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Autor/inn/enLittle, Todd D.; Bontempo, Daniel; Rioux, Charlie; Tracy, Allison
TitelOn the Merits of Longitudinal Multiple Group Modelling: An Alternative to Multilevel Modelling for Intervention Evaluations
QuelleIn: International Journal of Research & Method in Education, 45 (2022) 5, S.437-449 (13 Seiten)Infoseite zur Zeitschrift
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ZusatzinformationORCID (Little, Todd D.)
Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1743-727X
DOI10.1080/1743727X.2021.1973992
SchlagwörterLongitudinal Studies; Hierarchical Linear Modeling; Alternative Assessment; Intervention; Affordances; Robustness (Statistics); Error of Measurement; Scoring; Scaling
AbstractMultilevel modelling (MLM) is the most frequently used approach for evaluating interventions with clustered data. MLM, however, has some limitations that are associated with numerous obstacles to model estimation and valid inferences. Longitudinal multiple-group (LMG) modelling is a longstanding approach for testing intervention effects using cluster-sampled data that has been superseded by the rise of MLM approaches, but the LMG approach can have advantages when research questions do not pertain to predicting variability at the higher levels. In this paper, we first review the advantages and limitations of MLM and LMG approaches. Second, steps in the estimation of an LMG model are presented, with some recent upgrades and changes in the modelling strategy that have particular utility for evaluating interventions. We discuss the advantages of the LMG approach as a guided confirmatory model-testing framework and how the approach places a premium on avoiding Type II errors, particularly when complex interactions are potentially at play. (As Provided).
AnmerkungenRoutledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2024/1/01
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