Purrble RCT Analyses

Recording Keeping:

There are two master files that we are using for analyses. They are essentially the same file, though one is in wide format and the other is in long format.

The wide format dataset is called “Purrble_Master_Wide.” The long dataset format dataset is called “Purrble_Long_Master.” The wide dataset has all of the pre and posttest variables calculated, while the long does not. Otherwise, they do not differ.

This dataset includes the N=153 participants who were included in the randomized control trial examining Purrble with a population of university students. All participants were members of the LGTBQ+ community.

These analyses were conducted on February 18-19 by Aubrey Rhodes. We use the “final” datasets in which we removed participant C72, who had no information on gender identity.

Descriptive Analyses

Sample Characteristics

These tables report the count of participants by condition, identity group, and by condition x identity group.

Table 1: Number of Participants by Condition

condition	Count
Purrble Treatment	76
Waitlist Control	77
Total	153

Table 2: Number of Participants by Gender Identity

identity_group	Count
Cisgender	76
Transgender	77
Total	153

Table 3: Cross-tabulation of Condition by Gender Identity

condition	Cisgender	TGD
Purrble Treatment	39	37
Waitlist Control	37	40

Age: Descriptives and Check for Baseline differences

Summarizes age (Mean, SD, Min, Max) by condition and runs a t-test comparing age by condition.

Table: Descriptive Statistics for Age by Condition (APA Format)

condition	Mean	SD	Min	Max
Purrble Treatment	20.44	2.29	16.00	25.00
Waitlist Control	20.09	2.46	16.00	25.00

Dependent Variable	t	df	p	d	95% CI
age	0.92	151.17	.361	0.15	[-0.17, 0.46]

Race, Nationality, and Sexual Orientation

Sexual Orientation- Simplified

Table: Sexual Orientation (so_simplified) by Condition (Counts and Percentages)

so_simplified	Purrble Treatment	Waitlist Control	Total
asexual	13 (17.1%)	9 (11.7%)	22 (14.4%)
bisexual	28 (36.8%)	25 (32.5%)	53 (34.6%)
demisexual	2 (2.6%)	1 (1.3%)	3 (2%)
gay/lesbian	11 (14.5%)	18 (23.4%)	29 (19%)
heterosexual	1 (1.3%)	0 (0%)	1 (0.7%)
pansexual	8 (10.5%)	9 (11.7%)	17 (11.1%)
queer	13 (17.1%)	15 (19.5%)	28 (18.3%)

Sexual Orientation- Not simplified

Table: Sexual Orientation by Condition (Counts and Percentages)

so	Purrble Treatment	Waitlist Control	Total
aroace	1 (1.3%)	0 (0%)	1 (0.7%)
aromatic & asexual (aroace)	0 (0%)	1 (1.3%)	1 (0.7%)
asexual	9 (11.8%)	6 (7.8%)	15 (9.8%)
asexual aromantic	1 (1.3%)	0 (0%)	1 (0.7%)
asexual panromantic	0 (0%)	1 (1.3%)	1 (0.7%)
asexual, bisexual	1 (1.3%)	0 (0%)	1 (0.7%)
bi-demisexual	1 (1.3%)	0 (0%)	1 (0.7%)
bi/pansexual	1 (1.3%)	0 (0%)	1 (0.7%)
biromantic demisexual	1 (1.3%)	0 (0%)	1 (0.7%)
biromantic, asexual spectrum.	1 (1.3%)	0 (0%)	1 (0.7%)
bisexual	26 (34.2%)	25 (32.5%)	51 (33.3%)
bisexuality	1 (1.3%)	0 (0%)	1 (0.7%)
demisexual biromantic	0 (0%)	1 (1.3%)	1 (0.7%)
gay	1 (1.3%)	1 (1.3%)	2 (1.3%)
heterosexual	1 (1.3%)	0 (0%)	1 (0.7%)
homosexual	4 (5.3%)	0 (0%)	4 (2.6%)
homosexual, demiromantic, asexual	1 (1.3%)	0 (0%)	1 (0.7%)
homosexual, demisexual	0 (0%)	1 (1.3%)	1 (0.7%)
homosexual/gay	0 (0%)	1 (1.3%)	1 (0.7%)
lesbian	5 (6.6%)	13 (16.9%)	18 (11.8%)
lesbian demisexual	0 (0%)	1 (1.3%)	1 (0.7%)
pan/demisexual/asexual	1 (1.3%)	0 (0%)	1 (0.7%)
panromanric asexual	0 (0%)	1 (1.3%)	1 (0.7%)
pansexual	5 (6.6%)	8 (10.4%)	13 (8.5%)
pansexual/queer	1 (1.3%)	0 (0%)	1 (0.7%)
queer	13 (17.1%)	15 (19.5%)	28 (18.3%)
queer or bisexual	1 (1.3%)	0 (0%)	1 (0.7%)
queer/ pansexual	0 (0%)	1 (1.3%)	1 (0.7%)
queer/lesbian/gay	0 (0%)	1 (1.3%)	1 (0.7%)

Nationality

Table: Nationality by Condition (Counts and Percentages)

Nationality	Waitlist Control	Purrble Treatment	Total
bangladeshi	1 (1.3%)	0 (0%)	1 (0.7%)
british	36 (46.8%)	34 (44.7%)	70 (45.8%)
british-carribean	1 (1.3%)	1 (1.3%)	2 (1.3%)
british-indian	0 (0%)	1 (1.3%)	1 (0.7%)
british-japanese	1 (1.3%)	0 (0%)	1 (0.7%)
british-pakistani	1 (1.3%)	0 (0%)	1 (0.7%)
chinese	5 (6.5%)	1 (1.3%)	6 (3.9%)
filipino	0 (0%)	1 (1.3%)	1 (0.7%)
indian	5 (6.5%)	3 (3.9%)	8 (5.2%)
indonesian	1 (1.3%)	0 (0%)	1 (0.7%)
iranian	1 (1.3%)	0 (0%)	1 (0.7%)
irish	1 (1.3%)	1 (1.3%)	2 (1.3%)
irish-american	0 (0%)	1 (1.3%)	1 (0.7%)
irish-carribean	1 (1.3%)	0 (0%)	1 (0.7%)
malaysian chinese	1 (1.3%)	0 (0%)	1 (0.7%)
mexican	0 (0%)	1 (1.3%)	1 (0.7%)
nr	20 (26%)	29 (38.2%)	49 (32%)
pakistani	0 (0%)	1 (1.3%)	1 (0.7%)
polish	2 (2.6%)	2 (2.6%)	4 (2.6%)

Race

Table: Race Counts and Percentages by Condition

Race	Purrble Treatment		Waitlist Control		Total
Race	count_Purrble Treatment	percentage_Purrble Treatment	count_Waitlist Control	percentage_Waitlist Control	total_count	total_percentage
Race_Arabic	0	0.0	1	1.3	1	0.7
Race_Asian	10	13.2	17	22.1	27	17.6
Race_Black	1	1.3	3	3.9	4	2.6
Race_Hispanic	2	2.6	0	0.0	2	1.3
Race_White	60	78.9	55	71.4	115	75.2
Race_unknown	9	11.8	5	6.5	14	9.2

5 people in the Purrble Treatment condition reported multiple racial identities.
4 people in the Waitlist Control condition reported multiple racial identities.

Participation Over Time and Attrition

Count of Participation by Group Over Time

Participation in Each Week over Time Note: Week 0 was “intake.” Weeks 1-3 were considered “pre-test.” Purrble was given (or not) after week 3. Weeks 11-13 are considered “Post-test”. For each week (0–13), we count the number of unique participants overall, and then break down participation by condition. These summaries help us understand attendance trends during intake, pre-test, intervention, and post-test phases.

Table 1: Count of Total Participation by Week

Week	n_participants
0	151
1	147
2	148
3	149
4	142
5	139
6	138
7	140
8	142
9	128
10	130
11	128
12	117
13	130

Table: Count of Participation by Week and Condition

Week	Purrble Treatment	Waitlist Control
0	74	77
1	74	73
2	74	74
3	75	74
4	72	70
5	68	71
6	67	71
7	68	72
8	69	73
9	61	67
10	63	67
11	62	66
12	50	67
13	62	68

Number of Sessions Attended

Table 2: Overall Total Sessions Attended

mean_sessions	sd_sessions
12.60784	2.155883

Table 3: Total Sessions Attended by Condition

condition	mean_sessions	sd_sessions	n
0	12.85714	2.056532	77
1	12.35526	2.237284	76

Table 4: Total Sessions Attended by Gender Identity

identity_group	mean_sessions	sd_sessions	n
0	12.53947	2.193571	76
1	12.67532	2.130243	77

Table 5: Total Sessions Attended by Condition and Gender Identity

condition	identity_group	mean_sessions	sd_sessions	n
0	0	13.13514	1.417395	37
0	1	12.60000	2.499231	40
1	0	11.97436	2.630661	39
1	1	12.75676	1.673410	37

Attrition Analysis

Attrition is defined here as not having attended any post-test session (i.e., no attendance during Weeks 11–13). We create a binary indicator for post-test completion (1 = attended at least one post-test session, 0 = none) and calculate attrition rates overall, by condition and by gender identity. We used a chi-square test to determine if attrition differed by condition; it did not.

Attrition by Condition

Results for Manuscript:

The conditions did not significantly differ on any of the baseline measures of outcomes or by age. Attrition rates were low across both conditions, with 9.2% of participants in the Purrble condition and 6.5% in the Waitlist Control condition not completing the study. Attrition did not differ by condition, χ²(1) = 0.11, p = .75, or by gender identity, χ²(1) < 0.01, p = 1.

Chi-square test for differences in attrition by condition:

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

data:  attrition_ct
X-squared = 0.10517, df = 1, p-value = 0.7457

Table 7: Attrition Rate by Condition (with Completed and Not Completed counts)

condition	n	Completed	Not_Completed	attrition_rate	attrition_percent
0	77	72	5	0.0649351	6.5
1	76	69	7	0.0921053	9.2

Attrition by baseline Outcomes

Two-way ANOVA results for Pre_DERS8_Sum :

Two-way ANOVA for Pre_DERS8_Sum by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	7.983	7.983	0.356	0.552
attrition_status	1	30.432	30.432	1.356	0.246
condition:attrition_status	1	2.561	2.561	0.114	0.736
Residuals	148	3320.444	22.435	NA	NA

Two-way ANOVA results for Pre_GAD7_Sum :

Two-way ANOVA for Pre_GAD7_Sum by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	0.658	0.658	0.041	0.841
attrition_status	1	1.190	1.190	0.073	0.787
condition:attrition_status	1	0.001	0.001	0.000	0.994
Residuals	148	2401.630	16.227	NA	NA

Two-way ANOVA results for Pre_PHQ9_Sum :

Two-way ANOVA for Pre_PHQ9_Sum by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	18.249	18.249	0.859	0.356
attrition_status	1	2.796	2.796	0.132	0.717
condition:attrition_status	1	4.207	4.207	0.198	0.657
Residuals	148	3144.123	21.244	NA	NA

Two-way ANOVA results for Pre_SHS_Pathways :

Two-way ANOVA for Pre_SHS_Pathways by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	35.106	35.106	1.907	0.169
attrition_status	1	3.918	3.918	0.213	0.645
condition:attrition_status	1	25.587	25.587	1.390	0.240
Residuals	144	2651.435	18.413	NA	NA

Two-way ANOVA results for Pre_SHS_Agency :

Two-way ANOVA for Pre_SHS_Agency by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	34.935	34.935	1.450	0.231
attrition_status	1	8.541	8.541	0.354	0.553
condition:attrition_status	1	79.905	79.905	3.315	0.071
Residuals	144	3470.489	24.101	NA	NA

Two-way ANOVA results for Pre_SHS_TotalHope :

Two-way ANOVA for Pre_SHS_TotalHope by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	140.081	140.081	2.039	0.155
attrition_status	1	24.029	24.029	0.350	0.555
condition:attrition_status	1	195.924	195.924	2.852	0.093
Residuals	144	9893.938	68.708	NA	NA

Two-way ANOVA results for Pre_ucla_Sum :

Two-way ANOVA for Pre_ucla_Sum by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	3.945	3.945	1.556	0.214
attrition_status	1	1.318	1.318	0.520	0.472
condition:attrition_status	1	13.182	13.182	5.199	0.024
Residuals	143	362.575	2.535	NA	NA

Two-way ANOVA results for Pre_pmerq_Focus_Avg :

Two-way ANOVA for Pre_pmerq_Focus_Avg by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	1.392	1.392	1.243	0.267
attrition_status	1	2.233	2.233	1.995	0.160
condition:attrition_status	1	1.281	1.281	1.144	0.287
Residuals	144	161.212	1.120	NA	NA

Two-way ANOVA results for Pre_pmerq_Distract_Avg :

Two-way ANOVA for Pre_pmerq_Distract_Avg by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	1.554	1.554	1.247	0.266
attrition_status	1	4.213	4.213	3.380	0.068
condition:attrition_status	1	0.038	0.038	0.031	0.861
Residuals	144	179.482	1.246	NA	NA

Two-way ANOVA results for Pre_pmerq_AD_Avg :

Two-way ANOVA for Pre_pmerq_AD_Avg by Condition and Attrition Status

term	df	sumsq	meansq	statistic	p.value
condition	1	1.472	1.472	1.762	0.186
attrition_status	1	3.145	3.145	3.766	0.054
condition:attrition_status	1	0.440	0.440	0.527	0.469
Residuals	144	120.256	0.835	NA	NA

NA

Attrition by baseline Outcomes follow-up/exploraiton

Descriptive Statistics for Pre_ucla_Sum by Condition and Attrition Status
condition	attrition_status	N	Mean	SD
0	Attriter	5	8.25	0.96
0	Completer	72	7.19	1.35
1	Attriter	7	5.67	1.51
1	Completer	69	7.03	1.83
Note. Means and standard deviations for Pre_ucla_Sum across four groups defined by condition (Purrble, Waitlist Control) and attrition status (Completer, Attriter).

Simple Effects Analysis: Pre_ucla_Sum by Attrition Status within the Purrble Condition

Dependent Variable	t	df	p	d	95% CI
Pre_ucla_Sum	-2.09	6.38	.079	-0.75	[-1.60, 0.09]

Simple Effects Analysis: Pre_ucla_Sum by Attrition Status within the Waitlist Control Condition

Dependent Variable	t	df	p	d	95% CI
Pre_ucla_Sum	2.10	3.73	.109	0.79	[-0.23, 1.81]

Attrition by Gender Identity

Chi-square test for differences in attrition by gender identity:

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

data:  attrition_ct
X-squared = 1.4323e-30, df = 1, p-value = 1

Table 8: Attrition Rate by Gender Identity (with Completed and Not Completed counts)

identity_group	n	Completed	Not_Completed	attrition_rate	attrition_percent
0	76	70	6	0.0789474	7.9
1	77	71	6	0.0779221	7.8

Preliminary Analysis

Baseline Differences in Outcomes by Condition

Outlier Detection and Visualization :

We first convert each pre‑test variable to z‑scores and flag any observations with an absolute z‑score greater than 3 as potential outliers. A summary table is created that lists the number of outliers for each variable. We then specifically inspect the outliers for the Pre_pmerq_Focus_Avg variable, which appears to have two cases exceeding our threshold. To better understand the distribution of Pre_pmerq_Focus_Avg, we generate a boxplot (with jittered data points) that visually highlights the extreme values.

Summary of Potential Outliers (|z| > 3) for Pre-Test Variables:

Summary of Outliers for Pre-Test Variables (|z| > 3)

Variable	Outlier_Count
Pre_DERS8_Sum	0
Pre_GAD7_Sum	0
Pre_PHQ9_Sum	0
Pre_SHS_Pathways	0
Pre_SHS_Agency	0
Pre_SHS_TotalHope	0
Pre_ucla_Sum	0
Pre_pmerq_Focus_Avg	2
Pre_pmerq_Distract_Avg	0
Pre_pmerq_AD_Avg	0

Outliers for Pre_pmerq_Focus_Avg (|z| > 3):

Outliers for Pre_pmerq_Focus_Avg

psid	Pre_pmerq_Focus_Avg	z
C57	6	3.069197
C79	6	3.069197

Descriptive Statistics of Baseline Outcomes:

We compute descriptive statistics (mean, standard deviation, minimum, maximum, skewness, and kurtosis) for all pre‑test variables using the psych package. The resulting summary is then formatted into an APA‑style table using the nice_table function from the rempsyc package.

Variable	Mean	SD	Min	Max	Skew	Kurtosis
Pre_DERS8_Sum	28.15	4.72	14.33	38.33	-0.42	-0.13
Pre_GAD7_Sum	13.71	3.99	3.00	22.00	-0.17	-0.46
Pre_PHQ9_Sum	15.04	4.58	3.00	26.67	-0.02	-0.10
Pre_SHS_Pathways	13.29	4.30	3.00	24.00	-0.13	-0.42
Pre_SHS_Agency	10.70	4.94	3.00	24.00	0.34	-0.66
Pre_SHS_TotalHope	23.99	8.35	8.00	46.00	0.29	-0.30
Pre_ucla_Sum	7.08	1.62	3.00	9.00	-0.50	-0.66
Pre_pmerq_Focus_Avg	2.74	1.06	1.00	6.00	0.42	-0.10
Pre_pmerq_Distract_Avg	4.23	1.12	1.00	6.00	-0.86	0.70
Pre_pmerq_AD_Avg	3.49	0.92	1.00	6.00	-0.33	0.52

Baseline Equivalence of Baseline Outcomes (t‑Tests):

Finally, we run independent samples t‑tests comparing the two experimental conditions on each pre‑test variable using nice_t_test from rempsyc. This provides t‑statistics, degrees of freedom, p‑values, effect sizes (Cohen’s d), and confidence intervals, all formatted into an APA‑style table.

Result: No differences by chance.

Dependent Variable	t	df	p	d	95% CI
Pre_DERS8_Sum	0.60	146.06	.551	0.10	[-0.22, 0.41]
Pre_GAD7_Sum	-0.20	147.61	.840	-0.03	[-0.35, 0.29]
Pre_PHQ9_Sum	-0.93	147.00	.353	-0.15	[-0.47, 0.17]
Pre_SHS_Pathways	1.38	144.81	.168	0.23	[-0.10, 0.55]
Pre_SHS_Agency	1.20	144.38	.234	0.20	[-0.13, 0.52]
Pre_SHS_TotalHope	1.42	145.96	.157	0.23	[-0.09, 0.56]
Pre_ucla_Sum	1.23	134.26	.219	0.20	[-0.12, 0.53]
Pre_pmerq_Focus_Avg	1.11	145.19	.269	0.18	[-0.14, 0.51]
Pre_pmerq_Distract_Avg	1.11	145.84	.268	0.18	[-0.14, 0.51]
Pre_pmerq_AD_Avg	1.32	143.85	.190	0.22	[-0.11, 0.54]

Main Effects Analyses

Dependent Variable	Predictor	df	b	t	p	sr2	95% CI
Post_DERS8_Sum	condition_num	135	-3.04	-3.20	.002**	.04	[0.00, 0.09]
	Pre_DERS8_Sum	135	0.92	9.21	< .001***	.35	[0.23, 0.48]
	identity_group_num	135	1.69	1.72	.088	.01	[0.00, 0.04]
	age	135	0.13	0.60	.549	.00	[0.00, 0.01]
Post_pmerq_Focus_Avg	condition_num	121	0.31	1.96	.052	.02	[0.00, 0.05]
	Pre_pmerq_Focus_Avg	121	0.73	9.40	< .001***	.39	[0.26, 0.52]
	identity_group_num	121	-0.27	-1.61	.110	.01	[0.00, 0.04]
	age	121	0.02	0.45	.654	.00	[0.00, 0.01]
Post_pmerq_Distract_Avg	condition_num	121	0.25	1.49	.138	.01	[0.00, 0.05]
	Pre_pmerq_Distract_Avg	121	0.48	6.48	< .001***	.25	[0.12, 0.38]
	identity_group_num	121	0.20	1.19	.238	.01	[0.00, 0.04]
	age	121	0.02	0.64	.526	.00	[0.00, 0.02]
Post_pmerq_AD_Avg	condition_num	121	0.30	2.28	.024*	.02	[0.00, 0.06]
	Pre_pmerq_AD_Avg	121	0.70	9.54	< .001***	.42	[0.29, 0.55]
	identity_group_num	121	-0.04	-0.32	.747	.00	[0.00, 0.01]
	age	121	0.03	1.06	.290	.01	[0.00, 0.02]
Post_GAD7_Sum	condition_num	135	-1.35	-2.04	.044*	.02	[0.00, 0.05]
	Pre_GAD7_Sum	135	0.74	8.98	< .001***	.35	[0.23, 0.48]
	identity_group_num	135	0.75	1.08	.281	.01	[0.00, 0.02]
	age	135	0.27	1.84	.068	.01	[0.00, 0.05]
Post_PHQ9_Sum	condition_num	135	-2.60	-3.64	< .001***	.04	[0.00, 0.09]
	Pre_PHQ9_Sum	135	1.00	12.96	< .001***	.53	[0.42, 0.65]
	identity_group_num	135	0.25	0.34	.734	.00	[0.00, 0.00]
	age	135	0.29	1.86	.064	.01	[0.00, 0.03]
Post_SHS_Pathways	condition_num	122	0.09	0.14	.889	.00	[0.00, 0.00]
	Pre_SHS_Pathways	122	0.46	6.04	< .001***	.21	[0.09, 0.34]
	identity_group_num	122	-0.84	-1.19	.237	.01	[0.00, 0.04]
	age	122	-0.28	-1.86	.065	.02	[0.00, 0.06]
Post_SHS_Agency	condition_num	122	0.44	0.53	.595	.00	[0.00, 0.01]
	Pre_SHS_Agency	122	0.53	6.57	< .001***	.26	[0.13, 0.39]
	identity_group_num	122	-0.47	-0.55	.582	.00	[0.00, 0.01]
	age	122	-0.17	-0.96	.337	.01	[0.00, 0.03]
Post_SHS_TotalHope	condition_num	122	0.62	0.46	.648	.00	[0.00, 0.01]
	Pre_SHS_TotalHope	122	0.53	6.71	< .001***	.26	[0.13, 0.39]
	identity_group_num	122	-1.16	-0.82	.414	.00	[0.00, 0.02]
	age	122	-0.43	-1.45	.151	.01	[0.00, 0.04]
Post_ucla_Sum	condition_num	121	-0.09	-0.40	.688	.00	[0.00, 0.01]
	Pre_ucla_Sum	121	0.70	10.02	< .001***	.43	[0.30, 0.56]
	identity_group_num	121	0.52	2.20	.030*	.02	[0.00, 0.06]
	age	121	0.11	2.12	.036*	.02	[0.00, 0.05]

Emotion Regulation Outcomes: Moderation Models

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_DERS8_Sum	condition_num	134	-0.21	-3.17	.002**	.04	[0.00, 0.09]
	Pre_DERS8_Sum	134	0.60	9.18	< .001***	.35	[0.23, 0.48]
	identity_group_num	134	0.12	1.71	.089	.01	[0.00, 0.04]
	age	134	0.04	0.53	.595	.00	[0.00, 0.01]
	condition_num × Pre_DERS8_Sum	134	-0.04	-0.65	.517	.00	[0.00, 0.01]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_Focus_Avg	condition_num	120	0.13	1.93	.056	.02	[0.00, 0.05]
	Pre_pmerq_Focus_Avg	120	0.65	9.35	< .001***	.39	[0.26, 0.52]
	identity_group_num	120	-0.13	-1.74	.085	.01	[0.00, 0.04]
	age	120	0.03	0.49	.625	.00	[0.00, 0.01]
	condition_num × Pre_pmerq_Focus_Avg	120	-0.07	-1.02	.309	.00	[0.00, 0.02]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_Distract_Avg	condition_num	120	0.11	1.45	.150	.01	[0.00, 0.05]
	Pre_pmerq_Distract_Avg	120	0.52	6.50	< .001***	.25	[0.12, 0.38]
	identity_group_num	120	0.10	1.18	.241	.01	[0.00, 0.04]
	age	120	0.06	0.66	.510	.00	[0.00, 0.02]
	condition_num × Pre_pmerq_Distract_Avg	120	-0.05	-0.67	.505	.00	[0.00, 0.02]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_AD_Avg	condition_num	120	0.15	2.24	.027*	.02	[0.00, 0.06]
	Pre_pmerq_AD_Avg	120	0.67	9.45	< .001***	.42	[0.29, 0.55]
	identity_group_num	120	-0.03	-0.36	.722	.00	[0.00, 0.01]
	age	120	0.08	1.07	.288	.01	[0.00, 0.02]
	condition_num × Pre_pmerq_AD_Avg	120	-0.03	-0.38	.704	.00	[0.00, 0.01]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_DERS8_Sum	condition_num	134	-0.21	-3.23	.002**	.04	[0.00, 0.09]
	identity_group_num	134	0.12	1.75	.082	.01	[0.00, 0.04]
	Pre_DERS8_Sum	134	0.59	9.24	< .001***	.35	[0.23, 0.47]
	age	134	0.04	0.59	.558	.00	[0.00, 0.01]
	condition_num × identity_group_num	134	0.13	2.10	.038*	.02	[0.00, 0.05]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_Focus_Avg	condition_num	120	0.13	2.01	.046*	.02	[0.00, 0.05]
	identity_group_num	120	-0.11	-1.55	.124	.01	[0.00, 0.04]
	Pre_pmerq_Focus_Avg	120	0.68	9.65	< .001***	.41	[0.28, 0.54]
	age	120	0.03	0.48	.630	.00	[0.00, 0.01]
	condition_num × identity_group_num	120	0.12	1.79	.076	.01	[0.00, 0.04]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_Distract_Avg	condition_num	120	0.12	1.49	.139	.01	[0.00, 0.05]
	identity_group_num	120	0.10	1.19	.238	.01	[0.00, 0.04]
	Pre_pmerq_Distract_Avg	120	0.51	6.46	< .001***	.25	[0.12, 0.38]
	age	120	0.05	0.63	.528	.00	[0.00, 0.02]
	condition_num × identity_group_num	120	0.03	0.37	.708	.00	[0.00, 0.01]

Dependent Variable	Predictor	df	b*	t	p	sr2	95% CI
Post_pmerq_AD_Avg	condition_num	120	0.16	2.31	.023*	.02	[0.00, 0.06]
	identity_group_num	120	-0.02	-0.30	.766	.00	[0.00, 0.01]
	Pre_pmerq_AD_Avg	120	0.68	9.65	< .001***	.43	[0.30, 0.56]
	age	120	0.08	1.09	.279	.01	[0.00, 0.02]
	condition_num × identity_group_num	120	0.09	1.30	.197	.01	[0.00, 0.03]

Follow up: DERS 8

Since the interaction of condition by identity group was signifiacnt, I have to probe it using simple slopes.

Result:

For cisgender participants, controlling for pre‑test emotion regulation, condition significantly predicted post‑test scores, with the intervention yielding lower (i.e., better) scores (b = –4.90, SE = 1.41, t(67) = –3.47, p = .001, adjusted R² = .47). In contrast, for transgender/gender diverse participants, condition was not a significant predictor of post‑test emotion regulation (b = –1.07, SE = 1.23, t(67) = –0.87, p = .39, adjusted R² = .37). sad.

Call:
lm(formula = Post_DERS8_Sum ~ condition_num + Pre_DERS8_Sum, 
    data = filter(Purrble_Master_Wide, identity_group == "0"))

Residuals:
    Min      1Q  Median      3Q     Max 
-15.085  -3.353   1.433   3.929  14.517 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     4.8137     4.9268   0.977  0.33206    
condition_num  -4.9030     1.4137  -3.468  0.00092 ***
Pre_DERS8_Sum   1.0170     0.1502   6.771 3.89e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.885 on 67 degrees of freedom
  (6 observations deleted due to missingness)
Multiple R-squared:  0.484, Adjusted R-squared:  0.4686 
F-statistic: 31.43 on 2 and 67 DF,  p-value: 2.361e-10


Call:
lm(formula = Post_DERS8_Sum ~ condition_num + Pre_DERS8_Sum, 
    data = filter(Purrble_Master_Wide, identity_group == "1"))

Residuals:
     Min       1Q   Median       3Q      Max 
-12.1803  -2.3719   0.0348   3.7168  10.4756 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     6.1183     4.1405   1.478    0.144    
condition_num  -1.0671     1.2265  -0.870    0.387    
Pre_DERS8_Sum   0.8226     0.1274   6.456 1.41e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.13 on 67 degrees of freedom
  (7 observations deleted due to missingness)
Multiple R-squared:  0.3885,    Adjusted R-squared:  0.3703 
F-statistic: 21.29 on 2 and 67 DF,  p-value: 6.971e-08

Dependent Variable	Predictor	df	b	t	p	sr2	95% CI
Post_DERS8_Sum	condition_num	135	-3.04	-3.20	.002**	.04	[0.00, 0.09]
	Pre_DERS8_Sum	135	0.92	9.21	< .001***	.35	[0.23, 0.48]
	identity_group_num	135	1.69	1.72	.088	.01	[0.00, 0.04]
	age	135	0.13	0.60	.549	.00	[0.00, 0.01]
Post_pmerq_Focus_Avg	condition_num	121	0.31	1.96	.052	.02	[0.00, 0.05]
	Pre_pmerq_Focus_Avg	121	0.73	9.40	< .001***	.39	[0.26, 0.52]
	identity_group_num	121	-0.27	-1.61	.110	.01	[0.00, 0.04]
	age	121	0.02	0.45	.654	.00	[0.00, 0.01]
Post_pmerq_Distract_Avg	condition_num	121	0.25	1.49	.138	.01	[0.00, 0.05]
	Pre_pmerq_Distract_Avg	121	0.48	6.48	< .001***	.25	[0.12, 0.38]
	identity_group_num	121	0.20	1.19	.238	.01	[0.00, 0.04]
	age	121	0.02	0.64	.526	.00	[0.00, 0.02]
Post_pmerq_AD_Avg	condition_num	121	0.30	2.28	.024*	.02	[0.00, 0.06]
	Pre_pmerq_AD_Avg	121	0.70	9.54	< .001***	.42	[0.29, 0.55]
	identity_group_num	121	-0.04	-0.32	.747	.00	[0.00, 0.01]
	age	121	0.03	1.06	.290	.01	[0.00, 0.02]
Post_GAD7_Sum	condition_num	135	-1.35	-2.04	.044*	.02	[0.00, 0.05]
	Pre_GAD7_Sum	135	0.74	8.98	< .001***	.35	[0.23, 0.48]
	identity_group_num	135	0.75	1.08	.281	.01	[0.00, 0.02]
	age	135	0.27	1.84	.068	.01	[0.00, 0.05]
Post_PHQ9_Sum	condition_num	135	-2.60	-3.64	< .001***	.04	[0.00, 0.09]
	Pre_PHQ9_Sum	135	1.00	12.96	< .001***	.53	[0.42, 0.65]
	identity_group_num	135	0.25	0.34	.734	.00	[0.00, 0.00]
	age	135	0.29	1.86	.064	.01	[0.00, 0.03]
Post_SHS_Pathways	condition_num	122	0.09	0.14	.889	.00	[0.00, 0.00]
	Pre_SHS_Pathways	122	0.46	6.04	< .001***	.21	[0.09, 0.34]
	identity_group_num	122	-0.84	-1.19	.237	.01	[0.00, 0.04]
	age	122	-0.28	-1.86	.065	.02	[0.00, 0.06]
Post_SHS_Agency	condition_num	122	0.44	0.53	.595	.00	[0.00, 0.01]
	Pre_SHS_Agency	122	0.53	6.57	< .001***	.26	[0.13, 0.39]
	identity_group_num	122	-0.47	-0.55	.582	.00	[0.00, 0.01]
	age	122	-0.17	-0.96	.337	.01	[0.00, 0.03]
Post_SHS_TotalHope	condition_num	122	0.62	0.46	.648	.00	[0.00, 0.01]
	Pre_SHS_TotalHope	122	0.53	6.71	< .001***	.26	[0.13, 0.39]
	identity_group_num	122	-1.16	-0.82	.414	.00	[0.00, 0.02]
	age	122	-0.43	-1.45	.151	.01	[0.00, 0.04]
Post_ucla_Sum	condition_num	121	-0.09	-0.40	.688	.00	[0.00, 0.01]
	Pre_ucla_Sum	121	0.70	10.02	< .001***	.43	[0.30, 0.56]
	identity_group_num	121	0.52	2.20	.030*	.02	[0.00, 0.06]
	age	121	0.11	2.12	.036*	.02	[0.00, 0.05]

Linear Mixed Effects Models

### Outcome: DERS8_Sum 

Mixed-Effects Model for DERS8_Sum controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	22.165	3.733	5.937
fixed	NA	Week	-0.265	0.064	-4.120
fixed	NA	conditionWaitlist Control	-0.105	0.828	-0.127
fixed	NA	identity_groupTGD	0.930	0.824	1.129
fixed	NA	age	0.277	0.174	1.588
fixed	NA	Week:conditionWaitlist Control	0.284	0.090	3.152
ran_pars	psid	sd__(Intercept)	4.594	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.103	NA	NA
ran_pars	psid	sd__Week	0.468	NA	NA
ran_pars	Residual	sd__Observation	3.608	NA	NA

### Outcome: pmerq_Focus_Avg 

Mixed-Effects Model for pmerq_Focus_Avg controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	3.943	0.738	5.345
fixed	NA	Week	0.048	0.012	4.139
fixed	NA	conditionWaitlist Control	0.258	0.188	1.372
fixed	NA	identity_groupTGD	-0.476	0.163	-2.927
fixed	NA	age	-0.059	0.034	-1.705
fixed	NA	Week:conditionWaitlist Control	-0.035	0.016	-2.192
ran_pars	psid	sd__(Intercept)	0.799	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.454	NA	NA
ran_pars	psid	sd__Week	0.021	NA	NA
ran_pars	Residual	sd__Observation	0.640	NA	NA

### Outcome: pmerq_Distract_Avg 

Mixed-Effects Model for pmerq_Distract_Avg controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	5.349	0.709	7.543
fixed	NA	Week	0.031	0.013	2.307
fixed	NA	conditionWaitlist Control	0.265	0.202	1.310
fixed	NA	identity_groupTGD	0.086	0.156	0.552
fixed	NA	age	-0.066	0.033	-2.006
fixed	NA	Week:conditionWaitlist Control	-0.035	0.019	-1.849
ran_pars	psid	sd__(Intercept)	0.906	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.412	NA	NA
ran_pars	psid	sd__Week	0.057	NA	NA
ran_pars	Residual	sd__Observation	0.648	NA	NA

### Outcome: pmerq_AD_Avg 

Mixed-Effects Model for pmerq_AD_Avg controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	4.685	0.625	7.501
fixed	NA	Week	0.040	0.010	4.079
fixed	NA	conditionWaitlist Control	0.261	0.161	1.622
fixed	NA	identity_groupTGD	-0.202	0.138	-1.465
fixed	NA	age	-0.064	0.029	-2.205
fixed	NA	Week:conditionWaitlist Control	-0.035	0.014	-2.568
ran_pars	psid	sd__(Intercept)	0.674	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.999	NA	NA
ran_pars	psid	sd__Week	0.009	NA	NA
ran_pars	Residual	sd__Observation	0.552	NA	NA

### Outcome: GAD7_Sum 

Mixed-Effects Model for GAD7_Sum controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	10.890	2.892	3.766
fixed	NA	Week	-0.156	0.046	-3.411
fixed	NA	conditionWaitlist Control	-0.065	0.681	-0.095
fixed	NA	identity_groupTGD	1.253	0.637	1.967
fixed	NA	age	0.110	0.135	0.815
fixed	NA	Week:conditionWaitlist Control	0.103	0.064	1.608
ran_pars	psid	sd__(Intercept)	3.702	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.240	NA	NA
ran_pars	psid	sd__Week	0.293	NA	NA
ran_pars	Residual	sd__Observation	3.220	NA	NA

### Outcome: PHQ9_Sum 

Mixed-Effects Model for PHQ9_Sum controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	14.141	3.462	4.085
fixed	NA	Week	-0.177	0.048	-3.705
fixed	NA	conditionWaitlist Control	-1.216	0.753	-1.614
fixed	NA	identity_groupTGD	1.630	0.764	2.133
fixed	NA	age	0.038	0.162	0.234
fixed	NA	Week:conditionWaitlist Control	0.222	0.067	3.320
ran_pars	psid	sd__(Intercept)	4.187	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.056	NA	NA
ran_pars	psid	sd__Week	0.313	NA	NA
ran_pars	Residual	sd__Observation	3.262	NA	NA

### Outcome: SHS_Pathways 

Mixed-Effects Model for SHS_Pathways controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	18.228	2.760	6.604
fixed	NA	Week	0.180	0.053	3.423
fixed	NA	conditionWaitlist Control	0.879	0.807	1.088
fixed	NA	identity_groupTGD	-1.888	0.605	-3.122
fixed	NA	age	-0.246	0.128	-1.924
fixed	NA	Week:conditionWaitlist Control	-0.058	0.074	-0.783
ran_pars	psid	sd__(Intercept)	3.517	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.431	NA	NA
ran_pars	psid	sd__Week	0.201	NA	NA
ran_pars	Residual	sd__Observation	2.669	NA	NA

### Outcome: SHS_Agency 

Mixed-Effects Model for SHS_Agency controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	11.036	3.221	3.426
fixed	NA	Week	0.239	0.063	3.810
fixed	NA	conditionWaitlist Control	0.986	0.897	1.099
fixed	NA	identity_groupTGD	-1.511	0.707	-2.136
fixed	NA	age	-0.045	0.150	-0.300
fixed	NA	Week:conditionWaitlist Control	-0.069	0.088	-0.782
ran_pars	psid	sd__(Intercept)	3.946	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.349	NA	NA
ran_pars	psid	sd__Week	0.294	NA	NA
ran_pars	Residual	sd__Observation	2.928	NA	NA

### Outcome: SHS_TotalHope 

Mixed-Effects Model for SHS_TotalHope controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	29.159	5.488	5.313
fixed	NA	Week	0.419	0.103	4.076
fixed	NA	conditionWaitlist Control	1.843	1.525	1.209
fixed	NA	identity_groupTGD	-3.422	1.205	-2.840
fixed	NA	age	-0.285	0.255	-1.118
fixed	NA	Week:conditionWaitlist Control	-0.125	0.144	-0.869
ran_pars	psid	sd__(Intercept)	7.134	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.419	NA	NA
ran_pars	psid	sd__Week	0.522	NA	NA
ran_pars	Residual	sd__Observation	4.604	NA	NA

### Outcome: ucla_Sum 

Mixed-Effects Model for ucla_Sum controlling for identity_group and age

effect	group	term	estimate	std.error	statistic
fixed	NA	(Intercept)	6.511	1.162	5.602
fixed	NA	Week	-0.028	0.017	-1.668
fixed	NA	conditionWaitlist Control	0.301	0.295	1.019
fixed	NA	identity_groupTGD	0.498	0.256	1.948
fixed	NA	age	0.013	0.054	0.240
fixed	NA	Week:conditionWaitlist Control	-0.008	0.023	-0.366
ran_pars	psid	sd__(Intercept)	1.389	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.052	NA	NA
ran_pars	psid	sd__Week	0.045	NA	NA
ran_pars	Residual	sd__Observation	0.888	NA	NA

NA

Mixed-Effects Model for DERS8_Sum with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	22.165	3.733	5.937	14.848	29.481
fixed	NA	Week	-0.265	0.064	-4.120	-0.391	-0.139
fixed	NA	conditionWaitlist Control	-0.105	0.828	-0.127	-1.729	1.518
fixed	NA	identity_groupTGD	0.930	0.824	1.129	-0.685	2.545
fixed	NA	age	0.277	0.174	1.588	-0.065	0.619
fixed	NA	Week:conditionWaitlist Control	0.284	0.090	3.152	0.108	0.461
ran_pars	psid	sd__(Intercept)	4.594	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.103	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.468	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	3.608	NA	NA	NA	NA

NULL

# R2 for Mixed Models

  Conditional R2: 0.717
     Marginal R2: 0.037

Mixed-Effects Model for DERS8_Sum with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	22.165	3.733	5.937	14.848	29.481
fixed	NA	Week	-0.265	0.064	-4.120	-0.391	-0.139
fixed	NA	conditionWaitlist Control	-0.105	0.828	-0.127	-1.729	1.518
fixed	NA	identity_groupTGD	0.930	0.824	1.129	-0.685	2.545
fixed	NA	age	0.277	0.174	1.588	-0.065	0.619
fixed	NA	Week:conditionWaitlist Control	0.284	0.090	3.152	0.108	0.461
ran_pars	psid	sd__(Intercept)	4.594	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.103	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.468	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	3.608	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.717
     Marginal R2: 0.037

Mixed-Effects Model for pmerq_Focus_Avg with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	3.943	0.738	5.345	2.497	5.389
fixed	NA	Week	0.048	0.012	4.139	0.025	0.070
fixed	NA	conditionWaitlist Control	0.258	0.188	1.372	-0.111	0.628
fixed	NA	identity_groupTGD	-0.476	0.163	-2.927	-0.794	-0.157
fixed	NA	age	-0.059	0.034	-1.705	-0.126	0.009
fixed	NA	Week:conditionWaitlist Control	-0.035	0.016	-2.192	-0.067	-0.004
ran_pars	psid	sd__(Intercept)	0.799	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.454	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.021	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.640	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.680
     Marginal R2: 0.060

Mixed-Effects Model for pmerq_Distract_Avg with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	5.349	0.709	7.543	3.959	6.739
fixed	NA	Week	0.031	0.013	2.307	0.005	0.057
fixed	NA	conditionWaitlist Control	0.265	0.202	1.310	-0.132	0.662
fixed	NA	identity_groupTGD	0.086	0.156	0.552	-0.219	0.391
fixed	NA	age	-0.066	0.033	-2.006	-0.131	-0.002
fixed	NA	Week:conditionWaitlist Control	-0.035	0.019	-1.849	-0.071	0.002
ran_pars	psid	sd__(Intercept)	0.906	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.412	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.057	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.648	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.650
     Marginal R2: 0.031

Mixed-Effects Model for pmerq_AD_Avg with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	4.685	0.625	7.501	3.461	5.909
fixed	NA	Week	0.040	0.010	4.079	0.021	0.059
fixed	NA	conditionWaitlist Control	0.261	0.161	1.622	-0.054	0.576
fixed	NA	identity_groupTGD	-0.202	0.138	-1.465	-0.471	0.068
fixed	NA	age	-0.064	0.029	-2.205	-0.121	-0.007
fixed	NA	Week:conditionWaitlist Control	-0.035	0.014	-2.568	-0.062	-0.008
ran_pars	psid	sd__(Intercept)	0.674	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.999	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.009	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.552	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.661
     Marginal R2: 0.042

Mixed-Effects Model for GAD7_Sum with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	10.890	2.892	3.766	5.222	16.558
fixed	NA	Week	-0.156	0.046	-3.411	-0.246	-0.066
fixed	NA	conditionWaitlist Control	-0.065	0.681	-0.095	-1.400	1.270
fixed	NA	identity_groupTGD	1.253	0.637	1.967	0.004	2.502
fixed	NA	age	0.110	0.135	0.815	-0.154	0.374
fixed	NA	Week:conditionWaitlist Control	0.103	0.064	1.608	-0.023	0.228
ran_pars	psid	sd__(Intercept)	3.702	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.240	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.293	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	3.220	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.606
     Marginal R2: 0.024

Mixed-Effects Model for PHQ9_Sum with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	14.141	3.462	4.085	7.356	20.926
fixed	NA	Week	-0.177	0.048	-3.705	-0.271	-0.083
fixed	NA	conditionWaitlist Control	-1.216	0.753	-1.614	-2.692	0.261
fixed	NA	identity_groupTGD	1.630	0.764	2.133	0.132	3.127
fixed	NA	age	0.038	0.162	0.234	-0.279	0.355
fixed	NA	Week:conditionWaitlist Control	0.222	0.067	3.320	0.091	0.353
ran_pars	psid	sd__(Intercept)	4.187	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	0.056	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.313	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	3.262	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.703
     Marginal R2: 0.024

Mixed-Effects Model for SHS_Pathways with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	18.228	2.760	6.604	12.818	23.638
fixed	NA	Week	0.180	0.053	3.423	0.077	0.283
fixed	NA	conditionWaitlist Control	0.879	0.807	1.088	-0.704	2.461
fixed	NA	identity_groupTGD	-1.888	0.605	-3.122	-3.074	-0.703
fixed	NA	age	-0.246	0.128	-1.924	-0.497	0.005
fixed	NA	Week:conditionWaitlist Control	-0.058	0.074	-0.783	-0.202	0.087
ran_pars	psid	sd__(Intercept)	3.517	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.431	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.201	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	2.669	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.630
     Marginal R2: 0.072

Mixed-Effects Model for SHS_Agency with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	11.036	3.221	3.426	4.723	17.350
fixed	NA	Week	0.239	0.063	3.810	0.116	0.361
fixed	NA	conditionWaitlist Control	0.986	0.897	1.099	-0.773	2.745
fixed	NA	identity_groupTGD	-1.511	0.707	-2.136	-2.897	-0.124
fixed	NA	age	-0.045	0.150	-0.300	-0.338	0.249
fixed	NA	Week:conditionWaitlist Control	-0.069	0.088	-0.782	-0.240	0.103
ran_pars	psid	sd__(Intercept)	3.946	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.349	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.294	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	2.928	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.669
     Marginal R2: 0.051

Mixed-Effects Model for SHS_TotalHope with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	29.159	5.488	5.313	18.402	39.915
fixed	NA	Week	0.419	0.103	4.076	0.217	0.620
fixed	NA	conditionWaitlist Control	1.843	1.525	1.209	-1.146	4.831
fixed	NA	identity_groupTGD	-3.422	1.205	-2.840	-5.784	-1.060
fixed	NA	age	-0.285	0.255	-1.118	-0.785	0.215
fixed	NA	Week:conditionWaitlist Control	-0.125	0.144	-0.869	-0.406	0.157
ran_pars	psid	sd__(Intercept)	7.134	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.419	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.522	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	4.604	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.714
     Marginal R2: 0.070

Mixed-Effects Model for ucla_Sum with 95% CI

effect	group	term	estimate	std.error	statistic	2.5 %	97.5 %
fixed	NA	(Intercept)	6.511	1.162	5.602	4.232	8.789
fixed	NA	Week	-0.028	0.017	-1.668	-0.060	0.005
fixed	NA	conditionWaitlist Control	0.301	0.295	1.019	-0.278	0.880
fixed	NA	identity_groupTGD	0.498	0.256	1.948	-0.003	1.000
fixed	NA	age	0.013	0.054	0.240	-0.093	0.119
fixed	NA	Week:conditionWaitlist Control	-0.008	0.023	-0.366	-0.054	0.037
ran_pars	psid	sd__(Intercept)	1.389	NA	NA	NA	NA
ran_pars	psid	cor__(Intercept).Week	-0.052	NA	NA	NA	NA
ran_pars	psid	sd__Week	0.045	NA	NA	NA	NA
ran_pars	Residual	sd__Observation	0.888	NA	NA	NA	NA

# R2 for Mixed Models

  Conditional R2: 0.729
     Marginal R2: 0.030