Investigation of the Effect of Parameter Estimation and Classification Accuracy in Mixture IRT Models under Different Conditions

Autor/inn/en	Saatcioglu, Fatima Munevver; Atar, Hakan Yavuz
Titel	Investigation of the Effect of Parameter Estimation and Classification Accuracy in Mixture IRT Models under Different Conditions
Quelle	In: International Journal of Assessment Tools in Education, 9 (2022) 4, S.1013-1029 (17 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Saatcioglu, Fatima Munevver) ORCID (Atar, Hakan Yavuz)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Accuracy; Classification; Item Response Theory; Programming Languages; Simulation; Item Analysis; Computer Software; Data Analysis; Difficulty Level; Guessing (Tests); Sample Size; Correlation; Test Items; Responses + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Item-Response-Theorie; Simulation program; Simulationsprogramm; Itemanalyse; Auswertung; Schwierigkeitsgrad; Erraten; Korrelation; Test content; Testaufgabe
Abstract	This study aims to examine the effects of mixture item response theory (IRT) models on item parameter estimation and classification accuracy under different conditions. The manipulated variables of the simulation study are set as mixture IRT models (Rasch, 2PL, 3PL); sample size (600, 1000); the number of items (10, 30); the number of latent classes (2, 3); missing data type (complete, missing at random (MAR) and missing not at random (MNAR)), and the percentage of missing data (10%, 20%). Data were generated for each of the three mixture IRT models using the code written in R program. "MplusAutomation" package, which provides the automation of R and Mplus program, was used to analyze the data. The mean RMSE values for item difficulty, item discrimination, and guessing parameter estimation were determined. The mean RMSE values as to the Mixture Rasch model were found to be lower than those of the Mixture 2PL and Mixture 3PL models. Percentages of classification accuracy were also computed. It was noted that the Mixture Rasch model with 30 items, 2 classes, 1000 sample size, and complete data conditions had the highest classification accuracy percentage. Additionally, a factorial ANOVA was used to evaluate each factor's main effects and interaction effects. (As Provided).
Anmerkungen	International Journal of Assessment Tools in Education. Pamukkale University, Faculty of Education, Kinikli Campus, Denizli 20070, Turkey. e-mail: ijate.editor@gmail.com; Web site: https://ijate.net/index.php/ijate
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01