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ERIC Number: EJ1168026
Record Type: Journal
Publication Date: 2018
Pages: 30
Abstractor: As Provided
ISSN: ISSN-1530-5058
Spurious Latent Class Problem in the Mixed Rasch Model: A Comparison of Three Maximum Likelihood Estimation Methods under Different Ability Distributions
Sen, Sedat
International Journal of Testing, v18 n1 p71-100 2018
Recent research has shown that over-extraction of latent classes can be observed in the Bayesian estimation of the mixed Rasch model when the distribution of ability is non-normal. This study examined the effect of non-normal ability distributions on the number of latent classes in the mixed Rasch model when estimated with maximum likelihood estimation methods (conditional, marginal, and joint). Three information criteria fit indices (Akaike information criterion, Bayesian information criterion, and sample size adjusted BIC) were used in a simulation study and an empirical study. Findings of this study showed that the spurious latent class problem was observed with marginal maximum likelihood and joint maximum likelihood estimations. However, conditional maximum likelihood estimation showed no overextraction problem with non-normal ability distributions.
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Publication Type: Journal Articles; Reports - Research
Education Level: Grade 4
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Austria; Norway; Armenia; Iran; Tunisia
Identifiers - Assessments and Surveys: Trends in International Mathematics and Science Study
Grant or Contract Numbers: N/A