The Science of Decision-making: Behaviours Related to Gambling

Abstract

The behaviour analysis of decision-making has studied three realms of behaviour that play a role in gambling: risk taking, behavioural persistence and self-control. Research from our laboratory and others suggests that in all three areas, the manner in which choices are presented to subjects is of central importance. The paper argues that a behavioural perspective may be useful in understanding the factors controlling gambling and compares this perspective with one based on a cognitive orientation.     

Sample Selection in Models of Academic Performance

This article shows how admission and enrollment processes affect the interpretation of simple validation studies of academic performance. In a competitive market for students, optimal behavior of admissions committees and applicants drives the simple correlation between test scores and performance toward zero, regardless of the relationship in the population of prospective students. Data from our university's MBA program support the prediction that applicants exhibit a higher correlation between test scores and undergraduate GPAs than do current students. This suggests that standard validation studies will understate the importance of GMAT scores in predicting performance of potential MBA students. (JEL C24 , I121 , C52 )     

Bayesian inference for pairwise interacting point processes

Pairwise interacting point processes are commonly used to model spatial point patterns. To perform inference, the established frequentist methods can produce good point estimates when the interaction in the data is moderate, but some methods may produce severely biased estimates when the interaction in strong. Furthermore, because the sampling distributions of the estimates are unclear, interval estimates are typically obtained by parametric bootstrap methods. In the current setting however, the behavior of such estimates is not well understood. In this article we propose Bayesian methods for obtaining inferences in pairwise interacting point processes. The requisite application of Markov chain Monte Carlo (MCMC) techniques is complicated by an intractable function of the parameters in the likelihood. The acceptance probability in a Metropolis-Hastings algorithm involves the ratio of two likelihoods evaluated at differing parameter values. The intractable functions do not cancel, and hence an intractable ratio r must be estimated within each iteration of a Metropolis-Hastings sampler. We propose the use of importance sampling techniques within MCMC to address this problem. While r may be estimated by other methods, these, in general, are not readily applied in a Bayesian setting. We demonstrate the validity of our importance sampling approach with a small simulation study. Finally, we analyze the Swedish pine sapling dataset (Strand 1972) and contrast the results with those in the literature.     

Practice and Research in Career Counseling and Development—2002

The authors present a review of the 2002 career development literature. The literature review is divided into the following areas: career theory, career assessment, career counseling interventions and practice, career counseling training and professional issues, and international issues in career counseling. The authors discuss the implications of the findings in this literature for career counseling practice.     

Morbidity measures in the presence of recurrent composite endpoints

The analysis of recurrent event data in clinical trials presents a number of difficulties. The statistician is faced with issues of event dependency, composite endpoints, unbalanced follow‐up times and informative dropout. It is not unusual, therefore, for statisticians charged with responsibility for providing reliable and valid analyses to need to derive new methods specific to the clinical indication under investigation. One method is proposed that appears to have possible advantages over those that are often used in the analysis of recurrent event data in clinical trials. Based on an approach that counts periods of time with events instead of single event counts, the proposed method makes an adjustment for patient time on study and incorporates heterogeneity by estimating an individual per‐patient risk of experiencing a morbid event. Monte Carlo simulations demonstrate that, with use of a real clinical study data, the proposed method consistently outperforms other measures of morbidity. Copyright © 2003 John Wiley & Sons, Ltd.     

AN ETHNOGRAPHY OF COUPLE AND THERAPIST EXPERIENCES IN REFLECTING TEAM PRACTICE

Couple and therapist perspectives about the use and process of reflecting team practice were analyzed using ethnographic research. A domain analysis was performed on postession interviews from both couples and therapists, and on field notes from each therapist. Seven couples and five therapists were interviewed at least twice over a 4-month period concerning their reactions to and perceptions of reflecting team practice. Six domains concerning reflecting team practice emerged from the analysis: (a)benefits of its use, (b) effects of gender, (c)recommended use, (d) contraindicated use, (e) the importance of creating spatial separateness between couple and team members, and (f) sequences of communication between the couple and team members that elicit change. The latter two domains described the process of reflecting team practice (i.e., steps in how it is used and how it is implemented). The role of ethnog rahy in reflecting team practice and in family therapy research is discussed.     

On the Utility of Robust and Resampling Procedures1

Abstract Kassab (1990) makes an important methodological contribution by urging the use of robust regression methods in the study of community economic impacts and by indicating the utility of the bootstrap in assessing standard errors in robust regression. By introducing the notion of a contaminating distribution, we reconcile differences between her claim that ordinary least squares (OLS) regression is biased when outliers are present and standard linear model theory that does not make assumptions about the shape of the residual distribution in proving OLS an unbiased estimator. The contaminating distribution provides a framework for rural sociologists to link their statistical assumptions to a substantive understanding of the phenomena being studied. We suggest an alternative regression estimation strategy that may be more robust than the technique she uses. We also discuss an approach to bootstrapping that is more appropriate for macro-level social indicator data than the one she describes. An appendix discusses the software available for implementing these methods.