Thinking twice about sum scores
Thinking twice about sum scores
复制标题
DOI：
10.3758/s13428-020-01398-0
复制
发表时间：
2020-04-22
影响因子：
5.4
通讯作者：
Wolf, Melissa Gordon
中科院分区：
文献类型：
--
作者：
McNeish, Daniel;Wolf, Melissa Gordon
从多项目量表中形成分数的常见方法是将所有项目的反应相加。虽然总和评分往往是对比因子分析作为一种竞争的方法，我们回顾因子分析和总和评分都属于更大的保护伞下的潜变量模型，总和评分是一个因素分析的约束版本。尽管有相似之处，报告的心理测量属性的总和得分或因素分析量表是完全不同的。此外，如果研究人员使用因子分析来验证量表，但随后对量表进行求和，则这采用了与验证模型不同的模型。通过在潜在变量框架内构建总和评分，我们的目标是提高以下认识：（a）总和评分需要相当严格的约束，（B）施加这些约束需要与任何其他潜在变量模型相同类型的理由，以及（c）总和评分对应于统计模型，而不是无模型的算术计算。我们讨论了如何不合理的总和评分可以有不良影响的有效性，可靠性和定性分类总和评分截止。我们还讨论了如何在随后的分析中使用量表分数的考虑，以及这些选择如何改变结论。总体目标是鼓励研究人员更严格地评估他们如何获得，证明和使用多项量表评分。
A common way to form scores from multiple-item scales is to sum responses of all items. Though sum scoring is often contrasted with factor analysis as a competing method, we review how factor analysis and sum scoring both fall under the larger umbrella of latent variable models, with sum scoring being a constrained version of a factor analysis. Despite similarities, reporting of psychometric properties for sum scored or factor analyzed scales are quite different. Further, if researchers use factor analysis to validate a scale but subsequently sum score the scale, this employs a model that differs from validation model. By framing sum scoring within a latent variable framework, our goal is to raise awareness that (a) sum scoring requires rather strict constraints, (b) imposing these constraints requires the same type of justification as any other latent variable model, and (c) sum scoring corresponds to a statistical model and is not a model-free arithmetic calculation. We discuss how unjustified sum scoring can have adverse effects on validity, reliability, and qualitative classification from sum score cut-offs. We also discuss considerations for how to use scale scores in subsequent analyses and how these choices can alter conclusions. The general goal is to encourage researchers to more critically evaluate how they obtain, justify, and use multiple-item scale scores.