Spurious relationships arising from aggregate variables in linear regression

Spurious relationships arising from aggregate variables in linear regression Linear regressions that use aggregated values from a group variable such as a school or a neighborhood are commonplace in the social sciences. This paper uses Monte Carlo methods to demonstrate that aggregated variables produce spurious relationships with other dependent and independent variables in a model even when there are no underlying relationships among those variables. The size of the spurious relationships (or postulated effects) increases as the number of observations per group decreases. Although this problem is remedied by including the individual-level variable in the regression, the problem has not been discussed in the methodological literature. Accordingly, studies using aggregate variables must be interpreted with caution if the individual-level measurements are not available. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Spurious relationships arising from aggregate variables in linear regression

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0335-0
Publisher site
See Article on Publisher Site

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