Final Project Scaffolding Assignment #1 - Refining Your Analysis Idea

Author

Jingyi Yang

#Prepare

library(haven)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyverse)
Warning: package 'tidyverse' was built under R version 4.4.3
Warning: package 'ggplot2' was built under R version 4.4.3
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ forcats   1.0.0     ✔ readr     2.1.5
✔ ggplot2   3.5.1     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(semPlot)
library(lavaan) 
This is lavaan 0.6-19
lavaan is FREE software! Please report any bugs.
library(psych)

Attaching package: 'psych'

The following object is masked from 'package:lavaan':

    cor2cov

The following objects are masked from 'package:ggplot2':

    %+%, alpha
library(skimr) 
library(corrplot)
corrplot 0.95 loaded

Data Import

GSS2022 <- read_dta("研一下/DACSS 790Q/Final Project/2022/GSS2022.dta")
head(GSS2022)
# A tibble: 6 × 1,185
  year         id wrkstat hrs1        hrs2        evwork      wrkslf  occ10     
  <dbl+lbl> <dbl> <dbl+l> <dbl+lbl>   <dbl+lbl>   <dbl+lbl>   <dbl+l> <dbl+lbl> 
1 2022          1 1 [wor…    40       NA(i) [iap] NA(i) [iap] 2 [som…  430 [man…
2 2022          2 5 [ret… NA(i) [iap] NA(i) [iap]     1 [yes] 2 [som…   50 [mar…
3 2022          3 1 [wor…    52       NA(i) [iap] NA(i) [iap] 2 [som… 4610 [per…
4 2022          4 3 [wit… NA(i) [iap]    25       NA(i) [iap] 2 [som… 4120 [foo…
5 2022          5 8 [oth… NA(i) [iap] NA(i) [iap]     1 [yes] 2 [som… 7330 [ind…
6 2022          6 1 [wor…    50       NA(i) [iap] NA(i) [iap] 2 [som… 4610 [per…
# ℹ 1,177 more variables: prestg10 <dbl+lbl>, prestg105plus <dbl+lbl>,
#   indus10 <dbl+lbl>, marital <dbl+lbl>, martype <dbl+lbl>, divorce <dbl+lbl>,
#   widowed <dbl+lbl>, spwrksta <dbl+lbl>, sphrs1 <dbl+lbl>, sphrs2 <dbl+lbl>,
#   spevwork <dbl+lbl>, cowrksta <dbl+lbl>, coevwork <dbl+lbl>,
#   cohrs1 <dbl+lbl>, cohrs2 <dbl+lbl>, spwrkslf <dbl+lbl>, sppres80 <dbl+lbl>,
#   spocc10 <dbl+lbl>, sppres10 <dbl+lbl>, sppres105plus <dbl+lbl>,
#   spind10 <dbl+lbl>, coocc10 <dbl+lbl>, coind10 <dbl+lbl>, …

Setting data set

final_data <- GSS2022 %>% dplyr::select(c(wkvsfam, famvswk, health, mntlhlth, wrkstat, hrs1, cowrksta, cohrs1, marital, childs_exp, age, degree, income, rincome, race, sex, weight_issp)) %>% mutate(wkvsfam= as.numeric(wkvsfam),famvswk= as.numeric(famvswk), health= as.numeric(health), mntlhlth= as.numeric(mntlhlth), wrkstat= as.numeric(wrkstat), hrs1= as.numeric(hrs1),cowrksta= as.numeric(cowrksta), cohrs1= as.numeric(cohrs1), marital= as.numeric(marital), childs_exp= as.numeric(childs_exp), age= as.numeric(age), degree= as.numeric(degree), income= as.numeric(income), rincome= as.numeric(rincome), race= as.numeric(race), sex= as.numeric(sex), weight_issp= as.numeric(weight_issp))

new_names <- c("job_interfere_with_family", "family_interfere_with_job", "health_status_general", "mental_health_status", "working_status", "working_hours", "partner_working_status", "partner_working_hours", "marriage_status", "children_under_18", "age","education", "family_income", "income", "race", "sex", "weight")

colnames(final_data) <- new_names #Apply new names to your data frame
head(final_data)
# A tibble: 6 × 17
  job_interfere_with_family family_interfere_with_job health_status_general
                      <dbl>                     <dbl>                 <dbl>
1                         2                         3                     2
2                        NA                        NA                     2
3                         3                         3                     2
4                         1                         4                     2
5                        NA                        NA                     3
6                         1                         2                     1
# ℹ 14 more variables: mental_health_status <dbl>, working_status <dbl>,
#   working_hours <dbl>, partner_working_status <dbl>,
#   partner_working_hours <dbl>, marriage_status <dbl>,
#   children_under_18 <dbl>, age <dbl>, education <dbl>, family_income <dbl>,
#   income <dbl>, race <dbl>, sex <dbl>, weight <dbl>
# Reverse Coding
test_data <- final_data %>%
  mutate(
    job_interfere_with_family = case_when(
      job_interfere_with_family == 1 ~ 4,
      job_interfere_with_family == 2 ~ 3,
      job_interfere_with_family == 3 ~ 2,
      job_interfere_with_family == 4 ~ 1,
      job_interfere_with_family == "NA" ~ NA_real_),
    job_interfere_with_family = labelled(job_interfere_with_family, 
      c(`Never` = 1, `Rarely` = 2, `Sometimes` = 3, `Often` = 4))) %>% 
  mutate(family_interfere_with_job = as.numeric(family_interfere_with_job),
    family_interfere_with_job = case_when(
      family_interfere_with_job == 1 ~ 4,
      family_interfere_with_job == 2 ~ 3,
      family_interfere_with_job == 3 ~ 2,
      family_interfere_with_job == 4 ~ 1,
      family_interfere_with_job == "NA" ~ NA_real_),
    family_interfere_with_job = labelled(family_interfere_with_job, 
      c(`Never` = 1, `Rarely` = 2, `Sometimes` = 3, `Often` = 4))) %>%
  mutate(health_status_general = case_when(
      health_status_general == 1 ~ 8,
      health_status_general == 2 ~ 3,
      health_status_general == 3 ~ 2,
      health_status_general == 4 ~ 1,
      health_status_general == "NA" ~ NA_real_),
    health_status_general = labelled(health_status_general, 
      c(`Poor` = 1, `Fair` = 2, `Good` = 3, `Excellent` = 4)))%>%
  mutate(marriage_status= case_when(
    marriage_status== 1~5, 
    marriage_status== 2~4,
    marriage_status== 3~3,
    marriage_status== 4~2,
    marriage_status== 5~1, 
    marriage_status=="NA" ~ NA_real_),
    marriage_status==labelled(marriage_status, c(`Never married`=1, `Separated`=2, `Divorced`=3, `Widowed`=4, `Married`=5))) %>%
  mutate(working_status= case_when(
    working_status== 1~8, 
    working_status== 2~7,
    working_status== 3~6,
    working_status== 4~5,
    working_status== 5~4,
    working_status== 6~3,
    working_status== 7~2,
    working_status== 8~1,
    working_status=="NA" ~ NA_real_),
    working_status==labelled(working_status, c(`Other`=1, `Keeping House`=2, `In School`=3, `Retired`=4, `Unemployed, Laid off, Looking for job`=5, `With a job, But not at work because of temporary illness`= 6, `Working Part Time`= 7, `Working Full Time`=8))) %>%
  mutate(partner_working_status= case_when(
    partner_working_status== 1~8, 
    partner_working_status== 2~7,
    partner_working_status== 3~6,
    partner_working_status== 4~5,
    partner_working_status== 5~4,
    partner_working_status== 6~3,
    partner_working_status== 7~2,
    partner_working_status== 8~1,
    partner_working_status=="NA" ~ NA_real_),
    partner_working_status==labelled(partner_working_status, c(`Other`=1, `Keeping House`=2, `In School`=3, `Retired`=4, `Unemployed, Laid off, Looking for job`=5, `With a job, But not at work because of temporary illness`= 6, `Working Part Time`= 7, `Working Full Time`=8)))
  
head(test_data$job_interfere_with_family)
<labelled<double>[6]>
[1]  3 NA  2  4 NA  4

Labels:
 value     label
     1     Never
     2    Rarely
     3 Sometimes
     4     Often
head(test_data$family_interfere_with_job)
<labelled<double>[6]>
[1]  2 NA  2  1 NA  3

Labels:
 value     label
     1     Never
     2    Rarely
     3 Sometimes
     4     Often
head(test_data$health_status_general)
<labelled<double>[6]>
[1] 3 3 3 3 2 8

Labels:
 value     label
     1      Poor
     2      Fair
     3      Good
     4 Excellent
head(test_data)
# A tibble: 6 × 18
  job_interfere_with_family family_interfere_with_job health_status_general
  <dbl+lbl>                 <dbl+lbl>                 <dbl+lbl>            
1  3 [Sometimes]             2 [Rarely]               3 [Good]             
2 NA                        NA                        3 [Good]             
3  2 [Rarely]                2 [Rarely]               3 [Good]             
4  4 [Often]                 1 [Never]                3 [Good]             
5 NA                        NA                        2 [Fair]             
6  4 [Often]                 3 [Sometimes]            8                    
# ℹ 15 more variables: mental_health_status <dbl>, working_status <dbl>,
#   working_hours <dbl>, partner_working_status <dbl>,
#   partner_working_hours <dbl>, marriage_status <dbl>,
#   children_under_18 <dbl>, age <dbl>, education <dbl>, family_income <dbl>,
#   income <dbl>, race <dbl>, sex <dbl>, weight <dbl>, `==...` <lgl>
skim(test_data)
Data summary
Name test_data
Number of rows 4149
Number of columns 18
_______________________
Column type frequency:
logical 1
numeric 17
________________________
Group variables None

Variable type: logical

skim_variable n_missing complete_rate mean count
==… 3758 0.09 1 TRU: 391

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
job_interfere_with_family 1767 0.57 2.31 0.93 1 2 2 3.00 4 ▅▇▁▇▂
family_interfere_with_job 1779 0.57 2.06 0.86 1 1 2 3.00 4 ▆▇▁▆▁
health_status_general 7 1.00 3.61 2.19 1 2 3 3.00 8 ▅▇▁▁▃
mental_health_status 1870 0.55 4.53 7.93 0 0 0 5.00 30 ▇▁▁▁▁
working_status 9 1.00 5.93 2.34 1 4 7 8.00 8 ▂▁▃▁▇
working_hours 1829 0.56 39.94 14.09 0 36 40 45.00 89 ▁▂▇▁▁
partner_working_status 3758 0.09 6.57 2.22 1 5 8 8.00 8 ▁▁▂▁▇
partner_working_hours 3881 0.06 42.41 13.42 0 40 40 45.25 89 ▁▁▇▁▁
marriage_status 16 1.00 3.23 1.74 1 1 3 5.00 5 ▆▁▃▁▇
children_under_18 2106 0.49 0.68 1.46 0 0 0 1.00 29 ▇▁▁▁▁
age 256 0.94 48.24 17.75 18 33 47 63.00 89 ▇▇▆▆▂
education 2 1.00 1.82 1.27 0 1 1 3.00 4 ▂▇▂▃▂
family_income 484 0.88 11.22 2.07 1 12 12 12.00 12 ▁▁▁▁▇
income 1804 0.57 10.90 2.42 1 11 12 12.00 12 ▁▁▁▁▇
race 64 0.98 1.51 0.76 1 1 1 2.00 3 ▇▁▂▁▂
sex 23 0.99 1.54 0.50 1 1 2 2.00 2 ▇▁▁▁▇
weight 2840 0.32 180.68 53.93 80 145 171 205.00 999 ▇▁▁▁▁

Separate two scales

data_wif<-test_data%>% dplyr::select(c(job_interfere_with_family, health_status_general, mental_health_status, working_status, working_hours, partner_working_status, partner_working_hours, marriage_status, children_under_18, age,education, family_income, income, race, sex, weight))

head(data_wif)
# A tibble: 6 × 16
  job_interfere_with_family health_status_general mental_health_status
  <dbl+lbl>                 <dbl+lbl>                            <dbl>
1  3 [Sometimes]            3 [Good]                                15
2 NA                        3 [Good]                                NA
3  2 [Rarely]               3 [Good]                                 0
4  4 [Often]                3 [Good]                                10
5 NA                        2 [Fair]                                NA
6  4 [Often]                8                                       NA
# ℹ 13 more variables: working_status <dbl>, working_hours <dbl>,
#   partner_working_status <dbl>, partner_working_hours <dbl>,
#   marriage_status <dbl>, children_under_18 <dbl>, age <dbl>, education <dbl>,
#   family_income <dbl>, income <dbl>, race <dbl>, sex <dbl>, weight <dbl>
data_fiw <- test_data%>% dplyr::select(c(family_interfere_with_job, health_status_general, mental_health_status, working_status, working_hours, partner_working_status, partner_working_hours, marriage_status, children_under_18, age,education, family_income, income, race, sex, weight))

head(data_fiw)
# A tibble: 6 × 16
  family_interfere_with_job health_status_general mental_health_status
  <dbl+lbl>                 <dbl+lbl>                            <dbl>
1  2 [Rarely]               3 [Good]                                15
2 NA                        3 [Good]                                NA
3  2 [Rarely]               3 [Good]                                 0
4  1 [Never]                3 [Good]                                10
5 NA                        2 [Fair]                                NA
6  3 [Sometimes]            8                                       NA
# ℹ 13 more variables: working_status <dbl>, working_hours <dbl>,
#   partner_working_status <dbl>, partner_working_hours <dbl>,
#   marriage_status <dbl>, children_under_18 <dbl>, age <dbl>, education <dbl>,
#   family_income <dbl>, income <dbl>, race <dbl>, sex <dbl>, weight <dbl>

Test Cronbach’s alpha

Evaluating Correlations

#Step 1: Evaluate correlations 
cor_matrix_wif<-cor(data_wif, use = "pairwise.complete.obs") #Saves correlation matrix
cor_matrix_fiw<-cor(data_fiw, use = "pairwise.complete.obs") #Saves correlation matrix
 
# Display correlation matrix as a table
cor_table_wif <- round(cor_matrix_wif, 2)
print(cor_table_wif, cut = 0.6)
                          job_interfere_with_family health_status_general
job_interfere_with_family                      1.00                 -0.06
health_status_general                         -0.06                  1.00
mental_health_status                           0.14                 -0.21
working_status                                 0.09                  0.12
working_hours                                  0.20                 -0.01
partner_working_status                        -0.05                  0.06
partner_working_hours                         -0.02                  0.07
marriage_status                                0.06                  0.03
children_under_18                              0.08                  0.02
age                                           -0.09                 -0.09
education                                      0.10                  0.15
family_income                                  0.06                  0.10
income                                         0.09                  0.05
race                                          -0.04                 -0.03
sex                                            0.00                 -0.01
weight                                        -0.01                 -0.15
                          mental_health_status working_status working_hours
job_interfere_with_family                 0.14           0.09          0.20
health_status_general                    -0.21           0.12         -0.01
mental_health_status                      1.00          -0.05          0.01
working_status                           -0.05           1.00          0.61
working_hours                             0.01           0.61          1.00
partner_working_status                   -0.03           0.19          0.07
partner_working_hours                     0.10          -0.05          0.28
marriage_status                          -0.14          -0.03          0.04
children_under_18                         0.01           0.06          0.01
age                                      -0.19          -0.30         -0.05
education                                -0.08           0.19          0.06
family_income                            -0.08           0.24          0.14
income                                   -0.08           0.29          0.24
race                                     -0.07           0.03          0.01
sex                                       0.12          -0.11         -0.16
weight                                   -0.05           0.12          0.02
                          partner_working_status partner_working_hours
job_interfere_with_family                  -0.05                 -0.02
health_status_general                       0.06                  0.07
mental_health_status                       -0.03                  0.10
working_status                              0.19                 -0.05
working_hours                               0.07                  0.28
partner_working_status                      1.00                  0.45
partner_working_hours                       0.45                  1.00
marriage_status                            -0.21                 -0.04
children_under_18                          -0.01                 -0.01
age                                        -0.28                  0.02
education                                   0.07                 -0.04
family_income                               0.22                 -0.04
income                                      0.06                 -0.17
race                                        0.06                  0.04
sex                                         0.05                  0.12
weight                                      0.03                  0.10
                          marriage_status children_under_18   age education
job_interfere_with_family            0.06              0.08 -0.09      0.10
health_status_general                0.03              0.02 -0.09      0.15
mental_health_status                -0.14              0.01 -0.19     -0.08
working_status                      -0.03              0.06 -0.30      0.19
working_hours                        0.04              0.01 -0.05      0.06
partner_working_status              -0.21             -0.01 -0.28      0.07
partner_working_hours               -0.04             -0.01  0.02     -0.04
marriage_status                      1.00              0.07  0.38      0.15
children_under_18                    0.07              1.00 -0.26     -0.03
age                                  0.38             -0.26  1.00      0.05
education                            0.15             -0.03  0.05      1.00
family_income                        0.18              0.00  0.03      0.23
income                               0.13             -0.03  0.11      0.26
race                                -0.10              0.10 -0.16     -0.02
sex                                 -0.03              0.04 -0.01      0.02
weight                              -0.03             -0.02 -0.02     -0.07
                          family_income income  race   sex weight
job_interfere_with_family          0.06   0.09 -0.04  0.00  -0.01
health_status_general              0.10   0.05 -0.03 -0.01  -0.15
mental_health_status              -0.08  -0.08 -0.07  0.12  -0.05
working_status                     0.24   0.29  0.03 -0.11   0.12
working_hours                      0.14   0.24  0.01 -0.16   0.02
partner_working_status             0.22   0.06  0.06  0.05   0.03
partner_working_hours             -0.04  -0.17  0.04  0.12   0.10
marriage_status                    0.18   0.13 -0.10 -0.03  -0.03
children_under_18                  0.00  -0.03  0.10  0.04  -0.02
age                                0.03   0.11 -0.16 -0.01  -0.02
education                          0.23   0.26 -0.02  0.02  -0.07
family_income                      1.00   0.60 -0.06 -0.07  -0.02
income                             0.60   1.00 -0.02 -0.12   0.02
race                              -0.06  -0.02  1.00  0.02  -0.02
sex                               -0.07  -0.12  0.02  1.00  -0.27
weight                            -0.02   0.02 -0.02 -0.27   1.00
cor_table_fiw <- round(cor_matrix_fiw, 2)
print(cor_table_fiw, cut = 0.6)
                          family_interfere_with_job health_status_general
family_interfere_with_job                      1.00                 -0.07
health_status_general                         -0.07                  1.00
mental_health_status                           0.09                 -0.21
working_status                                 0.02                  0.12
working_hours                                  0.10                 -0.01
partner_working_status                        -0.04                  0.06
partner_working_hours                         -0.10                  0.07
marriage_status                                0.09                  0.03
children_under_18                              0.18                  0.02
age                                           -0.07                 -0.09
education                                      0.11                  0.15
family_income                                  0.01                  0.10
income                                         0.02                  0.05
race                                           0.00                 -0.03
sex                                            0.03                 -0.01
weight                                        -0.07                 -0.15
                          mental_health_status working_status working_hours
family_interfere_with_job                 0.09           0.02          0.10
health_status_general                    -0.21           0.12         -0.01
mental_health_status                      1.00          -0.05          0.01
working_status                           -0.05           1.00          0.61
working_hours                             0.01           0.61          1.00
partner_working_status                   -0.03           0.19          0.07
partner_working_hours                     0.10          -0.05          0.28
marriage_status                          -0.14          -0.03          0.04
children_under_18                         0.01           0.06          0.01
age                                      -0.19          -0.30         -0.05
education                                -0.08           0.19          0.06
family_income                            -0.08           0.24          0.14
income                                   -0.08           0.29          0.24
race                                     -0.07           0.03          0.01
sex                                       0.12          -0.11         -0.16
weight                                   -0.05           0.12          0.02
                          partner_working_status partner_working_hours
family_interfere_with_job                  -0.04                 -0.10
health_status_general                       0.06                  0.07
mental_health_status                       -0.03                  0.10
working_status                              0.19                 -0.05
working_hours                               0.07                  0.28
partner_working_status                      1.00                  0.45
partner_working_hours                       0.45                  1.00
marriage_status                            -0.21                 -0.04
children_under_18                          -0.01                 -0.01
age                                        -0.28                  0.02
education                                   0.07                 -0.04
family_income                               0.22                 -0.04
income                                      0.06                 -0.17
race                                        0.06                  0.04
sex                                         0.05                  0.12
weight                                      0.03                  0.10
                          marriage_status children_under_18   age education
family_interfere_with_job            0.09              0.18 -0.07      0.11
health_status_general                0.03              0.02 -0.09      0.15
mental_health_status                -0.14              0.01 -0.19     -0.08
working_status                      -0.03              0.06 -0.30      0.19
working_hours                        0.04              0.01 -0.05      0.06
partner_working_status              -0.21             -0.01 -0.28      0.07
partner_working_hours               -0.04             -0.01  0.02     -0.04
marriage_status                      1.00              0.07  0.38      0.15
children_under_18                    0.07              1.00 -0.26     -0.03
age                                  0.38             -0.26  1.00      0.05
education                            0.15             -0.03  0.05      1.00
family_income                        0.18              0.00  0.03      0.23
income                               0.13             -0.03  0.11      0.26
race                                -0.10              0.10 -0.16     -0.02
sex                                 -0.03              0.04 -0.01      0.02
weight                              -0.03             -0.02 -0.02     -0.07
                          family_income income  race   sex weight
family_interfere_with_job          0.01   0.02  0.00  0.03  -0.07
health_status_general              0.10   0.05 -0.03 -0.01  -0.15
mental_health_status              -0.08  -0.08 -0.07  0.12  -0.05
working_status                     0.24   0.29  0.03 -0.11   0.12
working_hours                      0.14   0.24  0.01 -0.16   0.02
partner_working_status             0.22   0.06  0.06  0.05   0.03
partner_working_hours             -0.04  -0.17  0.04  0.12   0.10
marriage_status                    0.18   0.13 -0.10 -0.03  -0.03
children_under_18                  0.00  -0.03  0.10  0.04  -0.02
age                                0.03   0.11 -0.16 -0.01  -0.02
education                          0.23   0.26 -0.02  0.02  -0.07
family_income                      1.00   0.60 -0.06 -0.07  -0.02
income                             0.60   1.00 -0.02 -0.12   0.02
race                              -0.06  -0.02  1.00  0.02  -0.02
sex                               -0.07  -0.12  0.02  1.00  -0.27
weight                            -0.02   0.02 -0.02 -0.27   1.00

What survey/dataset have you decided to use?

How work–family conflict related to health outcomes: control multiples variables

What are the main outcome/dependent variables of interest needed for this project?

The main dependent variables of interest needed for this project are 1) “wkvsfam”, The frequency for the demands of your job interfere with family life. 2) “famvswk”, The frequency for for the demands of family life interfere with your job.

What are the main explanatory/independent variables of interest needed to answer your research question?

The independent variables include: 1) “health”, Would you say your own health, in general, is excellent, good, fair, or poor? 2) “mntlhlth”, Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?

Write out at least 1 initial hypothesis on how you think an independent variable might influence a dependent variable.

  1. The job interfere with family life will have positive relationship with poor health condition. 2)The family life interfere with job will have positive relationship with poor mental health condition.

Write out control variables that you might use.

The control variables I might use are include 1) respondents’ working status, 2) Respondents’ working hours, 3) respondents’ partner working status, 4) respondents’ partner working hours, 5) Respondents’ marriage status,6) number for children under 18 respndents have, 7) respondents’ age, 8) respondents’ education level, 9) respondents’ family income, 10) respondents’ income, 11) respondents’ race, 12) respondents’ sex, and 13) respondents weight.

I chose these variables based on the articles related to the topic. Here is the frequency table about the control variables appeared in the eight articles.

Variables Name Notes Frequency Number
age 8
gender 5
education 5
Marital status 5
Working hours 5
race 3
number of children living at home 3
family income (income) 2
income 2
body mass (wight) 2
Parental status 1 = child(ren) livingat home and 2 = no children at home 2
living arrangement Live with spouse, and others 2
family history of heart disease 1
heavy drinking 1
Socioeconomic Index for Occupations  1
Location 1
presence of a long term disease 1
Work schedule 1
work environment 1
psychological job demands 1
decision latitude 1
social support at work 1
Emotional demands 1
changing domestic roles 1
changing work characteristics 1
job category 1
Shift work 1
Socioeconomic position 1
height 1

Next Step

  1. combine some control variables. According to the correlations, it seems that variables like family income and income, working hours and working status are measuring same concept. It might worth to consider to combine some variables to make the analysis more concise.
  2. Might try some regression model, like ordinal regression, and report the results, like odd ratio, with table.

Notes: Data is from General Social Survey (GSS), which aim to monitor the changes in U.S. society.