election: Election Data
In EffectStars: Visualization of Categorical Response Models

election

R Documentation

Election Data

Description

The data set contains data from the German Longitudinal Election Study. The Response Categories refer to the five dominant parties in Germany. The explanatory variables refer to the declarations of single voters.

Usage

data(election)

Format

A data frame with 816 observations on the following 30 variables.

Age: Standardized age of the voter
AgeOrig: Unstandardized age of the voter
Partychoice: Party Choice with levels CDU, SPD, FDP, Greens and Left Party
Gender: Gender with levels female and male
West: Regional provenance (West-Germany or East-Germany) with levels east and west
Union: Member of a Union with levels no member and member
Highschool: Educational level with levels no highschool and highschool
Unemployment: Unemployment with levels not unemployed and unemployed
Pol.Interest: Political Interest with levels very interested and less interested
Democracy: Satisfaction with the functioning of democracy with levels satisfied and not satisfied
Religion: Religion with levels evangelical, catholic and other religion
Social_CDU: Difference in attitude towards the socioeconomic dimension of politics between respondent and CDU
Social_SPD: Difference in attitude towards the socioeconomic dimension of politics between respondent and SPD
Social_FDP: Difference in attitude towards the socioeconomic dimension of politics between respondent and FDP
Social_Greens: Difference in attitude towards the socioeconomic dimension of politics between respondent and the Greens
Social_Left: Difference in attitude towards the socioeconomic dimension of politics between respondent and the Left party
Immigration_CDU: Difference in attitude towards immigration of foreigners between respondent and CDU
Immigration_SPD: Difference in attitude towards immigration of foreigners between respondent and SPD
Immigration_FDP: Difference in attitude towards immigration of foreigners between respondent and FDP
Immigration_Greens: Difference in attitude towards immigration of foreigners between respondent and the Greens
Immigration_Left: Difference in attitude towards immigration of foreigners between respondent and the Left party
Nuclear_CDU: Difference in attitude towards nuclear energy between respondent and CDU
Nuclear_SPD: Difference in attitude towards nuclear energy between respondent and SPD
Nuclear_FDP: Difference in attitude towards nuclear energy between respondent and FDP
Nuclear_Greens: Difference in attitude towards nuclear energy between respondent and the Greens
Nuclear_Left: Difference in attitude towards nuclear energy between respondent and the Left party
Left_Right_CDU: Difference in attitude towards the positioning on a political left-right scale between respondent and CDU
Left_Right_SPD: Difference in attitude towards the positioning on a political left-right scale between respondent and SPD
Left_Right_FDP: Difference in attitude towards the positioning on a political left-right scale between respondent and FDP
Left_Right_Greens: Difference in attitude towards the positioning on a political left-right scale between respondent and the Greens
Left_Right_Left: Difference in attitude towards the positioning on a political left-right scale between respondent and the Left party

References

German Longitudinal Election Study (GLES)

Examples

## Not run: 
data(election)

# simple multinomial logit model
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election)

# Use effect coding for the categorical predictor religion
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 pred.coding = "effect")                 

# Use reference category "FDP" instead of symmetric side constraints
star.nominal(Partychoice ~ Age + Religion + Democracy + Pol.Interest + 
                 Unemployment + Highschool + Union + West + Gender, election,
                 refLevel = 3, symmetric = FALSE)
                 
# Use category-specific covariates, subtract values for reference 
# category CDU
election[,13:16] <- election[,13:16] - election[,12]
election[,18:21] <- election[,18:21] - election[,17]
election[,23:26] <- election[,23:26] - election[,22]
election[,28:31] <- election[,28:31] - election[,27]

election$Social <- election$Social_SPD
election$Immigration <- election$Immigration_SPD
election$Nuclear <- election$Nuclear_SPD
election$Left_Right <- election$Left_Right_SPD

star.nominal(Partychoice ~ Social + Immigration + Nuclear + Left_Right + Age + 
Religion + Democracy + Pol.Interest + Unemployment + Highschool + Union + West + 
Gender, data = election, 
xij = list(Social ~ Social_SPD + Social_FDP + Social_Greens + Social_Left,
Immigration ~ Immigration_SPD + Immigration_FDP + Immigration_Greens + Immigration_Left,
Nuclear ~ Nuclear_SPD + Nuclear_FDP + Nuclear_Greens + Nuclear_Left,
Left_Right ~ Left_Right_SPD + Left_Right_FDP + Left_Right_Greens + Left_Right_Left),
symmetric = FALSE)

## End(Not run)

EffectStars documentation built on Oct. 14, 2026, 5:07 p.m.