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.

Browse

Filters

2005 - 200912010 - 20111

Settings

Subject: search.filters.subject.Latent Class

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Examining Differential Item Functioning From A Latent Class Perspective
    (2005-06-16) Samuelsen, Karen M.; Dayton, C M; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Current approaches for studying differential item functioning (DIF) using manifest groups are problematic since these groups are treated as homogeneous in nature. Additionally, manifest variables such as sex and ethnicity are proxies for more fundamental differences - educational advantage/disadvantage attributes. A simulation study was conducted to highlight issues arising from the use of standard DIF detection procedures. Results of this study showed that as the amount of overlap between manifest groups and latent classes decreased, so did the power to correctly identify items with DIF. Furthermore, the true magnitude of the DIF was obscured making it increasingly more difficult to eliminate items on that basis. After some problems with manifest group approaches for DIF had been identified, a recovery study was conducted using the WINBUGS software in the analysis of the mixed Rasch model for detecting DIF. In this study the mixed Rasch model also showed a lack of power to detect items with DIF when the sample size was small. However, this approach was able to identify the proportion of and ability distribution for each manifest group within latent classes, thereby providing a mechanism for judging the appropriateness of using manifest variables as proxies for latent ones. Finally, a series of protocols was developed for examining DIF using a latent class approach, and these were used to examine differential item functioning on a test of language proficiency for English language learners. Results showed that 74% of Hispanic and 83% of Asian examinees were in one latent class, meaning any DIF found by comparing manifest groups would be an artifact of a relatively small number of examinees. Examination of the output from the latent class analysis provided potentially important insights into the causes of DIF, however covariates were not predictive of latent class membership.