Although authoritarianism has predominantly been studied among conservatives, newer work on left-wing authoritarianism (LWA) has suggested that authoritarian individuals exist on both poles of the political spectrum. A 39-item multidimensional measure, the Left-wing Authoritarianism Index, was recently developed to measure LWA. The present study used a fully automated machine learning approach (i.e., a genetic algorithm) in a large, demographically representative American sample (N = 834) to generate two abbreviated versions of the LWA Index. A second community sample (N = 477) was used to conduct extensive validational tests of the abbreviated measures, which comprise 25- and 13-items. The abbreviated forms demonstrated remarkable convergence with the full LWA Index in terms of their psychometric (e.g., internal consistency) and distributional (e.g., mean, standard deviation, skew, kurtosis) properties. Further, this convergence extended to virtually identical cross-measure patterns of correlations with 14 external criteria, including need for chaos, political violence, anomia, and low institutional trust. In light of these results, the LWA-25 and LWA-13 scales appeared to function effectively as measures of LWA. We conclude by examining the items retained (vs. excluded) by the genetic algorithm to clarify the central vs. peripheral conceptual elements of LWA.
An automated machine learning approach to predict brain age from cortical anatomical measures