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又在思考总分了,也许是最后一次,我们也不确定,哦不……:一则关于……的评论

Thinking About Sum Scores Yet Again, Maybe the Last Time, We Don't Know, Oh No . . .: A Comment on.

作者信息

Widaman Keith F, Revelle William

机构信息

University of California, Riverside, USA.

Northwestern University, Evanston, IL, USA.

出版信息

Educ Psychol Meas. 2024 Aug;84(4):637-659. doi: 10.1177/00131644231205310. Epub 2023 Oct 13.

Abstract

The relative advantages and disadvantages of sum scores and estimated factor scores are issues of concern for substantive research in psychology. Recently, while championing estimated factor scores over sum scores, McNeish offered a trenchant rejoinder to an article by Widaman and Revelle, which had critiqued an earlier paper by McNeish and Wolf. In the recent contribution, McNeish misrepresented a number of claims by Widaman and Revelle, rendering moot his criticisms of Widaman and Revelle. Notably, McNeish chose to avoid confronting a key strength of sum scores stressed by Widaman and Revelle-the greater comparability of results across studies if sum scores are used. Instead, McNeish pivoted to present a host of simulation studies to identify relative strengths of estimated factor scores. Here, we review our prior claims and, in the process, deflect purported criticisms by McNeish. We discuss briefly issues related to simulated data and empirical data that provide evidence of strengths of each type of score. In doing so, we identified a second strength of sum scores: superior cross-validation of results across independent samples of empirical data, at least for samples of moderate size. We close with consideration of four general issues concerning sum scores and estimated factor scores that highlight the contrasts between positions offered by McNeish and by us, issues of importance when pursuing applied research in our field.

摘要

总分与估计因子分数的相对优缺点是心理学实证研究中备受关注的问题。最近,在推崇估计因子分数优于总分的同时,麦克尼什对维达曼和雷维尔的一篇文章给出了尖锐回应,该文章批评了麦克尼什和沃尔夫之前的一篇论文。在最近的这篇文章中,麦克尼什歪曲了维达曼和雷维尔的一些观点,使得他对维达曼和雷维尔的批评变得毫无意义。值得注意的是,麦克尼什选择回避维达曼和雷维尔所强调的总分的一个关键优势——如果使用总分,研究结果在不同研究之间具有更高的可比性。相反,麦克尼什转而展示了一系列模拟研究,以确定估计因子分数的相对优势。在此,我们回顾我们之前的观点,并在此过程中反驳麦克尼什所谓的批评。我们简要讨论与模拟数据和实证数据相关的问题,这些问题为每种分数类型的优势提供了证据。在此过程中,我们确定了总分的第二个优势:在独立的实证数据样本中,结果具有更好的交叉验证性,至少对于中等规模的样本是如此。最后,我们考虑了关于总分和估计因子分数的四个一般性问题,这些问题突出了麦克尼什和我们所提出观点之间的差异,这些问题在我们这个领域进行应用研究时具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1b7c/11268387/db83a30aa1de/10.1177_00131644231205310-fig1.jpg

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