Longitudinal Models for Ordinal Data With Many Zeros and Varying Numbers of Response Categories

Autor:	Shelley A. Blozis, Melissa McTernan
Rok vydání:	2014
Předmět:	Ordinal data Mixed model Class (set theory) Sociology and Political Science Multilevel model General Decision Sciences Ordinal regression Outcome (probability) Outcome variable Modeling and Simulation Statistics Econometrics General Economics Econometrics and Finance Ordinal response Mathematics
Zdroj:	Structural Equation Modeling: A Multidisciplinary Journal. 22:216-226
ISSN:	1532-8007 1070-5511
DOI:	10.1080/10705511.2014.935915
Popis:	Ordinal response scales are often used to survey behaviors, including data collected in longitudinal studies. Advanced analytic methods are now widely available for longitudinal data. This study evaluates the performance of 4 methods as applied to ordinal measures that differ by the number of response categories and that include many zeros. The methods considered are hierarchical linear models (HLMs), growth mixture mixed models (GMMMs), latent class growth analysis (LCGA), and 2-part latent growth models (2PLGMs). The methods are evaluated by applying each to empirical response data in which the number of response categories is varied. The methods are applied to each outcome variable, first treating the outcome as continuous and then as ordinal, to compare the performance of the methods given both a different number of response categories and treatment of the variables as continuous versus ordinal. We conclude that although the 2PLGM might be preferred, no method might be ideal.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::77b5a3174fbd450bd17188cdab187127 https://doi.org/10.1080/10705511.2014.935915 Zobrazit plný text záznamu