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"Covers the commonly ignored topic of heteroskedasticity (unequal error variances) in regression analyses and provides a practical guide for how to proceed in terms of testing and correction."-- Publisher description

Women and minorities have entered higher paying occupations, but their overall earnings still lag behind those of white men. Why? Looking nationwide at workers across all employment levels and occupations, the author examines the unexpected ways that prejudice and workplace discrimination continue to plague the labor market. He probes the mechanisms by which race and sex groups are sorted into "appropriate" jobs, showing how the resulting segregation undercuts earnings. He also uses an innovative integration of race-sex queuing and segmented-market theories to show how economic and social contexts shape these processes. His analysis reveals how race, sex, stereotyping, and devaluation interact to create earnings disparities, shedding new light on a vicious cycle that continues to the leave women and minorities behind

A revised version of abstract 92S26109. 2 Tables, 4 Figures, 14 References.

This article proposes a method for analyzing data from panels and pooled cross-sections that can separate the effects of exogenous variables on the endogenous variable into cross-unit and longitudinal components. Monte Carlo results suggest that estimation using estimated generalized least squares is close to theoretical expectations for unbiasedness and reasonably reproduces the expected distribution of confidence intervals for a variety of combinations of number of units and time points. To illustrate the substantive utility of the decomposition, an analysis is presented of the effects of unionization and economic conditions on race-gender group industrial earnings inequality that yields dramatically different longitudinal and cross-unit effects.

Using a Monte Carlo simulation, the research in this article addresses two key questions about the accuracy of cluster analysis in reproducing a known true cluster model. First, how is the accuracy affected by different ways of measuring interunit similarity; in this case, different ways of using principal components analysis. Second, how is the accuracy affected by the quality of the characteristics data and by different procedures for handling missing information? The results indicate that using principal components analysis is superior to not using it and that the choice of how to utilize the principal components results may be critical. The results also indicate that the impact of data quality differences may be minimal, but that there are important differentials among the procedures for handling missing data.

Supporting the positive development of a special needs child is especially challenging when parents have separated or divorced. Invariably, there is an increased need for collaborative co‐parenting wherein information is shared and intervention plans can be implemented effectively. In this article, the evolving literature on parental gatekeeping is applied to families with special needs children, as it offers a useful model for understanding the strengths and liabilities of co‐parenting relationships. We describe some of the typical and unique gatekeeping dynamics that occur when children suffer from developmental, physical, and/or psychiatric syndromes that require specific treatment and specialized parenting skills. Examples of both restrictive and facilitative gatekeeping are described as they manifest in these families. Implications for decision making are also discussed.

Family courts are seeing an increasing number of separating or divorced families who have a special needs child. These cases present complex challenges for family law professionals charged with crafting parenting plans based on best interests standards. For many of these children, the typical developmentally based custodial arrangements may not be suitable, given the child's specific symptoms and treatment needs. We present a model for understanding how the general and specific needs of these children, as well as the demands on parents, can be assessed and understood in the context of divorce. This includes an analysis of risk and protective factors that inform timeshare and custodial recommendations and determinations. The risk assessment model is then applied to three of the most commonly occurring childhood neurodevelopmental and psychiatric disorders likely to be encountered in family court, namely, attention deficit/hyperactivity disorder, depressive disorders, and autistic spectrum disorders.
Key Points for the Family Court Community
There has been a dramatic rise in the population of children with neurodevelopmental, psychiatric, and medical syndromes whose parents are disputing custody in the family courts.
Family law professionals of all disciplines should develop a fundamental knowledge base about the most commonly seen special needs children in family court, such as those with neurodevelopmental conditions like autistic spectrum disorder, attention deficit/hyperactivity disorder, and severe depressive disorders (especially with teenagers), which may involve suicidal or self‐harming behaviors.
Commonly recommended parenting plans may be inappropriate for many special needs children, as some function significantly below their chronological age and pose extreme behavioral challenges.
A systematic analysis of risk and protective factors should inform timeshare arrangements and determinations with this varied population, including the safety of the child and severity of the disorder, parental commitment and availability to pursue medical, educational, and therapeutic services, the parental attunement and insightful about the problem, and the differential parenting skills of each parent.

Are African‐American men, compared with white men, more likely to report being stopped by police for traffic law violations? Are African‐American men and Hispanic drivers less likely to report that police had a legitimate reason for the stop and less likely to report that police acted properly? This study answers these questions using citizen self‐reports of their traffic stop encounters with the police. Net of other important explanatory variables, the data indicate that police make traffic stops for Driving While Black and male. In addition, African‐American and Hispanic drivers are less likely to report that police had a legitimate reason for the stop and are less likely to report that police acted properly. The study also discusses the validity of citizen self‐report data and outlines an agenda for future research.

Widespread recognition that many sociological processes occur at multiple levels has led researchers to search for valid forms of cross-level modeling such as contextual analysis. In this article, we propose an alternative cross-level method for examining the effects of macro-factors on macro-processes while controlling for processes at a lower-level of aggregation. This method applies existing statistical techniques but in a new fashion. It has the advantages of more precise control for lower-order effects, reduction of problems with degrees of freedom, and more error-free prediction of the higher-level relationships than using alternative estimation strategies. As a result, parameter estimates of the macro-level effects are more efficient. An empirical example studying macro-determinants of unemployment rates in states in the United States demonstrates that estimation of this model leads to more valid conclusions than more commonly used methods.