A huge portion of what we know about how humans develop, learn, behave, and interact is based on survey data. Researchers use longitudinal growth modeling to understand the development of students on psychological and social-emotional learning constructs across elementary and middle school. In these designs, students are typically administered a consistent set of self-report survey items across multiple school years, and growth is measured either based on sum scores or scale scores produced based on item response theory (IRT) methods. Although there is great deal of guidance on scaling and linking IRT-based large-scale educational assessment to facilitate the estimation of examinee growth, little of this expertise is brought to bear in the scaling of psychological and social-emotional constructs. Through a series of simulation and empirical studies, we produce scores in a single-cohort repeated measure design using sum scores as well as multiple IRT approaches and compare the recovery of growth estimates from longitudinal growth models using each set of scores. Results indicate that using scores from multidimensional IRT approaches that account for latent variable covariances over time in growth models leads to better recovery of growth parameters relative to models using sum scores and other IRT approaches. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

中文翻译：

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避免增长估计中总分的偏差：对基于 IRT 的纵向调查响应评分方法的检查。

我们对人类如何发展、学习、行为和互动的了解有很大一部分是基于调查数据。研究人员使用纵向成长模型来了解学生在中小学心理和社会情感学习结构方面的发展。在这些设计中，学生通常会在多个学年接受一组一致的自我报告调查项目，并且基于总分或基于项目响应理论 (IRT) 方法产生的量表分数来衡量学生的成长。尽管有大量关于缩放和链接基于 IRT 的大规模教育评估以促进对考生成长的估计的指导，但这些专业知识很少用于心理和社会情感结构的缩放。通过一系列模拟和实证研究，我们使用总分和多种 IRT 方法在单队列重复测量设计中产生分数，并使用每组分数比较纵向增长模型的增长估计恢复。结果表明，相对于使用总分和其他 IRT 方法的模型，使用来自多维 IRT 方法的分数来解释潜在变量协方差随时间变化的增长模型可以更好地恢复生长参数。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）结果表明，相对于使用总分和其他 IRT 方法的模型，使用来自多维 IRT 方法的分数来解释潜在变量协方差随时间变化的增长模型可以更好地恢复生长参数。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）结果表明，相对于使用总分和其他 IRT 方法的模型，使用来自多维 IRT 方法的分数来解释潜在变量协方差随时间变化的增长模型可以更好地恢复生长参数。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）