Choice Certainty and Consistency in Repeated Choice Experiments

Abstract

The main objective of this study is to examine how repeated choice affects preference learning in stated preference experiments. We test different hypotheses related to preference learning by analyzing response patterns and asking respondents in a choice experiment to report their experienced certainty when going through the choice tasks. In a split-sample test, we show that follow-up choice certainty questions are procedural invariant. The self-reported certainty results indicate that learning occurs, but econometric testing procedures do not identify any significant impact of learning effects on parameter estimates or variance across choice tasks. Additional tests of choice consistency suggest that preferences in the choice experiment are stable and coherent.

Motivations matter: Behavioural determinants of preferences for remote and unfamiliar environmental goods

“…Similarly, components of the TPB might impact other moderators of stated choices, such as choice certainty (Brouwer et al 2010, Olsen et al 2011, Hensher et al 2012. These relationships were not tested in the framework of this study but should be investigated further when testing for construct validity in stated preference surveys, especially when regarding remote and unfamiliar environmental goods.…”

Are Fast Responses More Random? Testing the Effect of Response Time on Scale in an Online Choice Experiment

“…It is standard practice in stated preference surveys to ask respondents how certain they are of their choices (or responses in general) (e.g. Li and Mattsson 1995;Brouwer et al 2010). It is straightforward to expect that self-rated choice certainty should have a positive effect on scale.…”

Controlling for the Effects of Information in a Public Goods Discrete Choice Model

“…This "tedious, but straightforward" procedure results in unique maximum for the log likelihood function, at least for the linear-in-parameters MNL model for which the log likelihood is concave and remains probably the most commonly used way to test for scale differences between two datasets, at least in environmental economics applications (e.g. von Haefen and Phaneuf 2008;Christie and Azevedo 2009;Olsen 2009;Brouwer et al 2010).…”