EffectStars (version 1.9-1)

election: 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)
# }