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Latent class logistic regression: application to marijuana use and attitudes among high school seniors

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  • Hwan Chung
  • Brian P. Flaherty
  • Joseph L. Schafer

Abstract

Summary. Analysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively captures attitudes toward its social and moral acceptability. One approach is to view the joint distribution of multiple use and attitude indicators as a mixture of latent classes. Pooling items from the annual ‘Monitoring the future’ surveys of American high school seniors from 1977 to 2001, we find that marijuana use and attitudes are well summarized by a four‐class model. Secular trends in class prevalences over this period reveal major shifts in use and attitudes. Applying a multinomial logistic model to the latent response, we investigate how class membership relates to demographic and life style factors, political beliefs and religiosity over time. Inferences about the parameters of the latent class logistic model are obtained by a combination of maximum likelihood and Bayesian techniques.

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  • Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:4:p:723-743
    DOI: 10.1111/j.1467-985X.2006.00419.x
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    2. Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119, February.
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    2. Vij, Akshay & Walker, Joan L., 2014. "Preference endogeneity in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 90-105.
    3. Jay, Flora & François, Olivier & Durand, Eric Y. & Blum, Michael G. B., 2015. "POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i09).
    4. Lee, Jung Wun & Chung, Hwan & Jeon, Saebom, 2021. "Bayesian multivariate latent class profile analysis: Exploring the developmental progression of youth depression and substance use," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    5. Chun Wang, 2021. "Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 167-189, March.
    6. Hwan Chung & Theodore Walls & Yousung Park, 2007. "A Latent Transition Model With Logistic Regression," Psychometrika, Springer;The Psychometric Society, vol. 72(3), pages 413-435, September.
    7. Hakaru Iguchi & Hajime Katayama & Junichi Yamanoi, 2022. "CEOs’ religiosity and corporate green initiatives," Small Business Economics, Springer, vol. 58(1), pages 497-522, January.
    8. Nilba Feijó-Cuenca & Nuria Ceular-Villamandos & Virginia Navajas-Romero, 2023. "Behavioral Patterns That Influence the Financing Choice Models of Small Enterprises in Ecuador through Latent Class Analysis," Sustainability, MDPI, vol. 15(8), pages 1-17, April.

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