The Lack of Memory Property in the Density Form for the Bivariate Setup

The lack of memory property is a characterizing property of the exponential distribution in the continuous domain. In the bivariate setup different generalizations of the same are available in terms of survival function. We extend this lack of memory property in terms of bivariate probability density function and examine its characterization properties. In this process the density version of the lack of memory property can be interlinked with conditionally specified exponential distribution, bivariate reciprocal coordinate subtangent of the density curve and a few other derived measures.     

Surviving Involuntary Unemployment Together: The Role of Resilience-Promoting Communication in Familial and Committed Relationships

Involuntary unemployment is an unfortunate circumstance not only for those without meaningful work, but also for families, family relationships, and committed long-term relationships. To explore the role that communication plays in helping family partners demonstrate resilience (i.e., competence and adaptation throughout the experience of a stressor), while dealing with involuntary unemployment, the current study surveyed a sample of 111 recently unemployed, relationally involved participants. The majority of these participants self-reported as married (40.6%) or indicated serious commitment (38.6%) with their relational partner. Communication strategies perceived as contributing to resilience with their partner were measured as Resilience-Promoting Communication (RPC). Bootstrapping mediation analysis determined that RPC dimensions emphasizing respect/harmony, partnership and tension release contributed to mediation of the relationship between feelings about job loss on relational satisfaction and commitment. In addition, respect/harmony moderated the relationship between both unemployment affect and commitment and satisfaction. Civility moderated the relationship between unemployment affect and satisfaction. Implications of these findings for theory and for families dealing with unemployment are discussed.     

The Effects of Misconceptions on the Properties of Friedman's Test

Friedman's test is a widely used rank-based alternative to the analysis of variance (ANOVA) F-test for identifying treatment differences in a randomized complete block design. Many texts provide incomplete or misleading information about when Friedman's test may be appropriately applied. We discuss the assumptions needed for the test and common misconceptions. We show via simulation that when the variance or skew of the treatment distributions differ, application of Friedman's test to detect differences in treatment location can result in Type I error probabilities larger than the nominal α, and even when α is unaffected, the power of the test can be less than expected.     

Public relations as “practice:” applying the theory of Alasdair MacIntyre

Public relation’s search for a unifying theory may be fulfilled through application of Alasdair MacIntyre’s concept of a “practice,” a very specific and value-laden concept. This article explores what it would mean to be a public relations practice in MacIntyre’s concept of the term and argues that such an approach to public relations is of value for the field.     

Nonparametric Bayesian Methods for Benchmark Dose Estimation

The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose‐response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model‐averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.     

A Simulation Comparison of Approximate Tests for Fixed Effects in Random Coefficients Growth Curve Models

Often, the response variables on sampling units are observed repeatedly over time. The sampling units may come from different populations, such as treatment groups. This setting is routinely modeled by a random coefficients growth curve model, and the techniques of general linear mixed models are applied to address the primary research aim. An alternative approach is to reduce each subject’s data to summary measures, such as within-subject averages or regression coefficients. One may then test for equality of means of the summary measures (or functions of them) among treatment groups. Here, we compare by simulation the performance characteristics of three approximate tests based on summary measures and one based on the full data, focusing mainly on accuracy of p-values. We find that performances of these procedures can be quite different for small samples in several different configurations of parameter values. The summary-measures approach performed at least as well as the full-data mixed models approach.     

Operationalism: A Philosophy for Social Work Education

This paper presents and utilizes an operational philosophy of social work education in an attempt to identify effective and useful educational experiences. The effects of educational philosophy on curriculum content and emphasis, selection of learning experiences, and the determination of learning outcomes is discussed. The value and utility of an explicit philosophy in educational decision making is also stressed.     

Sampling Richness and Qualitative Integrity: Challenges for Research With Families

Sampling is one of the most difficult and contentious aspects of qualitative research design. There are few guidelines for sampling decisions or for understanding saturation in qualitative family research. The authors frame the problematic of data quality in the selection of units of analysis and observation and consider how to enhance sample richness. They outline considerations for data quantity and sample size as well as case‐ and variable‐based approaches. With multiple examples from recent and classic studies to illustrate the consequences of sampling decisions, they explore links between saturation and validity. Finally, they encourage researchers to craft a coherent statement on qualitative integrity to demonstrate how their sampling decisions are rooted in epistemology, theory, and richness and quality of data.