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"The world is saturated with data. We are regularly presented with data in words, tables, and graphics. Students from many academic fields are now expected to be educated about data in one form or another. Yet the typical sequence of courses--introductory statistics and research methods--does not provide sufficient information about data, learning to work with data sets, or how to present data to various audiences. This book is designed for these purposes. It discusses how data are used in research projects, where to get data, how to manage them with software, and how to present them so that one's message comes through clearly. With few expectations beyond some familiarity with basic statistics and research methods, this book provides a comprehensive set of principles for understanding and using data as part of a research project"--Provided by publisher

"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book"--Provided by publisher

"Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented under the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book"--Provided by publisher

1. Theory and delinquency -- 2. Deterrence and delinquency -- 3. Biological theories of delinquency -- 4. Psychological theories of delinquency -- 5. Communities and delinquency -- 6. The stresses and strains of adolescence -- 7. Learning to be delinquent -- 8. Control theories of delinquency -- 9. Labeling, symbolic interaction, and delinquency -- 10. Conflict and radical theories of delinquency -- 11. Postmodern and feminist views of delinquency -- 12. Integrating and elaborating theories of delinquency -- 13. Developmental theories of delinquency and crime.

Research has shown that parent-child relationships and religiousness are negatively associated with substance use among adolescents, but few studies have addressed their relationship with substance use disorders (SUDs). This study explored whether high quality parent-child relations are negatively associated with the risk of an SUD among adolescents, especially when religiousness is high. The data used to assess this inquiry were from 4 years (2016–2019) of the U.S. National Survey on Drug Use and Health (NSDUH). SUDs in the past year were based on a set of questions consistent with criteria enumerated in the DSM-IV. Latent measures of parent-child relations and religiousness were also constructed. The results of an augmented inverse probability weighting (AIPW) model furnished empirical evidence in support of the notion that the lowest risk of an SUD occurred among those reporting high quality parent-child relations and high religiousness, even after adjusting for a substantial number of selection factors.

Building on models of the stress process, this study examined the consequences for binge alcohol use, marijuana use, and other illicit substance use of cumulative levels of stressful life events using data from the Family Health Study (FHS), an 8-year panel data set ( N = 840). The results of a latent trajectory analysis indicated a positive association between cumulative stressors and involvement in substance use during this period of the life course, especially among early adolescent users. However, there were no identifiable effects on adolescent-limited users. Implications of the results for theory and policy are discussed.

Although there have been general social, economic, and environmental hypotheses developed to explain the distribution of endangered species across the globe, there is a paucity of social science literature designed to specify these hypotheses in more detail or test them with comparative, national-level data. This study draws on several models from environmental sociology to specify conceptually the relationship between social and economic factors and rates of endangered mammals and birds. Specifically, I developed hypotheses based on neo-Malthusian, ecological modernization, treadmill of production, and world-systems theory and analyzed data on endangered species, population, land use, economic size and growth, energy use, and industrial pollutants from one hundred twenty nations. The results support ecological modernization's position that there is an inverted U-shaped relationship between GDP per capita and endangerment rates, but they also indicate that GDP growth is associated with a monotonic increase in these rates, thus supporting a treadmill of production argument. Yet these relationships vary by world-systems position. Endangerment rates in peripheral nations are affected more by GDP growth, whereas rates in semiperipheral nations are affected more by deforestation, GDP per capita, and growth in energy use per capita.

Research exploring the development of adolescent drug use has focused much attention on family and peer influences. Most of this research has concentrated on the direct effects of family structure, parent-child relations, or peer associations on marijuana and other illicit drug use. Peers typically have the greatest impact, followed by family relations, and finally family structure. However, research has failed to explore the potential indirect effects of family variables on adolescent drug use. The author develops and evaluates empirically several models that examine the direct and indirect effects of family structure and family relations on marijuana and polydrug use. The results suggest that peers have the strongest effect on adolescent drug use, yet parent-child involvement and family structure also have a direct effect. The indirect models indicate that family factors influence peers, and family structure affects family relations. These findings suggest that the family effects on adolescent drug use are complex and in need of greater attention.