Improving Factor Score Estimation Through the Use of Observed Background Characteristics

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.

心理科学领域的几乎所有研究都面临一项共同挑战：如何将多个测项最优整合为一份有效且可靠的分数，以供后续建模使用。当前最通用的方法是计算测项的平均值，但当下更前沿的研究方法则采用各类潜变量得分估计手段。无论采用何种方法，在大规模测评应用之外的场景中，得分模型极少纳入背景特征以提升分数质量。本研究采用蒙特卡洛（Monte Carlo）模拟设计，针对分别纳入与未纳入协变量（covariates）的不同心理测量模型（psychometric models）展开得分质量分析，实验设计涵盖了样本量、测项数量以及测量不变性（measurement invariance）程度三个变量的不同水平。研究发现，纳入协变量后，几乎所有实验设计因素下的分数质量均得到提升，且在任何情况下，相较于完全不考虑这类影响的情形，协变量的加入均不会降低分数质量。研究结果表明，纳入观测协变量可有效提升因子得分估计（factor score estimation）的效果。