David Goretzko,Melanie Viola Partsch,Philipp Sterner
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引用次数: 0
摘要
Manapat等人(2023)在他们的论文“评估探索性因素分析结果中可避免的异质性”中研究了探索性因素分析中异质性的不同来源。他们的研究是理解因素分析结果的波动性的重要一步,这些结果可能会损害心理学的复制尝试。在这篇简短的评论中,我们想要解决的问题是,哪些异质性实际上是“可以避免的”,哪些异质性在探索性分析中也是可取的。此外,我们强调在执行和报告探索性因素分析时需要更大的研究透明度,并讨论预登记的潜力,以避免不必要的或“可避免的”异质性。当对导致特定配置的方法决策和概念假设透明时,我们相信在探索性因素分析中接受异质性是可能的,并且仍然可以开发更健壮的测量模型。(PsycInfo Database Record (c) 2025 APA,版权所有)。
Embrace the heterogeneity in exploratory factor analysis but be transparent about what you do-A commentary on Manapat et al. (2023).
Manapat et al. (2023) investigated different sources of heterogeneity in exploratory factor analysis in their paper "Evaluating Avoidable Heterogeneity in Exploratory Factor Analysis Results." Their study is an important step toward understanding the volatility of factor analysis results that potentially impair replication attempts in psychology. In this short commentary, we want to address the question which heterogeneity is actually "avoidable" and which heterogeneity can also be desirable in an exploratory analysis. Furthermore, we emphasize the need of greater research transparency when performing and reporting exploratory factor analyses and discuss the potential of preregistrations to avoid unwanted or "avoidable" heterogeneity. When being transparent about methodological decisions and conceptual assumptions that lead to specific configurations, we believe that it is possible to embrace the heterogeneity in exploratory factor analysis and still develop more robust measurement models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.