Wang Wei, Lee Philseok, Joo Seang-Hwane, Stark Stephen, Louden Robert
University of Central Florida, Orlando, USA.
University of South Florida, Tampa, USA.
Appl Psychol Meas. 2016 Oct;40(7):551-553. doi: 10.1177/0146621616663682. Epub 2016 Aug 20.
In recent years, there has been a surge of interest in measuring noncognitive constructs in educational and managerial/organizational settings. For the most part, these noncognitive constructs have been and continue to be measured using Likert-type (ordinal response) scales, which are susceptible to several types of response distortion. To deal with these response biases, researchers have proposed using forced-choice format, which requires respondents or raters to evaluate cognitive, affective, or behavioral descriptors presented in blocks of two or more. The workhorse for this measurement endeavor is the item response theory (IRT) model developed by Zinnes and Griggs (Z-G), which was first used as the basis for a computerized adaptive rating scale (CARS), and then extended by many organizational scientists. However, applications of the Z-G model outside of organizational contexts have been limited, primarily due to the lack of publicly available software for parameter estimation. This research effort addressed that need by developing a Markov chain Monte Carlo (MCMC) estimation program, called MCMC Z-G, which uses a Metropolis-Hastings-within-Gibbs algorithm to simultaneously estimate Z-G item and person parameters. This publicly available computer program MCMC Z-G can run on both Mac OS and Windows platforms.
近年来，在教育和管理/组织环境中测量非认知结构的兴趣激增。在很大程度上，这些非认知结构一直并将继续使用李克特式（顺序响应）量表进行测量，而这种量表容易出现几种类型的响应偏差。为了应对这些响应偏差，研究人员提出使用强制选择格式，即要求受访者或评分者对以两个或更多为一组呈现的认知、情感或行为描述符进行评估。这种测量方法的主要工具是由津尼斯和格里格斯（Z-G）开发的项目反应理论（IRT）模型，该模型最初被用作计算机化自适应评分量表（CARS）的基础，后来被许多组织科学家扩展。然而，Z-G模型在组织环境之外的应用受到限制，主要是因为缺乏用于参数估计的公开可用软件。这项研究工作通过开发一个名为MCMC Z-G的马尔可夫链蒙特卡罗（MCMC）估计程序来满足这一需求，该程序使用吉布斯抽样中的梅特罗波利斯-黑斯廷斯算法来同时估计Z-G项目和人员参数。这个公开可用的计算机程序MCMC Z-G可以在Mac OS和Windows平台上运行。
