Zero Correlation,Independence, and Normality

Two easy classroom examples are given of marginally normal random variables that are dependent but have zero correlation. One example is similar in nature to Anderson's well-known illustration that marginal normality does not imply joint normality.     

Multiplicity: discussion points from the Statisticians in the Pharmaceutical Industry multiplicity expert group

In May 2012, the Committee of Health and Medicinal Products issued a concept paper on the need to review the points to consider document on multiplicity issues in clinical trials. In preparation for the release of the updated guidance document, Statisticians in the Pharmaceutical Industry held a one‐day expert group meeting in January 2013. Topics debated included multiplicity and the drug development process, the usefulness and limitations of newly developed strategies to deal with multiplicity, multiplicity issues arising from interim decisions and multiregional development, and the need for simultaneous confidence intervals (CIs) corresponding to multiple test procedures. A clear message from the meeting was that multiplicity adjustments need to be considered when the intention is to make a formal statement about efficacy or safety based on hypothesis tests. Statisticians have a key role when designing studies to assess what adjustment really means in the context of the research being conducted. More thought during the planning phase needs to be given to multiplicity adjustments for secondary endpoints given these are increasing in importance in differentiating products in the market place. No consensus was reached on the role of simultaneous CIs in the context of superiority trials. It was argued that unadjusted intervals should be employed as the primary purpose of the intervals is estimation, while the purpose of hypothesis testing is to formally establish an effect. The opposing view was that CIs should correspond to the test decision whenever possible. Copyright © 2013 John Wiley & Sons, Ltd.     

Model specification tests against non-nested alternatives

Non-nested hypothesis tests provide a way to test the specification of an econometric model against the evidence provided by one or more non-nested alternatives. This paper surveys the recent literature on non-nested hypothesis testing in the context of regression and related models. Much of the purely statistical 1iterature which has evolved from the fundamental work of Cox (1961, 1962) is discussed briefly or not at all. Instead, emphasis is placed on those techniques which are easy to employ in practice and are likely to be useful to applied workers.     

Partners in the Spiritual Dance: Learning Clients’ Steps While Minding All Our Toes

We present four case illustrations highlighting the complex interplay of therapists’ and clients’ spirituality in therapy. Complexity, in these cases, results from (a) degrees of similarity and difference, both real and perceived, between clients’ and therapists’ spiritual beliefs and practices; (b) degrees of spiritual disclosure; (c) characteristics of the therapeutic relationship; and (d) geographic and cultural influences. Practicing therapists and therapist training programs can benefit from addressing how therapist and client spirituality intersect and influence therapy, how both similarity and difference present obstacles and opportunities, and how ambiguity and assumptions can contribute to misunderstandings. We believe that both the therapist’s and the client’s spiritualities are key influences in therapy that can contribute to the frustration, and the growth, of clients and therapists alike.     

Performance of the weighted least squares apprdach to categorical data analysis

The weighted least squares method of Grizzle, Starmer and Koch is a common and convenient way to analyze categorical data. Tests of significance based on this method are typically performed by calculating a quadratic form which is asymptotically distrib-uted as a chi-square random variable under an appropriate null hy- pothesis.

This paper explores the validity of this distributional approximation for small samples in terms of the accuracy and power associated with this chi-square statistic. We report simulation results from the weighted least squares method applied to balanced factorial experiments, The simulations which constitute the majorpart of this work also provide insight into the relative performance of several response functions.     

On the asymptotic properties of fuzzy c-means cluster prototypes as estimators of mixture subpopulation centers

Several computational studies suggest that the fuzzy c-means (FCM) clustering scheme may be used successfully in some cases to obtain estimates for the parameters of a statistical mixture (e.g., for a mixture of normal distributions). While these (limited) simulation results for the fuzzy c-means approach support this hypothesis, we provide herein an example that shows that the FCM cluster prototypes cannot generally be statistically consistent estimators of the centers (means) of any univariate mixture having symmetric component distributions     

Social Media Policy in Social Work Education: A Review and Recommendations

Although social media use has grown dramatically, program policies have not kept pace. Some programs now state that student social media activities have led to professional conduct reviews and may violate ethical standards. This article reviews current social media policies and conceptualizes their key elements. A review of current social media policies is reported, pointing to the need for further policy development. Six domains of a social media policy are identified: understanding social media, ethical and legal obligations, implications of one’s personal and professional online presence, institutional obligations to the program and agency, productivity implications, and possible consequences for violations of the policy. A model social media policy is detailed, and unresolved issues are identified.     

Do Legacy Industrial Sites Produce Legacy Effects in Ethnic and Racial Residential Settlement? Environmental Inequality Formation in Rhode Island’s Industrial Core1

This study advances understanding of environmental inequality by examining its production through the interaction of two ubiquitous and ongoing urban-ecological processes: industrial land-use changes and changing patterns of residential segregation. We employ longitudinal data from the Rhode Island Directory of Manufacturers (1953–2012) and tract-level US Census data measured at four panel-years (1980, 1990, 2000, and 2010) to study the effects of these interactions in Rhode Island’s historical industrial cities of Providence, Pawtucket, and Central Falls. Spatial patterning analysis and hybrid spatial panel modeling of residential exposure to active and legacy industrial sites in relation to ongoing racial and industrial change reveal two novel findings. First, we find a “legacy effect,” suggesting that the spatial organization of industrial activities prior to the study period has measurable impacts on patterns of residential segregation in later decades. Our results also indicate that over the study period Latinx and African-American residents have become increasingly less likely to bear disproportionate impacts of active manufacturing sites, despite very different spatial patterns of population growth. This is strong evidence that environmental inequality formation is a variable process in which historically distinct pathways can lead to similarly disproportionate exposure to environmental risk.