slca (version 1.3.0)

gss7677: 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.

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)

# \donttest{
regress(model_lta, status ~ SEX, gss500)
regress(model_lta, status ~ SEX, gss500, method = "BCH")
regress(model_lta, status ~ SEX, gss500, method = "ML")
# }