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General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A
Juan Merlo1,2, Philippe Wagner1,3, Peter C Austin4
1Unit for Social Epidemiology, Department of Clinical Sciences, Faculty of Medicine, Lund University, CRC, Jan Waldenströms Street 35, SE- 214 21 Malmö, Sweden.
SSM - Population Health
|June 13, 2018
Summary
Contextual factors significantly impact public health. Ignoring the general contextual effect (GCE) in multilevel analysis can lead to misleading results about specific contextual effects (SCEs).
Area of Science:
- Public Health
- Biostatistics
- Social Epidemiology
Background:
- Public health relevance requires contexts (e.g., neighborhoods) to influence individual health.
- Intra-class correlation quantifies the general contextual effect (GCE).
- Ignoring GCE in regression analysis leads to overestimated precision of specific contextual effects (SCEs).
Purpose of the Study:
- To highlight the importance of the general contextual effect (GCE) in multilevel regression.
- To explain the paradoxical situation where less relevant contexts yield statistically significant specific contextual effects (SCEs).
- To encourage simultaneous consideration of GCE and SCEs in health research.
Main Methods:
- Multilevel regression analysis.
- Quantification of intra-class correlation to estimate GCE.
- Analysis of regression coefficients and confidence intervals to assess SCE precision.
Main Results:
- Ignoring intra-class correlation results in spuriously narrow confidence intervals for SCEs.
- This leads to an overestimation of the precision of SCEs.
- Paradoxically, less relevant contexts (small GCEs) make it easier to find statistically significant SCEs.
Conclusions:
- Multilevel regression is crucial for investigating both GCEs and SCEs.
- Researchers should simultaneously assess GCE and SCEs for accurate interpretation of contextual influences on health.
- Understanding the interplay between GCE and SCEs is vital for public health research and policy.
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