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MacKuen, Erikson, and Stimson's classic article "Macropartisanship" extended the study of political behavior from static analyses of American elections to the dynamics of partisanship between elections. This launched new frontiers of research, such as studying the effects of presidential approval and economic indices on aggregate party identification. However, the Macropartisanship literature made an important oversight: changes in partisanship between elections are usually from one partisan group to identification as an independent, or vice versa. A single measure of aggregate partisanship, like the original Macropartisanship measure, leaves out independents altogether. This has important theoretical and empirical consequences that are evident in an era of increasingly strong partisanship. We conceive of Macropartisanship as a compositional variable and study how its components are affected by changes in economic sentiment and presidential approval.

When citizens hold multiple values relevant to their policy opinions, they might experience value conflict, value reconciliation or make a value trade-off. Yet, it is unclear which individuals are able to manage their multiple values in these ways. We posit a sophistication-interaction theory of value pluralism where the most politically sophisticated individuals are able to reconcile the existence of multiple values, thus increasing the stability of their policy opinions. We test this hypothesis using a series of heteroskedastic graded item response theory models of public opinion toward policies related to climate change. We find that people structure their policy preferences toward climate change policies in values toward the environment and the economy, but only the most sophisticated citizens are able to reconcile the potential conflict between these values.

This workbook teaches students how to use the popular Stata statistical software program to assess causal relationships using real data. It parallels the main Kellstedt/Whitten/Tuch text, which allows students to apply the lessons and techniques they learn in each chapter in a statistical software setting.