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

20191

Settings

Author: search.filters.author.Lee, Daniel Yangsup

Search Results

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Handling of Missing Data with Growth Mixture Models
    (2019) Lee, Daniel Yangsup; Harring, Jeffrey R; Measurement, Statistics and Evaluation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The recent growth of applications of growth mixture models for inference with longitudinal data has introduced a wide range of research dedicated to testing the different aspects of the model. One area of research that has not drawn much attention, however, is the performance of growth mixture models with missing data and when using the various methods for dealing with them. Missing data are usually an inconvenience that must be addressed in any data analysis scenario, and the use of growth mixture models is no less an exception to this. While the literature on various other aspects of growth mixture models has grown, not much research has been conducted on the consequences of mishandling missing data. Although the literature on missing data has generally accepted the use of modern missing data handling techniques, these techniques are not free of problems nor have they been comprehensively tested in the context of growth mixture models. The purpose of this dissertation is to incorporate the various missing data handling techniques on growth mixture models and, by using Monte Carlo simulation techniques, to provide guidance on specific conditions in which certain missing data handling methods will produce accurate and precise parameter estimates typically compromised when using simple, ad hoc, missing data handling approaches, or incorrect techniques.