RACE IN THE WORKPLACE:

Previous research on job satisfaction largely ignores possible race differences in its determinants. This study examines two explanations of a race/job satisfaction relationship: a structural perspective focuses on objective job rewards; a dispositional, on the role of worker orientations. The former implies that characteristics of race; the latter, that race leads to distinctive job needs and values and thus contributes to black/white differences in the sources of satisfaction. Analysis of recent national survey data supports the structural model: similarities outweigh differences in the predictors of job satisfaction across races. Blacks' lower workplace satisfaction is a function, not of racial differences in the process that determines satisfaction, but of their lower scores on the factors that tend to promote it.     

A polemic on education reform in the American states

Building upon previous research on educational reforms in the states after A Nation at Risk, we attempt to evaluate the effect of reforms on the improvement of both schooling and learning in secondary schools. Only meager data are available to evaluate the question and they yield ambiguous results. Our argument is that the absence of good measures of educational outcomes prevent intelligent reform constituting a cause for alarm in the education community as well as the society at large.     

Last Chance For The Last Refuge

The theme of this article is the drastic reduction in residential care for the elderly which has met little opposition from within the health and social services despite the growing population of the very aged. The reasons for this professional compliance are examined and found to lie in a combination of mistaken confidence in community care policies and an uncritical acceptance of influential but largely outdated studies whose findings on the adverse effects of institutional care are seriously questioned by later research.     

Weighing Evidence “Steampunk” Style via the Meta-Analyser

The funnel plot is a graphical visualization of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modeling assumptions. Although perhaps not widely appreciated, a simple extension of the funnel plot can help to facilitate an intuitive interpretation of the mathematics underlying a meta-analysis at a more fundamental level, by equating it to determining the center of mass of a physical system. We used this analogy to explain the concepts of weighing evidence and of biased evidence to a young audience at the Cambridge Science Festival, without recourse to precise definitions or statistical formulas and with a little help from Sherlock Holmes! Following on from the science fair, we have developed an interactive web-application (named the Meta-Analyser) to bring these ideas to a wider audience. We envisage that our application will be a useful tool for researchers when interpreting their data. First, to facilitate a simple understanding of fixed and random effects modeling approaches; second, to assess the importance of outliers; and third, to show the impact of adjusting for small study bias. This final aim is realized by introducing a novel graphical interpretation of the well-known method of Egger regression.     

Evaluating hospital readmission rates in dialysis facilities; adjusting for hospital effects

Motivated by the national evaluation of readmission rates among kidney dialysis facilities in the United States, we evaluate the impact of including discharging hospitals on the estimation of facility-level standardized readmission ratios (SRRs). The estimation of SRRs consists of two steps. First, we model the dependence of readmission events on facilities and patient-level characteristics, with or without an adjustment for discharging hospitals. Second, using results from the models, standardization is achieved by computing the ratio of the number of observed events to the number of expected events assuming a population norm and given the case-mix in that facility. A challenging aspect of our motivating example is that the number of parameters is very large and estimation of high-dimensional parameters is troublesome. To solve this problem, we propose a structured Newton-Raphson algorithm for a logistic fixed effects model and an approximate EM algorithm for the logistic mixed effects model. We consider a re-sampling and simulation technique to obtain p-values for the proposed measures. Finally, our method of identifying outlier facilities involves converting the observed p-values to Z-statistics and using the empirical null distribution, which accounts for overdispersion in the data. The finite-sample properties of proposed measures are examined through simulation studies. The methods developed are applied to national dialysis data. It is our great pleasure to present this paper in honor of Ross Prentice, who has been instrumental in the development of modern methods of modeling and analyzing life history and failure time data, and in the inventive applications of these methods to important national data problem.     

The Effect of Migration on a Similarity Index

Fewster and Buckland (2001 Fewster , R. M. , Buckland , S. T. ( 2001 ). Similarity indices for spatial ecological data . Biometrics 57 : 495 – 501 . [Google Scholar]) defined a similarity index between two communities by allowing changes between sites to reduce the influence of local discrepancies. The similarity index of Fewster and Buckland is calculated to attain the maximum similarity between two communities in the presence of migration. Instead of maximizing similarity, we propose random migration to measure the similarity of two communities with two types of stochastic migration. The similarity values based on the proposed methods can be treated as the expected value of similarity under migration. We use computer simulation and empirical examples to demonstrate our approach.     

A bayesian approach to prediction in analysis of variance settings

This paper takes the results of Lindley and smith ( 1972 ) one step further, by finding the predictive distribution of an observation y^* whose distribution is normal, and centred at A^* ₁θ₁ We then apply this distribution to the case of prediction based on data obtained in one and two wau ANOVA situations. For Example, it turns out that for two way ANOVA with interaction, the predictive mean, (which we would use as the predictor) is a weighted combination of sample main effects and interaction effects     

A scenario for sequential experimentation

This tutorial emphasizes the role of differecnt types of experimental design in a multi–stage investigation. In the initial phase group–screeningo can reveal the really important factors among hundreds of factors. Resooulution III designs are useful immediately after the screening phase, to investigate firstorder effects, provided higher–order effects are unimportants, i.e., validation is necessary. Resulotion IV designs may explain why a first–order model is not valid, i.e., they may yield unbiased estimators of sums of interactions. Resolution V designs yield unbiased estimators of the individual two–factor interactions. They can be easily extended to central composite designs to estimate pure quadratic effects of quantitative factors. Smaller steps are also possible, e.g. one run at a time, for model discrimination and calibration.     

User Beware: Concerning Findings from the Post 2011–2012 U.S. Internal Revenue Service Migration Data

Population Research and Policy Review - The U.S. Internal Revenue Service (IRS) makes publicly and freely available period migration data at the state and county levels. Among their uses, these...     

Taking on the Role of Doctor: A Dramaturgical Analysis of Professionalization*

This paper applies dramaturgical analysis to the study of professional socialization in an innovative medical school. Using concepts from the theater, professionalization is seen as critically involving performances before legitimating audiences. The data were collected by means of participant observation and interviews of a cohort of students as they proceeded through the professionalizing career. Good acting demands that you are convincing in your part. The audience must be willing to take part in a ritual, in which you represent, say, Hamlet. You must be able to sustain this representation convincingly: that is to say, you must consistently satisfy the audience's imagination and never outrage its acceptance of the fact that yours is a convincing, or consistently credible, Hamlet,…or whatever character you are impersonating (Guthrie, 1971:11).