使用多维成对偏好模型的多维强制选择测试的链接方法

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Naidan Tu, Lavanya S. Kumar, Sean Joo, Stephen Stark
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引用次数: 0

摘要

在过去 20 年中,多维强迫选择(MFC)测试的应用大幅增加。然而,关于将不同样本的参数估计联系起来的方法的研究却少之又少。为了满足这一重要需求,本研究扩展了四种广泛使用的单维连接方法,并比较了基于多维配对偏好模型(MUPP)的 MFC 连接系数新估算算法的有效性。更具体地说,我们在蒙特卡罗研究中比较了多维测试特征曲线(TCC)、项目特征曲线(ICC;Haebara,1980 年)、均值/均值(M/M)和均值/西格玛(M/S)方法的功效。结果表明,ICC 方法优于 M/M 方法,M/M 方法优于 M/S 方法,而 TCC 方法效果最差。不过,随着 "每个维度 "的项目数和锚点项目比例的增加,ICC、M/M 和 M/S 方法之间的差异也在缩小。本文讨论了 MUPP 连接的研究意义和实用建议以及局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Linking Methods for Multidimensional Forced Choice Tests Using the Multi-Unidimensional Pairwise Preference Model
Applications of multidimensional forced choice (MFC) testing have increased considerably over the last 20 years. Yet there has been little, if any, research on methods for linking the parameter estimates from different samples. This research addressed that important need by extending four widely used methods for unidimensional linking and comparing the efficacy of new estimation algorithms for MFC linking coefficients based on the Multi-Unidimensional Pairwise Preference model (MUPP). More specifically, we compared the efficacy of multidimensional test characteristic curve (TCC), item characteristic curve (ICC; Haebara, 1980), mean/mean (M/M), and mean/sigma (M/S) methods in a Monte Carlo study that also manipulated test length, test dimensionality, sample size, percentage of anchor items, and linking scenarios. Results indicated that the ICC method outperformed the M/M method, which was better than the M/S method, with the TCC method being the least effective. However, as the number of items “per dimension” and the percentage of anchor items increased, the differences between the ICC, M/M, and M/S methods decreased. Study implications and practical recommendations for MUPP linking, as well as limitations, are discussed.
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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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