The reflecting team as a reflection of second order therapeutic ideals

The reflecting (R.T.) team is examined within its theoretical, therapeutic and training contexts. A distinction is made between the reflecting team technique (R.T.T.) and the reflecting team process (R.T.P.), in order to explore whether or not the practice of team reflection is consistent with the requirements of the second order family therapies, the theoretical development which supported the R.T.'s emergence and subsequent popularity. The critique of the R.T.P. suggests that although this therapeutic process does represent attempts towards increasing acceptance of complexity, reduced attempts to control, collaboration, interconnectedness and contextual respect, there is little room for complacency either theoretically or practically.     

Household Debt and Marital Instability: Evidence from the Korean Labor and Income Panel Study

Korean society has recently experienced rapid increases in household debt and divorce rates. This study investigates whether household debt and debt ratios lower marital stability and increase the probability of divorce among Korean families. Six-year panels from the Korean Labor and Income Panel Study (KLIPS) household surveys were used for analysis. The effect of household debt upon the probability of divorce was estimated through pooled and population-averaged Logit models. The results suggest that household financial strain measured by the amount of household debt and the debt-to-income ratios does not significantly affect the probability of divorce. This finding contradicts the widespread notion that households’ excessive borrowing has been partially responsible for the recent increase in divorce rates in Korea.

Prevalence and Diagnostic Correlates of DSM-IV Pathological Gambling in Psychiatric Outpatients

Studies of the prevalence of pathological gambling (PG) in psychiatric and substance abusing patients suggest that the disorder is not rare. Most studies have been of substance abusers in treatment, and the rate of PG has been found to be several times higher than the rate found in community based epidemiological surveys. However, only one study has examined the prevalence of PG in a heterogeneous sample of patients, and this was a study of psychiatric inpatients. We are not aware of any prior study of the prevalence of PG in a psychiatric outpatient sample. In the present report from the Rhode Island Methods to Improve Diagnosis and Services (MIDAS) project we examined the current and lifetime prevalence of PG in 1,709 psychiatric outpatients interviewed with a semi-structured diagnostic interview that included a module to diagnose DSM-IV PG. Forty (2.3%) patients had a lifetime history of DSM-IV PG, all of whom had at least one other DSM-IV axis I disorder. Patients with PG had significantly more axis I disorders than patients without PG, and had significantly higher rates of bipolar disorder, social phobia, panic disorder with agoraphobia, alcohol use disorder, and other impulse control disorders. Possible reasons for the low prevalence of PG in our sample are discussed.     

Decomposition of Scatter Ratios Used in Monitoring Multivariate Process Variability

Wilks’ ratio statistic can be defined in terms of the ratio of the sample generalized variances of two non-independent estimators of the same covariance matrix. Recently this statistic has been proposed as a control statistic for monitoring changes in the covariance matrix of a multivariate normal process in a Phase II situation, particularly when the dimension is larger than the sample size. In this article we derive a technique for decomposing Wilks’ ratio statistic into the product of independent factors that can be associated with the components of the covariance matrix. With these results, we demonstrate that, when a signal is detected in a control procedure for the Phase II monitoring of process variability using the ratio statistic, the signaling value can be decomposed and the process variables contributing to the signal can be specifically identified.     

Detection and Interpretation of a Multivariate Signal Using Combined Charts

A control procedure is presented in this article that is based on jointly using two separate control statistics in the detection and interpretation of signals in a multivariate normal process. The procedure detects the following three situations: (i) a mean vector shift without a shift in the covariance matrix; (ii) a shift in process variation (covariance matrix) without a mean vector shift; and (iii) both a simultaneous shift in the mean vector and covariance matrix as the result of a change in the parameters of some key process variables. It is shown that, following the occurrence of a signal on either of the separate control charts, the values from both of the corresponding signaling statistics can be decomposed into interpretable elements. Viewing the two decompositions together helps one to specifically identify the individual components and associated variables that are being affected. These components may include individual means or variances of the process variables as well as the correlations between or among variables. An industrial data set is used to illustrate the procedure.     

Re-examining two tests for bivariate normality

Many goodness of fit tests for bivariate normality are not rigorous procedures because the distributions of the proposed statistics are unknown or too difficult to manipulate. Two familiar examples are the ring test and the line test. In both tests the statistic utilized generally is approximated by a chi-square distribution rather than compared to its known beta distribution. These two procedures are re-examined and re-evaluated in this paper. It is shown that the chi-square approximation can be too conservative and can lead to unnecessary

rejection of normality.     

Moment properties of estimators for a type 1 extreme-value regression model

A regression model is considered in which the response variable has a type 1 extreme-value distribution for smallest values. Bias approximations for the maximum likelihood estimators are pivm and a bias reduction estimator for the scale parameter is proposed. The small sample moment properties of the maximum likelihood estimators are compared with the properties of the ordinary least squares estimators and the best linear unbiased estimators based on order statistics for grouped data.