Bayesian Inference for Correlations in the Presence of Measurement Error and Estimation Uncertainty

Whenever parameter estimates are uncertain or observations are contaminated by measurement error, the Pearson correlation coefficient can severely underestimate the true strength of an association. Various approaches exist for inferring the correlation in the presence of estimation uncertainty and measurement error, but none are routinely applied in psychological research. Here we focus on a Bayesian hierarchical model proposed by Behseta, Berdyyeva, Olson, and Kass (2009) that allows researchers to infer the underlying correlation between error-contaminated observations. We show that this approach may be also applied to obtain the underlying correlation between uncertain parameter estimates as well as the correlation between uncertain parameter estimates and noisy observations. We illustrate the Bayesian modeling of correlations with two empirical data sets; in each data set, we first infer the posterior distribution of the underlying correlation and then compute Bayes factors to quantify the evidence that the data provide for the presence of an association.Multivariate analysis of psychological dat

The effects of customer equity drivers on loyalty across services industries and firms

Customer equity drivers (CEDs)—value equity, brand equity, and relationship equity—positively affect loyalty intentions, but this effect varies across industries and firms. We empirically examine potential industry and firm characteristics that explain why the CEDs–loyalty link varies across services industries and firms in the Netherlands. The results show that (1) some previously assumed industry and firm characteristics have moderating effects while others do not and (2) firm-level advertising expenditures constitute the most crucial moderator because they influence all three loyalty strategies (significant for value equity and brand equity; marginally significant for relationship equity), while three industry contexts (i.e., innovative markets, visibility to others, and complexity of purchase decisions) each influence two of the three loyalty strategies. Our results clearly show that specific industry and firm characteristics affect the effectiveness of specific loyalty strategies

An Information Theory Account of Preference Prediction Accuracy [Dataset]

Knowledge about other people's preferences is essential for successful social interactions, but what exactly are the driving factors that determine how well we can predict the likes and dislikes of people around us? To investigate the accuracy of couples' preference predictions we outline and empirically test three hypotheses: The positive valence hypothesis predicts that predictions for likes are more accurate than for dislikes. The negative valence hypothesis predicts the opposite, namely that dislikes are predicted more accurately than dislikes. The base rate hypothesis predicts that preference knowledge critically depends on the base rates of likes and dislikes within a given domain. In a series of studies we show that predicting likes over dislikes has relatively little effect compared with base rates. That is, accuracy is greater for relatively rare events regardless of whether they are liked or disliked. Our findings further suggest that when predicting preferences, people seem to rely on a combination of general, stereotypical knowledge of common preferences on the one hand and specific, idiosyncratic knowledge of rare preferences on the other