GLES17 {MultOrdRS} | R Documentation |
German Longitudinal Election Study 2017 (GLES17)
Description
Data from the German Longitudinal Election Study (GLES) from 2017 (Rossteutscher et al., 2017, https://doi.org/10.4232/1.12927). The GLES is a long-term study of the German electoral process. It collects pre- and post-election data for several federal elections, the data used here originate from the pre-election study for 2017.
Format
A data frame containing data from the German Longitudinal Election Study with 2036 observations. The data contain socio-demographic information about the participants as well as their responses to items about specific political fears.
- RefugeeCrisis
How afraid are you due to the refugee crisis? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- ClimateChange
How afraid are you due to the global climate change? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- Terrorism
How afraid are you due to the international terrorism? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- Globalization
How afraid are you due to the globalization? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- Turkey
How afraid are you due to the political developments in Turkey? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- NuclearEnergy
How afraid are you due to the use of nuclear energy? (Likert scale from 1 (not afraid at all) to 7 (very afraid))
- Age
Age in years
- Gender
0: male, 1: female
- EastWest
0: West Germany, 1: East Germany
- Abitur
High School Diploma, 1: Abitur/A levels, 0: else
- Unemployment
1: currently unemployed, 0: else
Source
https://www.gesis.org/en/gles/about-gles and doi:10.4232/1.12927
References
Rossteutscher, S., Schmitt-Beck, R., Schoen, H., Wessels, B., Wolf, C., Bieber, I., Stovsand, L.-C., Dietz, M., and Scherer, P. (2017). Pre-election cross section (GLES 2017). GESIS Data Archive, Cologne, ZA6800 Data file Version 2.0.0., doi:10.4232/1.12927.
Schauberger, Gunther and Tutz, Gerhard (2021): Multivariate Ordinal Random Effects Models Including Subject and Group Specific Response Style Effects, Statistical Modelling, doi:10.1177/1471082X20978034
Examples
###############################################################
## Examples from Schauberger and Tutz (2020)
## Data from the German Longitudinal Election Study (GLES) 2017
###############################################################
####
## Source: German Longitudinal Election Study 2017
## Rossteutscher et al. 2017, https://doi.org/10.4232/1.12927
####
## load GLES data
data(GLES17)
## scale data
GLES17[,7:11] <- scale(GLES17[,7:11])
## define formula
f.GLES <- as.formula(cbind(RefugeeCrisis, ClimateChange, Terrorism,
Globalization, Turkey, NuclearEnergy) ~
Age + Gender + Unemployment + EastWest + Abitur)
## fit adjacent categories model without and with response style parameters
m.GLES0 <- multordRS(f.GLES, data = GLES17, control = ctrl.multordRS(RS = FALSE, cores = 6))
m.GLES <- multordRS(f.GLES, data = GLES17, control = ctrl.multordRS(cores = 6))
m.GLES0
m.GLES
plot(m.GLES, main = "Adjacent categories model")
## fit cumulative model without and with response style parameters (takes pretty long!!!)
m.GLES20 <- multordRS(f.GLES, data = GLES17, model="cumul",
control = ctrl.multordRS(opt.method = "nlminb", cores = 6, RS = FALSE))
m.GLES2 <- multordRS(f.GLES, data = GLES17, model="cumul",
control = ctrl.multordRS(opt.method = "nlminb", cores = 6))
m.GLES20
m.GLES2
plot(m.GLES2, main = "Cumulative model")