UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Author: search.filters.author.Yumoto, Futoshi

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    EFFECTS OF UNMODELED LATENT CLASSES ON MULTILEVEL GROWTH MIXTURE ESTIMATION IN VALUE-ADDED MODELING
    (2011) Yumoto, Futoshi; Hancock, Gregory R; Mislevy, Robert J; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Fairness is necessary to successful evaluation, whether the context is simple and concrete or complex and abstract. Fair evaluation must begin with careful data collection, with clear operationalization of variables whose relationship(s) will represent the outcome(s) of interest. In particular, articulating what it is in the data that needs to be modeled, as well as the relationships of interest, must be specified before conducting any research; these two features will inform both study design and data collection. Heterogeneity is a key characteristic of data that can complicate the data collection design, and especially analysis and interpretation, interfering with or influencing the perception of the relationship(s) that the data will be used to investigate or evaluate. However, planning for, and planning to account for, heterogeneities in data are also critical to the research process, to support valid interpretation of results from any statistical analysis. The multilevel growth mixture model is a new analytic method specifically developed to accommodate heterogeneity so as to minimize the effect of variability on precision in estimation and to reduce bias that may arise in hierarchical data. This is particularly important in the Value Added Model context - where decisions and evaluations about teaching effectiveness are made, because estimates could be contaminated, biased, or simply less precise when data are modeled inappropriately. This research will investigate the effects of un-accounted for heterogeneity at level 1 on the precision of level-2 estimates in multilevel data utilizing the multilevel growth mixture model and multilevel linear growth model.