MultOrdRS (version 0.1-3)

GLES17: 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

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., tools:::Rd_expr_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, tools:::Rd_expr_doi("10.1177/1471082X20978034")

Examples

# \donttest{
###############################################################
## 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")

# }