Multiple contexts as cross-classified models: the Labour vote in the British General Election of 1992

Voters make their decisions in social and geographical contexts that can be seen as different levels in an overall data structure. Increasingly these structures are being analyzed by multilevel models, but this approach has so far been limited to structures that are strictly hierarchical. This paper outlines the approach of cross-classified multilevel models in which units at lower levels in the structure can be nested in more than one higher-level unit simultaneously. An appropriate modeling framework is outlined, models are specified, and particular attention is paid to efficient computation. The approach is illustrated through a cross-classified logit analysis of Labor versus Conservative support for a nationally representative sample of voting behavior for the 1992 British General Election. The data is structured so that individual voters at level 1 are nested within constituencies at level 2 which are cross-classified by geographical and functional regionalizations at level 3. A conclusion discusses the general utility of a cross-classified approach to geographically based contextual research, while two technical appendices provide details on model estimation.