An autoregressive growth model for longitudinal item analysis

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• 作者：Minjeong Jeon ; Sophia Rabe-Hesketh
• 刊名：Psychometrika
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：81
• 期：3
• 页码：830-850
• 全文大小：715 KB
• 刊物主题：Psychometrics; Assessment, Testing and Evaluation; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Statistical Theory and Methods;
• 出版者：Springer US
• ISSN：1860-0980
• 卷排序：81

文摘

A first-order autoregressive growth model is proposed for longitudinal binary item analysis where responses to the same items are conditionally dependent across time given the latent traits. Specifically, the item response probability for a given item at a given time depends on the latent trait as well as the response to the same item at the previous time, or the lagged response. An initial conditions problem arises because there is no lagged response at the initial time period. We handle this problem by adapting solutions proposed for dynamic models in panel data econometrics. Asymptotic and finite sample power for the autoregressive parameters are investigated. The consequences of ignoring local dependence and the initial conditions problem are also examined for data simulated from a first-order autoregressive growth model. The proposed methods are applied to longitudinal data on Korean students’ self-esteem.Keywordsautoregressive modelsinitial conditions problemmeasurement invarianceserial dependence