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Multilevel Multivariate Meta-analysis with Application to Choice Overload.

Blakeley B McShane1, Ulf Böckenholt2

  • 1Kellogg School of Management, Northwestern University, Evanston, IL, 60208, USA. b-mcshane@kellogg.northwestern.edu.

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Summary
This summary is machine-generated.

We developed a new multilevel multivariate meta-analysis method to analyze complex psychological data. This approach reveals significant variation and interactions in choice overload research, offering deeper insights than previous methods.

Keywords:
choicemeta-analysismultilevelmultivariateoverload

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Area of Science:

  • Psychological research methodology
  • Statistical modeling
  • Meta-analysis

Background:

  • Contemporary psychological research generates complex data with nested structures.
  • Previous meta-analyses have limitations in fully accounting for data complexity.

Purpose of the Study:

  • Introduce a novel multilevel multivariate meta-analysis methodology.
  • Address the complexity of psychological research data, including variation, covariation, and nesting.
  • Provide richer insights into the choice overload hypothesis.

Main Methods:

  • Developed a multilevel multivariate meta-analysis technique.
  • Directly modeled observations, accounting for variation and covariation across dependent measures, moderators, and nested structures (papers, studies, subjects, conditions).
  • Applied the methodology to data from choice overload hypothesis studies.

Main Results:

  • The new methodology fully accounts for choice overload data complexity.
  • Choice overload significantly varies across six dependent measures and four moderators.
  • Identified potentially important interactions among dependent measures and moderators.
  • Demonstrated that up to the fifth (paper) level of nesting is necessary to capture variation and covariation.

Conclusions:

  • The developed methodology offers a more comprehensive analysis of complex psychological data.
  • Results provide substantial implications for future choice overload research.
  • Highlights the importance of considering multilevel structures and interactions in meta-analyses.