Investigation of Student Growth Recovery in a Fixed-Item Linking Procedure With a Fixed-Person Prior Distribution for Mixed-Format Test Data

Abstract

When the item response theory (IRT) model uses the marginal maximum likelihood estimation, person parameters are usually treated as random parameters following a certain distribution as a prior distribution to estimate the structural parameters in the model. For example, both PARSCALE (Muraki & Bock, 1999) and BILOG 3 (Mislevy & Bock, 1990) use a standard normal distribution as a default person prior. When the fixed-item linking method is used with an IRT program having a fixed-person prior distribution, it biases person ability growth downward or upward depending on the direction of the growth due to the misspecification of the prior. This study demonstrated by simulation how much biasing impact there is on person ability growth from the use of the fixed prior distribution in fixed-item linking for mixed-format test data. In addition, the study demonstrated how to recover growth through an iterative prior update calibration procedure. This shows that fixed-item linking is still a viable linking method for a fixed-person prior IRT calibration.