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Disaggregating level-specific effects in cross-classified multilevel models.

Yingchi Guo1, Jeneesha Dhaliwal2, Jason D Rights2

  • 1Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T1Z4, Canada. yingchi.guo@ubc.ca.

Behavior Research Methods
|November 22, 2023
PubMed
Summary
This summary is machine-generated.

Researchers can now disaggregate level-specific effects in cross-classified multilevel models. This avoids conflating multiple predictor effects, improving the accuracy of psychological and other research findings.

Keywords:
CenteringContextual effectsCross-classificationLevel-specific effectsMultilevel modeling

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Area of Science:

  • Multilevel modeling
  • Cross-classified data structures
  • Psychological research methods

Background:

  • Data in psychology often exhibit cross-classified structures, with observations nested in multiple non-hierarchical clusters.
  • Existing multilevel models may conflate effects of lower-level predictors in cross-classified contexts, a problem often overlooked.
  • This conflation can lead to ambiguous interpretations of predictor effects in research.

Purpose of the Study:

  • To clarify the issue of conflated effects in cross-classified multilevel models.
  • To introduce methods for disaggregating level-specific effects in these models.
  • To provide guidance on model specification and interpretation for researchers.

Main Methods:

  • Developed novel model specifications including fully cluster-mean-centered, partially cluster-mean-centered, and contextual effect models.
  • Clarified methods to avoid both fixed and random effect conflation.
  • Utilized simulation studies and pedagogical examples to illustrate disaggregation techniques.

Main Results:

  • Demonstrated how common modeling practices can erroneously blend multiple predictor effects.
  • Showcased how new model specifications allow for unique interpretations of level-specific effects.
  • Simulation results highlighted the negative impact of conflation in cross-classified models.

Conclusions:

  • Disaggregating level-specific effects is crucial for accurate interpretation in cross-classified multilevel models.
  • The proposed methods and model specifications offer researchers clearer insights into complex data structures.
  • New software is available to assist researchers in implementing these advanced modeling techniques.