library(lmerTest)

## Loading required package: lme4

## Loading required package: Matrix

## 
## Attaching package: 'lmerTest'

## The following object is masked from 'package:lme4':
## 
##     lmer

## The following object is masked from 'package:stats':
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library(tidyverse)

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data <- read.csv("~/Google drive/My Drive/YEAR 1/PROJECTS/SHAI/Dominance and Job Preference/Data/study1.csv") %>% 
  slice(-c(1:2)) %>% 
  filter(attn == 24)%>% 
  filter(employment == 1 | employment == 2)

Descriptives

Manufacturing
Healthcare
Engineering
Real Estate
Sales
Retail
Tech
Entertainment and Sports
Government
Finance
Accounting
Military
Pharmaceutical
e-Commerce

data_social_good <- data %>% 
  select(ResponseId, industry_social_good_2:industry_social_good_15, FL_8_DO, politicalIdeology, ZSB_DO) %>% 
  pivot_longer(cols = industry_social_good_2:industry_social_good_15,
               names_to = "industry",
               values_to = "value") %>% 
  mutate(value = as.numeric(value)) %>% 
  rename(socialgood = value) %>%
  separate(industry, into = c("termindustry", "termsocial", "termgood", "industry"), sep = "_") %>%
  select(ResponseId, industry, socialgood, FL_8_DO, politicalIdeology)

summary_social <- data_social_good %>% 
  group_by(industry) %>% 
  summarise(mean(socialgood, na.rm = T)) %>% 
  ungroup()

# Least = Government, most = Engineering

data_zsb <- data %>% 
  mutate(zsb_2_all = ifelse(ZSB_DO == "zsb_1", zsb_2,
                            ifelse(ZSB_DO == "zsb_2", zsb_2.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_2.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_2.3, NA))))) %>% 
  mutate(zsb_3_all = ifelse(ZSB_DO == "zsb_1", zsb_3,
                            ifelse(ZSB_DO == "zsb_2", zsb_3.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_3.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_3.3, NA))))) %>% 
  mutate(zsb_4_all = ifelse(ZSB_DO == "zsb_1", zsb_4,
                            ifelse(ZSB_DO == "zsb_2", zsb_4.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_4.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_4.3, NA))))) %>% 
  mutate(zsb_5_all = ifelse(ZSB_DO == "zsb_1", zsb_5,
                            ifelse(ZSB_DO == "zsb_2", zsb_5.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_5.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_5.3, NA))))) %>% 
  mutate(zsb_6_all = ifelse(ZSB_DO == "zsb_1", zsb_6,
                            ifelse(ZSB_DO == "zsb_2", zsb_6.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_6.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_6.3, NA))))) %>% 
  mutate(zsb_7_all = ifelse(ZSB_DO == "zsb_1", zsb_7,
                            ifelse(ZSB_DO == "zsb_2", zsb_7.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_7.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_7.3, NA))))) %>% 
  mutate(zsb_8_all = ifelse(ZSB_DO == "zsb_1", zsb_8,
                            ifelse(ZSB_DO == "zsb_2", zsb_8.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_8.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_8.3, NA))))) %>% 
  mutate(zsb_9_all = ifelse(ZSB_DO == "zsb_1", zsb_9,
                            ifelse(ZSB_DO == "zsb_2", zsb_9.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_9.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_9.3, NA))))) %>% 
  mutate(zsb_10_all = ifelse(ZSB_DO == "zsb_1", zsb_10,
                            ifelse(ZSB_DO == "zsb_2", zsb_10.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_10.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_10.3, NA))))) %>% 
  mutate(zsb_11_all = ifelse(ZSB_DO == "zsb_1", zsb_11,
                            ifelse(ZSB_DO == "zsb_2", zsb_11.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_11.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_11.3, NA))))) %>% 
  mutate(zsb_12_all = ifelse(ZSB_DO == "zsb_1", zsb_12,
                            ifelse(ZSB_DO == "zsb_2", zsb_12.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_12.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_12.3, NA))))) %>%
  mutate(zsb_13_all = ifelse(ZSB_DO == "zsb_1", zsb_13,
                            ifelse(ZSB_DO == "zsb_2", zsb_13.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_13.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_13.3, NA))))) %>% 
  mutate(zsb_14_all = ifelse(ZSB_DO == "zsb_1", zsb_14,
                            ifelse(ZSB_DO == "zsb_2", zsb_14.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_14.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_14.3, NA))))) %>% 
  mutate(zsb_15_all = ifelse(ZSB_DO == "zsb_1", zsb_15,
                            ifelse(ZSB_DO == "zsb_2", zsb_15.1,
                                   ifelse(ZSB_DO == "zsb_3", zsb_15.2,
                                          ifelse(ZSB_DO == "zsb_4", zsb_15.3, NA))))) %>%
  select(ResponseId, zsb_2_all:zsb_15_all, FL_8_DO, politicalIdeology, ZSB_DO, orgType) %>% 
  pivot_longer(cols = zsb_2_all:zsb_15_all,
               names_to = "industry",
               values_to = "value") %>% 
  mutate(value = as.numeric(value)) %>% 
  rename(zsb = value) %>%
  separate(industry, into = c("termzsb", "industry"), sep = "_") %>%
  select(ResponseId, industry, zsb, FL_8_DO, politicalIdeology, ZSB_DO, orgType)

## Warning: Expected 2 pieces. Additional pieces discarded in 5404 rows [1, 2, 3, 4, 5, 6,
## 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].

summary_zsb <- data_zsb %>% 
  group_by(industry) %>% 
  summarise(mean(zsb, na.rm = T)) %>% 
  ungroup()

# Least = Healthcare, most = Government

Primary model

full <- data_social_good %>% 
  full_join(data_zsb)

## Joining with `by = join_by(ResponseId, industry, FL_8_DO, politicalIdeology)`

model1 <- full %>%
  lmer(zsb ~ socialgood + (1|ResponseId) + (1|industry) + (1|ZSB_DO), .)

summary(model1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: zsb ~ socialgood + (1 | ResponseId) + (1 | industry) + (1 | ZSB_DO)
##    Data: .
## 
## REML criterion at convergence: 17543.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1047 -0.5972  0.0095  0.6015  4.7127 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  ResponseId (Intercept) 1.24269  1.1148  
##  industry   (Intercept) 0.17046  0.4129  
##  ZSB_DO     (Intercept) 0.06683  0.2585  
##  Residual               1.22363  1.1062  
## Number of obs: 5404, groups:  ResponseId, 386; industry, 14; ZSB_DO, 4
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)    4.23358    0.18020    7.24712   23.49 4.17e-08 ***
## socialgood    -0.16893    0.01417 5294.04377  -11.92  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr)
## socialgood -0.061

ggplot(data = full,
       aes(x = socialgood,
           y = zsb))+
  geom_jitter(alpha = 0.1)+
  facet_wrap(~industry) +
  geom_smooth(method = "lm")

## `geom_smooth()` using formula = 'y ~ x'

Exploratory analyses

model2 <- full %>%
  lmer(zsb ~ socialgood * as.factor(FL_8_DO) + (1|ResponseId) + (1|industry)  + (1|ZSB_DO), .)

summary(model2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: zsb ~ socialgood * as.factor(FL_8_DO) + (1 | ResponseId) + (1 |  
##     industry) + (1 | ZSB_DO)
##    Data: .
## 
## REML criterion at convergence: 17548
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1153 -0.5983  0.0078  0.6014  4.7132 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  ResponseId (Intercept) 1.2402   1.1137  
##  industry   (Intercept) 0.1712   0.4137  
##  ZSB_DO     (Intercept) 0.0668   0.2584  
##  Residual               1.2235   1.1061  
## Number of obs: 5404, groups:  ResponseId, 386; industry, 14; ZSB_DO, 4
## 
## Fixed effects:
##                                     Estimate Std. Error         df t value
## (Intercept)                          4.17887    0.18967    8.88410  22.033
## socialgood                          -0.14791    0.01899 5282.23773  -7.789
## as.factor(FL_8_DO)FL_9               0.11182    0.11937  402.95940   0.937
## socialgood:as.factor(FL_8_DO)FL_9   -0.04252    0.02553 5246.87814  -1.666
##                                   Pr(>|t|)    
## (Intercept)                       4.63e-09 ***
## socialgood                        8.07e-15 ***
## as.factor(FL_8_DO)FL_9              0.3495    
## socialgood:as.factor(FL_8_DO)FL_9   0.0958 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) soclgd a.(FL_
## socialgood  -0.075              
## a.(FL_8_DO) -0.310  0.106       
## s:.(FL_8_DO  0.050 -0.666 -0.166

## approaching significance

model3 <- full %>%
  lmer(zsb ~ socialgood + as.factor(orgType) + (1|ResponseId) + (1|industry)  + (1|ZSB_DO), .)

summary(model3)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: zsb ~ socialgood + as.factor(orgType) + (1 | ResponseId) + (1 |  
##     industry) + (1 | ZSB_DO)
##    Data: .
## 
## REML criterion at convergence: 17542.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.1064 -0.5961  0.0087  0.6010  4.7091 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  ResponseId (Intercept) 1.2379   1.1126  
##  industry   (Intercept) 0.1703   0.4127  
##  ZSB_DO     (Intercept) 0.0688   0.2623  
##  Residual               1.2236   1.1062  
## Number of obs: 5404, groups:  ResponseId, 386; industry, 14; ZSB_DO, 4
## 
## Fixed effects:
##                       Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)            4.28464    0.18383    7.51616  23.307 2.77e-08 ***
## socialgood            -0.16967    0.01417 5288.90356 -11.971  < 2e-16 ***
## as.factor(orgType)2   -0.22418    0.15915  380.65083  -1.409    0.160    
## as.factor(orgType)3   -0.43235    0.34036  381.33497  -1.270    0.205    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) soclgd a.(T)2
## socialgood  -0.064              
## as.fctr(T)2 -0.147  0.030       
## as.fctr(T)3 -0.072  0.012  0.078

Study 1: Social Good and ZSB

Samantha Grayson

2024-04-11

Descriptives

Primary model

Exploratory analyses