General and specific contextual effects in multilevel regression analyses and their paradoxical relationship: A conceptual tutorial

Abstract

To be relevant for public health, a context (e.g., neighborhood, school, hospital) should influence or affect the health status of the individuals included in it. The greater the influence of the shared context, the higher the correlation of subject outcomes within that context is likely to be. This intra-context or intra-class correlation is of substantive interest and has been used to quantify the magnitude of the general contextual effect (GCE). Furthermore, ignoring the intra-class correlation in a regression analysis results in spuriously narrow 95% confidence intervals around the estimated regression coefficients of the specific contextual variables entered as covariates and, thereby, overestimates the precision of the estimated specific contextual effects (SCEs).

Multilevel regression analysis is an appropriate methodology for investigating both GCEs and SCEs. However, frequently researchers only report SCEs and disregard the study of the GCE, unaware that small GCEs lead to more precise estimates of SCEs so, paradoxically, the less relevant the context is, the easier it is to detect (and publish) small but “statistically significant” SCEs. We describe this paradoxical situation and encourage researchers performing multilevel regression analysis to consider simultaneously both the GCE and SCEs when interpreting contextual influences on individual health.

Highlights

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  The intra-context correlation is a measure of the general contextual effect (GCE).

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  Contextual measures of association inform on specific contextual effects (SCEs).

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  Many multilevel regression analyses only report SCEs.

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  Paradoxically, the lower the GCE the easier it is to detect “statistically significant” SCEs.

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  Multilevel regression analysis need to consider both GCEs and SCEs.