Developing a Rubric for Supervision of Students Counseling LGBTQ Clients

More than 100 pieces of anti-lesbian, gay, bisexual, transgender (LGBT) legislation are active in 2018. In this current political climate of “conscience clause” legislation, counselor educators and supervisors are poised to adopt more holistic practices in assessing and evaluating student and supervisee readiness to work with LGBT queer (Q) clients. The purpose of this article is to present a dispositional framework for assessment and evaluation and introduce the flexibility, integrity, awareness, stability, compassion, and openness (FIASCO) rubric of professional counseling dispositions. Two court cases, Keeton v. Anderson-Wiley and Ward v. Wilbanks, are used as case examples for applying the FIASCO rubric to situations involving LGBTQ clients.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Strategic planning in smaller rapid growth companies

The research reported in this article is based on a study of the strategic planning practices of the 500 fastest growing privately held smaller companies in the United States ranked according to percentage of sales increases from 1978 to 1982.^p1 Based on the information provided, a clear picture emerges regarding the actual role of strategic planning in rapid growth companies.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.     

Flipping and ex post aggregation

We show that Bayesian ex post aggregation is unstable with respect to refinements. Suppose a group of Bayesians use ex post aggregation. Since it is a joint problem, each agent's problem is captured by the same model, but probabilities and utilities may vary. If they analyze the same situation in more detail, their refined analysis should preserve their preferences among acts. However, ex post aggregation could bring about a preference reversal on the group level. Ex post aggregation thus depends on how much information is used and may keep oscillating (“flipping”) as one keeps adding more information. Received: 16 April 2002/Accepted: 27 May 2002     

Buying a Dream: Alternative Models of Demand for Lotto

Existing lotto demand models utilize effective price, computed as the face value of a ticket minus the expected value of prize money per ticket, as their primary explanatory variable. By contrast, this article proposes a key role for consumption benefit or "fun" in the demand for gambling in general and lotto demand in particular. It develops an alternative model of lotto demand that focuses on the maximum possible prize. When this is tested against the traditional model using data from the U.K. National Lottery, we find that jackpot considerations exert an influence over and above that of variations in effective price.     

Drug testing laws and employment injuries

Employer use of employee drugscreening procedures is widespread in the U.S. A state-by-state analysis of statutory law applicable to the drug testing issue is combined with state and industry data to isolate how drug testing laws affect workplace injury rates. Based on our data, injury rates are not statistically related to the state’s legal environment.