Manapat Patrick D, Edwards Michael C
Arizona State University, Tempe, USA.
Educ Psychol Meas. 2022 Oct;82(5):967-988. doi: 10.1177/00131644211063453. Epub 2022 Jan 7.
When fitting unidimensional item response theory (IRT) models, the population distribution of the latent trait (θ) is often assumed to be normally distributed. However, some psychological theories would suggest a nonnormal θ. For example, some clinical traits (e.g., alcoholism, depression) are believed to follow a positively skewed distribution where the construct is low for most people, medium for some, and high for few. Failure to account for nonnormality may compromise the validity of inferences and conclusions. Although corrections have been developed to account for nonnormality, these methods can be computationally intensive and have not yet been widely adopted. Previous research has recommended implementing nonnormality corrections when θ is not "approximately normal." This research focused on examining how far θ can deviate from normal before the normality assumption becomes untenable. Specifically, our goal was to identify the type(s) and degree(s) of nonnormality that result in unacceptable parameter recovery for the graded response model (GRM) and 2-parameter logistic model (2PLM).
在拟合单维项目反应理论（IRT）模型时，通常假定潜在特质（θ）的总体分布呈正态分布。然而，一些心理学理论表明θ是非正态的。例如，一些临床特质（如酗酒、抑郁）被认为遵循正偏态分布，即大多数人的该特质水平较低，部分人的水平中等，少数人的水平较高。未能考虑非正态性可能会损害推断和结论的有效性。尽管已经开发出了针对非正态性的校正方法，但这些方法计算量可能很大，尚未得到广泛应用。先前的研究建议，当θ并非“近似正态”时，应实施非正态性校正。本研究着重考察在正态性假设变得站不住脚之前，θ可以偏离正态多远。具体而言，我们的目标是确定导致等级反应模型（GRM）和双参数逻辑斯蒂模型（2PLM）的参数恢复不可接受的非正态类型和程度。
