Parameter estimation bias of dichotomous logistic item response theory models using different variables
| Autor: | C. Deha Doğan, Alper Köse |
|---|---|
| Přispěvatelé: | BAİBÜ, Eğitim Fakültesi, Eğitim Bilimleri Bölümü, Köse, Alper |
| Rok vydání: | 2019 |
| Předmět: |
Estimation theory
media_common.quotation_subject 05 social sciences 050401 social sciences methods 050301 education Ability distribution Logistic regression Item response theory Education Normal distribution 0504 sociology Sample size determination Parameter recovery Statistics Trait Probability distribution 0503 education Normality Simulation media_common Mathematics |
| Popis: | 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. file:///C:/Users/gokhan/Downloads/Parameter_estimation_bias_of_dichotomous_logistic_.pdf |
| Databáze: | OpenAIRE |
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