Advisor
Lalonde, Trent L.
Committee Member
Schaffer, Jay Ryan, 1969-
Committee Member
Hutchinson, Susan R.
Committee Member
Mostowfi, Mehrgan
Department
Applied Statistics and Research Methods
Institution
University of Northern Colorado
Type of Resources
Text
Place of Publication
Greeley (Colo.)
Publisher
University of Northern Colorado
Date Created
8-2017
Genre
Thesis
Extent
166 pages
Digital Origin
Born digital
Abstract
Longitudinal data occur in different fields such as biomedical and health studies, education, engineering, and social studies. Planning advantageous research projects with both high power and minimum sample size is an important step in any study. The extensive use of longitudinal data in different fields and the importance of their power estimation, yet the limited resources about their respective power estimation tools, made it worthwhile to study their power estimation techniques. The presence of time-dependent covariates triggers the need to use more efficient models such as generalized method of moments than the existing models which are based on generalized estimating equations. Not taking into consideration the correlation among observations and the covariates that change over time while calculating power and minimum sample size will cause expensive research being conducted without using data that are capable of answering the research questions (Williams, 1995). Two different power estimation and minimum sample size calculation techniques for longitudinal data in the presence of time-dependent covariate using generalized method of moments approaches are constructed in this study and their performances are evaluated.
Notes
Recipient of Dean's Citation for Excellence
Degree type
PhD
Degree Name
Doctoral
Language
English
Local Identifiers
Ramezani_unco_01610D_10596
Rights Statement
Copyright is held by the author.
