What's wrong with sociology? Its public image

In response to editor Stephen Cole's question—"What's wrong with sociology?"—the answer is nothing that cannot be overcome by clearer communications and better public relations. The problem is that the field is not held in high esteem by college administrators, legislators, and the public. Many see us as rebels of the 1960s, political liberals, and impractical thinkers with a penchant for jargon. Even our research is derided by some as a waste of money. The solution consists of strategies for overcoming our poor public image. Seven strategies for doing so are discussed: (1) Write clearly. (2) Write for a mass audience. (3) Seek board positions. (4) Strengthen our identity. (5) Teach sociology in high school. (6) Develop academic partnerships. (7) Emphasize outcomes: increase in tolerance and research skills.     

On the shrinkage of local linear curve estimators

Local linear curve estimators are typically constructed using a compactly supported kernel, which minimizes edge effects and (in the case of the Epanechnikov kernel) optimizes asymptotic performance in a mean square sense. The use of compactly supported kernels can produce numerical problems, however. A common remedy is ridging, which may be viewed as shrinkage of the local linear estimator towards the origin. In this paper we propose a general form of shrinkage, and suggest that, in practice, shrinkage be towards a proper curve estimator. For the latter we propose a local linear estimator based on an infinitely supported kernel. This approach is resistant against selection of too large a shrinkage parameter, which can impair performance when shrinkage is towards the origin. It also removes problems of numerical instability resulting from using a compactly supported kernel, and enjoys very good mean squared error properties.     

“Facing” leaders: Facial expression and leadership perception

This experimental study investigated the effect of a leader's expression of happy versus nervous emotions on subsequent perceptions of leadership and ratings of traits associated with implicit leadership theories (ILTs). Being fast and universally understood, emotions are ideal stimuli for investigating the dynamic effects of ILTs, which were understood in this study in terms of the constraints that expressed emotions impose on the connectionist networks that activate ILTs. The experimental design contrasted videotaped and still frame presentations of a leadership event; however, this methodological factor had no significant effects and analyses were thus collapsed across this factor. Key findings were that the expression of a happy versus nervous emotion at the end of a problem-solving sequence had multiple effects: happy emotions resulted in higher leadership ratings, higher trait ratings, greater correlations among trait ratings, and greater dependence of trait ratings on leadership perceptions. An exploratory model suggested that leadership impressions mediated the effects of facial emotions on trait ratings. The discussion further links the study findings with interpretations in terms of ILTs and many types of constraints on these cognitive structures. It also suggests ways to integrate these ideas with advances in neuroscience research.     

University Education as a Compensation Strategy Among Second‐Generation Immigrants

The aim of this study is to determine whether immigrants and their children use a compensation strategy that involves achieving higher than expected education, given their parents’ level of education and income. The study uses data for all individuals in Sweden who finished elementary school from 1990 to 1992. Parents’ level of education and income is in general positively associated with higher odds of having university education. However, some immigrant groups show the reverse pattern of the impact of parents’ income. The results support a compensation strategy developed in groups and families with low level of integration in the labor market.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Toward a More Complex View of Career Exploration

This article examines the concept of career exploration in adult populations. In contrast to the prevailing positive view of career exploration, the authors present a more complex and balanced perspective of this process, addressing some of the barriers to career exploration and the applicability of this concept to different populations. They examine differences between voluntary exploration and forced or chance exploration, discuss how relationships may be barriers to exploration, consider various outcomes of career explorations, and call for a more holistic view of the individual in career counseling.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.