Political Analysis Using R by James E. Monogan

Author:James E. Monogan
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

After the first seven chapters of this volume, users should now be able to perform most of the basic tasks that statistical software is designed to do: manage data, compute simple statistics, and estimate common models. In the remaining four chapters of this book, we now turn to the unique features of R that allow the user greater flexibility to apply advanced methods with packages developed by other users and tools for programming in R.

7.4 Practice Problems

1.Logistic regression: Load the foreign library, and download a subset of Singh’s (2015) cross-national survey data on voter turnout, the file stdSingh.dta, available from the Dataverse listed on page vii or the chapter content listed on page 97. The outcome variable is whether the survey respondent voted (voted). A key predictor, with which several variables are interacted, is the degree to which a citizen is subject to mandatory voting rules. This is measured with a scale of how severe the compulsory voting rules are (severity). Five predictors should be interacted with severity: age (age), political knowledge (polinfrel), income (income), efficacy (efficacy), and partisanship (partyID). Five more predictors should be included only for additive effects: district magnitude (dist_magnitude), number of parties (enep), victory margin (vicmarg_dist), parliamentary system (parliamentary), and per capita GDP (development). All of the predictor variables have been standardized. a.Estimate a logistic regression model with these data, including the five interaction terms.

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