An Omega-Hierarchical Extension Index for Second-Order Constructs With Hierarchical Measuring Instruments.

IF 2.1 3区 心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Educational and Psychological Measurement Pub Date : 2025-01-14 DOI:10.1177/00131644241302284
Tenko Raykov, Christine DiStefano, Yusuf Ransome
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Abstract

An index extending the widely used omega-hierarchical coefficient is discussed, which can be used for evaluating the influence of a second-order factor on the interrelationships among the components of a hierarchical measuring instrument. The index represents a useful and informative complement to the traditional omega-hierarchical measure of explained overall scale score variance by that underlying construct. A point and interval estimation procedure is outlined for the described index, which is based on model reparameterization and is developed within the latent variable modeling framework. The method is readily applicable with popular software and is illustrated with examples.

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具有层次测量仪器的二阶结构的ω -层次可拓指标。
讨论了一种扩展广泛使用的- - -等级系数的指标,该指标可用于评价二阶因子对等级测量仪器各组成部分之间相互关系的影响。该指数代表了一个有用的和翔实的补充,传统的omega-分层测量解释的总体规模得分方差的基础结构。在潜在变量建模框架内,提出了基于模型重参数化的点和区间估计方法。该方法易于应用于流行的软件,并通过实例加以说明。
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来源期刊
Educational and Psychological Measurement
Educational and Psychological Measurement 医学-数学跨学科应用
CiteScore
5.50
自引率
7.40%
发文量
49
审稿时长
6-12 weeks
期刊介绍: Educational and Psychological Measurement (EPM) publishes referred scholarly work from all academic disciplines interested in the study of measurement theory, problems, and issues. Theoretical articles address new developments and techniques, and applied articles deal with innovation applications.
期刊最新文献
"What If Applicants Fake Their Responses?": Modeling Faking and Response Styles in High-Stakes Assessments Using the Multidimensional Nominal Response Model. A Comparison of the Next Eigenvalue Sufficiency Test to Other Stopping Rules for the Number of Factors in Factor Analysis. An Omega-Hierarchical Extension Index for Second-Order Constructs With Hierarchical Measuring Instruments. The Impact of Missing Data on Parameter Estimation: Three Examples in Computerized Adaptive Testing. Item Classification by Difficulty Using Functional Principal Component Clustering and Neural Networks.
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