Outlier and influence diagnostics for meta‐analysis | Zendy

Viechtbauer Wolfgang | Zendy; Cheung Mike W.L. | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Premium

Outlier and influence diagnostics for meta‐analysis

Author(s) -

Viechtbauer Wolfgang,

Cheung Mike W.L.

Publication year - 2010

Publication title -

research synthesis methods

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.376

H-Index - 35

eISSN - 1759-2887

pISSN - 1759-2879

DOI - 10.1002/jrsm.11

Subject(s) - outlier , meta analysis , robustness (evolution) , computer science , context (archaeology) , random effects model , econometrics , data mining , artificial intelligence , mathematics , medicine , paleontology , biochemistry , chemistry , biology , gene

The presence of outliers and influential cases may affect the validity and robustness of the conclusions from a meta‐analysis. While researchers generally agree that it is necessary to examine outlier and influential case diagnostics when conducting a meta‐analysis, limited studies have addressed how to obtain such diagnostic measures in the context of a meta‐analysis. The present paper extends standard diagnostic procedures developed for linear regression analyses to the meta‐analytic fixed‐ and random/mixed‐effects models. Three examples are used to illustrate the usefulness of these procedures in various research settings. Issues related to these diagnostic procedures in meta‐analysis are also discussed. Copyright © 2010 John Wiley & Sons, Ltd.

This content is not available in your region!

Continue researching here.

Having issues? You can contact us here

Accelerating Research