EVALUATION OF 2 METHODS FOR ESTIMATING ITEM RESPONSE THEORY PARAMETERS WHEN ASSESSING DIFFERENTIAL ITEM FUNCTIONING

Recent legal developments appear to sanction the use of psychometrically unsound procedures for examining differential item functioning (DIF) on standardized tests. More appropriate approaches involve the use of item response theory (IRT). However, many IRT-based DIF studies have used Lord's (1968) joint maximum likelihood procedure, which can lead to incorrect and misleading results. A Monte Carlo simulation was conducted to evaluate the effectiveness of two other methods of parameter estimation: marginal maximum likelihood estimation and Bayes modal estimation. Sample size and data dimensionality were manipulated in the simulation. Results indicated that both estimation methods (a) provided more accurate parameter estimates and less inflated Type I error rates than joint maximum likelihood, (b) were robust to multidimensionality, and (c) produced more accurate parameter estimates and higher rates of identifying DIF with larger samples.