使用有序数据的机器学习进行因子保留。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Applied Psychological Measurement Pub Date : 2022-07-01 Epub Date: 2022-05-04 DOI:10.1177/01466216221089345
David Goretzko, Markus Bühner
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引用次数: 6

摘要

确定探索性因素分析中的因素数量可能是进行分析时最关键的决定,因为它明显影响结果的意义(即,析因效度)。最近开发了一种将数据模拟和机器学习相结合的新方法“因子森林”。这种基于模拟数据的方法对多变量正态数据具有很高的精度,但尚未对有序数据进行测试。因此,在本模拟研究中,我们基于不同数量的类别(2-6个类别)使用有序数据评估因子森林,并将其与常见的因子保留标准进行比较。它显示所有类型的有序数据的总体准确性高于所有用于比较的常见因素保留标准(平行分析,比较数据,经验凯撒标准和凯撒古特曼规则)。结果表明,因子森林在大多数情况下适用于至少5个类别的有序数据(问卷研究中的典型量表),在样本量较大的情况下适用于基于较少类别项目的二元或有序数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Factor Retention Using Machine Learning With Ordinal Data.

Determining the number of factors in exploratory factor analysis is probably the most crucial decision when conducting the analysis as it clearly influences the meaningfulness of the results (i.e., factorial validity). A new method called the Factor Forest that combines data simulation and machine learning has been developed recently. This method based on simulated data reached very high accuracy for multivariate normal data, but it has not yet been tested with ordinal data. Hence, in this simulation study, we evaluated the Factor Forest with ordinal data based on different numbers of categories (2-6 categories) and compared it to common factor retention criteria. It showed higher overall accuracy for all types of ordinal data than all common factor retention criteria that were used for comparison (Parallel Analysis, Comparison Data, the Empirical Kaiser Criterion and the Kaiser Guttman Rule). The results indicate that the Factor Forest is applicable to ordinal data with at least five categories (typical scale in questionnaire research) in the majority of conditions and to binary or ordinal data based on items with less categories when the sample size is large.

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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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