Development and Initial Validation of Two Brief Measures of Left-Wing Authoritarianism: A Machine Learning Approach

Abstract

Although authoritarianism has predominantly been studied among political conservatives, authoritarian individuals exist on both “poles” of the political spectrum. A 39-item multidimensional measure of left-wing authoritarianism, the Left-wing Authoritarianism Index, was recently developed to extend the study of authoritarianism to members of the far-left. The present study coupled a fully automated machine learning approach (i.e., a genetic algorithm) with multidimensional item response theory in a large, demographically representative American sample (N = 834) to generate and evaluate two abbreviated versions of the Left-wing Authoritarianism Index. We subsequently used a second community sample (N = 477) 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. This convergence extended to virtually identical cross-measure patterns of correlations with 14 external criteria, including need for chaos, political violence, anomia, low institutional trust. In light of these results, the LWA-25 and LWA-13 scales appeared to function effectively as measures of LWA.