Effects of introspection on meta and structural attitude bases

Introspection is the process by which individuals question their attitudes; either questioning why they hold their attitudes (Why introspection), or how they feel about a particular attitude object (How introspection). Previous research has suggested that Why-introspection induces attitude change, and that Why and How introspection influence attitude-behaviour consistency,persuasion, and other effects. Generally, psychologists have assumed that affective and cognitive attitude bases are the mechanism by which introspection leads to these effects. Leading perspectives originating from these findings suggest that either Why introspection changes the content of cognitive attitude bases (the skewness hypothesis), or increases the salience of cognitive attitude bases (the dominance hypothesis); whereas How introspection may increase the salience of affective attitude bases (another part of the dominance hypothesis). However, direct evidence for these mechanisms is lacking, and the distinction between structural and meta bases has not been considered. Two studies investigated this gap in the existing literature. Both studies measured undergraduate students’ attitudes and attitude bases (both structural and meta, affective and cognitive) before and after engaging in an introspection manipulation (Why introspection / How introspection / control), and after reading a (affective / cognitive) persuasive passage about the attitude object. No evidence was found supporting either the skewness or dominance hypotheses. Furthermore, previous introspection effects were not replicated in the present data. Possible reasons for these null findings are proposed, and several unexpected effects are examined.

Estimating Dynamic Treatment Effects from Project STAR (Working Paper 36)

This paper considers the analysis of data from randomized trials which offer a sequence of interventions and suffer from a variety of problems in implementation. In experiments that provide treatment in multiple periods (T>1), subjects have up to 2^{T}-1 counterfactual outcomes to be estimated to determine the full sequence of causal effects from the study. Traditional program evaluation and non-experimental estimators are unable to recover parameters of interest to policy makers in this setting, particularly if there is non-ignorable attrition. We examine these issues in the context of Tennessee's highly influential randomized class size study, Project STAR. We demonstrate how a researcher can estimate the full sequence of dynamic treatment effects using a sequential difference in difference strategy that accounts for attrition due to observables using inverse probability weighting M-estimators. These estimates allow us to recover the structural parameters of the small class effects in the underlying education production function and construct dynamic average treatment effects. We present a complete and different picture of the effectiveness of reduced class size and find that accounting for both attrition due to observables and selection due to unobservable is crucial and necessary with data from Project STAR