gss7677 {slca} | R Documentation |
GSS 1976-1977 Data on Social Status and Tolerance towards Minorities
Description
This dataset contains responses from the General Social Survey (GSS) for the years 1976 and 1977, focusing on social status and tolerance towards minorities.
The dataset can be used to replicate the analyses conducted in McCutcheon (1985) and Bakk et al. (2014).
It includes covariates such as interview year, age, sex, race, education level, and income. Social status-related variables include father's occupation and education level, as well as mother's education level. Tolerance towards minorities is measured by agreement with three questions: (1) allowing public speaking, (2) allowing teaching, and (3) allowing literature publication.
Usage
gss7677
Format
A data frame with 2942 rows and 14 variables:
YEAR
Interview year (1976, 1977).
COHORT
Respondent's age cohort.
Levels: (1)YOUNG
, (2)YOUNG-MIDDLE
, (4)MIDDLE
, (5)OLD
.SEX
Respondent's sex.
Levels: (1)MALE
, (2)FEMALE
.RACE
Respondent's race.
Levels: (1)WHITE
, (2)BLACK
, (3)OTHER
.DEGREE
Respondent's education level.
Levels: (1)LT HS
, (2)HIGH-SCH
, (3)HIGHER
.REALRINC
Respondent's income.
PAPRES
Father's occupational prestige.
Levels: (1)LOW
, (2)MEDIUM
, (3)HIGH
.PADEG
Father's education level.
Levels: (1)LT HS
, (2)HIGH-SCH
, (3)COLLEGE
, (4)BACHELOR
, (5)GRADUATE
.MADEG
Mother's education level.
Levels: (1)LT HS
, (2)HIGH-SCH
, (3)COLLEGE
, (4)BACHELOR
, (5)GRADUATE
.TOLRAC
Tolerance towards racists.
TOLCOM
Tolerance towards communists.
TOLHOMO
Tolerance towards homosexuals.
TOLATH
Tolerance towards atheists.
TOLMIL
Tolerance towards militarists.
Source
General Social Survey (GSS) 1976, 1977
References
Bakk Z, Kuha J. (2021) Relating latent class membership to external variables: An overview. Br J Math Stat Psychol. 74(2):340-362.
McCutcheon, A. L. (1985). A latent class analysis of tolerance for nonconformity in the American public. Public Opinion Quarterly, 49, 474–488.
Examples
library(magrittr)
gss500 <- gss7677[1:500,] %>% na.omit
model_stat <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>%
estimate(data = gss500, control = list(em.tol = 1e-6))
summary(model_stat)
param(model_stat)
model_tol <- slca(tol(4) ~ TOLRAC + TOLCOM + TOLHOMO + TOLATH + TOLMIL) %>%
estimate(data = gss500, control = list(em.tol = 1e-6))
summary(model_tol)
param(model_tol)
model_lta <- slca(
status(3) ~ PAPRES + PADEG + MADEG,
tol(4) ~ TOLRAC + TOLCOM + TOLHOMO + TOLATH + TOLMIL,
status ~ tol
) %>% estimate(data = gss500, control = list(em.tol = 1e-6))
summary(model_lta)
param(model_lta)
regress(model_lta, status ~ SEX, gss500)
regress(model_lta, status ~ SEX, gss500, method = "BCH")
regress(model_lta, status ~ SEX, gss500, method = "ML")