Multiple-Group Joint Modeling of Item Responses, Response Times, and Action Counts with the Conway-Maxwell-Poisson Distribution

Autor/inn/en	Qiao, Xin; Jiao, Hong; He, Qiwei
Titel	Multiple-Group Joint Modeling of Item Responses, Response Times, and Action Counts with the Conway-Maxwell-Poisson Distribution
Quelle	In: Journal of Educational Measurement, 60 (2023) 2, S.255-281 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Qiao, Xin) ORCID (He, Qiwei)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12349
Schlagwörter	Multivariate Analysis; Models; Item Response Theory; Statistical Distributions; Measurement; Bayesian Statistics; Monte Carlo Methods; Problem Solving; Gender Differences; Goodness of Fit; Reaction Time; Achievement Tests; Foreign Countries; International Assessment; Secondary School Students; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Multivariate Analyse; Analogiemodell; Item-Response-Theorie; Wahrscheinlichkeitsverteilung; Messverfahren; Monte-Carlo-Methode; Problemlösen; Geschlechterkonflikt; Reaktionsvermögen; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler
Abstract	Multiple group modeling is one of the methods to address the measurement noninvariance issue. Traditional studies on multiple group modeling have mainly focused on item responses. In computer-based assessments, joint modeling of response times and action counts with item responses helps estimate the latent speed and action levels in addition to latent ability. These two new data sources can also be used to further address the measurement noninvariance issue. One challenge, however, is to correctly model action counts which can be underdispersed, overdispersed, or equidispersed in real data sets. To address this, we adopted the Conway-Maxwell-Poisson distribution that accounts for different types of dispersion in action counts and incorporated it in the multiple group joint modeling of item responses, response times, and action counts. Bayesian Markov Chain Monte Carlo method was used for model parameter estimation. To illustrate an application of the proposed model, an empirical data analysis was conducted using the Programme for International Student Assessment (PISA) 2015 collaborative problem-solving items where potential measurement noninvariance issue existed between gender groups. Results indicated that Conway-Maxwell-Poisson model yielded better model fit than alternative count data models such as negative binomial and Poisson models. In addition, response times and action counts provided further information on performance differences between groups. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01