Parameter estimation bias of dichotomous logistic item response theory models using different variables

Alper Köse, C. Deha Doğan


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.


Ability distribution; Item response theory; Parameter recovery; Simulation

Full Text:




  • There are currently no refbacks.

Copyright (c) 2019 Institute of Advanced Engineering and Science

International Journal of Evaluation and Research in Education (IJERE)
p-ISSN: 2252-8822, e-ISSN: 2620-5440
The journal is published by Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) 

View IJERE Stats

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.