Applying Unidimensional Models for Semiordered Data to Scale Data with Neutral Responses

Autor/inn/en	Cohn, Sophie; Huggins-Manley, Anne Corinne
Titel	Applying Unidimensional Models for Semiordered Data to Scale Data with Neutral Responses
Quelle	In: Educational and Psychological Measurement, 80 (2020) 2, S.242-261 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Cohn, Sophie)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164419861143
Schlagwörter	Models; Responses; Evaluation Methods; Preservice Teachers; Pedagogical Content Knowledge; Technological Literacy + Suchen Sie Ihr Suchwort? Analogiemodell; Pädagogische Kompetenz; Technisches Wissen
Abstract	The purpose of this study is to evaluate whether a recently developed semiordered model can be used to explore the functioning of neutral response options in rating scale data. Huggins-Manley, Algina, and Zhou developed a class of unidimensional models for semiordered data within scale items (i.e., items with both ordered response categories and an additional nominal response category) and found promising results when applying them to scale data with Not Applicable response categories. In this study, we extended the application of the semi-partial credit model (PCM) to evaluate whether the semi-PCM can be used to calibrate potentially unordered neutral responses in rating scale data, and if so, how the approach compares with alternate methods of dealing with the neutral response option. Findings indicate that the semi-PCM can (a) assist practitioners in evaluating the ordered or unordered nature of neutral responses and (b) provide a viable alternative for [theta] estimation in the presence of an unordered neutral category. The process used in this study also provides a methodological framework for researchers and practitioners to use when dealing with neutral responses in their own data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01