## DISP student paper ##
library(readr)
library(jmv)
library(corrplot)
library(ggplot2)
library(GGally)
library(ggpubr)
pp <- read.csv("personal.projects.cleaned.v3.csv")
pp$gender <- as.factor(pp$gender)
pp$ethnicity <- as.factor(pp$ethnicity)
pp$student <- as.factor(pp$student)
pp$gender <- factor(pp$gender, levels = c(1,2,4),
labels = c("female", "male", "other identity"))
pp$ethnicity <- factor(pp$ethnicity, levels = c(1,2,3,4,5,6),
labels = c("white", "black", "asian", "indigenous", "latin", "other"))
pp$student <- factor(pp$student, levels = c(1,2),
labels = c("full time", "part time"))
source("http://pcwww.liv.ac.uk/~william/R/crosstab.r")
crosstab(pp, row.vars = "mh", col.vars = "perf", type = "f")
## perf adaptive maladaptive non-perfectionist Sum
## mh
## flourishing 2 19 18 39
## languishing 0 2 0 2
## moderate 24 63 63 150
## Sum 26 84 81 191
crosstab(pp, row.vars = "perf", col.vars = "mh", type = "f")
## mh flourishing languishing moderate Sum
## perf
## adaptive 2 0 24 26
## maladaptive 19 2 63 84
## non-perfectionist 18 0 63 81
## Sum 39 2 150 191
## getting scores on mhc-sf by type of perfectionism
mhc.by.perf<-with(pp, tapply(mhc, perf, mean))
mhc.by.perf
## adaptive maladaptive non-perfectionist
## 4.506551 3.870018 3.710128
## non-perf: 4.51 maladaptive = 3.87 adaptive = 3.71
## getting scores on aps by level of mental health
aps.by.mh<-with(pp, tapply(aps, mh, mean))
aps.by.mh
## flourishing languishing moderate
## 5.272136 6.239130 4.980463
#flourish: 5.27 languishing: 6.24 moderate: 4.98
## getting scores on aps subscales by level of mental health
s.by.mh<-with(pp, tapply(aps.standards, mh, mean))
s.by.mh
## flourishing languishing moderate
## 41.23077 44.50000 41.80000
#flourish: 41.23 languishing: 44.5 moderate: 41.8
d.by.mh<-with(pp, tapply(aps.discrepency, mh, mean))
d.by.mh
## flourishing languishing moderate
## 59.15385 77.00000 50.34000
#flourish: 59.15 languishing: 77 moderate: 50.34
mhc.perf.aov <- lm(mhc ~ perf, data = pp)
summary(mhc.perf.aov)
##
## Call:
## lm(formula = mhc ~ perf, data = pp)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4854 -0.7185 0.1360 0.6715 2.1300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.5066 0.1868 24.121 < 2e-16 ***
## perfmaladaptive -0.6365 0.2138 -2.977 0.003292 **
## perfnon-perfectionist -0.7964 0.2147 -3.709 0.000274 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9527 on 188 degrees of freedom
## Multiple R-squared: 0.06845, Adjusted R-squared: 0.05854
## F-statistic: 6.907 on 2 and 188 DF, p-value: 0.001275
## significantly different
descriptives(pp, aps.standards)
##
## DESCRIPTIVES
##
## Descriptives
## ────────────────────────────
## aps.standards
## ────────────────────────────
## N 191
## Missing 0
## Mean 41.71204
## Median 42
## Minimum 23
## Maximum 49
## ────────────────────────────
descriptives(pp, aps)
##
## DESCRIPTIVES
##
## Descriptives
## ───────────────────────
## aps
## ───────────────────────
## N 191
## Missing 0
## Mean 5.053199
## Median 5.000000
## Minimum 2.695652
## Maximum 7.000000
## ───────────────────────
cor.test(pp$mhc, pp$aps.standards, method=c("pearson"))
##
## Pearson's product-moment correlation
##
## data: pp$mhc and pp$aps.standards
## t = 3.0105, df = 189, p-value = 0.002965
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07418523 0.34540067
## sample estimates:
## cor
## 0.2139115
#correlation of 0.21, p = .003, CI = [.074, .345]
#demographic descriptives
descriptives(pp, age)
##
## DESCRIPTIVES
##
## Descriptives
## ───────────────────────
## age
## ───────────────────────
## N 177
## Missing 14
## Mean 20.71186
## Median 20
## Minimum 17
## Maximum 33
## ───────────────────────
eth = table(pp$ethnicity)
eth
##
## white black asian indigenous latin other
## 119 6 46 0 0 0
student = table(pp$student)
student
##
## full time part time
## 186 5
gender = table(pp$gender)
gender
##
## female male other identity
## 168 22 1
boxplot(mhc ~ perf, data = pp, xlab = "perfectionism type", ylab = "mental health",
plot = TRUE)
boxplot(aps.standards ~ perf, data = pp, xlab = "perfectionistic standards", ylab = "perfectionism type",
plot= TRUE)
boxplot(aps.standards ~ mh, data = pp, xlab = "mental health level", ylab = "perfectionistic standards",
plot = TRUE)
