title: The impact of ignoring multiple membership data structures in multilevel models.
abstract: This study compared the use of the conventional multilevel model (MM) with that of the multiple membership multilevel model (MMMM) for handling multiple membership data structures. Multiple membership data structures are commonly encountered in longitudinal educational data sets in which, for example, mobile students are members of more than one higher-level unit (e.g., school). While the conventional MM requires the user either to delete mobile students' data or to ignore prior schools attended, MMMM permits inclusion of mobile students' data and models the effect of all schools attended on student outcomes. The simulation study identified underestimation of the school-level predictor coefficient, as well as underestimation of the level-two variance component with corresponding overestimation of the level-one variance when multiple membership data structures were ignored. Results are discussed along with limitations and ideas for future MMMM methodological research as well as implications for applied researchers.
authors: Department of Psychology, John Jay College of Criminal Justice, City University of New York, 10019, USA. hchung@jjay.cuny.edu
authors: Hyewon
authors: H
authors: Chung
authors: S Natasha
authors: SN
authors: Beretvas
mesh_terms: D003198:Computer Simulation / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D055361:Multilevel Analysis / Q000379:methods*; D012574:Schools / Q000706:statistics & numerical data; D013334:Students / Q000706:statistics & numerical data