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This article analyses how the classroom context contributes to attitude change in adolescence. By analysing the relationship that the primary school classroom context has on anti-immigrant attitudes over time, it addresses the single factor fallacy that has troubled previous research on classrooms, which has largely tested the contact hypothesis. The dataset includes 849 participants over five-time points from 2010 to 2015. Findings show that over time individual’s anti-immigrant attitudes increased in classrooms with a higher average level of anti-immigrant sentiment net of the effect of classroom heterogeneity. However, this finding was true only while students were still enrolled in the same class over the first three waves of the study. After students entered high school, the classroom/time interaction effect disappears, suggesting that other contextual influences take over. This article highlights the crucial importance of classroom context on attitude development in adolescence.

Research has explored many different relationships between contextual influences, such as levels of immigration or economic condition, and attitudes about immigrants, with mixed results. These have largely been international comparative studies using cross-sectional data, therefore they have been unable to make claims about changes in environmental context translating to changes in attitudes of respondents. Furthermore, the previous literature has almost exclusively tested these relationships using data from adults, despite research showing that attitudes are most subject to change during adolescence. This study addresses these issues by using a longitudinal data set of repeated measures of 2,328 German adolescents (about 14–18 years old) over four response waves (2010–2014). Using a multilevel analysis, results show that contextual changes, including the percentage of foreign-born people and unemployment rates within respondents’ states, correspond to changes in attitudes toward immigrants consistent with group threat theory. These results were stable even when controlling individual-level factors.

Scholarship, including seminal research on prejudice, identifies adolescence as a critical period for the development of attitudes. Yet most sociological research on prejudice, especially in the form of anti-immigrant sentiment, focuses on the relationship between contemporaneous social conditions and attitudes towards out-groups while neglecting the demographic context during one’s impressionable years. Therefore, we design research to investigate the relationship among temporally distal and temporally proximal sub-national contexts and native-born attitudes towards immigration and immigrants. To do this, we merge geocoded data from the General Social Survey (1994–2016) with a unique US state-level dataset (1900–2015). Results from multilevel models reveal that immigrant presence during adolescence is a more consistent predictor of attitudes towards immigration and immigrants in adulthood. Thus, while the majority of sociological research on anti-immigrant sentiment asks ifsocietal conditions matter, our results suggest that a more important question is when the context matters.

Research investigating how social conditions influence attitudes about immigrants has focused primarily on demographic and economic factors as potential threat inducing contexts that lead to anti-immigrant sentiment. However, the empirical evidence supporting this link is mixed, while social cohesion indicators such as the influence of social trust, have largely been left unexamined. This article uses the European Social Survey (2002–2016) to test how differences in social trust, both within and between countries influence attitudes about immigrants. Results from longitudinal analyses show that countries with higher levels of social trust have more favorable attitudes toward immigrants, and while changes in social trust over time are small, they result in comparably large changes in anti-immigrant attitudes, even when controlling for other social factors. These results are robust across different model specifications and data sources.