B e r i c h t e u n d d i s k u s s i o n e nZusammenfassung: Logit-und Probitregression werden als multivariate Analyseverfahren zur Analyse von dichotomen abhängigen Variablen in den sozialwissenschaften routinemäßig eingesetzt. Beide Verfahren können so interpretiert werden, dass sich aus einer linearen Modellierung einer unbeobachteten Variable y* eine nichtlineare Modellierung der Wahrscheinlichkeiten für y = 1 ergibt. Wir zeigen erstens, dass diese nichtlinearität im Vergleich zu linearen regressionsverfahren zu Problemen bei der interpretation der Modellergebnisse führt. insbesondere die in der logistischen regression häufig verwendeten odds ratios (exponierte Logit-koeffizienten) sind unseres erachtens problematisch. stattdessen empfehlen wir neben graphischen interpretationshilfen die Verwendung von (korrigierten) durchschnittlich marginalen effekten (AMe). Zweitens zeigen wir anhand einer serie von Monte-carlo-simulationen, dass die üblichen regressionskoeffizienten bei Logit-und Probitanalysen nicht zwischen verschachtelten Modellen verglichen werden können. da in den sozialwissenschaften bei der Modellbildung jedoch häufig schrittweise vorgegangen wird, wäre ein Verfahren, das einen validen Vergleich von effektstärken zwischen den Modellen erlaubt, sehr nützlich. Wie wir anhand unserer simulationsstudie zeigen, führen durchschnittlich marginale effekte und koeffizienten, die nach dem Vorschlag von karlson et al. ( Sociological Methodology 42, 2012) korrigiert wurden, in sehr verschiedenen situationen zu gültigen ergebnissen. y*-standardisierte koeffizienten sind für einen Modellvergleich hingegen weniger geeignet und koeffizienten eines linearen Wahrscheinlichkeitsmodells sollten ausschließlich bei normalverteilten Variablen verwendet werden.
Our study explores the adoption of Facebook and Twitter by candidates in the 2013 German Federal elections. Utilizing data from the German Longitudinal Election Study candidate survey fused with data gathered on the Twitter and Facebook use of candidates, we draw a clear distinction between Facebook and Twitter. We show that adoption of both channels is primarily driven by two factors: party and money. But the impact of each plays out differently for Facebook and Twitter. While the influence of money is homogenous for Facebook and Twitter with the more resources candidates have, the more likely they are to adopt, the effect is stronger for Facebook. Conversely, a party's impact on adoption is heterogeneous across channels, a pattern we suggest is driven by the different audiences Facebook and Twitter attract. We also find candidates' personality traits only correlate with Twitter adoption, but their impact is minimal. Our findings demonstrate that social media adoption by politicians is far from homogenous, and that there is a need to differentiate social media channels from one another when exploring motivations for their use.
The author seeks to answer the question ``How secularized is Germany?'' on the basis of different perspectives and databases. The meaning of the term secularization is limited for this study to the decline of religiosity and its consequences, and a distinction is made between church-related and individual religiosity. First, evidence for the decline of church-related religiosity in Germany is presented. Next, it is shown that individual religiosity, i.e. religiosity not necessarily related to organized religion, is also declining. Then the analysis is extended to investigate the relationship between religiosity and non-religious attitudes. Taking two illustrative examples, it is claimed that religion today is far less salient for the way we live and see the world than it has been in the past. Finally, the focus of the analysis is broadened to an international perspective in which Germany is compared with other countries. The author ends with some thoughts on the future of secularization in Germany.
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