Introduction to methods for analysis of combined individual and aggregate social science data

The lecture notes from this workshop provide an introduction to a new class of multilevel models – termed hierarchical related regressions (HRR) – for estimating individual-level associations using a combination of aggregate (group level) and individual-level data. HRR differs from other methods by enabling analysts to model individual and aggregate data simultaneously, while including information on the dependent variable at the aggregate level (e.g. constituency election results), and data from aggregation units not available at the individual level (e.g. census data from all constituencies or output areas in the country). The workshop will also discuss HRR as a method of improving ecological inference (analyses that aim to make inference on the relationship between individual-level quantities using aggregate data). The HRR models combine features of standard ecological regression models for aggregate data and multilevel models for clustered individual-level data, and have been shown to reduce bias and improve precision in many situations.