Devastation in the aftermath of Hurricane Katrina as a state crime: social audience reactions

In the 5 years since Hurricane Katrina struck the Gulf Coast, scholars in many disciplines have speculated on the sources of the widespread devastation. While many of these studies have focused on objective evidence of the violation of human rights following Hurricane Katrina, this study reviews the human rights violations and goes a step further by examining social audience reactions (both victims and the general public) to the government’s response efforts. Relying on Green and Ward’s (Green 2009; Green and Ward Social Justice, 27, 101–115, 2000; 2004) human rights/organizational deviance definition, which sees state crime as human rights violations that result from state organizational deviance, we attempt to provide further evidence of this case as one of state crime. This article presents results from binary logistic regression analyses that assess the likelihood of respondents disapproving of the actions of officials from various levels of government after the hurricane using data from a survey of Hurricane Katrina evacuees completed by The Washington Post/Kaiser Family Foundation/Harvard University (The Washington Post/Kaiser Family Foundation/Harvard University 2005) in the weeks after the storm, and data from a special topics ABC News/Washington Post public opinion survey completed in September of 2005 (ABC News/The Washington Post 2006). Key findings in this study include a strong similarity of results across data sets for race, sex, and religion with regards to respondents disapproving responses to the storm devastation at the federal level. A clear majority of respondents in both data sets disapproved of the actions of some level of government, further implying negative audience reactions, and thus the need to recognize the government’s response to Hurricane Katrina as a state crime.     

Hair analysis for drugs of abuse. Hair color and race differentials or systematic differences in drug preferences?

There is currently a debate in the literature on chemical drug analysis concerning the contribution of biophysical attributes associated with specimens and specimen donors to assay outcome. In recent years this debate has focused on hair analysis, but has in the past also been raised in urinalysis interpretation. In this article we examine several aspects of that controversy. First, we present data regarding the effects of hair color on the distribution of positive hair testing results for three drug classes. We compare these results to negative hair samples from comparable donors. This data is derived from head hair from preemployment donors that was classified according to seven visual color categories. We determined the distribution of colors for hair samples devoid of any of three assayed drugs (amphetamines, cocaine, and cannabinoids). Subsequently, this distribution was compared with the distributions for hairs that had tested positive for amphetamines, cocaine or cannabinoids. We examined a total of 2000 randomly selected samples; 500 negative hair samples and 500 positive samples for each of three drugs: cannabinoids, cocaine, and amphetamine. We also evaluated ethnic/racial factors in relation to positive urinalyses for various ethnic/racial groups. We examined approximately 4000 urine specimens from two different groups, each constituting around 2000 specimens. In addition to ethnicity/race and urinalysis outcome, we also examined the relationship between the hair color distributions of urine donors and the corresponding urinalysis results for the three drug classes. We also compared them to drug-negative samples. Our summary impression is that the observed outcome patterns were largely consistent with differences in drug preferences among the various societal groups. There was little evidence of a pattern attributable to hair color bias alone or selective binding of drugs to hair of a particular color. Likewise, there was no discernible pattern associated with race or ethnicity that would lend support to a "race effect" in drug analysis.     

Verbal and Physical Aggression in Couples Where the Female Partner is Drinking Heavily

This study evaluated (i) frequencies of aggression in maritally distressed problem drinking (DP) women relative to controls, (ii) aggression, marital satisfaction, and partner drinking in predicting female drinking, and (iii) discrepant within-couple drinking in predicting marital distress. The sample included 27 DP women, 24 maritally distressed nonproblem drinking women (DNP women), and 24 women with neither problem (NDNP women). DP women reported frequencies of physical aggression similar to DNP women, but less male verbal aggression than DNP women. Predictors of female drinking were marital satisfaction and male drinking, but aggression did not predict female drinking. Female marital satisfaction was predicted by interspousal discrepancies in drinking after accounting for verbal aggression.     

Persistence of intimate partner violence among families referred to child welfare

The purpose of this study is to examine the longitudinal course of intimate partner violence (IPV) among female caregivers of children receiving child welfare services. Data are derived from the National Survey of Child and Adolescent Well-Being, a national probability study of children investigated for child abuse and neglect in the United States. Caregivers (n = 861) are interviewed about demographic characteristics, mental health, substance use, and physical violence by a partner at the close of the investigation and at an 18-month follow-up. Polychotomous logistic regression examines the associations of severe and minor IPV controlling for caregiver and environmental characteristics. The results suggest that factors related to initial risk for IPV do not affect the continuation of IPV and that patterns of IPV differ for racial and ethnic groups.     

Dynamic Models for Dynamic Theories: The Ins and Outs of Lagged Dependent Variables

Nathan J. Kelly Department of Political Science, University of Tennessee, 1001 McClung Tower, Knoxville, TN 37996-0410 e-mail: luke.keele{at}mail.polisci.ohio-state.edu (corresponding author) e-mail: nathan.j.kelly{at}gmail.com A lagged dependent variable in an OLS regression is often usedas a means of capturing dynamic effects in political processesand as a method for ridding the model of autocorrelation. Butrecent work contends that the lagged dependent variable specificationis too problematic for use in most situations. More specifically,if residual autocorrelation is present, the lagged dependentvariable causes the coefficients for explanatory variables tobe biased downward. We use a Monte Carlo analysis to assessempirically how much bias is present when a lagged dependentvariable is used under a wide variety of circumstances. In ouranalysis, we compare the performance of the lagged dependentvariable model to several other time series models. We showthat while the lagged dependent variable is inappropriate insome circumstances, it remains an appropriate model for thedynamic theories often tested by applied analysts. From theanalysis, we develop several practical suggestions on when andhow to use lagged dependent variables on the right-hand sideof a model.