Parameter estimation bias of dichotomous logistic item response theory models using different variables
The aim of this study was to examine the precision of item parameter estimation in different sample sizes and test lengths under three parameter logistic model (3PL) item response theory (IRT) model, where the trait measured by a test was not normally distributed or had a skewed distribution.In the...
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| Hoofdauteurs: | , |
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| Formaat: | EJournal Article |
| Gepubliceerd in: |
Institute of Advanced Engineering and Science,
2019-09-01.
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| Online toegang: | Get fulltext |
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| Samenvatting: | The aim of this study was to examine the precision of item parameter estimation in different sample sizes and test lengths under three parameter logistic model (3PL) item response theory (IRT) model, where the trait measured by a test was not normally distributed or had a skewed distribution.In the study, number of categories (1-0), and item response model were identified as fixed conditions, and sample size, test length variables, and the ability distributions were selected as manipulated conditions. This is a simulation study. So data simulation and data analysis were done via packages in the R programming language. Results of the study showed that item parameter estimations performed under normal distribution were much stronger and bias-free compared to non-normal distribution. Moreover, the sample size had some limited positive effect on parameter estimation. However, the test length had no effect parameter estimation. As a result the importance of normality assumptions for IRT models were highlighted and findings were discussed based on relevant literature. |
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| Beschrijving item: | https://ijere.iaescore.com/index.php/IJERE/article/view/19807 |