This study compares traditional and nontraditional training techniques with regard to computer related training. Its purpose was to determine which training methods could best be utilized in computer related training to maximize a trainee's retention of material and transfer of learning. A field experiment was conducted using two hundred members of active duty U.S. Naval Construction Battalion as subjects. Evaluation of trainees included a pre-training screening, post-training evaluation (immediately after training), and a follow-up session (four weeks after the post-training session) utilizing previously validated instruments. Training treatments included instruction (lecture), exploration (independent study), and a nontraditional technique—behavior modeling (an enhanced combination of the other two methods). Performance outcomes were operationalized using hands-on task performance and comprehension of the computer system as dependent variables. End-user satisfaction with the computer system was also measured. Two covariates, cognitive ability and system use, were also introduced into the study. The use of hands-on training methods, especially behavior modeling, resulted in superior retention of knowledge, transfer of learning, and end-user satisfaction. Cognitive ability failed to be a good predictor of trainee success but a connection was established between training methodology, system use, and end-user satisfaction.

PurposeThe present paper examines the effects of two decision‐framing inductions on young adults' set of career options: first, whether young adults use abilities or interests as the grounds for their vocational choices and, second, whether young adults approach the decision‐making task by including all career options to which they feel positively or by eliminating all career options to which they feel negatively.Design/methodology/approachA 2 × 2 experimental design was used to collect data from a diverse group of college undergraduates.FindingsThe results suggest that individuals who choose careers on the basis of skills and who use the inclusion decision‐making procedure are significantly more likely to narrow down their sets of career options.Research limitations/implicationsThe paper also suggests that Holland's model of vocational choice (RIASEC) may be differentially useful in guiding students to appropriate vocations for themselves. Students with a “social” profile, for example, have a much larger and more diffuse set of career options available to them than students with “realistic” or “investigative” profiles.Practical implicationsThe findings here suggest that the prevalent practice of focusing students' attention on finding activities they like may be less successful in helping students identify appropriate careers than focusing students' attention on their skills and abilities.Originality/valueThe paper addresses a career decision‐making phenomenon that has received increasing attention in the press and among educators.

In this study we compare the probability judgment accuracy of subjects from the United States and Turkey. Three different response modes were employed numerical probabilities, pie diagrams, and odds. The questions employed in the study were restricted to two-alternative, general-knowledge items. The observed pattern of differences in the components of probability judgment accuracy paralleled those of studies that have compared Western and Asian subjects. In particular, Turkish subjects exhibited better discrimination but worse calibration than their US counterparts. This result persisted across all three response modes. These findings lend support to previous assertions that observed crossnational differences arise from socioeconomic rather than Asian versus Western cultural differences. However, the consistency of the observed differences across response modes refutes a previous assertion that observed cultural differences are merely the result of response bias.

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The generalized Bayes’ rule (GBR) can be used to conduct ‘quasi-Bayesian’ analyses when prior beliefs are represented by imprecise probability models. We describe a procedure for deriving coherent imprecise probability models when the event space consists of a finite set of mutually exclusive and exhaustive events. The procedure is based on Walley’s theory of upper and lower prevision and employs simple linear programming models. We then describe how these models can be updated using Cozman’s linear programming formulation of the GBR. Examples are provided to demonstrate how the GBR can be applied in practice. These examples also illustrate the effects of prior imprecision and prior-data conflict on the precision of the posterior probability distribution. Copyright Springer 2005imprecise probability, generalized Bayes’ rule, second-order probability, quasi-Bayesian analysis,