Contradictory Behavior During Genocides1

In all large‐scale genocides, rescuing occurs alongside killing. Some members of the aggressors’ ethnic group even risk their own lives to save members of the targeted group. Killing and rescuing occur closely together, and even the same persons may engage in both behaviors—killing on one occasion and rescuing on another. This article examines such cases—where the same individuals kill and rescue—and discusses their relevance to the explanation of genocide. Both collectivistic and individualistic theories of killing and rescuing—which explain these behaviors with the properties of groups or persons—are inadequate in accounting for those who do both. Using Donald Black’s (1995, 2000) strategy of pure sociology and my theory of genocide ( Campbell, 2009 ), I offer an explanation of contradictory behavior by individuals during genocide. The behaviors themselves occur in different structures—killing where there is social distance and rescuing where there is social closeness. Individuals who exhibit contradictory behavior thus kill those who are distant and save those who are close. One feature of this analysis is its demonstration of the explanatory power of pure sociology, which is uniquely capable of explaining extreme variations in individual behavior.     

A critical appraisal of participatory methods in development research

Anthropologists and others working in the field of development are making increasing use of participatory research methods. While aware of the value of such methods, this paper outlines a number of methodological issues that need to be carefully considered. Such issues, when taken together with the problem of combining participatory with other forms of qualitative and quantitative research, argue strongly not only for caution but the need to undertake basic research on the participatory methods themselves. This paper looks at the development of participatory rural appraisal (PRA) in development research, and critically examines three methods/techniques--interviewing, visualization and ranking/scoring--in terms of their relation to qualitative research. Finally, the issue of the validity of PRA is discussed in relation to arguments about the role of sequencing/triangulating research techniques that are shown to be as problematic as the unexamined use of PRA methods.     

Partitioning chi-squape in contingency tables: A teaching approach

A representation of sums and differences of the form 2n log n, the lnn function, is introduced to express likelihood-ratio chi-square test statistics in contingency table analysis. This is a concise explicit form to display when partitioning chi-square statistics in accordance with hierarchical models. The lnn representation gives students insights into the construction of test statistics, and assists in relating identical forms under differing model sets. Hierarchies are presented for independence and equi-probability in two-way tables, for symmetry in correlated square tables, for independence-and-homogeneity of two-way responses across levels of a factor, and for mutual independence in three-way tables, along with relevant partitions of chi-square.     

An application of lomax distributions in receiver operating characteristic(roc)curve analysis

Receiver operating characteristic(ROC)curves are useful for studying the performance of diagnostic tests. ROC curves occur in many fields of applications including psychophysics, quality control and medical diagnostics. In practical situations, often the responses to a diagnostic test are classified into a number of ordered categories. Such data are referred to as ratings data. It is typically assumed that the underlying model is based on a continuous probability distribution. The ROC curve is then constructed from such data using this probability model. Properties of the ROC curve are inherited from the model. Therefore, understanding the role of different probability distributions in ROC modeling is an interesting and important area of research. In this paper the Lomax distribution is considered as a model for ratings data and the corresponding ROC curve is derived. The maximum likelihood estimation procedure for the related parameters is discussed. This procedure is then illustrated in the analysis of a neurological data example.     

An Application of Bayesian Methods to Small Area Poverty Rate Estimates

Efforts to estimate various sociodemographic variables in small geographic areas are proving difficult with the replacement of the Census long-form with the American Community Survey (ACS). Researchers interested in subnational demographic processes have previously relied on Census 2000 long-form data products in order to answer research questions. ACS data products promise to begin providing up-to-date profiles of the nation’s population and economy; however, unit- and item-level nonresponse in the ACS have left researchers with gaps in subnational coverage resulting in unstable and unreliable estimates for basic demographic measures. Borrowing information from neighboring areas and across time with a spatiotemporal smoothing process based on Bayesian statistical methods, it is possible to generate more stable and accurate estimates of rates for geographic areas not represented in the ACS. This research evaluates this spatiotemporal smoothing process in its ability to derive estimates of poverty rates at the county level for the contiguous United States. These estimates are then compared to more traditional estimates produced by the US Census Bureau, and comparisons between the two methods of estimation are carried out to evaluate the practical application of this smoothing method. Our findings suggest that by using available data from the ACS only, we are able to recreate temporal and spatial patterns of poverty in US counties even in years where data are sparse. Results show that the Bayesian methodology strongly agrees with the estimates produced by the SAIPE program, even in years with little data. This methodology can be expanded to other demographic and socioeconomic data with ease.     

High School Dropouts After They Exit School: Challenges and Directions for Sociological Research

Sociological research on high school dropouts is largely concerned with who drops out of school and why. Research on the consequences of dropping out is less common. There are clear theoretical reasons to expect dropping out to have a direct and negative effect on life outcomes; however, empirically evaluating these theories is difficult because students who drop out are different from high school graduates in meaningful and complex ways. In this article, I first provide a brief review of sociological research on high school dropouts, emphasizing the demographics of dropouts and reasons for dropping out. I then discuss the possible role of human capital differences, signaling theory, and social closure in creating worse outcomes for high school dropouts and outline the empirical challenges to researching the effects of dropping out. I conclude by discussing avenues for future research and important unanswered questions about the consequences of dropping out.     

Risk factors for femicide-suicide in abusive relationships: results from a multisite case control study

The killing of women by men who then take their own lives (femicide-suicide) is the most common form of homicide-suicide. This study identified femicide-suicide risk factors in an 11-city case-control study of femicide in the United States. Perpetrator, victim, relationship, and incident characteristics were analyzed for femicide-suicide cases (n = 67) and controls (n = 356, women living in the community with nonfatal physical abuse) using logistic regression modeling. Two risk factors emerged that were unique to femicide-suicides cases compared to overall femicide risk analyses: prior perpetrator suicide threats and victims having ever been married to the perpetrator.     

Are Self‐Service Customers Satisfied or Stuck?

This paper investigates the impact of self‐service technology (SST) usage on customer satisfaction and retention. Specifically, we disentangle the distinct effects of satisfaction and switching costs as drivers of retention among self‐service customers. Our empirical analysis examines 26,924 multi‐channel customers of a nationwide retail bank. We track each customer's channel usage, overall satisfaction, and retention over a 1‐year period. We find that, relative to face‐to‐face service, customers who use self‐service channels for a greater proportion of their transactions are either no more satisfied, or less satisfied with the service they receive, depending on the channel. However, we also find that these same customers are predictably less likely to defect to a competitor if they are heavily reliant on self‐service channels characterized by high switching costs. Through a mediation model, we demonstrate that, when self‐service usage promotes retention, it does so in a way that is consistent with switching costs. As a robustness check, we examine the behavior of channel enthusiasts, who concentrate transactions among specific channels. Relative to more diversified customers, we find that self‐service enthusiasts in low switching cost channels defect with greater frequency, while self‐service enthusiasts in high switching cost channels are retained with greater frequency.