The current research tested whether the passing of government legislation, signaling the prevailing attitudes of the local majority, was associated with changes in citizens’ attitudes. Specifically, with ∼1 million responses over a 12-y window, we tested whether state-by-state same-sex marriage legislation was associated with decreases in antigay implicit and explicit bias. Results across five operationalizations consistently provide support for this possibility. Both implicit and explicit bias were decreasing before same-sex marriage legalization, but decreased at a sharper rate following legalization. Moderating this effect was whether states passed legislation locally. Although states passing legislation experienced a greater decrease in bias following legislation, states that never passed legislation demonstrated increased antigay bias following federal legalization. Our work highlights how government legislation can inform individuals’ attitudes, even when these attitudes may be deeply entrenched and socially and politically volatile.

Across many diverse areas of research, it is common to average a series of observations, and to use these averages in subsequent analyses. Research using this approach faces the challenge of knowing when these averages are stable. Meaning, to what extent do these averages change when additional observations are included? Using averages that are not stable introduces a great deal of error into any analysis. The current research develops a tool, implemented in R, to assess when averages are stable. Using a sequential sampling approach, it determines how many observations are needed before additional observations would no longer meaningfully change an average. The utility of this tool is illustrated in the context of impression formation, demonstrating that averages of some perceived traits (e.g., happy) stabilize with fewer observations than others (e.g., assertive). A tutorial regarding how to utilize this tool in researchers’ own data is provided.

The present research adopts a data-driven approach to identify how characteristics of the environment are related to different types of regional in-group biases. After consolidating a large data set of environmental attributes ( N = 813), we used modern model selection techniques (i.e., elastic net regularization) to develop parsimonious models for regional implicit and explicit measures of race-, religious-, sexuality-, age-, and health-based in-group biases. Developed models generally predicted large amounts of variance in regional biases, up to 62%, and predicted significantly and substantially more variance in regional biases than basic regional demographics. Human features of the environment and events in the environment strongly and consistently predicted biases, but nonhuman features of the environment and population characteristics inconsistently predicted biases. Results implicate shared psychological causes of different regional intergroup biases, reveal distinctions between biases, and contribute to developing theoretical models of regional bias.

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The present research adopts a data-driven approach to identify how characteristics of the environment are related to different types of regional ingroup biases. After consolidating a large dataset of environmental attributes (n = 813), we used modern model selection techniques (i.e., elastic net regularization) to develop parsimonious models for regional implicit and explicit measures of race-, religious-, sexuality-, age-, and health-based ingroup biases. Developed models generally predicted large amounts of variance in regional biases, up to 62%, and predicted significantly and substantially more variance in regional biases than basic regional demographics. Human features of the environment and events in the environment strongly and consistently predicted biases, but non-human features of the environment and population characteristics inconsistently predicted biases. Results implicate shared psychological causes of different regional intergroup biases, reveal distinctions between biases, and contribute to developing theoretical models of regional bias.