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.

This article maps and investigates public support for different types of differentiated integration (DI) in the European Union. We examine citizens’ preferences for DI using novel survey data from eight EU member states. The data reveals substantive differences in support for different types of DI. Factor analyses reveal two dimensions that seem to structure citizens’ evaluations of DI. The first dimension relates to the effect of DI on the European integration project, the second concerns the safeguarding of national autonomy. Citizens’ attitudes on this second dimension vary substantively across countries. General EU support is the most important correlate of DI support, correlating positively with the first and negatively with the second dimension. Our results underline that while citizens generally care about the fairness of DI, balancing out their different concerns can be a challenging political task.

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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.