6  Characteristics of Fixed Effects Variables for Mixed Models

The following code reproduces the factor coding and descriptive statistics reported in Table 3 of the main text.

6.1 Dependent Variables

for (p in str_perception_levels) {
  cat(paste0("#### ", p, "\n\n"))

  df <- data_rq1 %>%
    filter(perception == p) %>%
    group_by(Timepoint) %>%
    get_summary_stats(rating, type = "common") %>%
    mutate(across(c(mean, sd), ~ scales::number(.x, accuracy = .01)))

  print(kable(df))

  cat("\n")

  rm(p, df)
}

Confidence

Timepoint	variable	n	min	max	median	iqr	mean	sd	se	ci
Baseline	rating	1490	1	6	4	2	3.69	1.44	0.037	0.073
Posttest	rating	1785	1	6	4	2	3.88	1.46	0.035	0.068

Anxiety

Timepoint	variable	n	min	max	median	iqr	mean	sd	se	ci
Baseline	rating	1490	1	6	3	2	3.31	1.41	0.036	0.071
Posttest	rating	1785	1	6	3	2	3.00	1.37	0.033	0.064

Difficulty

Timepoint	variable	n	min	max	median	iqr	mean	sd	se	ci
Baseline	rating	1490	1	6	4	1	3.55	1.26	0.033	0.064
Posttest	rating	1785	1	6	4	2	3.43	1.30	0.031	0.060

6.2 Covariates

Cohort

data_rq1$Cohort %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels") %>%
  round_all_doubles()

Factor Levels	Cohort 2	Cohort 3
Cohort 1	-0.11	-0.48
Cohort 2	0.89	-0.48
Cohort 3	-0.11	0.52

Semester Week

data_rq1 %>%
  select(Participant, Semester_Week) %>%
  unique() %>%
  get_summary_stats(Semester_Week, type = "common")

variable	n	min	max	median	iqr	mean	sd	se	ci
Semester_Week	149	-4.087	4.913	-1.087	5	0	2.79	0.229	0.452

Test Version

data_rq1$Test_Version %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels") %>%
  round_all_doubles()

Factor Levels	B
A	-0.50
B	0.50

Item-Level Accuracy

Factor Coding:

data_rq1$`Item-Level Accuracy` %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels") %>%
  round_all_doubles()

Factor Levels	Correct
Incorrect	-0.44
Correct	0.56

Baseline and Posttest Means and Standard Deviations:

data_rq1 %>%
  select(Participant, Timepoint, Item, Accuracy_Raw) %>%
  unique() %>%
  group_by(Timepoint) %>%
  get_summary_stats(type = "common") %>%
  mutate(across(c(mean, sd), ~ scales::number(.x, accuracy = .01)))

Timepoint	variable	n	min	max	median	iqr	mean	sd	se	ci
Baseline	Accuracy_Raw	1490	0	1	0	1	0.43	0.50	0.013	0.025
Posttest	Accuracy_Raw	1785	0	1	0	1	0.44	0.50	0.012	0.023

Baseline Threat

data_rq1 %>%
  select(Participant, Baseline_Threat) %>%
  unique() %>%
  get_summary_stats(Baseline_Threat, type = "common")

variable	n	min	max	median	iqr	mean	sd	se	ci
Baseline_Threat	149	-3.102	2.865	-0.035	1.933	0	1.297	0.106	0.21

6.3 Main Independent Variables of Interest

Timepoint

data_rq1$Timepoint %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels")

Factor Levels	Posttest
Baseline	0
Posttest	1

Condition

data_rq1$Condition %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels")

Factor Levels	Mindfulness
Control	-0.5
Mindfulness	0.5

Gender

data_rq1$Gender %>%
  contrasts() %>%
  as_tibble(rownames = "Factor Levels") %>%
  round_all_doubles()

Factor Levels	Women or Non-binary
Men	-0.56
Women or Non-binary	0.44