A challenge facing nearly all studies in the psychological sciences is how to best combine multiple items into a valid and reliable score to be used in subsequent modeling. The most ubiquitous method is to compute a mean of items, but more contemporary approaches use various forms of latent score estimation. Regardless of approach, outside of large-scale testing applications, scoring models rarely include background characteristics to improve score quality. This article used a Monte Carlo simulation design to study score quality for different psychometric models that did and did not include covariates across levels of sample size, number of items, and degree of measurement invariance. The inclusion of covariates improved score quality for nearly all design factors, and in no case did the covariates degrade score quality relative to not considering the influences at all. Results suggest that the inclusion of observed covariates can improve factor score estimation.

中文翻译：

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通过使用观察到的背景特征改进因子得分估计


几乎所有心理科学研究都面临的一个挑战是如何最好地将多个项目组合成一个有效且可靠的分数，以便在后续建模中使用。最普遍的方法是计算项目的平均值，但更现代的方法使用各种形式的潜在分数估计。无论采用哪种方法，除了大规模测试应用之外，评分模型很少包含背景特征以提高评分质量。本文使用蒙特卡洛模拟设计来研究不同心理测量模型的评分质量，这些模型包含或不包含跨样本大小、项目数量和测量不变性程度的协变量。包含协变量提高了几乎所有设计因素的评分质量，并且与根本不考虑影响相比，协变量在任何情况下都不会降低评分质量。结果表明，纳入观察到的协变量可以改善因子得分估计。