Effect of Sample Size on Irt Equating of Uni-Dimensional Tests in Common Item Non-Equivalent Group Design: a Monte Carlo Simulation Study
Test equating is important to large-scale testing programs because of the following two reasons: strict test security is a key concern for high-stakes tests and fairness of test equating is important for test takers. The question of adequacy of sample size often arises in test equating. However, most recommendations in the existing literature are based on classical test equating. Very few research studies systematically investigated the minimal sample size which leads to reasonably accurate equating results based on item response theory (IRT). The main purpose of this study was to examine the minimal sample size for desired IRT equating accuracy for the common-item nonequivalent groups design under various conditions. Accuracy was determined by examining the relative magnitude of six accuracy statistics. Two IRT equating methods were carried out on simulated tests with combinations of test length, test format, group ability difference, similarity of the form difficulty, and parameter estimation methods for 14 sample sizes using Monte Carlo simulations with 1,000 replications per cell. Observed score equating and true score equating were compared to the criterion equating to obtain the accuracy statistics. The results suggest that different sample size requirements exist for different test lengths, test formats and parameter estimation methods. Additionally, the results show the following: first, the results for true score equating and observed score equating are very similar. Second, the longer test has less accurate equating than the shorter one at the same sample size level and as the sample size decreases, the gap is greater. Third, concurrent parameter estimation method produced less equating error than separate estimation at the same sample size level and as the sample size reduces, the difference increases. Fourth, the cases with different group ability have larger and less stable error comparing to the base case and the cases with different test difficulty, especially when using separate parameter estimation method with sample size less than 750. Last, the mixed formatted test is more accurate than the single formatted one at the same sample size level.