Getting to Know the Data

The relationship between depression and rumination is well-established. The purpose of this analysis is to measure this relationship in a sample of college students, who responded to an online survey about how they use Facebook and outcomes of use.

Facebook<-read.delim(file="~/Dropbox/Writing/Blogging A to Z 2018/small_facebook_set.txt", header=TRUE)

Among other measures, detailed here, participants completed the Ruminative Response Scale, which assesses Depression-Related Rumination, Brooding, and Reflecting, and the Center for Epidemiologic Studies Depression Scale. These measures were scored following instructions from the test authors:

reverse<-function(max,min,x) {
  y<-(max+min)-x
  return(y)
}
Facebook$Dep4R<-reverse(3,0,Facebook$Dep4)
Facebook$Dep8R<-reverse(3,0,Facebook$Dep8)
Facebook$Dep12R<-reverse(3,0,Facebook$Dep12)

Facebook$RRS<-rowSums(Facebook[,3:24])
Facebook$RRS_D<-rowSums(Facebook[,c(3,4,5,6,8,10,11,16,19,20,21,24)])
Facebook$RRS_R<-rowSums(Facebook[,c(9,13,14,22,23)])
Facebook$RRS_B<-rowSums(Facebook[,c(7,12,15,17,18)])

Facebook$CESD<-rowSums(Facebook[,c(96,97,98,100,101,102,104,105,106,108,109,110,111,112,113,114)])

The following scatterplot shows the relationship between total Rumination score and total Depression score in this sample:

library(ggplot2)
ggplot(Facebook, aes(x=RRS, y=CESD)) + geom_point() + geom_smooth(method="lm")

Linear Model

First, a simple linear regression was conducted with total Rumination predicting Depression.

RumDep<-lm(CESD~RRS, data=Facebook)
summary(RumDep)
## 
## Call:
## lm(formula = CESD ~ RRS, data = Facebook)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12.3020  -3.3885  -0.7835   2.4140  17.2783 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.45024    0.86992   9.714   <2e-16 ***
## RRS          0.19753    0.02132   9.264   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.107 on 255 degrees of freedom
## Multiple R-squared:  0.2518, Adjusted R-squared:  0.2489 
## F-statistic: 85.82 on 1 and 255 DF,  p-value: < 2.2e-16

Next, a regression was performed with the 3 Rumination subscales as predictors of Depression. Standardized regression coefficients were also requested to directly compare the impact of the 3 types of rumination.

RumDep2 <-lm(CESD~RRS_D+RRS_R+RRS_B, data=Facebook)
summary(RumDep2)
## 
## Call:
## lm(formula = CESD ~ RRS_D + RRS_R + RRS_B, data = Facebook)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -13.944  -3.308  -0.677   2.572  18.271 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  8.12981    0.86644   9.383  < 2e-16 ***
## RRS_D        0.36845    0.06312   5.838 1.62e-08 ***
## RRS_R        0.04613    0.09928   0.465    0.643    
## RRS_B       -0.05401    0.12766  -0.423    0.673    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5.045 on 253 degrees of freedom
## Multiple R-squared:  0.2757, Adjusted R-squared:  0.2671 
## F-statistic:  32.1 on 3 and 253 DF,  p-value: < 2.2e-16
library(QuantPsyc)
## Loading required package: boot
## Loading required package: MASS
## 
## Attaching package: 'QuantPsyc'
## The following object is masked from 'package:base':
## 
##     norm
lm.beta(RumDep2)
##       RRS_D       RRS_R       RRS_B 
##  0.53245571  0.03262223 -0.03693183

Overall, the present study confirms the strong relationship between rumination and depression, and highlights that depression-related rumination (fixating on one’s negative thoughts and traits) drives this relationship.