Abstract
Researchers frequently have only categorical data to analyze and cannot, for theoretical or methodological reasons, assume that the observed variables are discrete representations of an underlying continuous variable. We present latent class analysis as an alternative method of measuring latent variables in these circumstances. Latent class analysis does not require the assumptions of factor analyses about the nature of manifest and latent variables, but does allow the use of more precise model selection than techniques such as cluster analysis. We modeled the lifetime substance use of American Indian youth. The latent class model of American Indian teenagers' substance use had four classes: Abstaining, Predominantly Alcohol, Predominantly Alcohol and Marijuana, and Plural Substance. We then demonstrated the usefulness of this latent variable by using it to differentiate levels of several variables in a manner consistent with Social Cognitive Theory.
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Mitchell, C.M., Plunkett, M. The Latent Structure of Substance Use Among American Indian Adolescents: An Example Using Categorical Variables. Am J Community Psychol 28, 105–125 (2000). https://doi.org/10.1023/A:1005146530634
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DOI: https://doi.org/10.1023/A:1005146530634