Hutton Poster
Mia Agramonte & Heather Perkins
2025-03-27
1 Load Libraries
library(psych)
library(kableExtra)
library(ggplot2)
library(tidyr)
library(dplyr)
library(nFactors)
library(corrplot)
library(sjPlot)2 Load Data
df1 <- read.csv(file="data/sona.csv", header=T)
labels1 <- data.frame(t(cbind(df1[1,],samp="Sample Source")))
df1 <- df1[-c(1,2), ]
df1 <- subset(df1, Progress == "100", select=-c(4,14:15))
df1$samp <- "SONA"
df2 <- read.csv(file="data/prolific.csv", header=T)
labels2 <- data.frame(t(cbind(df2[1,],samp="Sample Source")))
df2 <- df2[-c(1,2), ]
df2_quality <- subset(df2, select=c(8,173:175))
df2 <- subset(df2, Progress == "100" & Q45 == 2 & Q46 < 4, select=-c(15:17,173:175))
colnames(df2)[169] <- "id"
df2$samp <- "Prolific"
df <- rbind.data.frame(df1,df2)2.1 Attention Checks
attn <- subset(labels1, grepl("attention", X1))
attn_chk <- rownames(attn)
# 3, 2, 5, 1, 1
correct_responses <- c(3, 2, 5, 1, 1)
# Check if the values in df match the correct responses
correct_counts <- apply(df[attn_chk], 1, function(row) sum(row == correct_responses))
# Display results
table(correct_counts)## correct_counts
## 0 1 2 3 4 5
## 11 13 31 37 21 402df$attn <- correct_counts
df <- subset(df, attn > 3)
bad_ids <- c("663e9d84425d4157a22f3167",
"666f47d2c81ed466b97cd82d",
"634ef87aec0966557c825573",
"669d8f03a8742ea4d11b35d8",
"6701a4d42a2e8c434e0fc50a",
"671736c2a43ff993ff9a070b",
"67348c5f2fd2234d5e8f7d58")
df <- subset(df, !(id %in% bad_ids))3 Sample
samp <- subset(df, select=c(156:169))
samp$gen[is.na(samp$Q32)] <- NA
samp$gen[samp$Q32 == 1] <- "W"
samp$gen[samp$Q32 == 2] <- "M"
samp$gen[samp$Q32 == 3] <- "N"
samp$gen[samp$Q32 == 4] <- "N"
samp$gen[samp$Q32 == 5] <- "N"
samp$gen[samp$Q32 == 6] <- "N"
samp$gen[samp$Q32 == 7] <- "N"
samp$gen[samp$Q32 == "1,2"] <- "N"
samp$gen[samp$Q32 == "1,4"] <- "N"
samp$gen[samp$Q32 == "1,5"] <- "W"
samp$gen[samp$Q32 == "1,6"] <- "W"
samp$gen[samp$Q32 == "2,3,6"] <- "N"
samp$gen[samp$Q32 == "2,5"] <- "M"
samp$gen[samp$Q32 == "2,6"] <- "M"
samp$gen[samp$Q32 == "3,4"] <- "N"
samp$gen[samp$Q32 == "4,6"] <- "N"
table(samp$Q32, useNA = "always")##
## 1 1,2 1,4 1,5 1,6 2 2,3,6 2,5 2,6 3 3,4 4
## 1 261 1 1 3 1 139 1 2 1 1 1 5
## 4,6 6 <NA>
## 1 1 0table(samp$gen, useNA = "always")##
## M N W <NA>
## 142 12 265 1table(samp$Q32, samp$gen, useNA = "always")##
## M N W <NA>
## 0 0 0 1
## 1 0 0 261 0
## 1,2 0 1 0 0
## 1,4 0 1 0 0
## 1,5 0 0 3 0
## 1,6 0 0 1 0
## 2 139 0 0 0
## 2,3,6 0 1 0 0
## 2,5 2 0 0 0
## 2,6 1 0 0 0
## 3 0 1 0 0
## 3,4 0 1 0 0
## 4 0 5 0 0
## 4,6 0 1 0 0
## 6 0 1 0 0
## <NA> 0 0 0 0# 1 == Woman
# 2 == Man
# 3 == Agender
# 4 == Non-binary
# 5 == Cisgender
# 6 == Transgender
# 7 == Another gender not listed
######
# N == Trans, Non-binary, Agender, or Another Gender Not Listed4 Descriptives
4.1 Subset
rm(df1, df2)
df2 <- df
df2 <- subset(df, select = grep("^Q9|^Q18", colnames(df), value = TRUE))
df2 <- subset(df2, select = setdiff(colnames(df2), attn_chk))4.2 Describe
# Convert all columns in df2 to numeric
df2[] <- lapply(df2, function(x) as.numeric(x))
# Get descriptive statistics for df2
desc_stats <- describe(df2)
# Create a new column to identify rows with skew or kurtosis greater than 2
desc_stats <- desc_stats %>%
mutate(highlight = ifelse(abs(skew) > 2 | abs(kurtosis) > 2, "highlight", "no"))
# Create a table with highlighted rows for high skewness or kurtosis
desc_stats %>%
kable(format = "html", digits = 2) %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover", "condensed")) %>%
row_spec(which(desc_stats$highlight == "highlight"), background = "red")| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | highlight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Q9_1 | 1 | 420 | 2.19 | 1.11 | 2 | 2.06 | 1.48 | 1 | 5 | 4 | 0.83 | -0.08 | 0.05 | no |
| Q9_2 | 2 | 420 | 2.11 | 1.09 | 2 | 1.97 | 1.48 | 1 | 5 | 4 | 0.91 | 0.08 | 0.05 | no |
| Q9_3 | 3 | 419 | 2.89 | 1.34 | 3 | 2.87 | 1.48 | 1 | 5 | 4 | 0.16 | -1.24 | 0.07 | no |
| Q9_4 | 4 | 419 | 1.70 | 1.03 | 1 | 1.48 | 0.00 | 1 | 5 | 4 | 1.64 | 2.11 | 0.05 | highlight |
| Q9_5 | 5 | 420 | 2.11 | 1.18 | 2 | 1.95 | 1.48 | 1 | 5 | 4 | 0.95 | -0.05 | 0.06 | no |
| Q9_6 | 6 | 420 | 2.22 | 1.31 | 2 | 2.04 | 1.48 | 1 | 5 | 4 | 0.84 | -0.50 | 0.06 | no |
| Q9_7 | 7 | 420 | 2.90 | 1.31 | 3 | 2.88 | 1.48 | 1 | 5 | 4 | 0.25 | -1.21 | 0.06 | no |
| Q9_8 | 8 | 420 | 1.94 | 1.15 | 2 | 1.74 | 1.48 | 1 | 5 | 4 | 1.20 | 0.50 | 0.06 | no |
| Q9_9 | 9 | 419 | 2.38 | 1.31 | 2 | 2.23 | 1.48 | 1 | 5 | 4 | 0.63 | -0.73 | 0.06 | no |
| Q9_10 | 10 | 419 | 2.42 | 1.30 | 2 | 2.28 | 1.48 | 1 | 5 | 4 | 0.59 | -0.79 | 0.06 | no |
| Q9_11 | 11 | 420 | 1.87 | 1.13 | 1 | 1.65 | 0.00 | 1 | 5 | 4 | 1.32 | 0.88 | 0.06 | no |
| Q9_12 | 12 | 420 | 2.10 | 1.17 | 2 | 1.93 | 1.48 | 1 | 5 | 4 | 0.97 | 0.01 | 0.06 | no |
| Q9_13 | 13 | 420 | 2.49 | 1.28 | 2 | 2.37 | 1.48 | 1 | 5 | 4 | 0.50 | -0.87 | 0.06 | no |
| Q9_14 | 14 | 420 | 2.55 | 1.36 | 2 | 2.43 | 1.48 | 1 | 5 | 4 | 0.50 | -1.01 | 0.07 | no |
| Q9_15 | 15 | 420 | 2.77 | 1.34 | 2 | 2.71 | 1.48 | 1 | 5 | 4 | 0.31 | -1.14 | 0.07 | no |
| Q9_16 | 16 | 420 | 2.80 | 1.37 | 3 | 2.74 | 1.48 | 1 | 5 | 4 | 0.24 | -1.23 | 0.07 | no |
| Q18_1 | 17 | 420 | 1.96 | 0.86 | 2 | 1.90 | 1.48 | 1 | 4 | 3 | 0.48 | -0.66 | 0.04 | no |
| Q18_2 | 18 | 419 | 2.28 | 0.98 | 2 | 2.23 | 1.48 | 1 | 4 | 3 | 0.07 | -1.11 | 0.05 | no |
| Q18_3 | 19 | 420 | 2.46 | 1.01 | 3 | 2.45 | 1.48 | 1 | 4 | 3 | -0.10 | -1.11 | 0.05 | no |
| Q18_4 | 20 | 420 | 2.49 | 1.02 | 3 | 2.49 | 1.48 | 1 | 4 | 3 | -0.12 | -1.12 | 0.05 | no |
| Q18_5 | 21 | 420 | 2.37 | 0.99 | 2 | 2.33 | 1.48 | 1 | 4 | 3 | 0.06 | -1.08 | 0.05 | no |
| Q18_6 | 22 | 420 | 2.53 | 1.04 | 3 | 2.54 | 1.48 | 1 | 4 | 3 | -0.17 | -1.16 | 0.05 | no |
| Q18_7 | 23 | 420 | 1.82 | 0.78 | 2 | 1.74 | 1.48 | 1 | 4 | 3 | 0.72 | 0.12 | 0.04 | no |
| Q18_8 | 24 | 420 | 1.66 | 0.79 | 1 | 1.54 | 0.00 | 1 | 4 | 3 | 1.00 | 0.27 | 0.04 | no |
| Q18_9 | 25 | 420 | 1.93 | 0.95 | 2 | 1.83 | 1.48 | 1 | 4 | 3 | 0.60 | -0.79 | 0.05 | no |
| Q18_11 | 26 | 420 | 2.30 | 1.02 | 2 | 2.24 | 1.48 | 1 | 4 | 3 | 0.16 | -1.14 | 0.05 | no |
| Q18_12 | 27 | 420 | 1.86 | 0.92 | 2 | 1.76 | 1.48 | 1 | 4 | 3 | 0.64 | -0.76 | 0.05 | no |
| Q18_13 | 28 | 420 | 2.79 | 0.87 | 3 | 2.84 | 1.48 | 1 | 4 | 3 | -0.33 | -0.56 | 0.04 | no |
| Q18_14 | 29 | 420 | 2.76 | 0.87 | 3 | 2.82 | 1.48 | 1 | 4 | 3 | -0.45 | -0.39 | 0.04 | no |
| Q18_15 | 30 | 420 | 2.24 | 0.91 | 2 | 2.19 | 1.48 | 1 | 4 | 3 | 0.21 | -0.82 | 0.04 | no |
| Q18_16 | 31 | 420 | 2.11 | 0.79 | 2 | 2.10 | 1.48 | 1 | 4 | 3 | 0.24 | -0.50 | 0.04 | no |
5 Factor Analysis
5.1 Difficulties in Emotion Regulation Scale (DERS-16)
d <- na.omit(subset(df2, select = grep("^Q9", colnames(df2), value = TRUE)))
d <- subset(d, select=-c(14,16))
# colnames(d) <- c("I have difficulty making sense out of my feelings",
# "I am confused about how I feel.",
# "When I am upset, I have difficulty getting work done.",
# "When I am upset, I become out of control.",
# "When I am upset, I believe that I will remain that way for a long time.",
# "When I am upset, I believe that I’ll end up feeling very depressed.",
# "When I am upset, I have difficulty focusing on other things.",
# "When I am upset, I feel out of control.",
# "When I am upset, I feel ashamed with myself for feeling that way.",
# "When I am upset, I feel like I am weak.",
# "When I am upset, I have difficulty controlling my behaviors.",
# "When I am upset, I believe that there is nothing I can do to make myself feel better.",
# "When I am upset, I become irritated with myself for feeling that way.",
# # "When I am upset, I start to feel very bad about myself.",
# "When I am upset, I have difficulty thinking about anything else.")
# # "When I am upset, my emotions feel overwhelming.")
ev <- eigen(cor(d)) # get eigenvalues
ap <- parallel(subject=nrow(d),var=ncol(d),rep=100,cent=.05) # run the parallel analysis, gives us another perspective on how many factors should be used in the model
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea) # creates the scree plot
plotnScree(nS) # shows us the scree plot, look for the elbows
# EFA <- factanal(d, factors = 1, rotation = "promax")
# print(EFA, digits=3, cutoff=.4, sort=F)
# EFA <- factanal(d, factors = 2, rotation = "promax")
# print(EFA, digits=3, cutoff=.4, sort=F)
EFA <- factanal(d, factors = 5, rotation = "promax")
print(EFA, digits=3, cutoff=.4, sort=T)##
## Call:
## factanal(x = d, factors = 5, rotation = "promax")
##
## Uniquenesses:
## Q9_1 Q9_2 Q9_3 Q9_4 Q9_5 Q9_6 Q9_7 Q9_8 Q9_9 Q9_10 Q9_11 Q9_12 Q9_13
## 0.153 0.152 0.190 0.131 0.287 0.230 0.136 0.227 0.194 0.274 0.305 0.296 0.267
## Q9_15
## 0.254
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5
## Q9_9 0.896
## Q9_10 0.776
## Q9_13 0.882
## Q9_4 0.994
## Q9_8 0.776
## Q9_11 0.767
## Q9_3 0.866
## Q9_7 0.954
## Q9_15 0.665
## Q9_5 0.724
## Q9_6 0.876
## Q9_12 0.658
## Q9_1 0.916
## Q9_2 0.871
##
## Factor1 Factor2 Factor3 Factor4 Factor5
## SS loadings 2.254 2.221 2.137 1.797 1.639
## Proportion Var 0.161 0.159 0.153 0.128 0.117
## Cumulative Var 0.161 0.320 0.472 0.601 0.718
##
## Factor Correlations:
## Factor1 Factor2 Factor3 Factor4 Factor5
## Factor1 1.000 -0.582 0.673 -0.660 0.791
## Factor2 -0.582 1.000 -0.569 0.621 -0.656
## Factor3 0.673 -0.569 1.000 -0.612 0.695
## Factor4 -0.660 0.621 -0.612 1.000 -0.732
## Factor5 0.791 -0.656 0.695 -0.732 1.000
##
## Test of the hypothesis that 5 factors are sufficient.
## The chi square statistic is 76.1 on 31 degrees of freedom.
## The p-value is 1.16e-055.2 Factor Scores
df2 <- df2 %>%
mutate(clarity = rowMeans(select(., Q9_1, Q9_2), na.rm = TRUE)) %>%
mutate(goals = rowMeans(select(., Q9_3, Q9_7, Q9_15), na.rm = TRUE)) %>%
mutate(impulse = rowMeans(select(., Q9_4, Q9_8, Q9_11), na.rm = TRUE)) %>%
mutate(strategies = rowMeans(select(., Q9_5, Q9_6, Q9_12), na.rm = TRUE)) %>%
mutate(nona = rowMeans(select(., Q9_9, Q9_10, Q9_13), na.rm = TRUE)) %>%
mutate(idm_ib = rowMeans(select(., Q18_3:Q18_6), na.rm = TRUE)) %>%
mutate(idm_dp = rowMeans(select(., Q18_12:Q18_15), na.rm = TRUE))
# psych::alpha(select(df2, starts_with("Q39")), na.rm = TRUE)
# psych::alpha(select(df2, Q39_1:Q39_2), na.rm = TRUE)
# psych::alpha(select(df2, Q39_3:Q39_9), na.rm = TRUE)
# psych::alpha(select(df2, starts_with("Q40")), na.rm = TRUE)
# psych::alpha(select(df2, starts_with("Q18")), na.rm = TRUE)
# psych::alpha(select(df2, Q18_2, Q18_8:Q18_11), na.rm = TRUE)
# psych::alpha(select(df2, Q18_3:Q18_6), na.rm = TRUE)
# psych::alpha(select(df2, Q18_12:Q18_15), na.rm = TRUE)
# Get descriptive statistics for df2
desc_stats <- describe(subset(df2, select=c(clarity, nona, impulse, goals, strategies, idm_ib, idm_dp)))
# Create a new column to identify rows with skew or kurtosis greater than 2
desc_stats <- desc_stats %>%
mutate(highlight = ifelse(abs(skew) > 2 | abs(kurtosis) > 2, "highlight", "no"))
# Create a table with highlighted rows for high skewness or kurtosis
desc_stats %>%
kable(format = "html", digits = 2) %>%
kable_styling(full_width = FALSE, bootstrap_options = c("striped", "hover", "condensed")) %>%
row_spec(which(desc_stats$highlight == "highlight"), background = "red")| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | highlight | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| clarity | 1 | 420 | 2.15 | 1.05 | 2.00 | 2.02 | 1.48 | 1 | 5 | 4 | 0.85 | 0.03 | 0.05 | no |
| nona | 2 | 420 | 2.43 | 1.18 | 2.33 | 2.32 | 1.48 | 1 | 5 | 4 | 0.58 | -0.62 | 0.06 | no |
| impulse | 3 | 420 | 1.84 | 1.01 | 1.33 | 1.65 | 0.49 | 1 | 5 | 4 | 1.37 | 1.30 | 0.05 | no |
| goals | 4 | 420 | 2.85 | 1.23 | 2.67 | 2.81 | 1.48 | 1 | 5 | 4 | 0.27 | -1.11 | 0.06 | no |
| strategies | 5 | 420 | 2.14 | 1.10 | 2.00 | 2.00 | 0.99 | 1 | 5 | 4 | 0.88 | -0.16 | 0.05 | no |
| idm_ib | 6 | 420 | 2.46 | 0.88 | 2.50 | 2.47 | 1.11 | 1 | 4 | 3 | -0.12 | -0.98 | 0.04 | no |
| idm_dp | 7 | 420 | 2.41 | 0.66 | 2.50 | 2.41 | 0.74 | 1 | 4 | 3 | 0.05 | -0.25 | 0.03 | no |
6 Correlation Matrix
# Select the relevant columns from df2
df2_subset <- df2 %>% select(clarity, nona, impulse, goals, strategies, idm_ib, idm_dp)
# Compute the correlation matrix with significance tests
cor_results <- corr.test(df2_subset, use = "pairwise.complete.obs")
# Extract the correlation matrix
cor_matrix <- cor_results$r
# Extract the p-values matrix
p_matrix <- cor_results$p
# Display the correlation matrix and p-values
# print(cor_matrix)
# print(p_matrix)
rownames(cor_matrix) <- c("1 Lack of Emotional Clarity",
"2 Nonacceptance of Emotional Responses",
"3 Impulse Control Difficulties",
"4 Difficulties Engaging in Goal-Directed Behavior",
"5 Limited Access to Emotion Regulation Strategies",
"6 Impact of Betrayal",
"7 Defensive Processing")
colnames(cor_matrix) <- c("1",
"2",
"3",
"4",
"5",
"6",
"7")
corrplot(cor_matrix,
method = "color", # Use color for correlation visualization
type = "upper", # Display only upper triangle
addCoef.col = "white", # Add coefficients in black
tl.col = "black", # Set text label color
number.cex = 0.8, # Adjust text size
p.mat = p_matrix, # Provide p-values for significance
sig.level = 0.05, # Set significance level
insig = "blank") # Hide insignificant correlations## Warning in corrplot(cor_matrix, method = "color", type = "upper", addCoef.col =
## "white", : p.mat and corr may be not paired, their rownames and colnames are
## not totally same!
7 Emotion Dysregulation & IDM
For a coefficient β, effect sizes between 0.10–0.29 are said to be only small, effect sizes between 0.30–0.49 are medium, and effect sizes of 0.50 or greater are large
df2$strategies_std <- c(scale(df2$strategies, center = T, scale= T))
df2$clarity_std <- c(scale(df2$clarity, center = T, scale= T))
df2$nona_std <- c(scale(df2$nona, center = T, scale= T))
df2$impulse_std <- c(scale(df2$impulse, center = T, scale= T))
df2$goals_std <- c(scale(df2$goals, center = T, scale= T))
df2$idm_dp_std <- c(scale(df2$idm_dp, center = T, scale= T))
df2$idm_ib_std <- c(scale(df2$idm_ib, center = T, scale= T))
df2$gender <- samp$gen
table(df2$gender)##
## M N W
## 142 12 265df3 <- subset(df2, gender!="N")
df3$gender[df3$gender == "M"] <- "Men"
df3$gender[df3$gender == "W"] <- "Women"
df3$gender <- as.factor(df3$gender)
reg_model <- lm(idm_dp_std ~ clarity_std + nona_std + impulse_std + strategies_std + goals_std, data = df3)
car::vif(reg_model)## clarity_std nona_std impulse_std strategies_std goals_std
## 1.927044 2.266653 2.128176 3.306924 2.788022plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = idm_dp_std ~ clarity_std + nona_std + impulse_std +
## strategies_std + goals_std, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.09564 -0.57126 -0.05099 0.55474 2.88918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.020746 0.044668 -0.464 0.642575
## clarity_std 0.036159 0.062476 0.579 0.563076
## nona_std 0.108410 0.066991 1.618 0.106387
## impulse_std -0.004926 0.064755 -0.076 0.939399
## strategies_std 0.310249 0.081280 3.817 0.000156 ***
## goals_std 0.021437 0.074704 0.287 0.774296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9007 on 401 degrees of freedom
## Multiple R-squared: 0.1893, Adjusted R-squared: 0.1792
## F-statistic: 18.73 on 5 and 401 DF, p-value: < 2.2e-16plot_model(reg_model, type="pred")$strategies_std +
xlab("Limited Access to Emotion Regulation Strategies") +
ylab("Inequality-Driven Mistrust: Defensive Processing") +
ylim(min(df3$idm_dp_std), max(df3$idm_dp_std)) +
ggtitle("Figure 2: Relationship between emotion dysregulation and defensive processing")## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
reg_model <- lm(idm_ib_std ~ clarity_std + nona_std + impulse_std + strategies_std + goals_std, data = df3)
car::vif(reg_model)## clarity_std nona_std impulse_std strategies_std goals_std
## 1.927044 2.266653 2.128176 3.306924 2.788022plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = idm_ib_std ~ clarity_std + nona_std + impulse_std +
## strategies_std + goals_std, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.12316 -0.68461 0.00562 0.60304 2.30601
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.009229 0.043739 -0.211 0.833
## clarity_std 0.098778 0.061176 1.615 0.107
## nona_std 0.029237 0.065597 0.446 0.656
## impulse_std -0.015363 0.063407 -0.242 0.809
## strategies_std 0.384879 0.079589 4.836 1.89e-06 ***
## goals_std 0.014232 0.073150 0.195 0.846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8819 on 401 degrees of freedom
## Multiple R-squared: 0.2279, Adjusted R-squared: 0.2183
## F-statistic: 23.67 on 5 and 401 DF, p-value: < 2.2e-16plot_model(reg_model, type="pred")$strategies_std +
xlab("Limited Access to Emotion Regulation Strategies") +
ylab("Inequality-Driven Mistrust: Impact of Betrayal") +
ylim(min(df3$idm_dp_std), max(df3$idm_dp_std)) +
ggtitle("Figure 3: Relationship between emotion dysregulation and impact of betrayal")## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
8 Gender & Emotion Dysregulation
reg_model <- lm(clarity_std ~ gender, data = df3)
# car::vif(reg_model)
plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = clarity_std ~ gender, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1793 -0.8830 -0.2294 0.5419 2.9166
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2071 0.0826 -2.508 0.0125 *
## genderWomen 0.2963 0.1024 2.895 0.0040 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9843 on 405 degrees of freedom
## Multiple R-squared: 0.02027, Adjusted R-squared: 0.01785
## F-statistic: 8.379 on 1 and 405 DF, p-value: 0.004001plot_model(reg_model, type="pred") +
xlab("Gender") + ylab("Lack of Emotional Clarity") +
ylim(min(df3$clarity_std), max(df3$clarity_std)) +
ggtitle("Figure 4: Gender differences in emotion dysregulation")## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
reg_model <- lm(impulse_std ~ gender, data = df3)
# car::vif(reg_model)
plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = impulse_std ~ gender, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9023 -0.6699 -0.3407 0.4142 3.2798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1557 0.0841 -1.852 0.0648 .
## genderWomen 0.2325 0.1042 2.231 0.0263 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.002 on 405 degrees of freedom
## Multiple R-squared: 0.01214, Adjusted R-squared: 0.009697
## F-statistic: 4.975 on 1 and 405 DF, p-value: 0.02626plot_model(reg_model, type="pred") +
xlab("Gender") + ylab("Impulse Control Difficulties") +
ylim(min(df3$impulse_std), max(df3$impulse_std)) +
ggtitle("Figure 5: Gender differences in emotion dysregulation")## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
reg_model <- lm(strategies_std ~ gender, data = df3)
# car::vif(reg_model)
plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = strategies_std ~ gender, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.1062 -0.8453 -0.1977 0.6270 2.7468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.15111 0.08357 -1.808 0.0713 .
## genderWomen 0.21902 0.10356 2.115 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9958 on 405 degrees of freedom
## Multiple R-squared: 0.01092, Adjusted R-squared: 0.008481
## F-statistic: 4.473 on 1 and 405 DF, p-value: 0.03505plot_model(reg_model, type="pred") +
xlab("Gender") + ylab("Limited Access to Emotion Regulation Strategies") +
ylim(min(df3$impulse_std), max(df3$impulse_std)) +
ggtitle("Figure 6: Gender differences in emotion dysregulation")## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
9 IDM & Gender
reg_model <- lm(idm_dp_std ~ gender, data = df3)
# car::vif(reg_model)
plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = idm_dp_std ~ gender, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.18401 -0.56724 0.09356 0.57154 2.46952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04020 0.08344 0.482 0.630
## genderWomen -0.09838 0.10340 -0.951 0.342
##
## Residual standard error: 0.9943 on 405 degrees of freedom
## Multiple R-squared: 0.00223, Adjusted R-squared: -0.0002334
## F-statistic: 0.9053 on 1 and 405 DF, p-value: 0.3419# plot_model(reg_model, type="pred")
reg_model <- lm(idm_ib_std ~ gender, data = df3)
# car::vif(reg_model)
plot(reg_model, 1)
plot(reg_model, 2)
plot(reg_model, 4)
plot(reg_model, 5)
plot(reg_model, 3)
summary(reg_model)##
## Call:
## lm(formula = idm_ib_std ~ gender, data = df3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7053 -0.7783 0.1488 0.7197 1.8616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10595 0.08362 -1.267 0.206
## genderWomen 0.14123 0.10363 1.363 0.174
##
## Residual standard error: 0.9964 on 405 degrees of freedom
## Multiple R-squared: 0.004565, Adjusted R-squared: 0.002107
## F-statistic: 1.857 on 1 and 405 DF, p-value: 0.1737# plot_model(reg_model, type="pred")