You are here

Combining Regression Slopes from Studies with Different Models in Meta-Analysis

Title: Combining Regression Slopes from Studies with Different Models in Meta-Analysis.
5 views
0 downloads
Name(s): Jeon, Sanghyun, author
Becker, Betsy Jane, 1956-, professor directing dissertation
Huffer, Fred W. (Fred William), university representative
Yang, Yanyun, committee member
Paek, Insu, committee member
Florida State University, degree granting institution
College of Education, degree granting college
Department of Educational Psychology and Learning Systems, degree granting department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (116 pages)
Language(s): English
Abstract/Description: Primary studies are using complex models more and more. Slopes from multiple regression analyses are reported in primary studies, but few scholars have dealt with how to combine multiple regression slopes. One of the problems in combining multiple regression slopes is that each study may use a different regression model. The purpose of this research is to propose a method for combining partial regression slopes from studies with different regression models. The method combines comparable covariance matrices to obtain a synthetic partial slope. The proposed method assumes the population is homogeneous, and that the different regression models are nested. Elements in the sample covariance matrix are not independent of each other, so missing elements should be imputed using conditional expectations. The Bartlett decomposition is used to decompose the sample covariance matrix into a parameter component and a sampling error component. The proposed method treats the sample-size weighted average as a parameter matrix and applies Bartlett’s decomposition to the sample covariance matrices to get their respective error matrices. Since missing elements in the error matrix are not correlated, missing elements can be estimated in the error matrices and hence in the parameter matrices. Finally the partial slopes can be computed from the combined matrices. Simulation shows the suggested method gives smaller standard errors than the listwise-deletion method and the pairwise-deletion method. An empirical examination shows the suggested method can be applied to heterogeneous populations.
Identifier: FSU_FALL2017_Jeon_fsu_0071E_14179 (IID)
Submitted Note: A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Fall Semester 2017.
Date of Defense: November 17, 2017.
Keywords: bartlett decomposition, cholesky decomposition, conditional covariance matrix, dependency in sample covariance matrix, meta-analysis, multiple regression analysis
Bibliography Note: Includes bibliographical references.
Advisory Committee: Betsy Jane Becker, Professor Directing Dissertation; Fred Huffer, University Representative; Yanyun Yang, Committee Member; Insu Paek, Committee Member.
Subject(s): Educational psychology
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_FALL2017_Jeon_fsu_0071E_14179
Host Institution: FSU

Choose the citation style.
Jeon, S. (2017). Combining Regression Slopes from Studies with Different Models in Meta-Analysis. Retrieved from http://purl.flvc.org/fsu/fd/FSU_FALL2017_Jeon_fsu_0071E_14179