# Socioeconomic Correlates of Anti-Science Attitudes in the US
## 3 Results
### Latent Structure of Anti-Science Attitudes  
A challenge posed by data coming from diverse groups, each with its own characteristics and behaviors, is that the heterogeneity may obscure the underlying relationships. As a result, a model learned from pooled data may offer few insights into the trends within the underlying groups [42]. To address this challenge, we use DoGR (see Materials and Methods) to discover the latent groups and estimate the regression coefficients within them. DoGR has a tunable parameter \(K\), the number of mixture components. According to [PERSON] information criterion (BIC), the county data are best explained with three components or groups. Figure 4 compares the composition of the three groups based on the mean of the variables over the counties assigned to that group (each county is assigned to the group with the highest membership probability). The first (orange) group is composed of counties with the largest share of [PERSON] voters, smallest population and fewest college-educated residents on average. These counties are also home to the largest share of anti-science users compared to other counties in our data (the average anti-science share is \(0.62\pm 0.021\)). The second (green) group has the largest share of White residents, who are also older. The last (blue) group is composed of the largest counties, with more college-educated  
Figure 2: Correlations between socioeconomic variables, political ideology, sentiment, and anti-science attitudes. The last two variables were measured from social media messages. All variables represent averages aggregated per county. Statistically significant correlations are marked by asterisks (***: <0.1%, ***: <1%, *: <5%). and affluent residents. They also have the fewest [PERSON] voters. These counties have the smallest share of anti-science users (average share of anti-science users is \(0.57\pm 0.022\)). To obtain a better idea of what these groups represent, we plotted the distribution of the rural-urban continuum codes of counties in each group. The rural-urban continuum codes ([[https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/](https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/)]([https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/](https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/documentation/)), accessed on 16 June 2021) categorize counties in the US as metropolitan vs. non-metropolitan. The categorization scheme distinguishes the metropolitan counties (codes 1-3) by the population size of their metro area and non-metropolitan counties (codes 4-9) by their degree of urbanization and adjacency to metro areas. Figure 4 shows the distribution of county codes. The blue group is mainly composed of the largest metropolitan counties (code 1) representing the largest urban centers. The green group also contains large metropolitan counties, but also many mid-side metro areas (codes 2-3). In contrast, the orange group corresponds to counties in the smallest metro areas (code 3) and also many counties in non-metro and rural areas (codes 4-9). Although these county codes were not used in the analysis, the DoGR method identifies the rural-urban divide as the factor explaining cultural differences. Figure 3 (right) shows regression coefficients within the groups. In rural counties (orange group), the fraction of anti-science users is negatively associated with the share of White households and is positively associated with the age of residents, household size, and the share of [PERSON] voters. It is also positively associated with income, though this relationship is less significant. Other associations with socioeconomic variables are not statistically significant.