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Latent Structure Models with Direct Effects between Indicators

Author

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  • JACQUES A. HAGENAARS

    (Tilburg University)

Abstract

A basic assumption of latent structure models is that of local independence: given the score on the latent variable, the scores on the manifest variables are independent of each other. This basic assumption is violated when test-retest effects, response consistency effects, correlated response errors, and so forth are present. However, it is possible to reformulate the latent class model in such a way that these direct relations between the indicators (manifest variables) can be accounted for. The reformulation proposed here, takes place within the framework of log-linear modeling.

Suggested Citation

  • Jacques A. Hagenaars, 1988. "Latent Structure Models with Direct Effects between Indicators," Sociological Methods & Research, , vol. 16(3), pages 379-405, February.
    • Handle: RePEc:sae:somere:v:16:y:1988:i:3:p:379-405
    DOI: 10.1177/0049124188016003002
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    References listed on IDEAS

    as
    1. Jacques Hagenaars, 1978. "Latent probability models with direct effects between indicators," Quality & Quantity: International Journal of Methodology, Springer, vol. 12(3), pages 205-222, September.
    2. C. Mitchell Dayton & George Macready, 1976. "A probabilistic model for validation of behavioral hierarchies," Psychometrika, Springer;The Psychometric Society, vol. 41(2), pages 189-204, June.
    3. Dean Harper, 1972. "Local dependence latent structure models," Psychometrika, Springer;The Psychometric Society, vol. 37(1), pages 53-59, March.
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    Cited by:

    1. Morey, Edward R. & Thiene, Mara, 2017. "Can Personality Traits Explain Where and With Whom You Recreate? A Latent-Class Site-Choice Model Informed by Estimates From Mixed-Mode LC Cluster Models With Latent-Personality Traits," Ecological Economics, Elsevier, vol. 138(C), pages 223-237.
    2. Willem E. Saris & Melanie Revilla, 2016. "Correction for Measurement Errors in Survey Research: Necessary and Possible," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 127(3), pages 1005-1020, July.
    3. Matthieu Marbac & Christophe Biernacki & Vincent Vandewalle, 2016. "Latent class model with conditional dependency per modes to cluster categorical data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 183-207, June.
    4. Forcina, Antonio, 2017. "A Fisher-scoring algorithm for fitting latent class models with individual covariates," Econometrics and Statistics, Elsevier, vol. 3(C), pages 132-140.
    5. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.

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