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基于拉施模型的评定量表最佳分类的验证性研究。

A confirmatory study of Rasch-based optimal categorization of a rating scale.

作者信息

Zhu Weimo

机构信息

University of Illinois at Urbana-Champaign, Department of Kinesiology, 227 Freer Hall, MC-052, IL 61801, USA.

出版信息

J Appl Meas. 2002;3(1):1-15.

PMID:11997582
Abstract

The purpose of this study was to determine if the characteristics of the optimal categorization identified by the Rasch analysis in a previous study can be maintained when the revised scale is applied to the same population. Based on the results of the previous Rasch analysis, a 23-item exercise barrier scale was modified from its original five-category structure ("Very often" = 1, "Often" = 2, "Sometimes" = 3, "Rarely" = 4, and "Never" = 5) to a three-category structure ("Very Often" = 1, "Sometimes" = 2, and "Never" = 3). The modified scale was then mailed to the original sample (N = 381), of which 206 returned the survey; a return rate 57.5%. The data was again analyzed using the Rasch Rating Scale model. Overall, the Rasch model fit data well and similar change patterns were observed in two category statistics provided by the Rasch analysis. The order of item severity was also well kept and the correlation of item severities generated from two studies was very high, with r = .98. In addition, similar results were also found in respondents' ability estimations, and the correlation between the two studies was moderately high, with r = .68. These results verified that the characteristics of the optimal categorization identified by the Rasch post-hoc analysis can be maintained after the original scale was modified based on such an analysis.

摘要

本研究的目的是确定当将修订后的量表应用于同一人群时,先前研究中通过拉施分析确定的最佳分类特征是否能够得以保持。基于先前拉施分析的结果,一个包含23个条目的运动障碍量表从其原来的五分类结构(“非常频繁”=1,“频繁”=2,“有时”=3,“很少”=4,“从不”=5)修改为三分类结构(“非常频繁”=1,“有时”=2,“从不”=3)。然后将修改后的量表邮寄给原始样本(N = 381),其中206人回复了调查问卷,回复率为57.5%。再次使用拉施评分量表模型对数据进行分析。总体而言,拉施模型对数据拟合良好,并且在拉施分析提供的两个类别统计中观察到了相似的变化模式。条目严重程度的顺序也保持良好,两项研究产生的条目严重程度之间的相关性非常高,r = 0.98。此外,在受访者能力估计方面也发现了相似的结果,两项研究之间的相关性中等偏高,r = 0.68。这些结果证实,在基于拉施事后分析对原始量表进行修改后,拉施事后分析确定的最佳分类特征能够得以保持。

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