Psi Chi R Contest - September 2023

Source: Psi Chi R Contest

#Load packages and import data

library(dplyr)
library(readr)
library(ggplot2)

september=read.csv('Sept_R_Data.csv')

Data processing (level 1)

Write a script that will filter out participants who are missing values for the ‘Age’ variable

Write a script that will filter out participants who are missing values for the ‘Stateborn’ variable

sept_clean=september %>% 
  dplyr::filter(!is.na(Age) & !is.na(Stateborn)) %>% 
  dplyr::filter(str_detect(Age,'^[0-9]+$') & str_detect(Stateborn,'^[0-9]+$'))

Descriptive Statistics (level 2)

Create a variable called ‘CFPSCompas’ by summing together the following variables: FPS7, FPS8, FPS9, FPS10 CFPSCompas is a variable that measures Compassionate Reverence

Create a variable called ‘FACEsatis’ by summing together the following variables: FACES53, FACES54, FACES55, FACES56, FACES57, FACES58, FACES59, FACES60, FACES61, FACES62 FACEsatis is a variable that measures Family Satisfaction

sept_clean=sept_clean %>% 
  mutate(FACEsatis=FACES53+FACES54+FACES55+FACES56+ FACES57+FACES58+FACES59+FACES60+ FACES61+FACES62,CFPSCompas=FPS7+FPS8+FPS9+FPS10) %>% 
  filter(!is.na(FACEsatis) & !is.na(CFPSCompas))

Data visualization (level 3)

Create a graph that shows the mean levels of Family Satisfaction by gender

#Create mean for FACC and keep `Gender` as 123 rather than mutate into M/F
sept_clean2=sept_clean %>% 
  filter(!is.na(Gender)) %>% 
  select(Gender,FACEsatis,CFPSCompas,dplyr::everything())

sept_gender_face=sept_clean2 %>% 
  group_by(Gender) %>% 
  summarise(FACE_mean=mean(FACEsatis)) 

#Create column graph
sept_gender_face %>% 
  ggplot(aes(x=Gender,y=FACE_mean))+
  geom_col(fill='darkgreen')+
  theme_bw()+
  expand_limits(y=40)+
  labs(title='Mean Levels of Family Satisfaction by Gender',x='Gender',y='Family Satisifaction')+
  theme(plot.title =element_text(hjust=.5))

Inferential statistics (level 4)

Test if there is an association between Compassionate Reverence and Family Satisfaction. Note any key statistics.

test_cor=cor(sept_clean2$CFPSCompas,sept_clean2$FACEsatis)
print(test_cor) #0.44

## [1] 0.4413071

Scatter Plot

#Optional scatter plot, for fun
sept_clean2 %>% 
  ggplot(aes(y=FACEsatis,x=CFPSCompas))+
  geom_point()+
  geom_smooth(method='lm',se=F)+
  theme_bw()+
  labs(title='Family Satisfaction by Compassionate Reverance',x='Compassionate Reverance',y='Family Satisfaction')+
  theme(plot.title = element_text(hjust=.5))

Test if there are any gender differences in mean levels of Family Satisfaction. Note any key statistics.

#Run ANOVA
anovatest=anova(lm(sept_clean2$FACEsatis~sept_clean2$Gender))

print(anovatest) #p-value = 0.7905

## Analysis of Variance Table
## 
## Response: sept_clean2$FACEsatis
##                     Df Sum Sq Mean Sq F value Pr(>F)
## sept_clean2$Gender   1      5   5.327  0.0706 0.7905
## Residuals          439  33106  75.411