The Political Reputation of Students in Upper Secondary School: Consequences for Their Collective Political Activities Four Years Later

This longitudinal study examines the role that students' political reputation in class plays in their future political activities offline together with other people. When aged 16, students were asked to nominate the classmates they considered politically knowledgeable and verbal in class, that is, as having a political reputation. This measure of political reputation was used to predict the participants' political activities offline four years later at age 20 and their attempts to take a stand in public in the national election the same year. The study controlled for individual differences in political interest, self-perceived political impact in class, the students' political activities at age 16, and also gender and immigrant status. About 300 Swedish students were followed up four years later. Political reputation in class positively predicted future political activities offline, membership of political organizations, and attempts to take a stand in public for a party in the forthcoming national election. At the same time, the role played by political reputation depended upon students' other characteristics, especially their levels of anger and popularity, as observed by classmates. Evidently, the group dynamics in class that give some students a political reputation have long-term consequences for their future political activities.     

Meta-Analytic Analysis of Invariance Across Samples: Introducing a Method That Does Not Require Raw Data

Abstract

Invariance of surveys across different groups means that the respondents interpret the items in the same way, as reflected in similar factor loadings, for example. Invariance can be assessed using various statistical procedures, such as Multi-Group Confirmatory Factor Analysis. However, these analyses require access to raw data. Here, we introduce a meta-analytic method that requires only the factor correlation matrices of samples as input. It compares the structures of intercorrelations of factors by correlating these values across two samples, yielding a value of overall similarity for how the factors intercorrelate in different samples. This method was tested in three different ways. We conclude that the method yields useful results and can assess invariance when raw data are not available.