Estimating the population distribution shape of political attitude: A Bayesian statistical modeling approach

Abstract

The shape of the population distribution of political attitudes regarding maintenance- innovation was investigated. Previously, this shape has been analyzed by using a single item political ideology scale. However, it has been pointed out that the shape was strongly influenced by response difficulty and reaction bias. Therefore, we proposed two methodological solutions to solve these problems. Firstly, we estimated the shape of the population distribution of attitudes using the Generalized Graded Unfolding Model (GGUM) with Skewed Generalized Error Distribution (SGED) as the prior distribution of political attitudes. Secondly, we adopted a statistical model to correct for the reaction bias of response data. We also examined how the shape changes based on the degree of political knowledge. Results indicated that the distribution of attitudes converged in the middle before removing the reaction bias, whereas the distribution approached a normal distribution after correcting the bias. Moreover, political knowledge affected the shape of the distribution of attitudes. The distribution of people with low political knowledge converged in middle, whereas the distribution of people with high political knowledge had a large variance and small kurtosis. Based on these results, we have discussed the possibility that mechanisms of political attitude formation could be inferred differently depending on the degree of political knowledge.