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Handbook of Meta-analysis in Ecology and Evolution$
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Julia Koricheva, Jessica Gurevitch, and Kerrie Mengersen

Print publication date: 2013

Print ISBN-13: 9780691137285

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691137285.001.0001

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Recovering Missing or Partial Data from Studies: a Survey of Conversions and Imputations for Meta-analysis

Recovering Missing or Partial Data from Studies: a Survey of Conversions and Imputations for Meta-analysis

Chapter:
(p.195) 13 Recovering Missing or Partial Data from Studies: A Survey of Conversions and Imputations for Meta-analysis
Source:
Handbook of Meta-analysis in Ecology and Evolution
Author(s):

Marc J. Lajeunesse

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691137285.003.0013

This chapter discusses possible solutions for dealing with partial information and missing data from published studies. These solutions can improve the amount of information extracted from individual studies, and increase the representation of data for meta-analysis. It begins with a description of the mechanisms that generate missing information within studies, followed by a discussion of how gaps of information can influence meta-analysis and the way studies are quantitatively reviewed. It then suggests some practical solutions to recovering missing statistics from published studies. These include statistical acrobatics to convert available information (e.g., t-test) into those that are more useful to compute effect sizes, as well as a heuristic approaches that impute (fill gaps) missing information when pooling effect sizes. Finally, the chapter discusses multiple-imputation methods that account for the uncertainty associated with filling gaps of information when performing meta-analysis.

Keywords:   partial information, missing data, published studies, meta-analysis, imputation methods

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