Thesis Figures, Method, Descriptives
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## as.character.polynomial polynomGeneral set up
Full_data <- read_csv("Full_data_all.csv")## Rows: 259 Columns: 208
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Group
## dbl (207): ID, B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5, B_IUS_6, B_IUS_7...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Demographics_excluded <- read_csv("Demographics.csv")## New names:
## Rows: 259 Columns: 11
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## (8): Group, Gender_value, Ethnicity_value, Country_residence, Ppt_educat... dbl
## (3): ...1, ID, Age
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`Acceptability_numeric <- read_csv("Acceptability.csv")## New names:
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## (1): Group dbl (6): ...1, ID, B_acceptability_understandable,
## B_acceptability_useful, B...
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## • `` -> `...1`Behavioural task data set up
BT_full_raw <- read_csv("Full_BT_data.csv") %>%
dplyr::select("ID", "A_PRE_samples", "B_POST_samples") # numbers in the PRE_samples and POST_samples columns are the number of times participants sampled across each set of 10 trials (maximum being 300 - one ppt has 308 due to a glitch)## New names:
## Rows: 259 Columns: 4
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## (4): ...1, ID, A_PRE_samples, B_POST_samples
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`# Adding in groups + excluding participants who only sampled (never made a choice = did not understand task)
BT_data <- merge(BT_full_raw,Full_data,
by=c("ID"),
all = TRUE) %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples", "A_PRE_IUS_total", "B_POST_IUS_total") %>%
filter(ID != "8892522", ID != "8892570", ID != "8892628", ID != "8892668", ID != "8892681", ID != "8892779", ID != "8892794", ID != "8893157", ID != "8893186", ID != "8892873", ID != "9113535", ID != "9113549", ID != "9113550") # excluding those not making a choiceHypothesis 1
Cognitive IU with Depression Symptoms
Plot_ius_phq <- ggplot(data = Full_data, aes(x = A_PRE_IUS_total, y = A_PRE_PHQ_total)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", color = "black") +
scale_x_continuous(name = "Baseline Intolerance of Uncertainty (IUS-12)") +
scale_y_continuous(name = "Baseline Depression Symptoms (PHQ-8)") +
coord_cartesian(xlim = c(12, 60)) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic()
print(Plot_ius_phq)## `geom_smooth()` using formula = 'y ~ x'
ggsave(plot=Plot_ius_phq, 'IUS_PHQ.png',
width=7, height=4,
dpi=500)## `geom_smooth()` using formula = 'y ~ x'Cognitive IU with Anxiety Symptoms
Plot_ius_gad <- ggplot(data = Full_data, aes(x = A_PRE_IUS_total, y = A_PRE_GAD_total)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", color = "black") +
scale_x_continuous(name = "Baseline Intolerance of Uncertainty (IUS-12)") +
scale_y_continuous(name = "Baseline Anxiety (GAD-7)") +
coord_cartesian(xlim = c(12, 60)) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic()
print(Plot_ius_gad)## `geom_smooth()` using formula = 'y ~ x'
ggsave(plot=Plot_ius_gad, 'IUS_GAD.png',
width=7, height=4,
dpi=500)## `geom_smooth()` using formula = 'y ~ x'Cognitive IU with Behavioural IU
### Excluding those who never sample
BT_removed <- BT_data %>%
filter(A_PRE_samples != "0")
Plot_ius_btremoved <- ggplot(data = BT_removed, aes(x = A_PRE_IUS_total, y = A_PRE_samples)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", color = "black") +
scale_x_continuous(name = "Baseline Cognitive Intolerance of Uncertainty (IUS-12)") +
scale_y_continuous(name = "Baseline Behavioural Intolerance of Uncertainty \n (Total Number of Samples)") +
coord_cartesian(xlim = c(12, 60)) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic()
print(Plot_ius_btremoved)## `geom_smooth()` using formula = 'y ~ x'
ggsave(plot=Plot_ius_btremoved, 'Plot_ius_btremoved.png',
width=7, height=4,
dpi=500)## `geom_smooth()` using formula = 'y ~ x'Cognitive IU with Functional Impairment
Plot_ius_fi <- ggplot(data = Full_data, aes(x = A_PRE_IUS_total, y = A_PRE_FI_total)) +
geom_point(alpha = 0.5) +
geom_smooth(method = "lm", color = "black") +
scale_x_continuous(name = "Baseline Intolerance of Uncertainty (IUS-12)") +
scale_y_continuous(name = "Baseline Functional Impairment") +
coord_cartesian(xlim = c(12, 60), ylim = c(0, 20)) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic()
print(Plot_ius_fi)## `geom_smooth()` using formula = 'y ~ x'
ggsave(plot=Plot_ius_fi, 'IUS_FI.png',
width=7, height=4,
dpi=500)## `geom_smooth()` using formula = 'y ~ x'Correlations
cor.test(BT_removed$A_PRE_IUS_total, BT_removed$A_PRE_samples, method="pearson")##
## Pearson's product-moment correlation
##
## data: BT_removed$A_PRE_IUS_total and BT_removed$A_PRE_samples
## t = -3.0031, df = 171, p-value = 0.003074
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.36096987 -0.07720195
## sample estimates:
## cor
## -0.2238241cor.test(Full_data$A_PRE_IUS_total, Full_data$A_PRE_PHQ_total, method="pearson")##
## Pearson's product-moment correlation
##
## data: Full_data$A_PRE_IUS_total and Full_data$A_PRE_PHQ_total
## t = 8.0215, df = 257, p-value = 3.716e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3443686 0.5399152
## sample estimates:
## cor
## 0.4474747cor.test(Full_data$A_PRE_IUS_total, Full_data$A_PRE_GAD_total, method="pearson")##
## Pearson's product-moment correlation
##
## data: Full_data$A_PRE_IUS_total and Full_data$A_PRE_GAD_total
## t = 9.5802, df = 257, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4171735 0.5975068
## sample estimates:
## cor
## 0.5129779cor.test(Full_data$A_PRE_IUS_total, Full_data$A_PRE_FI_total, method="pearson")##
## Pearson's product-moment correlation
##
## data: Full_data$A_PRE_IUS_total and Full_data$A_PRE_FI_total
## t = 11.26, df = 257, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4870121 0.6510571
## sample estimates:
## cor
## 0.5747811Hypothesis 2
Intolerance of Uncertainty
IUS_alltimepoints <- Full_data %>%
dplyr::select("A_PRE_IUS_total", "B_POST_IUS_total", "C_W1_IUS_total", "D_M1_IUS_total", "Group") %>%
pivot_longer(cols = c(A_PRE_IUS_total, B_POST_IUS_total, C_W1_IUS_total, D_M1_IUS_total),
names_to = "Time",
values_to = "IUS_Score")IUS_rain <- IUS_alltimepoints %>%
mutate(Time=case_when(
Time == "A_PRE_IUS_total" ~ "Baseline",
Time == "B_POST_IUS_total" ~ "Post",
Time == "C_W1_IUS_total" ~ "1 Week",
Time == "D_M1_IUS_total" ~ "1 Month"
)) %>%
mutate(Time=factor(Time, levels=c("Baseline", "Post", "1 Week", "1 Month"))) %>%
ggplot(mapping = aes(x = Group, y = IUS_Score, fill = Time)) +
ggdist::stat_halfeye(aes(fill = Time), position = position_nudge(x = 0.1, y = 0),
adjust = 0.5, width = 0.45, .width = 0, justification = -0.2,
trim = FALSE, alpha = .5, colour = NA) +
gghalves::geom_half_point(aes(colour = Time), side = "l", position = position_jitternudge(x=-0.1,width = 0.001), range_scale = 0.4, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5, position = position_dodge(width = 0.3), fatten=NULL) +
stat_summary(fun = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
width = 0.2, size = 1, linetype = "solid",
position = position_dodge (width = .3)) +
scale_x_discrete(name = "Group", labels = paste(c("Non-Active Control", "Psychoeducation Control", "Mindset Intervention"))) +
scale_y_continuous(name = "Intolerance of Uncertainty (IUS-12)") +
scale_color_manual(values = c("#4682B4", "#7ecf97", "#fba863", "#af5f90")) +
scale_fill_manual (values = c("#4682B4", "#7ecf97", "#fba863", "#af5f90")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
IUS_rain
ggsave(plot=IUS_rain, 'IUS_rain.png',
width=8, height=4,
dpi=500)Behavioural IU
Excluding NS
BT_BP_removed <- BT_data %>%
filter(A_PRE_samples != "0") %>% # only excluding from PRE
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP_long_removed <- BT_BP_removed %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score") %>%
mutate(Time = recode(Time, "A_PRE_samples"=1,"B_POST_samples"=2))BT_mem_r <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long_removed, REML = TRUE)
BT_mem_plot_r <- interact_plot(BT_mem_r, pred = Time, modx = Group, modx.values = c("A_ECs", "B_Controls", "C_Intervention"), interval = TRUE, x.label = "Time", y.label = "Behavioural Intolerance of Uncertainty", main.title = "",legend.main = "Group", colors = NULL) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic() +
coord_cartesian(ylim = c(0, 35))
BT_mem_plot_r
ggsave(plot=BT_mem_plot_r, 'BT_mem_plot_r.png',
width=7, height=4,
dpi=500)Including NS
BT_BP <- BT_data %>%
dplyr::select("ID", "Group", "A_PRE_samples", "B_POST_samples")
BT_BP_long <- BT_BP %>%
pivot_longer(cols = c(A_PRE_samples, B_POST_samples),
names_to = "Time",
values_to = "BT_Score") %>%
mutate(Time = recode(Time, "A_PRE_samples"=1,"B_POST_samples"=2))BT_mem <- lmer(BT_Score ~ Group * Time + (1|ID), data = BT_BP_long, REML = TRUE)
BT_mem_plot <- interact_plot(BT_mem, pred = Time, modx = Group, modx.values = c("A_ECs", "B_Controls", "C_Intervention"), interval = TRUE, x.label = "Time", y.label = "Behavioural Intolerance of Uncertainty", main.title = "",legend.main = "Group", colors = NULL) +
theme(text = element_text(size = 20), axis.text = element_text(size = 20), axis.title.x = element_text(size = 20), axis.title.y = element_text(size = 20)) +
theme_classic() +
coord_cartesian(ylim = c(0, 35))
BT_mem_plot
ggsave(plot=BT_mem_plot, 'BT_mem_plot.png',
width=7, height=4,
dpi=500)Growth Mindsets
GM_alltimepoints <- Full_data %>%
dplyr::select("A_PRE_GM", "B_POST_GM", "C_W1_GM", "D_M1_GM", "Group") %>%
pivot_longer(cols = c(A_PRE_GM, B_POST_GM, C_W1_GM, D_M1_GM),
names_to = "Time",
values_to = "GM_Score")GM_rain <- GM_alltimepoints %>%
mutate(Time=case_when(
Time == "A_PRE_GM" ~ "Baseline",
Time == "B_POST_GM" ~ "Post",
Time == "C_W1_GM" ~ "1 Week",
Time == "D_M1_GM" ~ "1 Month"
)) %>%
mutate(Time=factor(Time, levels=c("Baseline", "Post", "1 Week", "1 Month"))) %>%
ggplot(mapping = aes(x = Group, y = GM_Score, fill = Time)) +
ggdist::stat_halfeye(aes(fill = Time), position = position_nudge(x = .1, y = 0),
adjust = 0.5, width = 0.45, .width = 0, justification = -0.2,
trim = FALSE, alpha = .5, colour = NA) +
gghalves::geom_half_point(aes(colour = Time), side = "l", position = position_jitternudge(x=-.1,width = 0.001), range_scale = 0.4, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5, position = position_dodge (width = .3), fatten=NULL) +
stat_summary(fun = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
width = 0.2, size = 1, linetype = "solid",
position = position_dodge (width = .3)) +
scale_x_discrete(name = "Group", labels = paste(c("Non-Active Control", "Psychoeducation Control", "Mindset Intervention"))) +
scale_y_continuous(name = "Fixed Mindsets") +
scale_color_manual(values = c("#4682B4", "#7ecf97", "#fba863", "#af5f90")) +
scale_fill_manual (values = c("#4682B4", "#7ecf97", "#fba863", "#af5f90")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
GM_rain
ggsave(plot=GM_rain, 'GM_rain.png',
width=8, height=4,
dpi=500)Depression
PHQ_alltimepoints <- Full_data %>%
dplyr::select("A_PRE_PHQ_total", "C_W1_PHQ_total", "D_M1_PHQ_total", "Group") %>%
pivot_longer(cols = c(A_PRE_PHQ_total, C_W1_PHQ_total, D_M1_PHQ_total),
names_to = "Time",
values_to = "PHQ_Score")PHQ_rain <- PHQ_alltimepoints %>%
mutate(Time=case_when(
Time == "A_PRE_PHQ_total" ~ "Baseline",
Time == "B_POST_PHQ_total" ~ "Post",
Time == "C_W1_PHQ_total" ~ "1 Week",
Time == "D_M1_PHQ_total" ~ "1 Month"
)) %>%
mutate(Time=factor(Time, levels=c("Baseline", "Post", "1 Week", "1 Month"))) %>%
ggplot(mapping = aes(x = Group, y = PHQ_Score, fill = Time)) +
ggdist::stat_halfeye(aes(fill = Time), position = position_nudge(x = .1, y = 0),
adjust = 0.5, width = 0.45, .width = 0, justification = -0.2,
trim = FALSE, alpha = .5, colour = NA) +
gghalves::geom_half_point(aes(colour = Time), side = "l", position = position_jitternudge(x=-.1,width = 0.001), range_scale = 0.4, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5, position = position_dodge (width = .3), fatten=NULL) +
stat_summary(fun = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
width = 0.2, size = 1, linetype = "solid",
position = position_dodge (width = .3)) +
scale_x_discrete(name = "Group", labels = paste(c("Non-Active Control", "Psychoeducation Control", "Mindset Intervention"))) +
scale_y_continuous(name = "Depression Symptoms (PHQ-8)") +
scale_color_manual(values = c("#4682B4", "#fba863", "#af5f90")) +
scale_fill_manual (values = c("#4682B4", "#fba863", "#af5f90")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
PHQ_rain
ggsave(plot=PHQ_rain, 'PHQ_rain.png',
width=8, height=4,
dpi=500)Anxiety
GAD_alltimepoints <- Full_data %>%
dplyr::select("A_PRE_GAD_total", "C_W1_GAD_total", "D_M1_GAD_total", "Group") %>%
pivot_longer(cols = c(A_PRE_GAD_total, C_W1_GAD_total, D_M1_GAD_total),
names_to = "Time",
values_to = "GAD_Score")GAD_rain <- GAD_alltimepoints %>%
mutate(Time=case_when(
Time == "A_PRE_GAD_total" ~ "Baseline",
Time == "B_POST_GAD_total" ~ "Post",
Time == "C_W1_GAD_total" ~ "1 Week",
Time == "D_M1_GAD_total" ~ "1 Month"
)) %>%
mutate(Time=factor(Time, levels=c("Baseline", "Post", "1 Week", "1 Month"))) %>%
ggplot(mapping = aes(x = Group, y = GAD_Score, fill = Time)) +
ggdist::stat_halfeye(aes(fill = Time), position = position_nudge(x = .1, y = 0),
adjust = 0.5, width = 0.45, .width = 0, justification = -0.2,
trim = FALSE, alpha = .5, colour = NA) +
gghalves::geom_half_point(aes(colour = Time), side = "l", position = position_jitternudge(x=-.1,width = 0.001), range_scale = 0.4, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5, position = position_dodge (width = .3), fatten=NULL) +
stat_summary(fun = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
width = 0.2, size = 1, linetype = "solid",
position = position_dodge (width = .3)) +
scale_x_discrete(name = "Group", labels = paste(c("Non-Active Control", "Psychoeducation Control", " Mindset Intervention"))) +
scale_y_continuous(name = "Anxiety Symptoms (GAD-7)") +
scale_color_manual(values = c("#4682B4", "#fba863", "#af5f90")) +
scale_fill_manual (values = c("#4682B4", "#fba863", "#af5f90")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
GAD_rain
ggsave(plot=GAD_rain, 'GAD_rain.png',
width=8, height=4,
dpi=500)Functional Impairment
FI_alltimepoints <- Full_data %>%
dplyr::select("A_PRE_FI_total", "C_W1_FI_total", "D_M1_FI_total", "Group") %>%
pivot_longer(cols = c(A_PRE_FI_total, C_W1_FI_total, D_M1_FI_total),
names_to = "Time",
values_to = "FI_Score")FI_rain <- FI_alltimepoints %>%
mutate(Time=case_when(
Time == "A_PRE_FI_total" ~ "Baseline",
Time == "B_POST_FI_total" ~ "Post",
Time == "C_W1_FI_total" ~ "1 Week",
Time == "D_M1_FI_total" ~ "1 Month"
)) %>%
mutate(Time=factor(Time, levels=c("Baseline", "Post", "1 Week", "1 Month"))) %>%
ggplot(mapping = aes(x = Group, y = FI_Score, fill = Time)) +
ggdist::stat_halfeye(aes(fill = Time), position = position_nudge(x = .1, y = 0),
adjust = 0.5, width = 0.45, .width = 0, justification = -0.2,
trim = FALSE, alpha = .5, colour = NA) +
gghalves::geom_half_point(aes(colour = Time), side = "l", position = position_jitternudge(x=-.1,width = 0.001), range_scale = 0.4, alpha = 0.5) +
geom_boxplot(width = 0.2, outlier.shape = NA, alpha = 0.5, position = position_dodge (width = .3), fatten=NULL) +
stat_summary(fun = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
width = 0.2, size = 1, linetype = "solid",
position = position_dodge (width = .3)) +
scale_x_discrete(name = "Group", labels = paste(c("Non-Active Control", "Psychoeducation Control", "Mindset Intervention"))) +
scale_y_continuous(name = "Functional Impairment") +
scale_color_manual(values = c("#4682B4", "#fba863", "#af5f90")) +
scale_fill_manual (values = c("#4682B4", "#fba863", "#af5f90")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"))
FI_rain
ggsave(plot=FI_rain, 'FI_rain.png',
width=8, height=4,
dpi=500)Method Analyses
Internal consistency
# Creating dataframes for each questionnaire
PRE_IUS_responses <- Full_data %>%
dplyr::select(B_IUS_1, B_IUS_2, B_IUS_3, B_IUS_4, B_IUS_5, B_IUS_6, B_IUS_7, B_IUS_8, B_IUS_9, B_IUS_10, B_IUS_11, B_IUS_12)
PRE_FI_responses <- Full_data %>%
dplyr::select(c(B_FI_1r, B_FI_2r, B_FI_3r, B_FI_4r, B_FI_5r))
PRE_PHQ_responses <- Full_data %>%
dplyr::select(c(B_PHQ_1r, B_PHQ_2r, B_PHQ_3r, B_PHQ_4r, B_PHQ_5r, B_PHQ_6r, B_PHQ_7r, B_PHQ_8r))
PRE_GAD_responses <- Full_data %>%
dplyr::select(c(B_GAD_1r, B_GAD_2r, B_GAD_3r, B_GAD_4r, B_GAD_5r, B_GAD_6r, B_GAD_7r))
POST_IUS_responses <- Full_data %>%
dplyr::select(c(POST_IUS_1, POST_IUS_2, POST_IUS_3, POST_IUS_4, POST_IUS_5, POST_IUS_6, POST_IUS_7, POST_IUS_8, POST_IUS_9, POST_IUS_10, POST_IUS_11, POST_IUS_12))
W1_IUS_responses <- Full_data %>%
dplyr::select(c(W1_IUS_1, W1_IUS_2, W1_IUS_3, W1_IUS_4, W1_IUS_5, W1_IUS_6, W1_IUS_7, W1_IUS_8, W1_IUS_9, W1_IUS_10, W1_IUS_11, W1_IUS_12))
W1_FI_responses <- Full_data %>%
dplyr::select(c(W1_FI_1r, W1_FI_2r, W1_FI_3r, W1_FI_4r, W1_FI_5r))
W1_PHQ_responses <- Full_data %>%
dplyr::select(c(W1_PHQ_1r, W1_PHQ_2r, W1_PHQ_3r, W1_PHQ_4r, W1_PHQ_5r, W1_PHQ_6r, W1_PHQ_7r, W1_PHQ_8r))
W1_GAD_responses <- Full_data %>%
dplyr::select(c(W1_GAD_1r, W1_GAD_2r, W1_GAD_3r, W1_GAD_4r, W1_GAD_5r, W1_GAD_6r, W1_GAD_7r))
M1_IUS_responses <- Full_data %>%
dplyr::select(c(M1_IUS_1, M1_IUS_2, M1_IUS_3, M1_IUS_4, M1_IUS_5, M1_IUS_6, M1_IUS_7, M1_IUS_8, M1_IUS_9, M1_IUS_10, M1_IUS_11, M1_IUS_12))
M1_FI_responses <- Full_data %>%
dplyr::select(c(M1_FI_1r, M1_FI_2r, M1_FI_3r, M1_FI_4r, M1_FI_5r))
M1_PHQ_responses <- Full_data %>%
dplyr::select(c(M1_PHQ_1r, M1_PHQ_2r, M1_PHQ_3r, M1_PHQ_4r, M1_PHQ_5r, M1_PHQ_6r, M1_PHQ_7r, M1_PHQ_8r))
M1_GAD_responses <- Full_data %>%
dplyr::select(c(M1_GAD_1r, M1_GAD_2r, M1_GAD_3r, M1_GAD_4r, M1_GAD_5r, M1_GAD_6r, M1_GAD_7r))
Acceptability_responses <- Acceptability_numeric %>%
filter(Group != "A_ECs") %>%
dplyr::select(c(B_acceptability_understandable, B_acceptability_useful, B_acceptability_recommend))Baseline
omega(PRE_IUS_responses) # omega total = 0.89## Loading required namespace: GPArotation
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.87
## G.6: 0.87
## Omega Hierarchical: 0.7
## Omega H asymptotic: 0.78
## Omega Total 0.89
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## B_IUS_1 0.65 0.55 0.73 0.27 0.58
## B_IUS_2 0.53 0.34 0.40 0.60 0.69
## B_IUS_3 0.53 0.27 0.36 0.64 0.78
## B_IUS_4 0.37 0.41 0.31 0.69 0.46
## B_IUS_5 0.51 0.34 0.66 0.78
## B_IUS_6 0.63 0.39 0.56 0.44 0.71
## B_IUS_7 0.70 0.43 0.68 0.32 0.73
## B_IUS_8 0.41 0.47 0.40 0.60 0.42
## B_IUS_9 0.56 0.31 0.44 0.56 0.72
## B_IUS_10 0.60 0.28 0.45 0.55 0.80
## B_IUS_11 0.40 0.53 0.44 0.56 0.35
## B_IUS_12 0.53 0.45 0.51 0.49 0.55
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.56 0.54 1.02 0.49
##
## general/max 3.47 max/min = 2.1
## mean percent general = 0.63 with sd = 0.15 and cv of 0.24
## Explained Common Variance of the general factor = 0.63
##
## The degrees of freedom are 33 and the fit is 0.15
## The number of observations was 259 with Chi Square = 37.19 with prob < 0.28
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.022 and the 10 % confidence intervals are 0 0.052
## BIC = -146.18
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 54 and the fit is 0.73
## The number of observations was 259 with Chi Square = 183.64 with prob < 5e-16
## The root mean square of the residuals is 0.1
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.096 and the 10 % confidence intervals are 0.081 0.112
## BIC = -116.43
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.86 0.57 0.74 0.67
## Multiple R square of scores with factors 0.74 0.32 0.54 0.44
## Minimum correlation of factor score estimates 0.48 -0.36 0.09 -0.11
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.89 0.80 0.78 0.71
## Omega general for total scores and subscales 0.70 0.61 0.45 0.45
## Omega group for total scores and subscales 0.14 0.19 0.33 0.26omega(PRE_FI_responses) # omega total = 0.75## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.68
## G.6: 0.66
## Omega Hierarchical: 0.56
## Omega H asymptotic: 0.74
## Omega Total 0.75
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## B_FI_1r 0.70 0.50 0.50 0.99
## B_FI_2r 0.30 0.32 0.21 0.79 0.44
## B_FI_3r 0.43 0.53 0.47 0.53 0.40
## B_FI_4r 0.45 0.70 0.69 0.31 0.29
## B_FI_5r 0.59 0.38 0.62 0.93
##
## With Sums of squares of:
## g F1* F2* F3*
## 1.33 0.80 0.00 0.13
##
## general/max 1.65 max/min = Inf
## mean percent general = 0.61 with sd = 0.32 and cv of 0.53
## Explained Common Variance of the general factor = 0.59
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 259 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.23
## The number of observations was 259 with Chi Square = 59.63 with prob < 1.4e-11
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.18
##
## RMSEA index = 0.205 and the 10 % confidence intervals are 0.161 0.254
## BIC = 31.85
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.81 0.78 0 0.39
## Multiple R square of scores with factors 0.65 0.61 0 0.15
## Minimum correlation of factor score estimates 0.31 0.21 -1 -0.70
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.75 0.73 NA 0.57
## Omega general for total scores and subscales 0.56 0.25 NA 0.56
## Omega group for total scores and subscales 0.14 0.48 NA 0.01omega(PRE_PHQ_responses) # omega total = 0.88
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.84
## G.6: 0.84
## Omega Hierarchical: 0.73
## Omega H asymptotic: 0.83
## Omega Total 0.88
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## B_PHQ_1r 0.61 0.41 0.59 0.92
## B_PHQ_2r 0.79 0.24 0.69 0.31 0.90
## B_PHQ_3r 0.59 0.69 0.83 0.17 0.42
## B_PHQ_4r 0.54 0.24 0.25 0.41 0.59 0.70
## B_PHQ_5r 0.44 0.26 0.20 0.30 0.70 0.64
## B_PHQ_6r 0.72 0.21 0.58 0.42 0.89
## B_PHQ_7r 0.50 0.64 0.65 0.35 0.38
## B_PHQ_8r 0.50 0.30 0.34 0.66 0.72
##
## With Sums of squares of:
## g F1* F2* F3*
## 2.85 0.13 0.62 0.63
##
## general/max 4.53 max/min = 4.76
## mean percent general = 0.7 with sd = 0.21 and cv of 0.3
## Explained Common Variance of the general factor = 0.67
##
## The degrees of freedom are 7 and the fit is 0.07
## The number of observations was 259 with Chi Square = 17.89 with prob < 0.012
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.05
## RMSEA index = 0.077 and the 10 % confidence intervals are 0.034 0.123
## BIC = -21
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.34
## The number of observations was 259 with Chi Square = 85.09 with prob < 5.3e-10
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.1
##
## RMSEA index = 0.112 and the 10 % confidence intervals are 0.088 0.137
## BIC = -26.05
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.31 0.81 0.74
## Multiple R square of scores with factors 0.79 0.10 0.66 0.55
## Minimum correlation of factor score estimates 0.59 -0.80 0.32 0.09
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.88 0.79 0.68 0.70
## Omega general for total scores and subscales 0.73 0.73 0.37 0.44
## Omega group for total scores and subscales 0.09 0.06 0.31 0.26omega(PRE_GAD_responses) # omega total = 0.91
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.88
## G.6: 0.87
## Omega Hierarchical: 0.81
## Omega H asymptotic: 0.89
## Omega Total 0.91
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## B_GAD_1r 0.65 0.24 0.48 0.52 0.88
## B_GAD_2r 0.80 0.29 0.72 0.28 0.88
## B_GAD_3r 0.78 0.29 0.69 0.31 0.88
## B_GAD_4r 0.76 0.30 0.69 0.31 0.84
## B_GAD_5r 0.58 0.45 0.55 0.45 0.62
## B_GAD_6r 0.57 0.82 1.00 0.00 0.32
## B_GAD_7r 0.63 0.45 0.55 0.89
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.31 0.27 0.32 0.68
##
## general/max 4.89 max/min = 2.54
## mean percent general = 0.76 with sd = 0.22 and cv of 0.28
## Explained Common Variance of the general factor = 0.72
##
## The degrees of freedom are 3 and the fit is 0
## The number of observations was 259 with Chi Square = 0.57 with prob < 0.9
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is 0.01
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.044
## BIC = -16.1
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.13
## The number of observations was 259 with Chi Square = 32.43 with prob < 0.0035
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.06
##
## RMSEA index = 0.071 and the 10 % confidence intervals are 0.039 0.104
## BIC = -45.37
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.91 0.40 0.60 0.95
## Multiple R square of scores with factors 0.83 0.16 0.36 0.91
## Minimum correlation of factor score estimates 0.65 -0.67 -0.29 0.82
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.91 0.84 0.77 0.99
## Omega general for total scores and subscales 0.81 0.74 0.64 0.32
## Omega group for total scores and subscales 0.08 0.10 0.14 0.67Post
omega(POST_IUS_responses) # omega total = 0.94
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.92
## G.6: 0.93
## Omega Hierarchical: 0.78
## Omega H asymptotic: 0.83
## Omega Total 0.94
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## POST_IUS_1 0.81 0.43 0.84 0.16 0.77
## POST_IUS_2 0.67 0.28 0.56 0.44 0.81
## POST_IUS_3 0.68 0.32 0.56 0.44 0.82
## POST_IUS_4 0.53 0.46 0.51 0.49 0.56
## POST_IUS_5 0.64 0.21 0.21 0.50 0.50 0.81
## POST_IUS_6 0.71 0.39 0.67 0.33 0.76
## POST_IUS_7 0.77 0.40 0.76 0.24 0.79
## POST_IUS_8 0.51 0.44 0.49 0.51 0.54
## POST_IUS_9 0.70 0.20 0.27 0.61 0.39 0.80
## POST_IUS_10 0.71 0.36 0.63 0.37 0.79
## POST_IUS_11 0.45 0.62 0.59 0.41 0.34
## POST_IUS_12 0.58 0.23 0.36 0.52 0.48 0.65
##
## With Sums of squares of:
## g F1* F2* F3*
## 5.15 0.69 1.08 0.33
##
## general/max 4.77 max/min = 3.27
## mean percent general = 0.7 with sd = 0.15 and cv of 0.21
## Explained Common Variance of the general factor = 0.71
##
## The degrees of freedom are 33 and the fit is 0.09
## The number of observations was 259 with Chi Square = 22.01 with prob < 0.93
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.02
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.015
## BIC = -161.36
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 54 and the fit is 0.96
## The number of observations was 259 with Chi Square = 242.4 with prob < 1.1e-25
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.116 and the 10 % confidence intervals are 0.102 0.131
## BIC = -57.67
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.91 0.60 0.80 0.62
## Multiple R square of scores with factors 0.82 0.35 0.63 0.39
## Minimum correlation of factor score estimates 0.64 -0.29 0.27 -0.22
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.94 0.89 0.83 0.81
## Omega general for total scores and subscales 0.78 0.72 0.52 0.66
## Omega group for total scores and subscales 0.10 0.17 0.31 0.151 Week
omega(W1_IUS_responses) # omega total = 0.93
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.91
## G.6: 0.91
## Omega Hierarchical: 0.71
## Omega H asymptotic: 0.77
## Omega Total 0.93
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## W1_IUS_1 0.68 0.33 0.62 0.38 0.76
## W1_IUS_2 0.71 0.71 1.00 0.00 0.50
## W1_IUS_3 0.56 0.38 0.46 0.54 0.67
## W1_IUS_4 0.54 0.43 0.48 0.52 0.62
## W1_IUS_5 0.49 0.36 0.38 0.62 0.63
## W1_IUS_6 0.60 0.58 0.70 0.30 0.52
## W1_IUS_7 0.61 0.51 0.64 0.36 0.58
## W1_IUS_8 0.57 0.43 0.51 0.49 0.63
## W1_IUS_9 0.58 0.29 0.26 0.50 0.50 0.68
## W1_IUS_10 0.60 0.52 0.63 0.37 0.57
## W1_IUS_11 0.59 0.47 0.57 0.43 0.61
## W1_IUS_12 0.57 0.32 0.47 0.53 0.70
##
## With Sums of squares of:
## g F1* F2* F3*
## 4.25 1.37 0.81 0.53
##
## general/max 3.09 max/min = 2.58
## mean percent general = 0.62 with sd = 0.07 and cv of 0.12
## Explained Common Variance of the general factor = 0.61
##
## The degrees of freedom are 33 and the fit is 0.21
## The number of observations was 259 with Chi Square = 53.03 with prob < 0.015
## The root mean square of the residuals is 0.03
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.048 and the 10 % confidence intervals are 0.022 0.072
## BIC = -130.35
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 54 and the fit is 1.25
## The number of observations was 259 with Chi Square = 314.4 with prob < 2e-38
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.136 and the 10 % confidence intervals are 0.122 0.152
## BIC = 14.34
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.86 0.75 0.68 0.87
## Multiple R square of scores with factors 0.75 0.56 0.47 0.76
## Minimum correlation of factor score estimates 0.49 0.12 -0.07 0.52
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.93 0.88 0.79 1.0
## Omega general for total scores and subscales 0.71 0.58 0.52 0.5
## Omega group for total scores and subscales 0.17 0.30 0.27 0.5omega(W1_FI_responses) # omega total = 0.80## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.72
## G.6: 0.7
## Omega Hierarchical: 0.62
## Omega H asymptotic: 0.77
## Omega Total 0.8
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## W1_FI_1r 0.85 0.73 0.27 1.00
## W1_FI_2r 0.33 0.49 0.36 0.64 0.31
## W1_FI_3r 0.36 0.68 0.59 0.41 0.22
## W1_FI_4r 0.53 0.48 0.55 0.45 0.52
## W1_FI_5r 0.62 0.20 0.44 0.56 0.89
##
## With Sums of squares of:
## g F1* F2* F3*
## 1.64 0.75 0.00 0.28
##
## general/max 2.19 max/min = Inf
## mean percent general = 0.59 with sd = 0.35 and cv of 0.59
## Explained Common Variance of the general factor = 0.62
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 259 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.23
## The number of observations was 259 with Chi Square = 58.67 with prob < 2.3e-11
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.17
##
## RMSEA index = 0.204 and the 10 % confidence intervals are 0.159 0.252
## BIC = 30.88
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.77 0 0.56
## Multiple R square of scores with factors 0.79 0.59 0 0.31
## Minimum correlation of factor score estimates 0.59 0.18 -1 -0.38
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.80 0.74 NA 0.64
## Omega general for total scores and subscales 0.62 0.41 NA 0.53
## Omega group for total scores and subscales 0.18 0.33 NA 0.12omega(W1_PHQ_responses) # omega total = 0.89
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.86
## G.6: 0.86
## Omega Hierarchical: 0.74
## Omega H asymptotic: 0.83
## Omega Total 0.89
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## W1_PHQ_1r 0.63 0.34 0.52 0.48 0.77
## W1_PHQ_2r 0.74 0.44 0.75 0.25 0.74
## W1_PHQ_3r 0.65 0.50 0.67 0.33 0.63
## W1_PHQ_4r 0.64 0.37 0.55 0.45 0.73
## W1_PHQ_5r 0.54 0.21 0.36 0.64 0.82
## W1_PHQ_6r 0.63 0.35 0.54 0.46 0.75
## W1_PHQ_7r 0.57 0.75 0.88 0.12 0.36
## W1_PHQ_8r 0.45 0.35 0.33 0.67 0.60
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.00 0.46 0.43 0.72
##
## general/max 4.17 max/min = 1.68
## mean percent general = 0.68 with sd = 0.15 and cv of 0.22
## Explained Common Variance of the general factor = 0.65
##
## The degrees of freedom are 7 and the fit is 0.03
## The number of observations was 259 with Chi Square = 6.51 with prob < 0.48
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.073
## BIC = -32.39
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.42
## The number of observations was 259 with Chi Square = 106.89 with prob < 7.2e-14
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.129 and the 10 % confidence intervals are 0.106 0.154
## BIC = -4.24
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.87 0.58 0.62 0.86
## Multiple R square of scores with factors 0.76 0.33 0.39 0.74
## Minimum correlation of factor score estimates 0.53 -0.34 -0.22 0.48
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.89 0.81 0.76 0.74
## Omega general for total scores and subscales 0.74 0.62 0.56 0.34
## Omega group for total scores and subscales 0.11 0.19 0.19 0.40omega(W1_GAD_responses) # omega total = 0.92
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.9
## G.6: 0.89
## Omega Hierarchical: 0.83
## Omega H asymptotic: 0.9
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## W1_GAD_1r 0.71 0.20 0.57 0.43 0.89
## W1_GAD_2r 0.77 0.41 0.76 0.24 0.78
## W1_GAD_3r 0.77 0.36 0.73 0.27 0.82
## W1_GAD_4r 0.82 0.57 1.00 0.00 0.68
## W1_GAD_5r 0.58 0.20 0.40 0.60 0.85
## W1_GAD_6r 0.66 0.38 0.58 0.42 0.75
## W1_GAD_7r 0.70 0.22 0.55 0.45 0.89
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.64 0.34 0.25 0.35
##
## general/max 10.28 max/min = 1.43
## mean percent general = 0.81 with sd = 0.08 and cv of 0.1
## Explained Common Variance of the general factor = 0.79
##
## The degrees of freedom are 3 and the fit is 0
## The number of observations was 259 with Chi Square = 0.81 with prob < 0.85
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.01
## RMSEA index = 0 and the 10 % confidence intervals are 0 0.058
## BIC = -15.86
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.19
## The number of observations was 259 with Chi Square = 47.58 with prob < 1.5e-05
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.07
##
## RMSEA index = 0.096 and the 10 % confidence intervals are 0.067 0.127
## BIC = -30.22
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.92 0.59 0.53 0.83
## Multiple R square of scores with factors 0.85 0.35 0.28 0.68
## Minimum correlation of factor score estimates 0.70 -0.30 -0.44 0.37
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.92 0.86 0.74 1.00
## Omega general for total scores and subscales 0.83 0.73 0.64 0.67
## Omega group for total scores and subscales 0.06 0.13 0.11 0.321 Month
omega(M1_IUS_responses) # omega total = 0.93
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.9
## G.6: 0.91
## Omega Hierarchical: 0.68
## Omega H asymptotic: 0.73
## Omega Total 0.93
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## M1_IUS_1 0.64 0.28 0.22 0.55 0.45 0.75
## M1_IUS_2 0.71 0.62 0.89 0.11 0.57
## M1_IUS_3 0.56 0.48 0.54 0.46 0.58
## M1_IUS_4 0.49 0.53 0.52 0.48 0.46
## M1_IUS_5 0.51 0.28 0.37 0.63 0.72
## M1_IUS_6 0.57 0.60 0.68 0.32 0.47
## M1_IUS_7 0.64 0.62 0.79 0.21 0.51
## M1_IUS_8 0.47 0.34 0.35 0.65 0.64
## M1_IUS_9 0.64 0.31 0.25 0.57 0.43 0.71
## M1_IUS_10 0.53 0.52 0.56 0.44 0.51
## M1_IUS_11 0.53 0.52 0.57 0.43 0.49
## M1_IUS_12 0.58 0.21 0.30 0.48 0.52 0.72
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.99 1.55 0.86 0.46
##
## general/max 2.58 max/min = 3.38
## mean percent general = 0.59 with sd = 0.11 and cv of 0.18
## Explained Common Variance of the general factor = 0.58
##
## The degrees of freedom are 33 and the fit is 0.16
## The number of observations was 259 with Chi Square = 39.7 with prob < 0.2
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.028 and the 10 % confidence intervals are 0 0.056
## BIC = -143.67
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 54 and the fit is 1.4
## The number of observations was 259 with Chi Square = 353.68 with prob < 1.3e-45
## The root mean square of the residuals is 0.14
## The df corrected root mean square of the residuals is 0.16
##
## RMSEA index = 0.146 and the 10 % confidence intervals are 0.132 0.161
## BIC = 53.61
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.85 0.78 0.72 0.74
## Multiple R square of scores with factors 0.72 0.61 0.51 0.55
## Minimum correlation of factor score estimates 0.45 0.22 0.03 0.10
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.93 0.89 0.77 0.89
## Omega general for total scores and subscales 0.68 0.57 0.46 0.50
## Omega group for total scores and subscales 0.18 0.32 0.31 0.38omega(M1_FI_responses) # omega total = 0.80## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.75
## G.6: 0.73
## Omega Hierarchical: 0.62
## Omega H asymptotic: 0.77
## Omega Total 0.8
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## M1_FI_1r 0.51 0.60 0.63 0.37 0.42
## M1_FI_2r 0.28 0.48 0.33 0.67 0.25
## M1_FI_3r 0.71 0.51 0.49 0.98
## M1_FI_4r 0.79 0.63 0.37 0.98
## M1_FI_5r 0.50 0.38 0.25 0.44 0.56 0.55
##
## With Sums of squares of:
## g F1* F2* F3*
## 1.71 0.00 0.74 0.11
##
## general/max 2.32 max/min = Inf
## mean percent general = 0.64 with sd = 0.33 and cv of 0.52
## Explained Common Variance of the general factor = 0.67
##
## The degrees of freedom are -2 and the fit is 0
## The number of observations was 259 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.2
## The number of observations was 259 with Chi Square = 51.76 with prob < 6e-10
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.18
##
## RMSEA index = 0.19 and the 10 % confidence intervals are 0.145 0.239
## BIC = 23.98
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.87 0 0.74 0.41
## Multiple R square of scores with factors 0.76 0 0.55 0.17
## Minimum correlation of factor score estimates 0.52 -1 0.10 -0.67
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.80 NA 0.70 0.73
## Omega general for total scores and subscales 0.62 NA 0.31 0.73
## Omega group for total scores and subscales 0.17 NA 0.39 0.00omega(M1_PHQ_responses) # omega total = 0.90
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.88
## G.6: 0.87
## Omega Hierarchical: 0.75
## Omega H asymptotic: 0.83
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## M1_PHQ_1r 0.70 0.37 0.63 0.37 0.77
## M1_PHQ_2r 0.72 0.48 0.75 0.25 0.68
## M1_PHQ_3r 0.59 0.21 0.42 0.58 0.82
## M1_PHQ_4r 0.80 0.59 1.00 0.00 0.65
## M1_PHQ_5r 0.56 0.29 0.43 0.57 0.73
## M1_PHQ_6r 0.64 0.30 0.53 0.47 0.78
## M1_PHQ_7r 0.58 0.24 0.27 0.47 0.53 0.73
## M1_PHQ_8r 0.46 0.55 0.52 0.48 0.41
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.27 0.57 0.40 0.50
##
## general/max 5.76 max/min = 1.41
## mean percent general = 0.7 with sd = 0.13 and cv of 0.18
## Explained Common Variance of the general factor = 0.69
##
## The degrees of freedom are 7 and the fit is 0.05
## The number of observations was 259 with Chi Square = 11.48 with prob < 0.12
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.04
## RMSEA index = 0.05 and the 10 % confidence intervals are 0 0.1
## BIC = -27.42
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 20 and the fit is 0.36
## The number of observations was 259 with Chi Square = 91.65 with prob < 3.8e-11
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.118 and the 10 % confidence intervals are 0.094 0.143
## BIC = -19.48
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.64 0.78 0.67
## Multiple R square of scores with factors 0.79 0.40 0.62 0.45
## Minimum correlation of factor score estimates 0.59 -0.19 0.23 -0.09
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.90 0.84 1.00 0.70
## Omega general for total scores and subscales 0.75 0.66 0.65 0.47
## Omega group for total scores and subscales 0.10 0.18 0.35 0.23omega(M1_GAD_responses) # omega total = 0.94
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.9
## G.6: 0.9
## Omega Hierarchical: 0.8
## Omega H asymptotic: 0.85
## Omega Total 0.94
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## M1_GAD_1r 0.71 0.34 0.62 0.38 0.80
## M1_GAD_2r 0.79 0.52 0.89 0.11 0.70
## M1_GAD_3r 0.77 0.26 0.20 0.71 0.29 0.84
## M1_GAD_4r 0.72 0.24 0.20 0.63 0.37 0.83
## M1_GAD_5r 0.61 0.79 0.99 0.01 0.37
## M1_GAD_6r 0.67 0.37 0.60 0.40 0.76
## M1_GAD_7r 0.70 0.28 0.58 0.42 0.85
##
## With Sums of squares of:
## g F1* F2* F3*
## 3.56 0.52 0.28 0.67
##
## general/max 5.33 max/min = 2.42
## mean percent general = 0.74 with sd = 0.17 and cv of 0.23
## Explained Common Variance of the general factor = 0.71
##
## The degrees of freedom are 3 and the fit is 0.01
## The number of observations was 259 with Chi Square = 3.74 with prob < 0.29
## The root mean square of the residuals is 0.01
## The df corrected root mean square of the residuals is 0.02
## RMSEA index = 0.031 and the 10 % confidence intervals are 0 0.114
## BIC = -12.93
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.37
## The number of observations was 259 with Chi Square = 93.42 with prob < 8.5e-14
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.1
##
## RMSEA index = 0.148 and the 10 % confidence intervals are 0.121 0.178
## BIC = 15.62
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.90 0.67 0.54 0.94
## Multiple R square of scores with factors 0.80 0.45 0.29 0.88
## Minimum correlation of factor score estimates 0.61 -0.10 -0.43 0.75
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.94 0.89 0.73 0.99
## Omega general for total scores and subscales 0.80 0.74 0.60 0.37
## Omega group for total scores and subscales 0.09 0.15 0.14 0.62omega(Acceptability_responses) # omega total = 0.72## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.67
## G.6: 0.61
## Omega Hierarchical: 0.03
## Omega H asymptotic: 0.04
## Omega Total 0.72
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## B_acceptability_understandable 0.37 0.19 0.81 0.06
## B_acceptability_useful 0.82 0.72 0.28 0.04
## B_acceptability_recommend 0.73 0.56 0.44 0.02
##
## With Sums of squares of:
## g F1* F2* F3*
## 0.05 1.35 0.06 0.00
##
## general/max 0.04 max/min = Inf
## mean percent general = 0.04 with sd = 0.02 and cv of 0.5
## Explained Common Variance of the general factor = 0.04
##
## The degrees of freedom are -3 and the fit is 0
## The number of observations was 209 with Chi Square = 0 with prob < NA
## The root mean square of the residuals is 0
## The df corrected root mean square of the residuals is NA
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 0 and the fit is 0.55
## The number of observations was 209 with Chi Square = 112.85 with prob < NA
## The root mean square of the residuals is 0.41
## The df corrected root mean square of the residuals is NA
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.18 0.88 0.31 0
## Multiple R square of scores with factors 0.03 0.77 0.10 0
## Minimum correlation of factor score estimates -0.93 0.53 -0.81 -1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.72 0.71 NA NA
## Omega general for total scores and subscales 0.03 0.03 NA NA
## Omega group for total scores and subscales 0.69 0.69 NA NADemographics
Age
Age_noNA <- Demographics_excluded %>%
filter(Age != "NA")
write.csv(Age_noNA, "Age_noNA.csv")
Age_numeric <- read_csv("Age_noNA.csv")## New names:
## Rows: 242 Columns: 12
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (8): Group, Gender_value, Ethnicity_value, Country_residence, Ppt_educat... dbl
## (4): ...1, ...2, ID, Age
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
## • `...1` -> `...2`# Total Age
Age_meanT <- Age_numeric %>%
dplyr::summarise(mean = mean(Age),
sd = sd(Age),
min = min(Age),
max = max(Age))
age_anova <- aov(Age ~ Group, data=Age_numeric)
summary(age_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 4.6 2.289 1.26 0.286
## Residuals 239 434.2 1.817# Intervention
Age_I <- Age_numeric %>%
filter(Group == "Intervention")
Age_meanI <- Age_I %>%
dplyr::summarise(mean = mean(Age),
sd = sd(Age),
min = min(Age),
max = max(Age))
# Controls
Age_C <- Age_numeric %>%
filter(Group == "Controls")
Age_meanC <- Age_C %>%
dplyr::summarise(mean = mean(Age),
sd = sd(Age),
min = min(Age),
max = max(Age))
# ECs
Age_EC <- Age_numeric %>%
filter(Group == "ECs")
Age_meanEC <- Age_EC %>%
dplyr::summarise(mean = mean(Age),
sd = sd(Age),
min = min(Age),
max = max(Age))Gender
# Total
Gender_count <- Demographics_excluded %>%
count(Gender_value)
# By group
Gender_count_G <- Demographics_excluded %>%
group_by(Group) %>%
count(Gender_value) %>%
ungroup()Gender_chi <- Demographics_excluded %>%
filter(Gender_value != "Prefer not to say") %>%
filter(Gender_value != "__other")
chisq.test(Gender_chi$Group, Gender_chi$Gender_value)##
## Pearson's Chi-squared test
##
## data: Gender_chi$Group and Gender_chi$Gender_value
## X-squared = 0.24926, df = 2, p-value = 0.8828Ethnicity
# Total
Ethnicity_count <- Demographics_excluded %>%
count(Ethnicity_value)
# By group
Ethnicity_count_G <- Demographics_excluded %>%
group_by(Group) %>%
count(Ethnicity_value) %>%
ungroup()Ethnicity_chi <- Demographics_excluded %>%
filter(Ethnicity_value != "Prefer not to say") %>%
filter(Ethnicity_value != "Aboriginal or Torres Strait Islander")
chisq.test(Ethnicity_chi$Group, Ethnicity_chi$Ethnicity_value)## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: Ethnicity_chi$Group and Ethnicity_chi$Ethnicity_value
## X-squared = 2.5676, df = 6, p-value = 0.8608Education
Education_chi <- Demographics_excluded %>%
filter(Ppt_education_value != "Prefer not to say") %>%
filter(Ppt_education_value != "Primary School")
chisq.test(Education_chi$Group, Education_chi$Ppt_education_value)## Warning in stats::chisq.test(x, y, ...): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: Education_chi$Group and Education_chi$Ppt_education_value
## X-squared = 4.6114, df = 4, p-value = 0.3295# Total
Education_count <- Demographics_excluded %>%
count(Ppt_education_value)
# By group
Education_count_G <- Demographics_excluded %>%
group_by(Group) %>%
count(Ppt_education_value) %>%
ungroup()Mental Health
MH_chi <- Demographics_excluded %>%
filter(MH_value != "Prefer not to say")
chisq.test(MH_chi$Group, MH_chi$MH_value)##
## Pearson's Chi-squared test
##
## data: MH_chi$Group and MH_chi$MH_value
## X-squared = 1.6109, df = 2, p-value = 0.4469# Total
MH_count <- Demographics_excluded %>%
count(MH_value)
# By group
MH_count_G <- Demographics_excluded %>%
group_by(Group) %>%
count(MH_value) %>%
ungroup()Module Acceptability
Intervention Acceptability
Acceptability_numeric %>%
filter(Group == "C_Intervention") %>%
count(B_acceptability_understandable)## # A tibble: 3 × 2
## B_acceptability_understandable n
## <dbl> <int>
## 1 2 4
## 2 3 98
## 3 NA 1Acceptability_numeric %>%
filter(Group == "C_Intervention") %>%
count(B_acceptability_useful)## # A tibble: 4 × 2
## B_acceptability_useful n
## <dbl> <int>
## 1 1 3
## 2 2 8
## 3 3 90
## 4 NA 2Acceptability_numeric %>%
filter(Group == "C_Intervention") %>%
count(B_acceptability_recommend)## # A tibble: 5 × 2
## B_acceptability_recommend n
## <dbl> <int>
## 1 0 1
## 2 1 1
## 3 2 9
## 4 3 88
## 5 NA 4Psychoeducation Acceptability
Acceptability_numeric %>%
filter(Group == "B_Controls") %>%
count(B_acceptability_understandable)## # A tibble: 5 × 2
## B_acceptability_understandable n
## <dbl> <int>
## 1 0 1
## 2 1 1
## 3 2 7
## 4 3 94
## 5 NA 3Acceptability_numeric %>%
filter(Group == "B_Controls") %>%
count(B_acceptability_useful)## # A tibble: 4 × 2
## B_acceptability_useful n
## <dbl> <int>
## 1 0 1
## 2 2 7
## 3 3 94
## 4 NA 4Acceptability_numeric %>%
filter(Group == "B_Controls") %>%
count(B_acceptability_recommend)## # A tibble: 4 × 2
## B_acceptability_recommend n
## <dbl> <int>
## 1 1 2
## 2 2 6
## 3 3 95
## 4 NA 3Descriptives set up
Baseline Measures
IUS_mean <- Full_data %>%
dplyr::summarise(mean = mean(A_PRE_IUS_total),
sd = sd(A_PRE_IUS_total))
IUS_mean_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_IUS_total),
sd = sd(A_PRE_IUS_total))
IUS_mean_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_IUS_total),
sd = sd(A_PRE_IUS_total))
IUS_mean_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_IUS_total),
sd = sd(A_PRE_IUS_total))PHQ_mean <- Full_data %>%
dplyr::summarise(mean = mean(A_PRE_PHQ_total),
sd = sd(A_PRE_PHQ_total))
PHQ_mean_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_PHQ_total),
sd = sd(A_PRE_PHQ_total))
PHQ_mean_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_PHQ_total),
sd = sd(A_PRE_PHQ_total))
PHQ_mean_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_PHQ_total),
sd = sd(A_PRE_PHQ_total))GAD_mean <- Full_data %>%
dplyr::summarise(mean = mean(A_PRE_GAD_total),
sd = sd(A_PRE_GAD_total))
GAD_mean_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_GAD_total),
sd = sd(A_PRE_GAD_total))
GAD_mean_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_GAD_total),
sd = sd(A_PRE_GAD_total))
GAD_mean_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_GAD_total),
sd = sd(A_PRE_GAD_total))FI_mean <- Full_data %>%
dplyr::summarise(mean = mean(A_PRE_FI_total),
sd = sd(A_PRE_FI_total))
FI_mean_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_FI_total),
sd = sd(A_PRE_FI_total))
FI_mean_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_FI_total),
sd = sd(A_PRE_FI_total))
FI_mean_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_FI_total),
sd = sd(A_PRE_FI_total))GM_mean <- Full_data %>%
dplyr::summarise(mean = mean(A_PRE_GM),
sd = sd(A_PRE_GM))
GM_mean_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_GM),
sd = sd(A_PRE_GM))
GM_mean_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_GM),
sd = sd(A_PRE_GM))
GM_mean_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_GM),
sd = sd(A_PRE_GM))BT_mean <- BT_BP %>%
dplyr::summarise(mean = mean(A_PRE_samples),
sd = sd(A_PRE_samples))
BT_mean_I <- BT_BP %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(A_PRE_samples),
sd = sd(A_PRE_samples))
BT_mean_C <- BT_BP %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(A_PRE_samples),
sd = sd(A_PRE_samples))
BT_mean_EC <- BT_BP %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(A_PRE_samples),
sd = sd(A_PRE_samples))Baseline Group comparisons
bt_anova <- aov(A_PRE_samples ~ Group, data=BT_BP)
summary(bt_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 1567 783.4 0.827 0.439
## Residuals 243 230206 947.3phq_anova <- aov(A_PRE_PHQ_total ~ Group, data=Full_data)
summary(phq_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 80 40.09 1.162 0.315
## Residuals 256 8833 34.50gad_anova <- aov(A_PRE_GAD_total ~ Group, data=Full_data)
summary(gad_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 63 31.28 1.037 0.356
## Residuals 256 7720 30.16ius_anova <- aov(A_PRE_IUS_total ~ Group, data=Full_data)
summary(ius_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 143 71.27 0.907 0.405
## Residuals 256 20115 78.57gm_anova <- aov(A_PRE_GM ~ Group, data=Full_data)
summary(gm_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 6.2 3.113 1.595 0.205
## Residuals 256 499.5 1.951fi_anova <- aov(A_PRE_FI_total ~ Group, data=Full_data)
summary(fi_anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Group 2 17 8.279 0.531 0.588
## Residuals 256 3989 15.581Post Measures
IUS_mean_BP <- Full_data %>%
dplyr::summarise(mean = mean(B_POST_IUS_total, na.rm = TRUE),
sd = sd(B_POST_IUS_total, na.rm = TRUE))
IUS_mean_BP_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(B_POST_IUS_total, na.rm = TRUE),
sd = sd(B_POST_IUS_total, na.rm = TRUE))
IUS_mean_BP_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(B_POST_IUS_total, na.rm = TRUE),
sd = sd(B_POST_IUS_total, na.rm = TRUE))
IUS_mean_BP_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(B_POST_IUS_total, na.rm = TRUE),
sd = sd(B_POST_IUS_total, na.rm = TRUE))GM_mean_BP <- Full_data %>%
dplyr::summarise(mean = mean(B_POST_GM, na.rm = TRUE),
sd = sd(B_POST_GM, na.rm = TRUE))
GM_mean_BP_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(B_POST_GM, na.rm = TRUE),
sd = sd(B_POST_GM, na.rm = TRUE))
GM_mean_BP_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(B_POST_GM, na.rm = TRUE),
sd = sd(B_POST_GM, na.rm = TRUE))
GM_mean_BP_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(B_POST_GM, na.rm = TRUE),
sd = sd(B_POST_GM, na.rm = TRUE))BT_mean_BP <- BT_BP %>%
dplyr::summarise(mean = mean(B_POST_samples, na.rm = TRUE),
sd = sd(B_POST_samples, na.rm = TRUE))
BT_mean_BP_I <- BT_BP %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(B_POST_samples, na.rm = TRUE),
sd = sd(B_POST_samples, na.rm = TRUE))
BT_mean_BP_C <- BT_BP %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(B_POST_samples, na.rm = TRUE),
sd = sd(B_POST_samples, na.rm = TRUE))
BT_mean_BP_EC <- BT_BP %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(B_POST_samples, na.rm = TRUE),
sd = sd(B_POST_samples, na.rm = TRUE))1 Week Measures
IUS_mean_W1 <- Full_data %>%
dplyr::summarise(mean = mean(C_W1_IUS_total, na.rm = TRUE),
sd = sd(C_W1_IUS_total, na.rm = TRUE))
IUS_mean_W1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(C_W1_IUS_total, na.rm = TRUE),
sd = sd(C_W1_IUS_total, na.rm = TRUE))
IUS_mean_W1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(C_W1_IUS_total, na.rm = TRUE),
sd = sd(C_W1_IUS_total, na.rm = TRUE))
IUS_mean_W1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(C_W1_IUS_total, na.rm = TRUE),
sd = sd(C_W1_IUS_total, na.rm = TRUE))PHQ_mean_W1 <- Full_data %>%
dplyr::summarise(mean = mean(C_W1_PHQ_total, na.rm = TRUE),
sd = sd(C_W1_PHQ_total, na.rm = TRUE))
PHQ_mean_W1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(C_W1_PHQ_total, na.rm = TRUE),
sd = sd(C_W1_PHQ_total, na.rm = TRUE))
PHQ_mean_W1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(C_W1_PHQ_total, na.rm = TRUE),
sd = sd(C_W1_PHQ_total, na.rm = TRUE))
PHQ_mean_W1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(C_W1_PHQ_total, na.rm = TRUE),
sd = sd(C_W1_PHQ_total, na.rm = TRUE))GAD_mean_W1 <- Full_data %>%
dplyr::summarise(mean = mean(C_W1_GAD_total, na.rm = TRUE),
sd = sd(C_W1_GAD_total, na.rm = TRUE))
GAD_mean_W1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(C_W1_GAD_total, na.rm = TRUE),
sd = sd(C_W1_GAD_total, na.rm = TRUE))
GAD_mean_W1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(C_W1_GAD_total, na.rm = TRUE),
sd = sd(C_W1_GAD_total, na.rm = TRUE))
GAD_mean_W1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(C_W1_GAD_total, na.rm = TRUE),
sd = sd(C_W1_GAD_total, na.rm = TRUE))FI_mean_W1 <- Full_data %>%
dplyr::summarise(mean = mean(C_W1_FI_total, na.rm = TRUE),
sd = sd(C_W1_FI_total, na.rm = TRUE))
FI_mean_W1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(C_W1_FI_total, na.rm = TRUE),
sd = sd(C_W1_FI_total, na.rm = TRUE))
FI_mean_W1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(C_W1_FI_total, na.rm = TRUE),
sd = sd(C_W1_FI_total, na.rm = TRUE))
FI_mean_W1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(C_W1_FI_total, na.rm = TRUE),
sd = sd(C_W1_FI_total, na.rm = TRUE))GM_mean_W1 <- Full_data %>%
dplyr::summarise(mean = mean(C_W1_GM, na.rm = TRUE),
sd = sd(C_W1_GM, na.rm = TRUE))
GM_mean_W1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(C_W1_GM, na.rm = TRUE),
sd = sd(C_W1_GM, na.rm = TRUE))
GM_mean_W1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(C_W1_GM, na.rm = TRUE),
sd = sd(C_W1_GM, na.rm = TRUE))
GM_mean_W1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(C_W1_GM, na.rm = TRUE),
sd = sd(C_W1_GM, na.rm = TRUE))1 Month Measures
IUS_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_IUS_total, na.rm = TRUE),
sd = sd(D_M1_IUS_total, na.rm = TRUE))
IUS_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_IUS_total, na.rm = TRUE),
sd = sd(D_M1_IUS_total, na.rm = TRUE))
IUS_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_IUS_total, na.rm = TRUE),
sd = sd(D_M1_IUS_total, na.rm = TRUE))
IUS_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_IUS_total, na.rm = TRUE),
sd = sd(D_M1_IUS_total, na.rm = TRUE))PHQ_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))GAD_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_GAD_total, na.rm = TRUE),
sd = sd(D_M1_GAD_total, na.rm = TRUE))
GAD_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_GAD_total, na.rm = TRUE),
sd = sd(D_M1_GAD_total, na.rm = TRUE))
GAD_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_GAD_total, na.rm = TRUE),
sd = sd(D_M1_GAD_total, na.rm = TRUE))
GAD_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_GAD_total, na.rm = TRUE),
sd = sd(D_M1_GAD_total, na.rm = TRUE))PHQ_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))
PHQ_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_PHQ_total, na.rm = TRUE),
sd = sd(D_M1_PHQ_total, na.rm = TRUE))FI_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_FI_total, na.rm = TRUE),
sd = sd(D_M1_FI_total, na.rm = TRUE))
FI_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_FI_total, na.rm = TRUE),
sd = sd(D_M1_FI_total, na.rm = TRUE))
FI_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_FI_total, na.rm = TRUE),
sd = sd(D_M1_FI_total, na.rm = TRUE))
FI_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_FI_total, na.rm = TRUE),
sd = sd(D_M1_FI_total, na.rm = TRUE))GM_mean_M1 <- Full_data %>%
dplyr::summarise(mean = mean(D_M1_GM, na.rm = TRUE),
sd = sd(D_M1_GM, na.rm = TRUE))
GM_mean_M1_I <- Full_data %>%
filter(Group == "C_Intervention") %>%
dplyr::summarise(mean = mean(D_M1_GM, na.rm = TRUE),
sd = sd(D_M1_GM, na.rm = TRUE))
GM_mean_M1_C <- Full_data %>%
filter(Group == "B_Controls") %>%
dplyr::summarise(mean = mean(D_M1_GM, na.rm = TRUE),
sd = sd(D_M1_GM, na.rm = TRUE))
GM_mean_M1_EC <- Full_data %>%
filter(Group == "A_ECs") %>%
dplyr::summarise(mean = mean(D_M1_GM, na.rm = TRUE),
sd = sd(D_M1_GM, na.rm = TRUE))Descriptives output
Note: IUS = Cognitive IU BT = Behavioural IU GM = Growth mindsets PHQ = Depression symptoms GAD = Anxiety symptoms FI = Functional impairment I = Mindset intervention group C = Psychoeducation control group EC = Non-active control group
Baseline
IUS_mean## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 42.3 8.86IUS_mean_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 43.1 9.14IUS_mean_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 42.0 9.45IUS_mean_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 41.1 6.74GM_mean## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.97 1.40GM_mean_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.90 1.36GM_mean_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 3.14 1.43GM_mean_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.74 1.40PHQ_mean## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.0 5.88PHQ_mean_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.7 5.62PHQ_mean_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.44 6.16PHQ_mean_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.96 5.77GAD_mean## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.71 5.49GAD_mean_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.28 5.47GAD_mean_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.49 5.79GAD_mean_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.02 4.85FI_mean## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.2 3.94FI_mean_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.5 3.91FI_mean_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.0 4.12FI_mean_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.86 3.65BT_mean## mean sd
## 1 14.25203 30.75727BT_mean_I## mean sd
## 1 11.61616 19.48034BT_mean_C## mean sd
## 1 17.18 42.2301BT_mean_EC## mean sd
## 1 13.57447 18.91715Post
IUS_mean_BP## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 38.1 10.7IUS_mean_BP_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 36.8 11.1IUS_mean_BP_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 38.2 10.7IUS_mean_BP_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 40.8 9.19GM_mean_BP## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.51 1.51GM_mean_BP_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.22 1.49GM_mean_BP_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.65 1.49GM_mean_BP_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.78 1.50BT_mean_BP## mean sd
## 1 9.847107 16.86625BT_mean_BP_I## mean sd
## 1 8.731959 12.03079BT_mean_BP_C## mean sd
## 1 9.408163 20.6127BT_mean_BP_EC## mean sd
## 1 13.06383 16.696011 Week
IUS_mean_W1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 40.0 9.99IUS_mean_W1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 38.8 10.1IUS_mean_W1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 40.2 10.5IUS_mean_W1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 42.0 8.52GM_mean_W1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.60 1.48GM_mean_W1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.33 1.33GM_mean_W1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.83 1.54GM_mean_W1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.67 1.58PHQ_mean_W1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.10 5.92PHQ_mean_W1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.22 5.78PHQ_mean_W1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.60 6.13PHQ_mean_W1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.92 5.78GAD_mean_W1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.14 5.75GAD_mean_W1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.26 5.94GAD_mean_W1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 7.90 5.82GAD_mean_W1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.38 5.25FI_mean_W1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.75 3.95FI_mean_W1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.5 3.77FI_mean_W1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.87 4.12FI_mean_W1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.98 4.011 Month
IUS_mean_M1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 40.2 10.3IUS_mean_M1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 38.4 10.3IUS_mean_M1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 40.5 10.9IUS_mean_M1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 43.4 8.08GM_mean_M1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.56 1.44GM_mean_M1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.24 1.39GM_mean_M1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.81 1.46GM_mean_M1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 2.68 1.38PHQ_mean_M1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.65 6.31PHQ_mean_M1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.36 6.15PHQ_mean_M1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 8.15 6.27PHQ_mean_M1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 10.3 6.58GAD_mean_M1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 7.79 5.91GAD_mean_M1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 7.60 5.78GAD_mean_M1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 7.34 5.90GAD_mean_M1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.14 6.13FI_mean_M1## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.41 4.15FI_mean_M1_I## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.29 4.04FI_mean_M1_C## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.37 4.40FI_mean_M1_EC## # A tibble: 1 × 2
## mean sd
## <dbl> <dbl>
## 1 9.73 3.90