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A Latent Transition Analysis Model for Latent-State-Dependent Nonignorable Missingness

Published online by Cambridge University Press:  01 January 2025

Sonya K. Sterba
Sonya K. Sterba*
Affiliation:
Vanderbilt University
*
Correspondence should be made to Sonya K. Sterba, Quantitative Methods Program, Department of Psychology and Human Development, Vanderbilt University, Peabody #552, 230 Appleton Place, Nashville, TN 37203, USA. Email: sonya.sterba@vanderbilt.edu
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Abstract

Psychologists often use latent transition analysis (LTA) to investigate state-to-state change in discrete latent constructs involving delinquent or risky behaviors. In this setting, latent-state-dependent nonignorable missingness is a potential concern. For some longitudinal models (e.g., growth models), a large literature has addressed extensions to accommodate nonignorable missingness. In contrast, little research has addressed how to extend the LTA to accommodate nonignorable missingness. Here we present a shared parameter LTA that can reduce bias due to latent-state-dependent nonignorable missingness: a parallel-process missing-not-at-random (MNAR-PP) LTA. The MNAR-PP LTA allows outcome process parameters to be interpreted as in the conventional LTA, which facilitates sensitivity analyses assessing changes in estimates between LTA and MNAR-PP LTA. In a sensitivity analysis for our empirical example, previous and current membership in high-delinquency states predicted adolescents’ membership in missingness states that had high nonresponse probabilities for some or all items. A conventional LTA overestimated the proportion of adolescents ending up in a low-delinquency state, compared to an MNAR-PP LTA.

Keywords

nonignorable missing datalatent transition analysismissing not at randomshared parameter modelmixture model
Type
Article
Information
Psychometrika , Volume 81 , Issue 2 , June 2016 , pp. 506 - 534
Copyright
Copyright © 2015 The Psychometric Society

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