Reliability is an important quantification of measurement precision based on a latent variable measurement model. Inspired by McDonald (2011), we present a regression framework of reliability, placing emphasis on whether latent or observed scores serve as the regression outcome. Our theory unifies two extant perspectives of reliability: (a) classical test theory (measurement decomposition), and (b) optimal prediction of latent scores (prediction decomposition). Importantly, reliability should be treated as a property of the observed score under a measurement decomposition, but a property of the latent score under a prediction decomposition. To facilitate the evaluation and interpretation of distinct reliability coefficients for complex measurement models, we introduce a Monte Carlo approach for approximate calculation of reliability. We illustrate the proposed computational procedure with an empirical data analysis, which concerns measuring susceptibility and severity of depressive symptoms using a two-dimensional item response theory model. We conclude with a discussion on computing reliability coefficients and outline future avenues of research.

翻译：可靠性是基于潜变量测量模型对测量精度的重要量化指标。受McDonald (2011)启发，我们提出一个关于可靠性的回归框架，重点探讨潜变量分数与观测分数何者作为回归结果变量。该理论统一了可靠性的两种现有视角：(a)经典测验理论（测量分解视角），(b)潜变量的最优预测（预测分解视角）。重要的是，在测量分解框架下，可靠性应被视为观测分数的属性，而在预测分解框架下则应视为潜变量的属性。为便于评估和解释复杂测量模型的不同可靠性系数，我们引入蒙特卡洛方法实现可靠性的近似计算。通过一项实证数据分析——该分析运用二维项目反应理论模型测量抑郁症状的易感性及严重程度——我们展示了所提出的计算流程。文章最后讨论了可靠性系数的计算方法，并展望了未来研究方向。