The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models.
The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11...
| Publicado en: | Educational & Psychological Measurement Vol. 83; no. 3; pp. 520 - 556 |
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| Autores principales: | , |
| Formato: | Artículo |
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Sage Publications Inc.
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=163453836&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 163453836 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Jun2023 vid: 83 iid: 3 pid: 344 pub: Sage Publications Inc. artinfo: ui: 163453836 10.1177/00131644221094325 ppf: 520 ppct: 36 formats: tig: atl: The Impact of Sample Size and Various Other Factors on Estimation of Dichotomous Mixture IRT Models. aug: au: Sen, Sedat Cohen, Allan S. affil: Harran University, Şanlıurfa, Turkey University of Georgia, Athens, GA su: Türkiye Computer simulation Psychometrics Structural equation modeling Computer adaptive testing Sample size (Statistics) Descriptive statistics Statistical models sug: subj: Computer simulation Psychometrics Türkiye Structural equation modeling Computer adaptive testing Sample size (Statistics) Descriptive statistics Statistical models keyword: dichotomous data maximum likelihood estimation mixture item response theory Monte Carlo simulation sample size dichotomous data maximum likelihood estimation mixture item response theory Monte Carlo simulation sample size ab: The purpose of this study was to examine the effects of different data conditions on item parameter recovery and classification accuracy of three dichotomous mixture item response theory (IRT) models: the Mix1PL, Mix2PL, and Mix3PL. Manipulated factors in the simulation included the sample size (11 different sample sizes from 100 to 5000), test length (10, 30, and 50), number of classes (2 and 3), the degree of latent class separation (normal/no separation, small, medium, and large), and class sizes (equal vs. nonequal). Effects were assessed using root mean square error (RMSE) and classification accuracy percentage computed between true parameters and estimated parameters. The results of this simulation study showed that more precise estimates of item parameters were obtained with larger sample sizes and longer test lengths. Recovery of item parameters decreased as the number of classes increased with the decrease in sample size. Recovery of classification accuracy for the conditions with two-class solutions was also better than that of three-class solutions. Results of both item parameter estimates and classification accuracy differed by model type. More complex models and models with larger class separations produced less accurate results. The effect of the mixture proportions also differentially affected RMSE and classification accuracy results. Groups of equal size produced more precise item parameter estimates, but the reverse was the case for classification accuracy results. Results suggested that dichotomous mixture IRT models required more than 2,000 examinees to be able to obtain stable results as even shorter tests required such large sample sizes for more precise estimates. This number increased as the number of latent classes, the degree of separation, and model complexity increased. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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