Review Essay: Constructing the Sociology of Social Problems: Spector and Kitsuse Twenty-Five Years Later

Sociological Forum -     

A Bayesian method for classification and discrimination

We discuss Bayesian analyses of traditional normal-mixture models for classification and discrimination. The development involves application of an iterative resampling approach to Monte Carlo inference, commonly called Gibbs sampling, and demonstrates routine application. We stress the benefits of exact analyses over traditional classification and discrimination techniques, including the ease with which such analyses may be performed in a quite general setting, with possibly several normal-mixture components having different covariance matrices, the computation of exact posterior classification probabilities for observed data and for future cases to be classified, and posterior distributions for these probabilities that allow for assessment of second-level uncertainties in classification.     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

A solution of the externality problem using strategic matching

This paper develops a Coase-like solution of the problem of inducing Pareto optimal behavior in the presence of reciprocal externalities. In place of Coasean direct compensation between the parties to an externality problem, actors strategically match each other's externality-producing activity, and thus induce counterparts to internalize the external benefits or costs of their actions. The analysis suggests a general framework for analyzing social interactions in the presence of reciprocal externalities. As an application of the theory, a solution of the duopoly problem is noted.We are indebted to L. Danziger, D. Samet, participants in faculty seminars at Bar-Ilan and New York Universities and at the University of Maryland, and two anonymous referees for helpful comments and discussions. All remaining errors are our own.     

Benchmark Analysis: Shopping with Proper Confidence

We discuss the issue of using benchmark doses for quantifying (excess) risk associated with exposure to environmental hazards. The paradigm of low-dose risk estimation in dose-response modeling is used as the primary application scenario. Emphasis is placed on making simultaneous inferences on benchmark doses when data are in the form of proportions, although the concepts translate easily to other forms of outcome data.     

Nonparametric smoothing in the analysis of air pollution and respiratory illness

While most of epidemiology is observational, rather than experimental, the culture of epidemiology is still derived from agricultural experiments, rather than other observational fields, such as astronomy or economics. The mismatch is made greater as focus has turned to continue risk factors, multifactorial outcomes, and outcomes with large variation unexplainable by available risk factors. The analysis of such data is often viewed as hypothesis testing with statistical control replacing randomization. However, such approaches often test restricted forms of the hypothesis being investigated, such as the hypothesis of a linear association, when there is no prior empirical or theoretical reason to believe that if an association exists, it is linear. In combination with the large nonstochastic sources of error in such observational studies, this suggests the more flexible alternative of exploring the association. Conclusions on the possible causal nature of any discovered association will rest on the coherence and consistency of multiple studies. Nonparametric smoothing in general, and generalized additive models in particular, represent an attractive approach to such problems. This is illustrated using data examining the relationship between particulate air pollution and daily mortality in Birmingham, Alabama; between particulate air pollution, ozone, and SO₂ and daily hospital admissions for respiratory illness in Philadelphia; and between ozone and particulate air pollution and coughing episodes in children in six eastern U.S. cities. The results indicate that airborne particles and ozone are associated with adverse health outcomes at very low concentrations, and that there are likely no thresholds for these relationships.     

Why Did House Members Vote for H.R. 4437?1

This article examines why members of the U.S. House of Representatives voted for H.R. 4437, the controversial 2005 bill to construct a 700‐mile immigration barrier along the U.S.‐Mexican border and to criminalize illegal presence and aid to undocumented immigrants. Logit analysis suggests that being a first‐term House member or a Republican and representing a district that was in the South or the West or heavily blue‐collar substantially boosted the odds of supporting H.R. 4437. If a member's district was disproportionately Asian, Latino, or, especially, African American, he or she was instead more likely to oppose the measure.     

Audience effects on solitary faces during imagery: Displaying to the people in your head

Subjects imagined situations in which they reported enjoying themselves either alone or with others. Electromyographic (EMG) activity was recorded bilaterally from regions overlying thezygomatic major muscles responsible for smiling. Controlling for equal rated happiness in the two conditions, subjects showed more smiling in high-sociality than low-sociality imagery. In confirming imaginary audience effects during imagery, these data corroborate hypotheses that solitary facial displays are mediated by the presence of imaginary interactants, and suggest caution in employing them as measures of felt emotion.Avery Gilbert and Amy Jaffey had compelling insights throughout the course of study. We thank Paul Ekman, Carroll Izard, and Paul Rozin for extensive comments on earlier drafts. We also thank Bernard Apfelbaum, Jon Baron, Janet Bavelas, John Cacioppo, Linda Camras, Dean Delis, Rob DeRubeis, Alan Fiske, Stephen Fowler, Greg McHugo, Harriet Oster, David Premack, W. John Smith, and David Williams for their valuable comments and suggestions.     

Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.