How to Improve the Regression Factor Score Predictor When Individuals Have Different Factor Loadings.

IF 2.3 3区心理学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Educational and Psychological Measurement Pub Date : 2025-08-15 DOI:10.1177/00131644251347530

André Beauducel, Norbert Hilger, Anneke C Weide

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Abstract

Previous research has shown that ignoring individual differences of factor loadings in conventional factor models may reduce the determinacy of factor score predictors. Therefore, the aim of the present study is to propose a heterogeneous regression factor score predictor (HRFS) with larger determinacy than the conventional regression factor score predictor (RFS) when individuals have different factor loadings. First, a method for the estimation of individual loadings is proposed. The individual loading estimates are used to compute the HRFS. Then, a binomial test for loading heterogeneity of a factor is proposed to compute the HRFS only when the test is significant. Otherwise, the conventional RFS should be used. A simulation study reveals that the HRFS has larger determinacy than the conventional RFS in populations with substantial loading heterogeneity. An empirical example based on subsamples drawn randomly from a large sample of Big Five Markers indicates that the determinacy can be improved for the factor emotional stability when the HRFS is computed.

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个体具有不同因子负荷时如何改进回归因子评分预测器。

以往的研究表明，在传统的因子模型中忽略因子负荷的个体差异可能会降低因子得分预测因子的确定性。因此，本研究的目的是在个体具有不同因子负荷时，提出一种比传统回归因子得分预测器具有更大确定性的异质性回归因子得分预测器。首先，提出了一种估计单个负荷的方法。单个负载估计用于计算HRFS。在此基础上，提出了一种因子加载异质性的二项检验，只有当检验显著时才计算HRFS。否则，应使用常规RFS。仿真研究表明，在负荷异质性较大的种群中，HRFS比传统RFS具有更大的确定性。基于从大五大标记的大样本随机抽取的子样本的实证示例表明，在计算HRFS时，情绪稳定性因素的确定性可以得到提高。

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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来源期刊

Educational and Psychological Measurement 医学-数学跨学科应用

CiteScore

5.50

自引率

7.40%

发文量

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.