Development and Consideration of Global Policies for Managing the Future Risks of Poliovirus Outbreaks: Insights and Lessons Learned Through Modeling

The success of the Global Polio Eradication Initiative promises to bring large benefits, including sustained improvements in quality of life (i.e., cases of paralytic disease and deaths avoided) and costs saved from cessation of vaccination. Obtaining and maintaining these benefits requires that policymakers manage the transition from the current massive use of oral poliovirus vaccine (OPV) to a world without OPV and free of the risks of potential future reintroductions of live polioviruses. This article describes the analytical journey that began in 2001 with a retrospective case study on polio risk management and led to development of dynamic integrated risk, economic, and decision analysis tools to inform global policies for managing the risks of polio. This analytical journey has provided several key insights and lessons learned that will be useful to future analysts involved in similar complex decision-making processes.     

Assessing the Effect of Model Misspecifications on Parameter Estimates in Structural Equation Models

Model misspecifications may have a systematic effect on parameters, causing biases in their estimates. In the application of structural equation models, every interesting model is fallible. When simultaneously evaluating a model, it is of interest to study whether all parameters are affected by a misspecification. This paper provides three procedures for evaluating such an effect: (1) analyzing the path, (2) using a functional relationship, and (3) using a significance test. Analyzing the path is illustrated through a confirmatory factor model. This method is ad hoc but intuitive. A more rigorous approach is built upon the concept of orthogonality of two sets of parameters. When parameter a is orthogonal to parameter b, omitting parameter b will not affect the estimation of parameter a. The functional relationship of two sets of parameters is used to check their orthogonality. The distribution of the difference between estimates based on different models is obtained, which provides a Hausman–like way to check significant parameter differences that are due to biases. Examples illustrate that these procedures can provide valuable information on identifying parameter estimates that are systematically affected by a model misspecification.     

On the Utility of Robust and Resampling Procedures1

Abstract Kassab (1990) makes an important methodological contribution by urging the use of robust regression methods in the study of community economic impacts and by indicating the utility of the bootstrap in assessing standard errors in robust regression. By introducing the notion of a contaminating distribution, we reconcile differences between her claim that ordinary least squares (OLS) regression is biased when outliers are present and standard linear model theory that does not make assumptions about the shape of the residual distribution in proving OLS an unbiased estimator. The contaminating distribution provides a framework for rural sociologists to link their statistical assumptions to a substantive understanding of the phenomena being studied. We suggest an alternative regression estimation strategy that may be more robust than the technique she uses. We also discuss an approach to bootstrapping that is more appropriate for macro-level social indicator data than the one she describes. An appendix discusses the software available for implementing these methods.     

How race affects dismissals of college football coaches

We employ a discretetime hazard model and a Blinder-decomposition approach to explore the possibility of racial discrimination in the dismissal and retention of college football coaches by university administrations. A rich data set consisting of 81 institutions over an 11-year period (1990–2000) that contains, in addition to a coach's race, variables on cumulative winning records, annual on-the-field improvements, and pre-/post-season game participation by institutions and their coaches is employed. Our study finds that black coaches, on average, face a dismissal probability that is 9.6 percentage points below that of their nonblack counterparts, ceteris paribus, suggesting that black head coaches may be the beneficiaries of favorable treatment by university administrators. Such a result likely stems from universities ' approach to social concerns involving race and gender issues. This finding also fits a construct that considers workplace discrimination as multi-dimensional. For example, black representation in the college football coaching ranks may be disproportionately low, possibly as a result of discrimination. However, once hired, black coaches are given more time to succeed than nonblacks, other things constant.     

RACE, GENDER, CLASS CONSCIOUSNESS AND UNION SUPPORT: AN ANALYSIS OF SOUTHERN TEXTILE WORKERS

This paper examines race and gender differences in class consciousness and union support among a random sample of textile workers in a small North Carolina city. Class consciousness is treated as an ordinal variable consisting of class verbalization, class action orientation, and endorsement of egalitarian change. Union support denotes a recognition of the union's instrumental role in improving work and working conditions. The analysis reveals that blacks are significantly more class conscious and prounion than whites. On the other hand, males and females tend to share similar levels of class consciousness and union support. Comparisons by race, within gender and by gender, within race, are provided for finer distinctions and extended discussion. A multiple classification analysis reveals that racial differences persist and that gender differences remain unimportant when the covariates of age, job dissatisfaction, education, income, and skill are considered.