Modeling achievement in the presence of student mobility : a growth curve model for multiple membership data

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dc.contributor.advisorBeretvas, Susan Natashaen
dc.contributor.committeeMemberDodd, Barbara G.en
dc.contributor.committeeMemberPituch, Keenan A.en
dc.contributor.committeeMemberWhittaker, Tiffany A.en
dc.contributor.committeeMemberMahometa, Michael J.en
dc.creatorGrady, Matthew William, 1981-en
dc.date.accessioned2010-12-03T20:59:46Zen
dc.date.accessioned2010-12-03T20:59:52Zen
dc.date.accessioned2017-05-11T22:20:49Z
dc.date.available2010-12-03T20:59:46Zen
dc.date.available2010-12-03T20:59:52Zen
dc.date.available2017-05-11T22:20:49Z
dc.date.issued2010-08en
dc.date.submittedAugust 2010en
dc.date.updated2010-12-03T20:59:52Zen
dc.descriptiontexten
dc.description.abstractThe current study evaluated a multiple-membership growth curve model that can be used to model growth in student achievement, in the presence of student mobility. The purpose of the study was to investigate the impact of ignoring multiple school membership when modeling student achievement across time. Part one of the study consisted of an analysis of real longitudinal student achievement data. This real data analysis compared parameter estimates, standard error estimates, and model-fit statistics obtained from a growth curve model that ignores multiple membership, to those obtained from a growth model that accounts for multiple school membership via the MMREM approach. Part two of the study consisted of a simulation study designed to determine the impact of ignoring multiple membership and the accuracy of parameter estimates obtained under the two modeling approaches, under a series of data conditions. The goal of the study was to assess the importance of incorporating a more flexible MMREM approach when modeling students’ academic achievement across time. Overall, the results of the current study indicated that the Cross-classified multiple membership growth curve model (CCMM-GCM) may provide more accurate parameter estimates than competing approaches for a number of data conditions. Both modeling approaches, however, yielded substantially biased estimates of parameters for some experimental conditions. Overall, results demonstrate that incorporating student mobility into achievement growth modeling can result in more accurate estimates of schools effects.en
dc.description.departmentEducational Psychologyen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-08-1632en
dc.language.isoengen
dc.subjectGrowth curve modelingen
dc.subjectStudent mobilityen
dc.subjectStudent achievementen
dc.subjectAcademic achievementen
dc.titleModeling achievement in the presence of student mobility : a growth curve model for multiple membership dataen
dc.type.genrethesisen

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