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Direct linkKaplan, David; Chen, Jianshen; Lyu, Weicong; Yavuz, Sinan – Large-scale Assessments in Education, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys
Download full textDavid Kaplan; Jianshen Chen; Weicong Lyu; Sinan Yavuz – Grantee Submission, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys
Lee, Daniel Y.; Harring, Jeffrey R.; Stapleton, Laura M. – Journal of Experimental Education, 2019
Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from…
Descriptors: Longitudinal Studies, Research Methodology, Research Problems, Data Analysis
Smith, Lindsey J. Wolff; Beretvas, S. Natasha – Journal of Experimental Education, 2017
Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…
Descriptors: Student Mobility, Academic Achievement, Simulation, Influences
Steiner, Peter M.; Cook, Thomas D.; Li, Wei; Clark, M. H. – Journal of Research on Educational Effectiveness, 2015
In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of…
Descriptors: Quasiexperimental Design, Bias, Selection, Observation
Kern, Justin L.; McBride, Brent A.; Laxman, Daniel J.; Dyer, W. Justin; Santos, Rosa M.; Jeans, Laurie M. – Grantee Submission, 2016
Measurement invariance (MI) is a property of measurement that is often implicitly assumed, but in many cases, not tested. When the assumption of MI is tested, it generally involves determining if the measurement holds longitudinally or cross-culturally. A growing literature shows that other groupings can, and should, be considered as well.…
Descriptors: Psychology, Measurement, Error of Measurement, Measurement Objectives
