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Immigration is a persistent and complex phenomenon intertwined with geographical, political, societal, and economic challenges. The number of international migrants has been continually increasing over the past five decades. The contributors to this volume dedicated to Professor Rebeca Raijman address various types of migrants like economic or labour migrants, forced migration and ethnic migrants. Implementing both qualitative and quantitative data and analyses, they provide insight on why individuals decide to migrate, how their decisions affect their own lives and the lives of their offspring, and how immigrants affect the receiving societies they arrive in

Bridging the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents strategies for analyzing cross-cultural data. Techniques are demonstrated through applications that employ cross-national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationally prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods. Syntax and graphical and verbal explanations of the techniques are included. Online resources include some of the data sets and syntax commands used in the book. The second edition includes six new chapters and two revised ones presenting exciting developments in the literature of cross-cultural analysis, including topics such as approximate measurement inariance, alignment optimization, sensitivity analyses, a mixed-methods approach to test for measurement invariance, and a multilevel structural equation modeling approach to explain noninvariance. This book is intended for researchers, practitiones, and advanced students interested in cross-cultural research. The book will appeal to researchers and students in psychology, political science, sociology, education, marketing and economics, geography, criminology, psychometrics, epidemiology, and public health, as well as those interested in methodology. It is also appropriate for an advanced methods course in cross-cultural analysis

In der vorliegenden kumulativen Dissertation werden einige der wichtigsten individuellen psychologischen Einflussfaktoren für die Erklärung von Vorurteilen gegenüber verschiedenen ethnischen und religiösen Minderheiten in Deutschland untersucht. Bei den Minderheiten, die in den einzelnen Beiträgen thematisiert werden, handelt es sich um Muslim*innen, Jüd*innen, Ausländer*innen im Allgemeinen, Sinti, Roma und Geflüchtete. Dabei stehen drei Aspekte besonders im Mittelpunkt: erstens soll überprüft werden, inwieweit sich etablierte psychologische Erklärungsansätze (wie z.B. Autoritarismus) bei der Erklärung von Vorurteilen gegenüber bestimmten Minderheiten in Deutschland eignen und ob eine empirische Differenzierung zwischen Vorurteilen und davon abzugrenzenden kritischen Einstellungen erfolgen kann. Zweitens soll gezeigt werden, wie stabil diese Erklärungsmodelle über verschiedene Stichproben und Zeitpunkte hinweg sind und wie gut sie sich langfristig zur Erklärung der Abwertung von Minderheiten eignen. Und drittens soll beleuchtet werden, welche kausalen Mechanismen es bei der Erklärung von Vorurteilen gibt und ob die Erklärungsmodelle für unterschiedliche Minderheiten in gleicher Weise angewendet werden können.

There has been a significant increase in cross-national and longitudinal data production in social science research in recent decades. Before drawing substantive conclusions based on cross-national and longitudinal survey data, researchers need to assess whether the constructs are measured in the same way across countries and time-points. If cross-national data are not tested for comparability, researchers risk confusing methodological artifacts as "real" substantive differences across countries. However, researchers often find it particularly difficult to establish the highest level of measurement invariance, that is, exact scalar invariance. When measurement invariance is rejected, it is crucial to understand why this was the case and to address its absence with approaches, such as alignment optimization or Bayesian structural equation modeling.

Comparisons of means or associations between theoretical constructs of interest in cross-national comparative research assume measurement invariance, that is, that the same constructs are measured in the same way across the various nations under study. While it is intuitive, this assumption needs to be statistically tested. An increasing number of sociological and social psychological studies have been published in the last decade in which the cross-national comparability of various scales such as human values, national identity, attitudes toward democracy, or religiosity, to name but afew, were tested. Many of these studies did not manage to fully achieve measurement invariance. In this study we review, in anontechnical manner, the methodological literature on measurement invariance testing. We explain what it is, how to test for it, and what to do when measurement invariance across countries is not given in the data. Several approaches have been recently proposed in the literature on how to deal with measurement noninvariance. We illustrate one of these approaches with alarge dataset of seven rounds from the European Social Survey (2002-2015) by estimating the most trustworthy means of human values, even when strict measurement invariance is not given in the data. We conclude with asummary and some critical remarks. ZusammenfassungVergleiche von Mittelwerten und von Beziehungen zwischen theoretischen Konstrukten, die im Rahmen international vergleichender Forschung untersucht werden, gehen davon aus, dass diese Konstrukte messinvariant sind, d.h., dass sie in den verschiedenen Landern identisch gemessen werden. Obwohl diese Annahme plausibel sein kann, muss sie jedoch statistisch getestet werden. Im letzten Jahrzehnt wurde eine zunehmende Zahl von soziologischen, politikwissenschaftlichen und sozialpsychologischen Studien veroffentlicht, in denen die internationale Vergleichbarkeit von verschiedenen Skalen zur Messung von z.B. menschlichen Werten, nationaler Identitat, Einstellungen zu Demokratie oder Religiositat uberpruft wurde. In vielen dieser Studien konnte Messinvarianz nicht vollig nachgewiesen werden. Die folgende Studie bietet in einer nicht technischen Art und Weise einen uberblick uber die methodologische Literatur zur Messinvarianz. Es wird erklart, was Messinvarianz ist, wie man sie uberpruft und was man tun kann, wenn sie in den Daten nicht gegeben ist. In der Literatur wurden in der letzten Zeit verschiedene Ansatze vorgeschlagen, wie man fehlende Messinvarianz behandeln kann. Die Autoren illustrieren eine dieser Herangehensweisen (Alignment) mit einem gro ss en Datensatz, der 7Befragungsrunden des European Social Survey (2002-2015) beinhaltet, und schatzen den vertrauenswurdigsten Durchschnitt menschlicher Werte, auch wenn strikte Messinvarianz in den Daten nicht vorhanden ist. Abschlie ss end folgen eine Zusammenfassung und einige kritische Anmerkungen.

AbstractPrevious explanations regarding transnational solidarity in the European Union (EU) have mainly focussed on factors including left–right self‐placement, support for European integration and European identity. We expand this model by considering deeper psychological determinants of transnational solidarity: values, operationalised as Schwartz's basic human values of universalism and security. We expect them to exert (1) direct effects on transnational solidarity – measured as support for pan‐European social benefits – and (2) indirect effects via the three aforementioned factors. We test and find evidence to support our theoretical framework using multigroup structural equation modelling and data from the European Social Survey. We further show that the effect size of the value of universalism on preferences for an EU social benefit scheme in each country is positively moderated by that country's net contribution to the EU budget, highlighting the interaction between material interests and psychological value motivations.

Extreme response style (ERS) may bias responses and hamper the validity of conclusions in substantive research. ERS can be controlled for by using an additional (random) sample of response style indicators (i.e., a separate, random sample of survey items). There are two options to draw response style indicators to control for ERS: from only one versus from multiple domains. In two studies (four samples in total), this paper examines the domain dependency of ERS across three domains: consumer behavior, interpersonal relationships and politics. We find in the four samples repeated evidence suggesting that ERS has a domain specific component. This finding calls into question the (often encountered) assumption that it does not matter from which domains ERS measures are drawn.