Lee Philseok, Joo Seang-Hwane, Stark Stephen, Chernyshenko Oleksandr S
South Dakota State University, Brookings, USA.
Katholieke Universiteit Leuven, Belgium.
Appl Psychol Meas. 2019 May;43(3):226-240. doi: 10.1177/0146621618768294. Epub 2018 Apr 23.
Historically, multidimensional forced choice (MFC) measures have been criticized because conventional scoring methods can lead to ipsativity problems that render scores unsuitable for interindividual comparisons. However, with the recent advent of item response theory (IRT) scoring methods that yield normative information, MFC measures are surging in popularity and becoming important components in high-stake evaluation settings. This article aims to add to burgeoning methodological advances in MFC measurement by focusing on statement and person parameter recovery for the GGUM-RANK (generalized graded unfolding-RANK) IRT model. Markov chain Monte Carlo (MCMC) algorithm was developed for estimating GGUM-RANK statement and person parameters directly from MFC rank responses. In simulation studies, it was examined that how the psychometric properties of statements composing MFC items, test length, and sample size influenced statement and person parameter estimation; and it was explored for the benefits of measurement using MFC triplets relative to pairs. To demonstrate this methodology, an empirical validity study was then conducted using an MFC triplet personality measure. The results and implications of these studies for future research and practice are discussed.
从历史上看，多维强制选择（MFC）测量方法一直受到批评，因为传统的计分方法可能会导致同质性问题，使分数不适用于个体间的比较。然而，随着最近产生规范信息的项目反应理论（IRT）计分方法的出现，MFC测量方法正迅速流行起来，并成为高风险评估环境中的重要组成部分。本文旨在通过关注GGUM-RANK（广义分级展开-RANK）IRT模型的项目和个体参数恢复，为MFC测量中迅速发展的方法学进展做出贡献。开发了马尔可夫链蒙特卡罗（MCMC）算法，用于直接从MFC等级反应中估计GGUM-RANK项目和个体参数。在模拟研究中，考察了构成MFC项目的项目心理测量特性、测验长度和样本量如何影响项目和个体参数估计；并探讨了使用MFC三元组相对于二元组进行测量的优势。为了演示这种方法，随后使用MFC三元组人格测量进行了一项实证效度研究。讨论了这些研究的结果及其对未来研究和实践的启示。
