Both sides of the story: Narratives of romantic infidelity

We applied a narrative identity approach to the study of romantic infidelity. In Study 1, participants provided narratives of the moment they discovered their partners' infidelity. In Study 2, participants were prompted for narratives of their unfaithful actions. In both studies, measures of personality traits, forgiveness, empathy, and self‐esteem were administered. Narratives were coded for themes of redemption (bad beginnings, positive endings) and exploration (engaging with the experience's emotional implications). After controlling for relevant covariates (e.g., story length), participants who formed redemptive stories of their partner's infidelity evinced higher levels of forgiveness and lower levels of empathy (Study 1), whereas participants who disclosed redemptive stories about their own infidelity demonstrated decreased forgiveness (Study 2). Across studies, exploratory narratives corresponded positively with conscientiousness.     

The effects of a video vignette on increasing treatment acceptability

The effects of a video vignette on the treatment acceptability ratings of four behavioral interventions were evaluated. Two interventions involved positive reinforcement (DRO and DRI) and two negative consequences (contingent physical restraint and contingent electric shock). Eighty-five individuals involved in programs for individuals with developmental disabilities were randomly assigned into two groups. The experimental group (n=41) viewed a video vignette of an extremely aggressive individual whereas the control group (n=44) did not. Acceptability ratings prior to the video showed no differences between the experimental and control groups except for the physical restraint intervention. The experimental group’s post video ratings indicated significant acceptability increases for electric shock and significant decreases for DRO. There also was a significant group by testing effect with the means for the experimental group rising whereas the control group means were stable. These results showed that acceptability is alterable through video. © 1997 John Wiley & Sons, Ltd.     

The effects of statistical training on choice heuristics in choice under uncertainty

Kahneman and Tversky (1984) proposed that decision makers perceive choice uncertainty in two ways: (1) as a distribution of possible outcomes or (2) as a single uncertain outcome. Using statistical training as a factor that influences these perceptions, and thus the type of decision approach individuals use, we found that individuals with different levels of experience displayed differences in the decisions they made and in the choice heuristics used to make those decisions. Statistically naive individuals were more likely to prefer loss-minimizing alternatives, use a more non-compensatory heuristic, and spend more time on loss-related information than their statistically experienced counterparts. When a distributional cue, indicating the distributional nature of choice outcomes, was presented to both experience groups, the naive group was found to use a decision approach similar to the experienced group and to make similar decisions. The results are discussed in terms of the need to include factors that alter individuals' approaches to uncertainty in future behavioral models of uncertain choice.     

Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Data integration in legal evaluations: Can clinicians deliver on their premises?

Clinicians often claim to base their conclusions on configural analysis and integration of most or all of the data. However, research suggests that subjective beliefs about complex interpretive strategies and their resultant success are largely illusory. Studies show that: (1) access to more data does not necessarily improve judgmental accuracy, (2) clinical judgments, which purportedly depend on configural analyses, can usually be duplicated by simple linear models, (3) individuals often have difficulty deciphering interactions or patterns among just two or three variables, and (4) actuarial methods, even those that simply add variables together, almost always equal or exceed the accuracy of clinicians who purport to integrate large amounts of information. Corrective suggestions are discussed that may help the clinician better manage large data sets.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.