EFFECTS OF SOLUTION FOCUSED THERAPY'S "FORMULA FIRST SESSION TASK" ON COMPLIANCE AND OUTCOME IN FAMILY THERAPY

This study investigated the immediate impact of a "solution focused" family therapy intervention on both the family and the therapist. Specifically, the study examined the differential effects of the formula first session task (FFST) recommended by de Shazer (1982, 1985), as well as the FFST plus solution focused therapy, as compared to a standard problem focused structural-strategic intervention. After one week, the groups receiving the FFST were significantly higher on measures of family compliance, clarity of treatment goals, and improvement in the presenting problem. However, there were no significant differences among groups on family optimism or on outcome after 10 sessions. The implications of these results are discussed.     

The Dilemmas of Regulating Casino Gambling

One of the major reasons for the reluctance to legalize many of the popular forms of gambling is the belief that these forms are particularly difficult to control honestly. It is generally believed that organized crime has been able to undermine regulatory mechanisms. This paper reports on the difficulties and successes of the Nevada Gambling Control Board's efforts to deal with a sophisticated and complex form of gambling organization and compares and contrasts that regulation with the regulation of casino gambling in England.     

Design and analysis of computer experiments when the output is highly correlated over the input space

To build a predictor, the output of a deterministic computer model or “code” is often treated as a realization of a stochastic process indexed by the code's input variables. The authors consider an asymptotic form of the Gaussian correlation function for the stochastic process where the correlation tends to unity. They show that the limiting best linear unbiased predictor involves Lagrange interpolating polynomials; linear model terms are implicitly included. The authors then develop optimal designs based on minimizing the limiting integrated mean squared error of prediction. They show through several examples that these designs lead to good prediction accuracy.     

Amateur craft‐artists: Marginal leisure roles in a marginal art world (1)

Amateurism is an important but marginal leisure role, in which the boundaries between work and leisure are blurred. This phenomenon has been studied by Stebbins as a subjective social reality. His conceptualization of amateurism was used as a model in the examination of survey data on craft‐artists and it was found to apply rather well to the analysis of quantitative data. As a self‐identified category of craft‐artists, amateurs were statistically marginal between professionals and dabblers.     

On Semiparametric Mode Regression Estimation

It has been found that, for a variety of probability distributions, there is a surprising linear relation between mode, mean, and median. In this article, the relation between mode, mean, and median regression functions is assumed to follow a simple parametric model. We propose a semiparametric conditional mode (mode regression) estimation for an unknown (unimodal) conditional distribution function in the context of regression model, so that any m-step-ahead mean and median forecasts can then be substituted into the resultant model to deliver m-step-ahead mode prediction. In the semiparametric model, Least Squared Estimator (LSEs) for the model parameters and the simultaneous estimation of the unknown mean and median regression functions by the local linear kernel method are combined to infer about the parametric and nonparametric components of the proposed model. The asymptotic normality of these estimators is derived, and the asymptotic distribution of the parameter estimates is also given and is shown to follow usual parametric rates in spite of the presence of the nonparametric component in the model. These results are applied to obtain a data-based test for the dependence of mode regression over mean and median regression under a regression model.     

On the relative behavior of estimators of reliability/survivability

Based on a decomposition of mean absolute error, a twofold technique is introduced whereby a pairwise comparison of point estimators of reliability/survivability can be made. Given two such estimators, the method examines (a) the “odds” in favor of one of the estimators being closer to the true value than is the other and (b) each estimator’s average closeness to the true value not only when it is closer than is the other but also when it is not. Joint consideration of these concepts is shown to form a basis for determining which of the two estimators is preferred in a given situation. An application of the theory is made by comparing the maximum likelihood and minimum variance unbiased estimators of reliability/survivability in the exponential failure model.     

On bagging and nonlinear estimation

We propose an elementary model for the way in which stochastic perturbations of a statistical objective function, such as a negative log-likelihood, produce excessive nonlinear variation of the resulting estimator. Theory for the model is transparently simple, and is used to provide new insight into the main factors that affect performance of bagging. In particular, it is shown that if the perturbations are sufficiently symmetric then bagging will not significantly increase bias; and if the perturbations also offer opportunities for cancellation then bagging will reduce variance. For the first property it is sufficient that the third derivative of a perturbation vanish locally, and for the second, that second and fourth derivatives have opposite signs. Functions that satisfy these conditions resemble sinusoids. Therefore, our results imply that bagging will reduce the nonlinear variation, as measured by either variance or mean-squared error, produced in an estimator by sinusoid-like, stochastic perturbations of the objective function. Analysis of our simple model also suggests relationships between the results obtained using different with-replacement and without-replacement bagging schemes. We simulate regression trees in settings that are far more complex than those explicitly addressed by the model, and find that these relationships are generally borne out.     

Occupational Advantage and the Impact of College Rank on Labor Market Outcomes*

Most analyses of the effects of college rank on labor market outcomes focus on its average impact across sub-populations and employment situations. The framework adopted in this paper, however, suggests that the effects of college rank may vary by individual characteristics and type of job. Using data from the 1973 Occupational Changes in a Generation Survey, we test three hypotheses suggesting that occupational advantage, as measured either by family of origin or current position, is a significant determinant of capacity to convert increments in college prestige into labor market success. The findings suggest that where one went to college is especially consequential for (1) the occupational status of individuals from professional families; (2) the earnings of individuals from both professional and managerial families; and (3) the earnings of individuals in both professional and managerial positions. Overall, these findings tend to confirm the study's underlying hypothesis that aggregate analyses of college rank may be misleading when applied across sub-populations and employment situations.