Theory and Validity of Life Satisfaction Scales

National accounts of subjective well-being are being considered and adopted by nations. In order to be useful for policy deliberations, the measures of life satisfaction must be psychometrically sound. The reliability, validity, and sensitivity to change of life satisfaction measures are reviewed. The scales are stable under unchanging conditions, but are sensitive to changes in circumstances in people’s lives. Several types of data indicate that the scales validly reflect the quality of respondents’ lives: (1) Differences between nations in life satisfaction associated with differences in objective conditions, (2) Differences between groups who live in different circumstances, (3) Correlations with nonself-report measures of life satisfaction, (4) Genetic and physiological associations with life satisfaction, (5) Systematic patterns of change in the scales before, during, and after significant life events, and (6) Prediction by life satisfaction scores of future behaviors such as suicide. The life satisfaction scales can be influenced by factors such as question order, current mood, and mode of presentation, but in most cases these can be controlled. Our model of life satisfaction judgments points to the importance of attention, values, standards, and top-down effects. Although the scales are useful in research on individual well-being, there are policy questions that need more analysis and research, such as which types of subjective well-being measures are most relevant to which types of policies, how standards influence scores, and how best to associate the scores with current policy deliberations.     

Why a time effect often has a limited impact on capture‐recapture estimates in closed populations

The author is concerned with log‐linear estimators of the size N of a population in a capture‐recapture experiment featuring heterogeneity in the individual capture probabilities and a time effect. He also considers models where the first capture influences the probability of subsequent captures. He derives several results from a new inequality associated with a dispersive ordering for discrete random variables. He shows that in a log‐linear model with inter‐individual heterogeneity, the estimator N is an increasing function of the heterogeneity parameter. He also shows that the inclusion of a time effect in the capture probabilities decreases N in models without heterogeneity. He further argues that a model featuring heterogeneity can accommodate a time effect through a small change in the heterogeneity parameter. He demonstrates these results using an inequality for the estimators of the heterogeneity parameters and illustrates them in a Monte Carlo experiment     

Michael Polanyi and the sociology of a free society

Michael Polanyi’s defense of freedom in science and society conflicts in major ways with Weber (process of rationalization, value neutrality of sociologists), Popper (objective knowledge, open society), and technological or oppositional sociology. Polanyi rejects positivism, utilitarianism, and Marxism, and defends freedom as a necessary condition for pursuit of spiritual ideals such as truth, justice, charity, and tolerance. Half truths about science seen as rejecting tradition, faith, authority, values, and the subjective, have helped bring valuable social results, but in the form taken by radical philosophical skepticism (doubt), also called objectivism, they also threaten freedom itself. A more truthful account is needed. Scientists and citizens who would maintain a free society are morally responsible persons, joined together in quest of truth and certain other ideals, demanding of themselves and each other that they be faithful to that quest. Polanyi’s thought has connections with that of Shils, and has implications for what Shils calls a consensual sociology. Louis H. Swartz teaches law, and is interested in the development of sociological theory and legal sociology, building upon the contributions of Polanyi and Shils.     

Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.     

The Impact of Racism on Social Work Education

One of the most difficult problems in teaching social services has to do with the development of an analytic framework for an understanding of the delivery of social services within the community. This paper examines a particular systems framework that allows the student to conceptualize and evaluate what services are being delivered, what specific inputs and connections of agency with community act to produce this service, and what changes in the system may be possible. The model allows ready connection with practice, policy, and research components of the curriculum.     

Continuity of utility functions representing fuzzy preferences

In a previous paper, we established necessary and sufficient conditions for a given binary fuzzy relation to be representable by a utility function. In this article, we construct a crisp order topology associated to a given weakly complete fuzzy pre-order and introduce the notion of “continuous fuzzy pre-order.” We show that this new condition and the conditions introduced in the previous paper are together necessary and sufficient for a numerical representation of a given weakly complete fuzzy pre-order by a continuous utility function.     

Confidence Intervals for a Binomial Parameter after Observing No Successes

The easily computed, one-sided confidence interval for the binomial parameter provides the basis for an interesting classroom example of scientific thinking and its relationship to confidence intervals. The upper limit can be represented as the sample proportion from a number of “successes” in a future experiment of the same sample size. The upper limit reported by most people corresponds closely to that producing a 95 percent classical confidence interval and has a Bayesian interpretation.