I want to conduct a LCA on three surveys (three different years), with covariates and sample weights. Each survey samples workers (between 3.300 and 5.200 each, depending on the years) in different industries and firm sizes. The class indicators are 14 items measuring up to what extent different organizational and physical aspects (i.e. monotony, lack of autonomy, physical effort, noise, insecurity, …) cause annoyances to the workers, measured on a 5-point likert scale (from not at all to very much). Data is highly skewed towards the first category so, to have less sparse tables, I’ve dichotomized the items. A) Is that correct? To have an idea of the possible model and number of classes, an EFA was requested on data from the last survey, showing 2 eigenvalues greater than one (7.687 and 1.441; the third was 0.814). Nevertheless, it seems impossible to fit an EFA in terms of the Chi-sq. with the WLSMV estimator (for the 2-factor solution, RMSEA=0.042 and RMSR=0.0457 but Chi=517.425; adding more factors leads to negative residual variances). B) It seems that the data is not well suited for factor analysis, should it also be concluded that the data is not well suited for LCA? Is there anyway to cope with this problem? I’ve read a reference where the authors do a LPA over principal components, but I don’t see support for this idea in this forum. Thanking you in advance, Fernando.
The 2- and 3- factor solutions are interpretable. I also like the 3- and (even more) 4- classes solutions, but some of the bivariate residuals (BVR) are high, and Pearson's Chi is significative. Fit indices for 1 to 7 classes are: C's BIC LL Param. Entrophy LRT (C-1) 1 90080.061 -44980.125 14 - - 2 91067.610 -45396.792 32 0.861 0.0000 3 74049.313 -36836.381 44 0.799 0.0000 4 72894.265 -36194.673 59 0.783 0.0004 5 72505.335 -35936.024 74 0.763 0.1368 6 72282.717 -35760.530 89 0.754 0.1200 7 72118.025 -35613.999 104 0.772 1.0000 Can I accept the 4-classes solution and then go with the covariates?
Thank you very much for your quick and helpful answer. I didn't use the bootstrapped LRT because I have sample weights. I didn't knew the FMA technique, it seems that is exactly what is needed, and I will explore it as soon as possible. The tobaco paper is quite clarifying, and I will work on it, and in example 7.27. Many thanks, Fernando.