Andrew Miles

The recent change in the general social survey (GSS) to a rotating panel design is a landmark development for social scientists. Sociological methodologists have argued that fixed-effects (FE) models are generally the best starting point for analyzing panel data because they allow analysts to control for unobserved time-constant heterogeneity. We review these treatments and demonstrate the advantages of FE models in the context of the GSS. We also show, however, that FE models have two rarely tested assumptions that can seriously bias parameter estimates when violated. We provide simple tests for these assumptions. We further demonstrate that FE models are extremely sensitive to the correct specification of temporal lags. We provide a simulation and a proof to show that the use of incorrect lags in FE models can lead to coefficients that are the opposite sign of the true parameter values.

Dual-process models are increasingly popular in sociology as a framework for theorizing the role of automatic cognition in shaping social behavior. However, empirical studies using dual-process models often rely on ad hoc measures such as forced-choice surveys, observation, and interviews whose relationships to underlying cognitive processes are not fully established. In this article, we advance dual-process research in sociology by (1) proposing criteria for measuring automatic cognition, and (2) assessing the empirical performance of two popular measures of automatic cognition developed by psychologists. We compare the ability of the Brief Implicit Association Test (BIAT), the Affect Misattribution Procedure (AMP), and traditional forced-choice measures to predict process-pure estimates of automatic influences on individuals’ behavior during a survey task. Results from three studies focusing on politics, morality, and racial attitudes suggest the AMP provides the most valid and co...