Latent Class Analysis (LCA) is a statistical technique that is used in factor, cluster, and regression techniques; it is a subset of structural equation modeling (SEM). LCA is a technique where constructs are identified and created from unobserved, or latent, subgroups, which are usually based on individual responses from multivariate categorical data. These constructs are then used for r further analysis. LCA models can also be referred to as finite mixture models.
Questions Answered:
What subtypes of disease exist within a given test?
What domains are found to exist among the different categorical symptoms?
Assumptions in latent class analysis:
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Key concepts and terms in LCA:
Resources
Biemer, P. P., & Wiesen, C. (2002). Measurement error evaluation of self-reported drug use: A latent class analysis of the U.S. National Household Survey on Drug Abuse. Journal of the Royal Statistical Society, 165(1), 97-119.
Chung, H., Flaherty, B. P., & Schafer, J. L. (2006). Latent class logistic regression: Application to marijuana use and attitudes among high school seniors. Journal of the Royal Statistical Society, 169(4), 723-743.
Clogg, C. C. (1995). Latent class models. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbook of statistical modeling for the social and behavioral sciences (pp. 311-359). New York: Plenum Press.
Clogg, C. C., & Goodman, L. A. (1984). Latent structure analysis of a set of multidimensional contingency tables. Journal of the American Statistical Association, 79(388), 762-771.
Croon, M. A. (1991). Investigating Mokken scalability of dichotomous items by means of ordinal latent class analysis. British Journal of Mathematical and Statistical Psychology, 44(2), 315-331.
Dayton, C. M. (1998). Latent class scaling analysis. Thousand Oaks, CA: Sage Publications.
Flaherty, B. P. (2002). Assessing the reliability of categorical substance use measures with latent class analysis. Drug and Alcohol Dependence, 69(1), 7-20.
Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215-231.
Hagenaars, J. A. (1993). Loglinear models with latent variables. Newbury Park, CA: Sage Publications.
Kolb, R. R., & Dayton, C. M. (1996). Correcting for nonresponse in latent class analysis. Multivariate Behavioral Research, 31(1), 7-32.
Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671-694.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent Structure Analysis. Boston: Houghton Mifflin.
Loken, E. (2004). Using latent class analysis to model temperament types. Multivariate Behavioral Research, 39(4), 625-652.
McCutcheon, A. L. (1987). Latent class analysis. Newbury Park, CA: Sage Publications.
Mooijaart, A., & van der Heijden, P. G. (1992). The EM algorithm for latent class analysis with equality constraints. Psychometrika, 56(4), 699-716.
Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class models (pp. 89-106). Cambridge, UK: Cambridge University Press.
Latent Variable and Latent Structure Models (Quantitative Methodology Series)
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