Department of Education and Human Development, Clemson University, 226 Holtzendorff Hall, Clemson, SC, 29634, USA.
Department of Educational Psychology, University of Georgia, 325P Aderhold Hall, Athens, GA, 30602, USA.
Psychometrika. 2018 Dec;83(4):963-990. doi: 10.1007/s11336-018-9638-5. Epub 2018 Sep 27.
A common assessment research design is the single-group pre-test/post-test design in which examinees are administered an assessment before instruction and then another assessment after instruction. In this type of study, the primary objective is to measure growth in examinees, individually and collectively. In an item response theory (IRT) framework, longitudinal IRT models can be used to assess growth in examinee ability over time. In a diagnostic classification model (DCM) framework, assessing growth translates to measuring changes in attribute mastery status over time, thereby providing a categorical, criterion-referenced interpretation of growth. This study introduces the Transition Diagnostic Classification Model (TDCM), which combines latent transition analysis with the log-linear cognitive diagnosis model to provide methodology for analyzing growth in a general DCM framework. Simulation study results indicate that the proposed model is flexible, provides accurate and reliable classifications, and is quite robust to violations to measurement invariance over time. The TDCM is used to analyze pre-test/post-test data from a diagnostic mathematics assessment.
一种常见的评估研究设计是单组前测/后测设计，其中被试在教学前接受一次评估，然后在教学后再接受一次评估。在这种类型的研究中，主要目的是衡量被试个体和集体的增长。在项目反应理论（IRT）框架中，可以使用纵向 IRT 模型来评估被试能力随时间的增长。在诊断分类模型（DCM）框架中，衡量增长转化为衡量属性掌握状态随时间的变化，从而为增长提供分类的、基于标准的解释。本研究引入了过渡诊断分类模型（TDCM），它将潜在过渡分析与对数线性认知诊断模型相结合，为在一般 DCM 框架中分析增长提供了方法。模拟研究结果表明，所提出的模型具有灵活性，能够提供准确可靠的分类，并且对随时间变化的测量不变性的违反具有很强的稳健性。TDCM 用于分析诊断数学评估的前测/后测数据。
