#queervibe_long %>% group_by(Group, Time) %>% summarise_at(vars(FS), list(Mean = mean, sd = SD))

RSES

The results of this suggest that there is a significant main effect of time, and an interaction between time and group. These differences are shown for the intervention group, with significant increases in self-esteem between baseline and post test and baseline and follow up. No difference between post test and follow up. The control group showed no differences.

lme_rses <- lme4::lmer(RSES ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA and pairwise comparisons
Anova(lme_rses, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: RSES
                F Df Df.res    Pr(>F)    
Time       7.2064  2     86 0.0012779 ** 
Group      1.0536  1     43 0.3104122    
Time:Group 9.4679  2     86 0.0001922 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

rses_emm <- emmeans(lme_rses, pairwise ~ Time | Group, adjust = "holm")
rses_emm

$emmeans
Group = Intervention:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            21.2 1.18 57.7     18.8     23.5
 Post Intervention   25.4 1.18 57.7     23.0     27.7
 Follow Up           23.8 1.18 57.7     21.5     26.2

Group = Control:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            22.3 1.20 57.7     19.9     24.7
 Post Intervention   22.1 1.20 57.7     19.7     24.5
 Follow Up           21.1 1.20 57.7     18.7     23.5

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                      estimate    SE df t.ratio p.value
 Baseline - Post Intervention    -4.217 0.769 86  -5.483  <.0001
 Baseline - Follow Up            -2.652 0.769 86  -3.448  0.0018
 Post Intervention - Follow Up    1.565 0.769 86   2.035  0.0449

Group = Control:
 contrast                      estimate    SE df t.ratio p.value
 Baseline - Post Intervention     0.182 0.786 86   0.231  0.8177
 Baseline - Follow Up             1.182 0.786 86   1.503  0.4097
 Post Intervention - Follow Up    1.000 0.786 86   1.272  0.4139

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 3 tests 

eff_size(rses_emm, sigma = sigma(lme_rses), edf = df.residual(lme_rses))

Since 'object' is a list, we are using the contrasts already present.

Group = Intervention:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)      -1.6169 0.312 86 -2.23680   -0.997
 (Baseline - Follow Up)              -1.0168 0.302 86 -1.61657   -0.417
 (Post Intervention - Follow Up)      0.6001 0.297 86  0.00911    1.191

Group = Control:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)       0.0697 0.302 86 -0.52974    0.669
 (Baseline - Follow Up)               0.4531 0.303 86 -0.14896    1.055
 (Post Intervention - Follow Up)      0.3834 0.302 86 -0.21791    0.985

sigma used for effect sizes: 2.608 
Degrees-of-freedom method: inherited from kenward-roger when re-gridding 
Confidence level used: 0.95 

# Output as regression
summary(lme_rses)

Linear mixed model fit by REML ['lmerMod']
Formula: RSES ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 739.1

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-1.98520 -0.48658 -0.01083  0.54138  2.55565 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 25.057   5.006   
 Residual              6.804   2.608   
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                         21.1739     1.1770  17.990
TimePost Intervention                4.2174     0.7692   5.483
TimeFollow Up                        2.6522     0.7692   3.448
GroupControl                         1.1443     1.6833   0.680
TimePost Intervention:GroupControl  -4.3992     1.1001  -3.999
TimeFollow Up:GroupControl          -3.8340     1.1001  -3.485

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.327                            
TimeFollwUp -0.327  0.500                     
GroupContrl -0.699  0.228  0.228              
TmPIntrv:GC  0.228 -0.699 -0.350 -0.327       
TmFlUp:GrpC  0.228 -0.350 -0.699 -0.327  0.500

tab_model(lme_rses)

# Calculate descriptives
#queervibe_long %>% group_by(Group, Time) %>% summarise_at(vars(RSES), list(Mean = mean, sd = SD))

CSES

The results of this suggest a significant interaction effect but the pairwise comparisons are not significant.

lme_cse <- lme4::lmer(CSE ~ Time*Group + (1|PTN), queervibe_long)

# ANOVA
Anova(lme_cse, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: CSE
                F Df Df.res  Pr(>F)  
Time       0.4465  2     86 0.64134  
Group      0.4547  1     43 0.50374  
Time:Group 4.6371  2     86 0.01223 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

cse_emm <- emmeans(lme_cse, pairwise ~ Time | Group, adjust = "holm")
cse_emm

$emmeans
Group = Intervention:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            51.1 1.79 70.3     47.5     54.7
 Post Intervention   54.3 1.79 70.3     50.7     57.8
 Follow Up           54.1 1.79 70.3     50.5     57.7

Group = Control:
 Time              emmean   SE   df lower.CL upper.CL
 Baseline            56.3 1.83 70.3     52.6     59.9
 Post Intervention   54.7 1.83 70.3     51.0     58.3
 Follow Up           53.0 1.83 70.3     49.4     56.6

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention    -3.174 1.50 86  -2.111  0.1130
 Baseline - Follow Up            -3.000 1.50 86  -1.995  0.1130
 Post Intervention - Follow Up    0.174 1.50 86   0.116  0.9082

Group = Control:
 contrast                      estimate   SE df t.ratio p.value
 Baseline - Post Intervention     1.591 1.54 86   1.035  0.5540
 Baseline - Follow Up             3.273 1.54 86   2.129  0.1084
 Post Intervention - Follow Up    1.682 1.54 86   1.094  0.5540

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 3 tests 

eff_size(cse_emm, sigma = sigma(lme_cse), edf = df.residual(lme_cse))

Since 'object' is a list, we are using the contrasts already present.

Group = Intervention:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)      -0.6225 0.297 86  -1.2138  -0.0312
 (Baseline - Follow Up)              -0.5884 0.297 86  -1.1792   0.0024
 (Post Intervention - Follow Up)      0.0341 0.295 86  -0.5521   0.6203

Group = Control:
 contrast                        effect.size    SE df lower.CL upper.CL
 (Baseline - Post Intervention)       0.3120 0.302 86  -0.2886   0.9127
 (Baseline - Follow Up)               0.6419 0.304 86   0.0372   1.2466
 (Post Intervention - Follow Up)      0.3299 0.302 86  -0.2709   0.9306

sigma used for effect sizes: 5.099 
Degrees-of-freedom method: inherited from kenward-roger when re-gridding 
Confidence level used: 0.95 

# Output as regression
summary(lme_cse)

Linear mixed model fit by REML ['lmerMod']
Formula: CSE ~ Time * Group + (1 | PTN)
   Data: queervibe_long

REML criterion at convergence: 885.4

Scaled residuals: 
     Min       1Q   Median       3Q      Max 
-2.54665 -0.45160 -0.00223  0.46453  1.93023 

Random effects:
 Groups   Name        Variance Std.Dev.
 PTN      (Intercept) 47.47    6.890   
 Residual             26.00    5.099   
Number of obs: 135, groups:  PTN, 45

Fixed effects:
                                   Estimate Std. Error t value
(Intercept)                          51.087      1.787  28.584
TimePost Intervention                 3.174      1.504   2.111
TimeFollow Up                         3.000      1.504   1.995
GroupControl                          5.186      2.556   2.029
TimePost Intervention:GroupControl   -4.765      2.150  -2.216
TimeFollow Up:GroupControl           -6.273      2.150  -2.917

Correlation of Fixed Effects:
            (Intr) TmPstI TmFllU GrpCnt TPI:GC
TmPstIntrvn -0.421                            
TimeFollwUp -0.421  0.500                     
GroupContrl -0.699  0.294  0.294              
TmPIntrv:GC  0.294 -0.699 -0.350 -0.421       
TmFlUp:GrpC  0.294 -0.350 -0.699 -0.421  0.500

tab_model(lme_cse)

# Calculate descriptives
#queervibe_long %>% group_by(Group, Time) %>% summarise_at(vars(CSE), list(Mean = mean, sd = SD))

Examining SSES over time

lme_sses <- lme4::lmer(Score ~ Time*Group + (1|PTN), queervibe_SSES)
Anova(lme_sses, type = "II", test.statistic = "F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Score
                F Df Df.res   Pr(>F)   
Time       2.8262  7    301 0.007246 **
Group      2.4995  1     43 0.121213   
Time:Group 2.1653  7    301 0.037170 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emmeans(lme_sses, pairwise ~ Time | Group, adjust = "holm")

$emmeans
Group = Intervention:
 Time              emmean   SE df lower.CL upper.CL
 Baseline            53.4 3.42 69     46.6     60.2
 Tutorial One        61.1 3.42 69     54.3     68.0
 Tutorial Two        61.7 3.42 69     54.8     68.5
 Tutorial Three      66.0 3.42 69     59.1     72.8
 Tutorial Four       63.4 3.42 69     56.6     70.2
 Tutorial Five       62.0 3.42 69     55.2     68.9
 Post Intervention   61.1 3.42 69     54.3     68.0
 Follow Up           59.7 3.42 69     52.9     66.6

Group = Control:
 Time              emmean   SE df lower.CL upper.CL
 Baseline            53.3 3.50 69     46.3     60.3
 Tutorial One        56.6 3.50 69     49.7     63.6
 Tutorial Two        55.0 3.50 69     48.1     62.0
 Tutorial Three      54.2 3.50 69     47.2     61.2
 Tutorial Four       53.5 3.50 69     46.5     60.4
 Tutorial Five       54.0 3.50 69     47.0     61.0
 Post Intervention   53.0 3.50 69     46.0     59.9
 Follow Up           54.0 3.50 69     47.1     61.0

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$contrasts
Group = Intervention:
 contrast                           estimate   SE  df t.ratio p.value
 Baseline - Tutorial One             -7.7391 2.40 301  -3.226  0.0335
 Baseline - Tutorial Two             -8.2609 2.40 301  -3.443  0.0164
 Baseline - Tutorial Three          -12.5652 2.40 301  -5.238  <.0001
 Baseline - Tutorial Four           -10.0000 2.40 301  -4.168  0.0011
 Baseline - Tutorial Five            -8.6522 2.40 301  -3.606  0.0094
 Baseline - Post Intervention        -7.7391 2.40 301  -3.226  0.0335
 Baseline - Follow Up                -6.3478 2.40 301  -2.646  0.1886
 Tutorial One - Tutorial Two         -0.5217 2.40 301  -0.217  1.0000
 Tutorial One - Tutorial Three       -4.8261 2.40 301  -2.012  0.9030
 Tutorial One - Tutorial Four        -2.2609 2.40 301  -0.942  1.0000
 Tutorial One - Tutorial Five        -0.9130 2.40 301  -0.381  1.0000
 Tutorial One - Post Intervention     0.0000 2.40 301   0.000  1.0000
 Tutorial One - Follow Up             1.3913 2.40 301   0.580  1.0000
 Tutorial Two - Tutorial Three       -4.3043 2.40 301  -1.794  1.0000
 Tutorial Two - Tutorial Four        -1.7391 2.40 301  -0.725  1.0000
 Tutorial Two - Tutorial Five        -0.3913 2.40 301  -0.163  1.0000
 Tutorial Two - Post Intervention     0.5217 2.40 301   0.217  1.0000
 Tutorial Two - Follow Up             1.9130 2.40 301   0.797  1.0000
 Tutorial Three - Tutorial Four       2.5652 2.40 301   1.069  1.0000
 Tutorial Three - Tutorial Five       3.9130 2.40 301   1.631  1.0000
 Tutorial Three - Post Intervention   4.8261 2.40 301   2.012  0.9030
 Tutorial Three - Follow Up           6.2174 2.40 301   2.592  0.2104
 Tutorial Four - Tutorial Five        1.3478 2.40 301   0.562  1.0000
 Tutorial Four - Post Intervention    2.2609 2.40 301   0.942  1.0000
 Tutorial Four - Follow Up            3.6522 2.40 301   1.522  1.0000
 Tutorial Five - Post Intervention    0.9130 2.40 301   0.381  1.0000
 Tutorial Five - Follow Up            2.3043 2.40 301   0.961  1.0000
 Post Intervention - Follow Up        1.3913 2.40 301   0.580  1.0000

Group = Control:
 contrast                           estimate   SE  df t.ratio p.value
 Baseline - Tutorial One             -3.3636 2.45 301  -1.371  1.0000
 Baseline - Tutorial Two             -1.7727 2.45 301  -0.723  1.0000
 Baseline - Tutorial Three           -0.9091 2.45 301  -0.371  1.0000
 Baseline - Tutorial Four            -0.1818 2.45 301  -0.074  1.0000
 Baseline - Tutorial Five            -0.7273 2.45 301  -0.296  1.0000
 Baseline - Post Intervention         0.3182 2.45 301   0.130  1.0000
 Baseline - Follow Up                -0.7727 2.45 301  -0.315  1.0000
 Tutorial One - Tutorial Two          1.5909 2.45 301   0.649  1.0000
 Tutorial One - Tutorial Three        2.4545 2.45 301   1.001  1.0000
 Tutorial One - Tutorial Four         3.1818 2.45 301   1.297  1.0000
 Tutorial One - Tutorial Five         2.6364 2.45 301   1.075  1.0000
 Tutorial One - Post Intervention     3.6818 2.45 301   1.501  1.0000
 Tutorial One - Follow Up             2.5909 2.45 301   1.056  1.0000
 Tutorial Two - Tutorial Three        0.8636 2.45 301   0.352  1.0000
 Tutorial Two - Tutorial Four         1.5909 2.45 301   0.649  1.0000
 Tutorial Two - Tutorial Five         1.0455 2.45 301   0.426  1.0000
 Tutorial Two - Post Intervention     2.0909 2.45 301   0.852  1.0000
 Tutorial Two - Follow Up             1.0000 2.45 301   0.408  1.0000
 Tutorial Three - Tutorial Four       0.7273 2.45 301   0.296  1.0000
 Tutorial Three - Tutorial Five       0.1818 2.45 301   0.074  1.0000
 Tutorial Three - Post Intervention   1.2273 2.45 301   0.500  1.0000
 Tutorial Three - Follow Up           0.1364 2.45 301   0.056  1.0000
 Tutorial Four - Tutorial Five       -0.5455 2.45 301  -0.222  1.0000
 Tutorial Four - Post Intervention    0.5000 2.45 301   0.204  1.0000
 Tutorial Four - Follow Up           -0.5909 2.45 301  -0.241  1.0000
 Tutorial Five - Post Intervention    1.0455 2.45 301   0.426  1.0000
 Tutorial Five - Follow Up           -0.0455 2.45 301  -0.019  1.0000
 Post Intervention - Follow Up       -1.0909 2.45 301  -0.445  1.0000

Degrees-of-freedom method: kenward-roger 
P value adjustment: holm method for 28 tests 

emmip(lme_sses, ~ Time | Group)

Plots

# SSES_plot <- emmip(lme_sses, ~ Time | Group, CI = TRUE, CIarg = list(lwd = 1, alpha = 1, color = "black")) + 
#                    theme_classic() +
#                    scale_linetype_manual(values=c("dashed")) +
#                    scale_x_discrete(guide = guide_axis(angle = 40)) +
#                    ylab("Estimated Marginal Mean SSES") +
#                    xlab("Time of Measurement")






# SSESplot <- ggplot(queervibe_SSES, aes(x = Time, y = Score)) +
#             geom_point() +
#             facet_wrap(~ Group)



SSESplot <- emmip(lme_sses, ~ Time | Group, CI = TRUE, plotit = FALSE)
SSES_plot1 <- ggplot(SSESplot,
                     aes(Time, yvar)) +
                     geom_line(group = "Group") +
                     geom_errorbar(aes(ymin= LCL, ymax= UCL), width=.2, position=position_dodge(.9)) +
                     geom_point() +
                     scale_x_discrete(guide = guide_axis(angle = 40)) +
                     ylab("Estimated Marginal Mean SSES") +
                     xlab("Time of Measurement") +
                     facet_wrap(~ Group) +
                     theme_classic()

ggsave("Figure2.png", plot = SSES_plot1, width = 30, height = 20, scale = 0.5, unit = "cm")

sessionInfo()

R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

Random number generation:
 RNG:     L'Ecuyer-CMRG 
 Normal:  Inversion 
 Sample:  Rounding 
 
locale:
[1] LC_COLLATE=English_United Kingdom.utf8  LC_CTYPE=English_United Kingdom.utf8    LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C                            LC_TIME=English_United Kingdom.utf8    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] sjPlot_2.8.11    car_3.1-0        carData_3.0-5    simr_1.0.6       mixedpower_0.1.0 lme4_1.1-30      Matrix_1.5-1     MVN_5.9          psych_2.2.9      forcats_0.5.2    stringr_1.4.1    dplyr_1.0.10    
[13] purrr_0.3.5      readr_2.1.3      tidyr_1.2.1      tibble_3.1.8     ggplot2_3.3.6    tidyverse_1.3.2  devtools_2.4.5   usethis_2.1.6    emmeans_1.8.1-1 

loaded via a namespace (and not attached):
  [1] readxl_1.4.1        backports_1.4.1     systemfonts_1.0.4   plyr_1.8.7          splines_4.2.1       RLRsim_3.1-8        digest_0.6.29       htmltools_0.5.3     rsconnect_0.8.28    fansi_1.0.3        
 [11] magrittr_2.0.3      memoise_2.0.1       googlesheets4_1.0.1 tzdb_0.3.0          remotes_2.4.2       modelr_0.1.9        vroom_1.6.0         prettyunits_1.1.1   colorspace_2.0-3    rvest_1.0.3        
 [21] textshaping_0.3.6   haven_2.5.1         xfun_0.33           callr_3.7.2         crayon_1.5.2        jsonlite_1.8.2      iterators_1.0.14    glue_1.6.2          gtable_0.3.1        gargle_1.2.1       
 [31] sjstats_0.18.1      sjmisc_2.8.9        pkgbuild_1.3.1      abind_1.4-5         scales_1.2.1        mvtnorm_1.1-3       DBI_1.1.3           ggeffects_1.1.4     miniUI_0.1.1.1      Rcpp_1.0.9         
 [41] plotrix_3.8-2       xtable_1.8-4        performance_0.10.0  bit_4.0.4           profvis_0.3.7       datawizard_0.6.3    htmlwidgets_1.5.4   httr_1.4.4          ellipsis_0.3.2      farver_2.1.1       
 [51] urlchecker_1.0.1    pkgconfig_2.0.3     sass_0.4.2          dbplyr_2.2.1        binom_1.1-1.1       utf8_1.2.2          labeling_0.4.2      tidyselect_1.2.0    rlang_1.0.6         later_1.3.0        
 [61] effectsize_0.8.1    munsell_0.5.0       cellranger_1.1.0    tools_4.2.1         cachem_1.0.6        cli_3.4.1           generics_0.1.3      moments_0.14.1      sjlabelled_1.2.0    broom_1.0.1        
 [71] evaluate_0.17       fastmap_1.1.0       ragg_1.2.3          yaml_2.3.5          processx_3.7.0      knitr_1.40          bit64_4.0.5         fs_1.5.2            nlme_3.1-157        mime_0.12          
 [81] xml2_1.3.3          compiler_4.2.1      pbkrtest_0.5.1      rstudioapi_0.14     curl_4.3.3          reprex_2.0.2        bslib_0.4.0         stringi_1.7.8       gsl_2.1-7.1         parameters_0.19.0  
 [91] ps_1.7.1            lattice_0.20-45     nloptr_2.0.3        vctrs_0.4.2         pillar_1.8.1        lifecycle_1.0.3     jquerylib_0.1.4     estimability_1.4.1  insight_0.18.6      httpuv_1.6.6       
[101] R6_2.5.1            promises_1.2.0.1    sessioninfo_1.2.2   boot_1.3-28         energy_1.7-10       MASS_7.3-57         assertthat_0.2.1    pkgload_1.3.0       withr_2.5.0         nortest_1.0-4      
[111] mnormt_2.1.1        mgcv_1.8-40         bayestestR_0.13.0   parallel_4.2.1      hms_1.1.2           grid_4.2.1          coda_0.19-4         minqa_1.2.4         rmarkdown_2.17      googledrive_2.0.0  
[121] shiny_1.7.2         lubridate_1.8.0