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A Rule for Inferring Individual-Level Relationships from Aggregate Data

Glenn Firebaugh
American Sociological Review
Vol. 43, No. 4 (Aug., 1978), pp. 557-572
Stable URL: http://www.jstor.org/stable/2094779
Page Count: 16
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
A Rule for Inferring Individual-Level Relationships from Aggregate Data
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Abstract

Under certain conditions aggregate-level data provide unbiased estimates of individual-level relationships. Here I present these conditions in the form of a single theoretical decision rule: bias is absent when, and only when, the group mean of the independent variable (X) has no effect on Y, with X controlled. This paper introduces this rule, demonstrates it for the general n-variable case, compares it with prior discussions of cross-level inference, and illustrates it with the 1930 census data used by Robinson (1950). The final section discusses the implications of this rule for the converse type of cross-level inference: the use of individual-level data to estimate aggregate-level relationships.

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