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This book covers applied statistics for the social sciences with upper-level undergraduate students in mind. The chapters are based on lecture notes from an introductory statistics course the author has taught for a number of years. The book integrates statistics into the research process, with early chapters covering basic philosophical issues underpinning the process of scientific research. These include the concepts of deductive reasoning and the falsifiability of hypotheses, the development of a research question and hypotheses, and the process of data collection and measurement. Probability theory is then covered extensively with a focus on its role in laying the foundation for statistical reasoning and inference. After illustrating the Central Limit Theorem, later chapters address the key, basic statistical methods used in social science research, including various z and t tests and confidence intervals, nonparametric chi square tests, one-way analysis of variance, correlation, simple regression, and multiple regression, with a discussion of the key issues involved in thinking about causal processes. Concepts and topics are illustrated using both real and simulated data. The penultimate chapter presents rules and suggestions for the successful presentation of statistics in tabular and graphic formats, and the final chapter offers suggestions for subsequent reading and study

Although Bayes' theorem has been around for more than 250 years, widespread application of the Bayesian approach only began in statistics in 1990. By 2000, Bayesian statistics had made considerable headway into social science, but even now its direct use is rare in articles in top sociology journals, perhaps because of a lack of knowledge about the topic. In this review, we provide an overview of the key ideas and terminology of Bayesian statistics, and we discuss articles in the top journals that have used or developed Bayesian methods over the last decade. In this process, we elucidate some of the advantages of the Bayesian approach. We highlight that many sociologists are, in fact, using Bayesian methods, even if they do not realize it, because techniques deployed by popular software packages often involve Bayesian logic and/or computation. Finally, we conclude by briefly discussing the future of Bayesian statistics in sociology.

Stereotype threat is a widely supported theory for understanding the racial achievement gap in college grade performance. However, today's minority college students are increasingly of immigrant origins, and it is unclear whether two dispositional mechanisms that may increase susceptibility to stereotype threat are applicable to immigrants. We use survey data to examine whether and how negative-ability stereotypes affect the grades of 1,865 first-, second-, and third-generation or higher (domestic) minority students at 28 selective American colleges. Structural equation model results indicate that first-generation immigrants are highly resistant to both dispositional identity threat mechanisms we consider. Second-generation immigrants experience only certain dispositional elements of identity threat. Drawing on research in social psychology, we suggest immigrants tend to resist stereotype threat in part due to the primacy of their immigrant identities and their connectedness to the opportunity structure of mainstream society.

In sociological research, it is often difficult to compare nonnested models and to evaluate the fit of models in which outcome variables are not normally distributed. In this article, the authors demonstrate the utility of Bayesian posterior predictive distributions specifically, as well as a Bayesian approach to modeling more generally, in tackling these issues. First, they review the Bayesian approach to statistics and computation. Second, they discuss the evaluation of model fit in a bivariate probit model. Third, they discuss comparing fixed- and random-effects hierarchical linear models. Both examples highlight the use of Bayesian posterior predictive distributions beyond these particular cases.

This paper examines the consequences of China's dramatic socioeconomic and political transformations for individual subjective well-being (SWB) from 1990 to 2007. Although many still consider China to be a collectivist country, and some scholars have argued that collectivist factors would be important predictors of individual well-being in such a context, our analysis demonstrates that the Chinese are increasingly prioritizing individualist factors in assessments of their own happiness and life satisfaction thus substantiating descriptions of their society as increasingly individualistic. While the vast majority of quality of life studies have focused on Westerners, this study contributes findings from the unique cultural context of China. Moreover, concentration on this particular period in Chinese history offers insight into the relationship between SWB and rapid socioeconomic and political change.

Extant approaches to constructing life tables generally rely on the use of population data, and differences between groups defined by discrete characteristics are examined by disaggregating the data before estimation. When sample data are used, few researchers have attempted to include covariates directly in the process of estimation, and fewer still have attempted to construct interval estimates for state expectancies when covariates are used. In this paper, we present a Bayesian approach that is useful for producing interval estimates for single-decrement, multiple-decrement, and multistate life tables. The method involves (1) estimating a hazard or survival model using Bayesian Markov chain Monte Carlo (MCMC) methods to produce a sample from the posterior distribution for the parameters of the model; (2) generating distributions of transition probabilities for selected values of covariates using the sample of model parameters; (3) using these distributions of transition probabilities as inputs for life table construction; and (4) summarizing the distribution of life table quantities. We illustrate the method on data simulated from the Berkeley Mortality Database, data from the National Health and Nutrition Examination Survey (and follow-ups), and data from the National Long Term Care Survey, and we show how the results can be used for hypothesis testing.

This book looks at the way we tax the poor in the United States, particularly in the American South, where poor families are often subject to income taxes, and where regressive sales taxes apply even to food for home consumption. Katherine S. Newman and Rourke L. O'Brien argue that these policies contribute in unrecognized ways to poverty-related problems like obesity, early mortality, the high school dropout rates, teen pregnancy, and crime. They show how, decades before California's passage of Proposition 13, many southern states implemented legislation that makes it almost impossible to raise property or corporate taxes, a pattern now growing in the western states. Taxing the Poor demonstrates how sales taxes intended to replace the missing revenue—taxes that at first glance appear fair—actually punish the poor and exacerbate the very conditions that drove them into poverty in the first place

Researchers have investigated the effects of ethnic heterogeneity on a range of socioeconomic and political outcomes. However, approaches to measuring ethnic diversity vary not only across fields of study but even within subfields. In this review, we systematically dissect the computational approaches of prominent measures of diversity, including polarization, and discuss where and how differences emerge in their relationships with outcomes of interest to sociologists (social capital and trust, economic growth and redistribution, conflict, and crime). There are substantial similarities across computations, which are often generalizations or specializations of one another. Differences in how racial and ethnic groupings are constructed and in level of geographic analysis explain many divergences in empirical findings. We conclude by summarizing the type of measurement technique preferred by outcome, when relevant, and provide considerations for future researchers contemplating how best to operationalize diversity. Finally, we highlight two less widely used yet promising measures of diversity.