Bayesian Approaches to Assessing the Parallel Lines Assumption in Cumulative Ordered Logit Models

Autor/inn/en	Xu, Jun; Bauldry, Shawn G.; Fullerton, Andrew S.
Titel	Bayesian Approaches to Assessing the Parallel Lines Assumption in Cumulative Ordered Logit Models
Quelle	In: Sociological Methods & Research, 51 (2022) 2, S.667-698 (32 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Xu, Jun)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119882461
Schlagwörter	Bayesian Statistics; Evaluation Methods; Models; Intervals; Regression (Statistics); Equations (Mathematics); Hypothesis Testing + Suchen Sie Ihr Suchwort? Analogiemodell; Regression; Regressionsanalyse; Equations; Mathematics; Gleichungslehre; Hypothesenprüfung; Hypothesentest
Abstract	We first review existing literature on cumulative logit models along with various ways to test the parallel lines assumption. Building on the traditional frequentist framework, we introduce a method of Bayesian assessment of null values to provide an alternative way to examine the parallel lines assumption using highest density intervals and regions of practical equivalence. Second, we propose a new r cumulative logit model that can improve upon existing ones in addressing several challenges where traditional modeling techniques fail. We use two empirical examples from health research to showcase the Bayesian approaches. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01