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Autor/inn/enLuo, Wen; Kwok, Oi-man
TitelThe Consequences of Ignoring Individuals' Mobility in Multilevel Growth Models: A Monte Carlo Study
QuelleIn: Journal of Educational and Behavioral Statistics, 37 (2012) 1, S.31-56 (26 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1076-9986
DOI10.3102/1076998610394366
SchlagwörterMobility; Statistical Analysis; Models; Longitudinal Studies; Data Analysis; Monte Carlo Methods; Statistical Inference; Computation; Student Mobility; Statistical Bias; Accuracy; Error of Measurement; Early Childhood Longitudinal Survey
AbstractIn longitudinal multilevel studies, especially in educational settings, it is fairly common that participants change their group memberships over time (e.g., students switch to different schools). Participant's mobility changes the multilevel data structure from a purely hierarchical structure with repeated measures nested within individuals and individuals nested within clusters to a cross-classified structure with repeated measures cross-classified by both individuals and clusters. If researchers fail to consider the cross-classified data structure and simply use the hierarchical linear models (HLMs) instead of the more appropriate cross-classified random-effects models (CCREMs) to analyze the data, there will be biases in the estimates of variance components and inaccurate statistical inference regarding the fixed effects. In addition, the impact of such model misspecification depends on factors including the rate of mobility and the pattern of mobility. (Contains 5 figures, 1 table, and 7 notes.) (As Provided).
AnmerkungenSAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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