Models of gambling and gambling addictions as perceptual filters

Models, in the sense of loosely entertained clusters of beliefs, attitudes, assumptions and expectations which direct the work of researchers and clinicians, also act as perceptual filters, tending to focus the attention of those who use them on certain aspects of reality and relegating to the background or ignoring altogether other aspects of the field of study and action. The central characteristics of social learning and of medical models are briefly reviewed and they are compared as perceptual filters for their distorting effects on research in gambling and in gambling addictions and on intervention strategies for problem gamblers. It is concluded that the exclusive predominance of any one model leads to the impoverishment of both research and intervention.     

AN ALGORITHM FOR THE DESIGN OF 2? FACTORIAL EXPERIMENTS ON CONTINUOUS PROCESSES

Saunders & Eccleston (1992) presented an approach to the design of 2-level factorial experiments for continuous processes. It determined sets of contrasts between the observations that could be well estimated, and then selected a design so that those contrasts estimated the parameters of interest. This paper shows that a well-estimated contrast must have a large number of changes of sign or level, and also be ‘paired’ in a particular sense. It develops an algorithm which constructs designs that must have a large number of changes of sign, evenly spread among the contrasts and optimal or near optimal. When such designs exist they are often preferable to those produced by the reverse foldover algorithm of Cheng & Steinberg (1991).     

NEURO-LINGUISTIC PROGRAMMING™ AND FAMILY THERAPY*

A brief introduction to Neuro-Linguistic Programming? (NLP?) as taught by the developers of NLP, Richard handler, John Grinder and their associates, is presented. This is followed by case examples. The cases illustrate what the authors believe are some of the substantive gains which NLP techniques have provided in their own and others' work with couples and families. NLP's major contributions involve understanding new models of human experience. This paper, however, is focused principally on a consideration of useful technology. The intent is to share from over three years' experiences certain fundamental and consistent benefits derived from the addition of NLP to clinical practice.     

Families and donor insemination: the views of semen donors

One of the consequences of donor insemination for the semen donor is that he becomes a member of two families: his own family in which he has a genetic and social connection with his offspring; and the family of the recipient couple, in which he has a genetic connection to the offspring, but usually no social connection with either the offspring or the recipient couple. This article challenges the dominant view that donor insemination as a practice of mainly medical or legal importance by looking at the attitudes of donors to both their own family and the family to which they have contributed their genetic material. Results are presented concerning who donors have told about their donation, who they think should be told, whether they think recipient couples should tell their donor insemination offspring about their conception, and concerning their opinions about factors that are considered in matching donors with recipient fathers.     

Influential observations in principal component analysis:a case study

A number of results have been derived recently concerning the influence of individual observations in a principal component analysis. Some of these results, particularly those based on the correlation matrix, are applied to data consisting of seven anatomical measurements on students. The data have a correlation structure which is fairly typical of many found in allometry. This case study shows that theoretical influence functions often provide good estimates of the actual changes observed when individual observations are deleted from a principal component analysis. Different observations may be influential for different aspects of the principal component analysis (coefficients, variances and scores of principal components); these differences, and the distinction between outlying and influential observations are discussed in the context of the case study. A number of other complications, such as switching and rotation of principal components when an observation is deleted, are also illustrated.