slca (version 1.0.0)

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 latent class models can be fitted using this dataset replicate the analysis carried on McCutcheon (1985) and Bakk et al. (2014).
The data contains some covariates including year of the interview, age, sex, race, degree, and income of respondents. The variables associating social status include father's occupation and education level, and mother's education level, while the variables associating tolerance towards minorities are created by agreeing three related questions: (1) allowing public speaking, (2) allowing teaching, and (3) allowing literatures.

Format

A data frame with 2942 rows and 14 variables:

YEAR

Interview year (1976, 1977)

COHORT

Respondent's age
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 degree
levels: (1)LT HS, (2)HIGH-SCH, (3)COLLEGE, (4) BACHELOR, (5)GRADUATE

REALRINC

Income of respondents

PAPRES

Father's prestige (occupation)
levels: (1)LOW, (2)MIDIUM, (2)HIGH

PADEG

Father's degree
levels: (1)LT HS, (2)HIGH-SCH, (3)COLLEGE, (4) BACHELOR, (5)GRADUATE

MADEG

Mother's degree
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)
data <- gss7677[gss7677$RACE == "BLACK",]
model_stat <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>%
   estimate(data = data)
summary(model_stat)
param(model_stat)

model_tol <- slca(tol(4) ~ TOLRAC + TOLCOM + TOLHOMO + TOLATH + TOLMIL) %>%
   estimate(data = data)
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 = data)
summary(model_lta)
param(model_lta)

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