@article{MunzertSelb, author = {Munzert, Simon and Selb, Peter}, title = {Can we directly survey adherence to non-pharmaceutical interventions? Evidence from a list experiment conducted in Germany during the early Corona pandemic.}, series = {Survey Research Methods}, volume = {14}, journal = {Survey Research Methods}, number = {2}, doi = {10.18148/srm/2020.v14i2.7759}, pages = {205 -- 209}, abstract = {Self-reports of adherence to non-pharmaceutical interventions in surveys may be subject to social desirability bias. Existing questioning techniques to reduce bias are rarely used to monitor adherence. We conducted a list experiment to elicit truthful answers to the question whether respondents met friends or acquaintances and thus disregarded the social distancing norm. Our empirical findings are mixed. Using the list experiment, we estimate the prevalence of non-compliant behavior at 28\%, whereas the estimate from a direct question is 22\%. However, a more permissively phrased direct question included later in the survey yields an estimate of 47\%. All three estimates vary consistently across social groups. Interestingly, only the list experiment reveals somewhat higher non-compliance rates among the highly educated compared to those with lower education, yet the variance of the list estimates is considerably higher. We conclude that the list experiment compared unfavorably to simpler direct measurements in our case.}, language = {en} } @article{NeunhoefferGschwendMunzertetal., author = {Neunhoeffer, Marcel and Gschwend, Thomas and Munzert, Simon and Stoetzer, Lukas F.}, title = {Ein Ansatz zur Vorhersage der Erststimmenanteile bei Bundestagswahlen}, series = {Politische Vierteljahresschrift}, volume = {61}, journal = {Politische Vierteljahresschrift}, doi = {10.1007/s11615-019-00216-3}, pages = {111 -- 130}, abstract = {Almost half of the total seats in the German Bundestag are awarded through first-past-the post elections at the electoral-district level. However, many election forecasting models do not consider this. In this paper we present an approach to predicting the candidate-vote shares at the district level for the German Federal Elections. To that end, we combine the national-level election prediction model from zweitstimme.org with two district-level prediction models, a linear regression and an artificial neural network, that both use the same candidate and district characteristics for their predictions. All data in our approach are publicly available prior to the respective election; thus, our model yields real forecasts. The model is therefore able to provide valuable information to running candidates and the interested public in future elections. Moreover, our prediction results are also relevant for substantive research: with the aid of the resulting odds of winning, better measures can be created to characterize the competitiveness of an electoral district and the expected closeness of electoral-district elections, which can influence political behaviour. Furthermore, the prediction allows empirical statements to be made about the expected size of the Bundestag as well as the composition of its personnel.}, language = {de} }