Evaluating Model Fit in Two-Level Mokken Scale Analysis

Psych Pub Date : 2023-08-07 DOI:10.3390/psych5030056
Letty Koopman, B. Zijlstra, L. A. van der Ark
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引用次数: 1

Abstract

Currently, two-level Mokken scale analysis for clustered test data is being developed. This paper contributes to this development by providing model-fit procedures for two-level Mokken scale analysis. New theoretical insights suggested that the existing model-fit procedure from traditional (one-level) Mokken scale analyses can be used for investigating model fit at both level 1 (respondent level) and level 2 (cluster level) of two-level Mokken scale analysis. However, the traditional model-fit procedure requires some modifications before it can be used at level 2. In this paper, we made these modifications and investigated the resulting model-fit procedure. For two model assumptions, monotonicity and invariant item ordering, we investigated the false-positive count and the sensitivity count of the level 2 model-fit procedure, with respect to the number of model violations detected, and the number of detected model violations deemed statistically significant. For monotonicity, the detection of model violations was satisfactory, but the significance test lacked power. For invariant item ordering, both aspects were satisfactory.
二水平Mokken量表分析模型拟合评价
目前正在开发针对聚类测试数据的两级Mokken量表分析。本文通过提供两级莫肯量表分析的模型拟合程序来促进这一发展。新的理论见解表明,传统(一级)Mokken量表分析的现有模型拟合程序可以用于研究两级Mokken量表分析的第一级(被调查者水平)和第二级(聚类水平)的模型拟合。然而,传统的模型拟合过程需要进行一些修改才能用于第2级。在本文中,我们进行了这些修改,并研究了由此产生的模型拟合过程。对于两个模型假设,即单调性和不变项目顺序,我们研究了2级模型拟合过程的假阳性计数和敏感性计数,涉及检测到的模型违规数量,以及检测到的模型违规数量被认为具有统计显著性。对于单调性,模型违例检测是令人满意的,但显著性检验缺乏效力。对于不变项排序,这两个方面都令人满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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