The impact of nonnormal and heteroscedastic level one residuals in partially clustered data

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dc.contributor.advisorBeretvas, Susan Natasha
dc.creatorTalley, Anna Elizabethen
dc.date.accessioned2013-12-11T15:41:08Zen
dc.date.accessioned2017-05-11T22:40:02Z
dc.date.available2017-05-11T22:40:02Z
dc.date.issued2013-08en
dc.date.submittedAugust 2013en
dc.date.updated2013-12-11T15:41:08Zen
dc.descriptiontexten
dc.description.abstractThe multilevel model (MLM) is easily parameterized to handle partially clustered data (see, for example, Baldwin, Bauer, Stice, & Rohde, 2011). The current study evaluated the performance of this model under various departures from underlying assumptions, including assumptions of normally distributed and homoscedastic Level 1 residuals. Two estimating models – one assuming homoscedasticity, the other allowing for the estimation of unique Level 1 variance components – were compared. Results from a Monte Carlo simulation suggest that the MLM for partially clustered data yields consistently unbiased parameter estimates, except for an underestimation of the Level 2 variance component under heteroscedastic generating conditions. However, this negative parameter bias desisted when the MLM allowed for Level 1 heteroscedasticity. Standard errors for variance component estimates at both levels were underestimated in the presence of nonnormally distributed Level 1 residuals. A discussion of results, as well as suggestions for future research, is provided.en
dc.description.departmentEducational Psychologyen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/22630en
dc.language.isoen_USen
dc.subjectPartially clustered dataen
dc.subjectMultilevel modelingen
dc.subjectIntervention studiesen
dc.subjectSimulation studiesen
dc.titleThe impact of nonnormal and heteroscedastic level one residuals in partially clustered dataen

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