Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

<p>Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. A series of Monte-Carlo simulations were conducted to investigate the performance of a robust variance estimator for each of these approaches. Key meta-analytic parameters were varied throughout the process. Also, two small scale, examples were conducted to illustrate the use of the robust variance estimator in each of these two approaches. In general, the robust variance estimator performed well. Robust confidence interval parameter recovery was close to the specified 95% under almost all conditions. Only when there were a larger number of slope estimates and a small number of study samples did the robust standard errors noticeably lose efficiency. Combining sample-dependent focal slope estimates provides biased point estimates, however, the results of this paper suggest that the robust standard errors are still accurate.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Theology

Year dc:date.available

2012

Author dc:creator

• Williams, Ryan T.

• Dependencies
• Meta-Analysis
• Multivariate
• Robust Variance
• Sandwich Estimator
• Quantitative Psychology

Repository record dc:identifier

https://ecommons.luc.edu/luc_diss/405

OAI identifier oai:identifier

oai:ecommons.luc.edu:luc_diss-1404