September 2016
It ’ s Nativism :
Explaining the Drivers of Trump ’ s Popular Support
Both models estimate the relative impact of each potential explanatory factor . Bayesian network analysis , in turn , identifies the inter-connectedness and causality of the explanatory factors — using a very exciting proprietary method developed at Ipsos .
Why is Bayes Net cool ? In cognitive psychology parlance , our memory is associative . The key is to identify this interconnectedness and causal sequencing . Practically speaking , the ability to identify memory clusters enhances our understanding of key behavioral triggers and allows us to optimize messaging for full impact .
SO WHAT DO WE FIND ?
Logistic Regression Analysis
Again , I estimate three models using three distinct dependent variables . In addition to the eight ideological variables mentioned above , I include standard demographic variables as controls : age , gender , education , and race / ethnicity .
Additionally , for easy perusal , I report each factor ’ s impact as the percent explained variance . This places all the explanatory factors on a standard 100 % scale for easy understanding .
Simply put , it is all about nativism ! Indeed , across all methods , the findings are incredibly consistent — those who support Trump are much more likely to hold strong nativist and anti-immigrant beliefs , controlling for other ideological and demographic variables . Most importantly , nativism is the most impactful driver of support for Trump . Let ’ s examine in detail .
Two caveats . First , the percent explained variance is a proxy metric as it excludes the overlapping variance among independent variables . Second , I convert logistic regression logit coefficients to standardized beta scores like those found in linear regression . This is a more heterodox approach but one which is more intuitively appealing . 7 Finally , output of the full models can be found in the appendix .
7 Menard ( 2004 ) “ Six Approaches to Calculating Standardized Logistic Regression Coefficients ” The American Statistician , Vol . 58 , No . 3 ( Aug ., 2004 ), pp . 218-223
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