Intelligence and personality in mate choice and marital satisfaction

This research examined how intellectual and personality similarity or complementarity between a couple relates to their marital choice and marital satisfaction. Eighty-one couples were tested using the WAIS-R and the MMPI and the Locke-Wallace Marital Adjustment Scale. As predicted, couples showed significant similarity for some IQ variables. However, the prediction that IQ would have a relationship to marital satisfaction was not borne out. Couples show some significant similarity on some personality variables but for different variables in the 39 Clinic couples and the 42 Non-clinic couples. A strong relationship was found between couples in both samples on the Pd scale. As far as marital satisfaction is concerned, similarity or complementarity in personality variables does not appear.     

Interventionist counterfactuals

A number of recent authors (Galles and Pearl, Found Sci 3 (1):151?C182, 1998; Hiddleston, No?s 39 (4):232?C257, 2005; Halpern, J Artif Intell Res 12:317?C337, 2000) advocate a causal modeling semantics for counterfactuals. But the precise logical significance of the causal modeling semantics remains murky. Particularly important, yet particularly under-explored, is its relationship to the similarity-based semantics for counterfactuals developed by Lewis (Counterfactuals. Harvard University Press, 1973b). The causal modeling semantics is both an account of the truth conditions of counterfactuals, and an account of which inferences involving counterfactuals are valid. As an account of truth conditions, it is incomplete. While Lewis??s similarity semantics lets us evaluate counterfactuals with arbitrarily complex antecedents and consequents, the causal modeling semantics makes it hard to ascertain the truth conditions of all but a highly restricted class of counterfactuals. I explain how to extend the causal modeling language to encompass a wider range of sentences, and provide a sound and complete axiomatization for the extended language. Extending the truth conditions for counterfactuals has serious consequences concerning valid inference. The extended language is unlike any logic of Lewis??s: modus ponens is invalid, and classical logical equivalents cannot be freely substituted in the antecedents of conditionals.     

Truthmaking without necessitation

I propose an account truthmaking that provides truthmakers for negative truths. The account replaces Truthmaker Necessitarianism with a ??Duplication Principle??, according to which a suitable entity T is a truthmaker for a proposition p just in case the existence of an appropriate counterpart of T entails the truth of p, where the counterpart relation is cashed out in terms of qualitative duplication. My account captures an intuitive notion of truthmakers as ??things the way they are??, validates two appealing principles about entailment and containment proposed by David Armstrong, and invalidates the controversial Disjunction Thesis.     

Women's Spirituality Across the Life Span: Implications for Counseling

Women's spirituality has unique characteristics that are often ignored within the spirituality literature. The authors review the literature on women's spirituality to reveal the major themes women have identified as relevant to their spiritual journeys across the life span. Implications for counseling and ideas for practice are included after major themes are examined.     

Use of Bibliotherapy in the Treatment of Grief and Loss: A Guide to Current Counseling Practices

Grief is a complex and dynamic process influencing individuals of all ages. This article provides an overview of historical and contemporary constructs of grief counseling. Bibliotherapy, a creative counseling tool, is presented as an appropriate intervention when counseling grieving clients. Guidelines for using bibliotherapy with grieving clients are considered.     

The Growing-Block: just one thing after another?

In this article, we consider two independently appealing theories—the Growing-Block view and Humean Supervenience—and argue that at least one is false. The Growing-Block view is a theory about the nature of time. It says that (a) past and present things exist, while future things do not, and (b) the passage of time consists in new things coming into existence. Humean Supervenience is a theory about the nature of entities like laws, nomological possibility, counterfactuals, dispositions, causation, and chance. It says that none of these entities are fundamental, since if they were, this would entail the existence of irreducible necessary connections between matters of fact. Instead, these entities supervene on a fundamental, non-nomological ‘Humean mosaic’ of property instances at spacetime points. We will further explain and motivate the Growing-Block view and Humean Supervenience in Sects. 2 and 3, but first, we turn to our master argument.     

Not every credible interval is credible: Evaluating robustness in the presence of contamination in Bayesian data analysis

As Bayesian methods become more popular among behavioral scientists, they will inevitably be applied in situations that violate the assumptions underpinning typical models used to guide statistical inference. With this in mind, it is important to know something about how robust Bayesian methods are to the violation of those assumptions. In this paper, we focus on the problem of contaminated data (such as data with outliers or conflicts present), with specific application to the problem of estimating a credible interval for the population mean. We evaluate five Bayesian methods for constructing a credible interval, using toy examples to illustrate the qualitative behavior of different approaches in the presence of contaminants, and an extensive simulation study to quantify the robustness of each method. We find that the “default” normal model used in most Bayesian data analyses is not robust, and that approaches based on the Bayesian bootstrap are only robust in limited circumstances. A simple parametric model based on Tukey’s “contaminated normal model” and a model based on the t-distribution were markedly more robust. However, the contaminated normal model had the added benefit of estimating which data points were discounted as outliers and which were not.