Disaggregating level-specific effects in cross-classified multilevel models

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.