genderrole {randomLCA} | R Documentation |
Opinions collected on gender roles in a study by Felling et al (1987). This was originally published in Heinen (1996) and subsequently in Galindo Garre and Vermunt (2006).
data(genderrole)
A data frame with 16 observations on the following 5 variables.
Q1
Q2
Q3
Q4
Q5
Freq
Galindo Garre and Vermunt (2006)
Felling, A., Peters, J., and Schreuder, O. (1987) Religion in Dutch society 85: Documentation of a national survey on religious and secular attitudes in 1985. Amsterdam: Steinmetz Archive.
Galindo Garre, F. and Vermunt, J.K. (2006) Avoiding boundary estimates in latent class analysis by Bayesian posterior mode estimation. Behaviormetrika, 33, 43–59.
Heinen, T. (1996) Latent Class and Discrete Latent Trait Models: Similarities and Differences.
data(genderrole) # standard latent class genderrole.lca1 <- randomLCA(genderrole[,1:5],freq=genderrole$Freq,nclass=1) genderrole.lca2 <- randomLCA(genderrole[,1:5],freq=genderrole$Freq) genderrole.lca3 <- randomLCA(genderrole[,1:5],freq=genderrole$Freq,nclass=3) # repeat with random effect genderrole.lca1random <- randomLCA(genderrole[,1:5],freq=genderrole$Freq,nclass=1,random=TRUE) genderrole.lca2random <- randomLCA(genderrole[,1:5],freq=genderrole$Freq,random=TRUE) genderrole.lca3random <- randomLCA(genderrole[,1:5],freq=genderrole$Freq,nclass=3,random=TRUE) # improved BIC for 1 class random print(c(BIC(genderrole.lca1),BIC(genderrole.lca2),BIC(genderrole.lca3))) print(c(BIC(genderrole.lca1random),BIC(genderrole.lca2random),BIC(genderrole.lca3random))) # can also repeat fits with blocksize=5 to give mixture of IRT models