Factorial Surveys with Multiple Ratings per Vignette: A Seemingly Unrelated Multilevel Regressions Framework

Factorial surveys are a prominent tool in the social sciences. Reanalyzing a literature sur­vey on the factorial survey approach (Wallander, 2009), I show that about a quarter of ap­plied factorial surveys asks respondents to provide multiple ratings on the same vignette. This paper is the first to propose a statistical modeling approach for precisely this situation. Data from factorial surveys with multiple ratings per vignette are afflicted with two sourc­es of statistical dependencies. First, each respondent answers multiple vignettes, which is typically accounted for via random effects models, and, second, each vignette prompts multiple ratings. The first problem is common for almost any factorial survey and has been addressed decades ago. The second problem is addressed here. I propose to apply a seem­ingly unrelated regression approach to account for the statistical dependencies between multiple ratings per vignette. Due to the use of a structural equation modeling approach, the model allows not only to correctly compare coefficients across ratings but also to ana­lyze the factor structure underlying these ratings. The proposed model is illustrated by two examples from recent research. All data and syntax are available online and allows for an easy adaption of the proposed model to readers’ own research

Institutions, culture and migrants' preference for state-provided welfare:Longitudinal evidence from Germany

Using the difference-in-differences estimator and data provided by the German Socio-Economic Panel, this article explores migrants' preferences for state-provided welfare. The study finds evidence that over time, the preferences of immigrants and natives become more similar. We interpret this finding as evidence that the culture of home countries does not have a time-invariant effect, and that immigrants' welfare preferences are subject to a socializing effect of the host countries' welfare regime

High-profile crime and perceived public safety: Evidence from Cologne’s New Year’s Eve in 2015

This study analyses the impact of a high-profile crime event on perceived public safety. At the 2015 New Year's Eve celebrations in Cologne (NYE), Germany, refugees allegedly committed over a thousand crimes, ranging from theft to sexual assault. The widespread media coverage of these incidents made a shift in the public’s perceived safety plausible. We empirically analyze this proposition using a difference-in-differences strategy. Using the European Social Survey, we estimate the differential response of German respondents to those of other European countries in terms of perceived safety after NYE. We find that Germans feel less safe after the NYE incidents. Women and individuals leaning toward the political right are affected the most. An analysis of search queries suggests that the loss of perceived safety may also translate into changed behavior, indicated by a higher demand for defense good

Die Säkularisierung Europas 2002 bis 2016

Das Ausgangsniveau der Säkularisierung der Länder Europas nach 1989 spiegelt zwei Spannungslinien wider: den rezenten politischen Konflikt zwischen West und Ost und die historische konfessionelle Spaltung zwischen Katholizismus, Orthodoxie und Protestantismus. Wie stark bestimmen beide den Fortgang der Säkularisierung? Um diese Frage zu behandeln, werden zunächst die beiden Spannungslinien, die entsprechende Gruppierung der Länder und die Datenbasis zu ihrer Untersuchung vorgestellt. Anschließend werden Ausgangspunkt und Fortgang der Säkularisierung zwischen Ländergruppen verglichen

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

Interactions in fixed effects regression models

An interaction in a fixed effects (FE) regression is usually specified by demeaning the product term. However, algebraic transformations reveal that this strategy does not yield a within-unit estimator. Instead, the standard FE interaction estimator reflects unit-level differences of the interacted variables. This property allows interactions of a time-constant variable and a time-varying variable in FE to be estimated but may yield unwanted results if both variables vary within units. In such cases, Monte Carlo experiments confirm that the standard FE estimator of x ⋅ z is biased if x is correlated with an unobserved unit-specific moderator of z (or vice versa). A within estimator of an interaction can be obtained by first demeaning each variable and then demeaning their product. This “double-demeaned” estimator is not subject to bias caused by unobserved effect heterogeneity. It is, however, less efficient than standard FE and only works with T < 2

Economic inequality and public demand for redistribution: combining cross-sectional and longitudinal evidence

One proposition of the popular median-voter hypothesis is a positive relationship between demand for redistribution and levels of inequality. However, empirical evidence of this relationship is scarce. A major shortcoming of previous research is that it is either cross-sectional, which casts general doubt on the causal nature of the estimates, or it is longitudinal and based on aggregated data, which makes it difficult to control for compositional effects or to analyze the individual-level implications of the hypothesis. This article estimates cross-sectional and longitudinal effects of inequality, while simultaneously controlling for the composition of data at the individual level. The article finds a positive within-country effect of inequality on demand for redistribution but no such relationship between countries. This finding points to an unobserved variable at the country level. Following the literature, the article considers welfare regimes as a possible factor capturing these unobserved country differences. However, none of the existing welfare regime typologies performs well in terms of capturing unobserved heterogeneity or in general explanatory power. All in all, the article finds robust support for the proposition that demand for redistribution is positively related to inequality, but it casts doubt on the utility of cross-sectional analysis and the welfare regime approach

Migration, migrant integration and support for social spending: The case of Switzerland

An extensive body of scholarship has claimed that the relationship between migration and the welfare state is a potentially troublesome one, because the native population might be concerned about the fiscal, economic and cultural threats this poses. At the same time, studies have argued that migrants differ', not only in their actual numbers but also in their similarities or differences compared with the native population. Taking these differences into account, we analyse the effect of the integration of migrants for natives' support for welfare. In detail, we test for the possibility that the integration of migrants might have a direct impact on the economic and cultural difficulties which natives associate with migration and in this way will have an indirect effect on their support for social spending. Our results show that the objective integration of migrants has only limited relevance for the relationship between migration and welfare support and point to the need to focus on subjectively perceived migration- and integration-related attitudes of natives

The Random Effects in Multilevel Models: Getting Them Wrong and Getting Them Right

Many surveys of respondents from multiple countries or subnational regions have now been fielded on multiple occasions. Social scientists are regularly using multilevel models to analyse the data generated by such surveys, investigating variation across both space and time. We show, however, that such models are usually specified erroneously. They typically omit one or more relevant random effects, thereby ignoring important clustering in the data, which leads to downward biases in the standard errors. These biases occur even if the fixed effects are specified correctly; if the fixed effects are incorrect, erroneous specification of the random effects worsens biases in the coefficients. We illustrate these problems using Monte Carlo simulations and two empirical examples. Our recommendation to researchers fitting multilevel models to comparative longitudinal survey data is to include random effects at all potentially relevant levels, thereby avoiding any mismatch between the random and fixed parts of their models

Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job

Giesselmann M, Schmidt-Catran AW. Getting the Within Estimator of Cross-Level Interactions in Multilevel Models with Pooled Cross-Sections: Why Country Dummies (Sometimes) Do Not Do the Job . Sociological Methodology. 2019;49(1):190-219.Multilevel models with persons nested in countries are increasingly popular in cross-country research. Recently, social scientists have started to analyze data with a three-level structure: persons at level 1, nested in year-specific country samples at level 2, nested in countries at level 3. By using a country fixed-effects estimator, or an alternative equivalent specification in a random-effects framework, this structure is increasingly used to estimate within-country effects in order to control for unobserved heterogeneity. For the main effects of country-level characteristics, such estimators have been shown to have desirable statistical properties. However, estimators of cross-level interactions in these models are not exhibiting these attractive properties: as algebraic transformations show, they are not independent of between-country variation and thus carry country-specific heterogeneity. Monte Carlo experiments consistently reveal the standard approaches to within estimation to provide biased estimates of cross-level interactions in the presence of an unobserved correlated moderator at the country level. To obtain an unbiased within-country estimator of a cross-level interaction, effect heterogeneity must be systematically controlled. By replicating a published analysis, we demonstrate the relevance of this extended country fixed-effects estimator in research practice. The intent of this article is to provide advice for multilevel practitioners, who will be increasingly confronted with the availability of pooled cross-sectional survey data