Combining Aggregate and Individual Level Data to Estimate an Individual Level Correlation Coefficient

Abstract

Methods are developed that use aggregate data, possibly based on a large number of individuals, and individual level data, from a small fraction of individuals from the same or similar population, to eliminate ecological bias inherent in the analysis of aggregate data. The primary focus is on estimating the individual level correlation coefficient but the proposed methodology can be extended to estimate regression coefficients. Two approaches, the method of moments and the maximum likelihood, are developed for a bivariate distribution, but can be extended to a multivariate distribution. The method of moments develops a corrected estimate of the within-group covariance matrix, which is then used to estimate the individual level correlation and regression coefficients. The second method assumes bivariate normality and maximizes the combined likelihood function based on the two data sets. The maximum likelihood estimates are obtained using the EM-algorithm. A simulation study investigates the repeated sampling properties of these procedures in terms of bias and the mean square error of the point estimates and the actual coverage of the confidence intervals. The maximum likelihood estimates are almost unbiased and the confidence intervals are well calibrated for simulation conditions considered. The method of moments estimates have the same desirable properties for some simulation conditions. Under all conditions, the correlation coefficient between aggregate variables is severely biased as an estimate of the individual level correlation coefficient.