Evaluating Anchor-Item Designs for Concurrent Calibration With the GGUM. (March 2017)

• Record Type: Journal Article
• Authors: Joo, Seang-Hwane; Lee, Philseok; Stark, Stephen
• Is Part Of: Applied psychological measurement. Volume 41:Number 2(2017)
• Page Start: 83
• Page End: 96
• Publication Date: 2017-03
• Subjects: concurrent calibration -- anchor-item design -- generalized graded unfolding model -- Markov chain Monte Carlo; Psychometrics -- Periodicals; Psychological tests -- Periodicals; Psychology, Applied -- Periodicals; 150.1519505
• DOI: 10.1177/0146621616673997
• Languages: English
• ISSNs: 0146-6216

Abstract

Concurrent calibration using anchor items has proven to be an effective alternative to separate calibration and linking for developing large item banks, which are needed to support continuous testing. In principle, anchor-item designs and estimation methods that have proven effective with dominance item response theory (IRT) models, such as the 3PL model, should also lead to accurate parameter recovery with ideal point IRT models, but surprisingly little research has been devoted to this issue. This study, therefore, had two purposes: (a) to develop software for concurrent calibration with, what is now the most widely used ideal point model, the generalized graded unfolding model (GGUM); (b) to compare the efficacy of different GGUM anchor-item designs and develop empirically based guidelines for practitioners. A Monte Carlo study was conducted to compare the efficacy of three anchor-item designs in vertical and horizontal linking scenarios. The authors found that a block-interlaced design provided the best parameter recovery in nearly all conditions. The implications of these findings for concurrent calibration with the GGUM and practical recommendations for pretest designs involving ideal point computer adaptive testing (CAT) applications are discussed.