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ERIC Number: EJ969738
Record Type: Journal
Publication Date: 2012
Pages: 23
Abstractor: As Provided
ISSN: ISSN-0027-3171
Augmenting Data with Published Results in Bayesian Linear Regression
de Leeuw, Christiaan; Klugkist, Irene
Multivariate Behavioral Research, v47 n3 p369-391 2012
In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this article is therefore to develop a Bayesian model in which a linear regression analysis on current data is augmented with the reported regression coefficients (and standard errors) of previous studies. Two versions of this model are presented. The first version incorporates previous studies through the prior density and is applicable when the current and all previous studies are exchangeable. The second version models all studies in a hierarchical structure and is applicable when studies are not exchangeable. Both versions of the model are assessed using simulation studies. Performance for each in estimating the regression coefficients is consistently superior to using current data alone and is close to that of an equivalent model that uses the data from previous studies rather than reported regression coefficients. Overall the results show that augmenting data with results from previous studies is viable and yields significant improvements in the parameter estimation. (Contains 4 footnotes, 3 tables, and 6 figures.)
Psychology Press. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A