Julian Schuessler

Conjoint experiments aiming to estimate average marginal component effects and related quantities have become a standard tool for social scientists. However, existing solutions for power analyses to find appropriate sample sizes for such studies have various shortcomings and accordingly, explicit sample size planning is rare. Based on recent advances in statistical inference for factorial experiments, we derive simple yet generally applicable formulae to calculate power and minimum required sample sizes for testing average marginal component effects (AMCEs), conditional AMCEs, as well as interaction effects in forced-choice conjoint experiments. The only input needed are expected effect sizes. Our approach only assumes random sampling of individuals or randomization of profiles and avoids any parametric assumption. Furthermore, we show that clustering standard errors on individuals is not necessary and does not affect power. Our results caution against designing conjoint experiments with small sample sizes, especially for detecting heterogeneity and interactions. We provide an R package that implements our approach.

Research on differentiated integration (DI) in the European Union has burgeoned in recent years. However, we still know little about citizens’ attitudes towards the phenomenon. In this article, we argue that at the level of individual citizens, liberal economic values increase support for DI. Stronger preferences for equality, in contrast, make opposition to the concept more likely. Similarly, concerns about discriminatory differentiation at the member state level lead citizens to oppose DI. We test the theoretical claims by analysing survey data on citizens’ attitudes towards a ‘multi-speed Europe’. Supporters of DI, indeed, are marked by liberal economic attitudes. In contrast to general EU support, we do not find robust correlations with socio-demographic variables. Moreover, the data reveal striking differences amongst macro-regions: support for DI has become much lower in Southern European states. We attribute this opposition to negative repercussions of the Eurozone crisis.

When using instrumental variables, researchers often assume that causal effects are only identified conditional on covariates. We show that the role of these covariates in applied research is often unclear, and that there exists confusion regarding their ability to mitigate violations of the exclusion restriction. We explain how existing adjustment strategies may lead to bias. We then discuss assumptions that are sufficient to identify various treatment effects, some of which are new, when the exclusion restriction only holds conditionally. In general, these assumptions are highly restrictive, albeit they sometimes are testable. We also show that other existing tests are generally misleading. Then, we introduce an alternative sensitivity analysis that uses information on variables influenced by the instrument to gauge the effect of potential violations of the exclusion restriction. Finally, we summarize our results in easy-to-understand guidelines.

Directed acyclic graphs (DAGs) are an increasingly popular tool to inform causal inferences in observational research. We demonstrate how DAGs can be used to encode and communicate theoretical assumptions about nonprobability samples and survey nonresponse, determine whether typical population parameters of interest to survey researchers can be identified from a sample, and support the choice of adjustment strategies. Following an introduction to basic concepts in graph and probability theory, we discuss sources of bias and assumptions for eliminating it in selection scenarios familiar from the missing data literature. We then introduce and analyze graphical representations of the multiple selection stages in the survey data collection process, in line with the Total Survey Error approach. Finally, we identify areas for future survey methodology research that can benefit from advances in causal graph theory.

The European Union targets up to a quarter of its budget towards underdeveloped regions. Do these investments have an impact on citizens' attitudes towards the EU? The previous literature on this question is scarce and inconclusive. I use a regression- discontinuity design to tackle this question. The analysis is based on a large dataset of geocoded individual-level survey responses from every member state that spans more than twenty years. Point estimates for the effect of the funds on public opinion are relatively small and statistically insignificant. Large effects can be ruled out. At the same time, I show that pre-existing attitudes towards European integration correlate with the EU's allocation decisions. Finally, I show that the funds do not have an impact on EU-related attitudes due to informational problems and explore whether the EU's activities may be misattributed to national political actors.