Data analysis
The 23 dichotomous caregiving
ability indicators were used for Latent Class Analysis (LCA)1125
Wang J, Wang X. Structural Equation Modeling: Applications Using
Mplus, 2d Edition. New York: John Wiley; 2020.. The LCA analysis was
started with a single-class solution and increased the number of classes
(to four) while comparing model fit. Model fit statistics and indices
include the Akaike Information Criterion (AIC), Bayesian Information
Criterion (BIC), adjusted Bayesian Information Criterion (aBIC),
Bootstrapped Likelihood Ratio Test (BLRT), Vuong-Lo-Mendell-Rubin
likelihood ratio test (LMR), Lo-Mendell-Rubin adjusted LRT test(aLMR)
and entropy score25. Lower values on AIC, BIC and aBIC
indicate better model fit; significant p-values on the BLRT, LMR, and
aLMR indicate the k class model is more preferable than the k-1 class
model25. BIC and BLRT perform better than other fit
statistics/indices in determining the number of latent classes. The
entropy statistic measures the certainty of class classification. The
values of entropy range from 0 to 1, and a value closer to 1 indicating
better classification25. For a LCA model with entropy
≥0.80, the latent class membership estimated from the model can be saved
as ”observed” categorical variables for further analysis22Clark
SL. Mixture Modeling with Behavioral Data. Doctor thesis. University
of California, Los Angeles, CA, 2010.. In the present study, we used
a multinomial logistic regression model to examine the effects of
demographic and clinical variables on the latent class membership. LCA
was estimated using Mplus 7.0, and other analyses were conducted using
SPSS 22.0. All tests of statistical significance were 2-sided at α=0.05
level.