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This book provides the student, researcher or practitioner with the tools to understand many of the most commonly used advanced statistical analysis tools in criminology and criminal justice, and also to apply them to research problems. The volume is structured around two main topics, giving the user flexibility to find what they need quickly. The first is “the general linear model” which is the main analytic approach used to understand what influences outcomes in crime and justice. It presents a series of approaches from OLS multivariate regression, through logistic regression and multi-nomial regression, hierarchical regression, to count regression. The volume also examines alternative methods for estimating unbiased outcomes that are becoming more common in criminology and criminal justice, including analyses of randomized experiments and propensity score matching. It also examines the problem of statistical power, and how it can be used to better design studies. Finally, it discusses meta analysis, which is used to summarize studies; and geographic statistical analysis, which allows us to take into account the ways in which geographies may influence our statistical conclusions.
Statistics for Criminal Justice and Criminology in Practice and Research—by Jack Fitzgerald and Jerry Fitzgerald—is an engaging and comprehensive introduction to the study of basic statistics for students pursuing careers as practitioners or researchers in both Criminal Justice and Criminology programs. This student-friendly text shows how to calculate a variety of descriptive and inferential statistics, recognize which statistics are appropriate for particular data analysis situations, and perform hypothesis tests using inferential statistics. But it is much more than a "cook book." It encourages readers to think critically about the strengths and limitations of the statistics they are calculating, as well as how they may be misapplied and misleading. Examples of statistics and statistical analyses are drawn from the worlds of the practitioner as well as the policymaker and researcher. Students will also gain a clear understanding of major ethical issues in conducting statistical analyses and reporting results, as well as insight into the realities of the life of researchers and practitioners as they use statistics and statistical analyses in their day-to-day activities.
Statistics in Criminal Justice takes an approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and researchers who want to gain a basic understanding of common statistical methods used in this field. In general, the text relies on a building-block approach, meaning that each chapter helps to prepare the student for the chapters that follow. It also means that the level of sophistication of the text increases as the text progresses. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. However, as the statistical methods discussed become more complex and demanding to compute, there is increasing use and integration of statistical software. This approach is meant to provide the reader with an accessible, yet sophisticated understanding of statistics that can be used to examine real-life criminal justice problems with popular statistical software programs. The primary goal of the text is to give students and researchers a basic understanding of statistical concepts and methods that will leave them with the confidence and the tools for tackling more complex problems on their own. New to the 4th Edition · New chapter on experimental design and the analysis of experimental data. · New chapter on multi-level models, including growth-curve models. · New computer exercises throughout the text to illustrate the use of both SPSS and Stata. · Revision of exercises at the end of each chapter that places greater emphasis on using statistical software. · Additional resources on the text’s web site for instructors and students, including answers to selected problems, syntax for replicating text examples in SPSS and Stata, and other materials that can be used to supplement the use of the text.
This introductory textbook takes a building-block approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and professionals who want to gain a basic understanding of common statistical methods used in criminology and criminal justice before advancing to more complex statistical analyses in future volumes. This book emphasizes comprehension and interpretation. As the statistical methods discussed become more complex and demanding to compute, it integrates statistical software. It provides readers with an accessible understanding of popular statistical programs used to examine real-life crime and justice problems (including SPSS, Stata, and R). In addition, the book includes supplemental resources such as a glossary of key terms, practice questions, and sample data. Basic Statistics in Criminology and Criminal Justice aims to give students and researchers a core understanding of statistical concepts and methods that will leave them with the confidence and tools to tackle the statistical problems in their own research work.
Data Analysis in Criminal Justice and Criminology: History, Concept, and Application breaks down various data analysis techniques to help students build their conceptual understanding of key methods and processes. The information in the text encourages discussion and consideration of how and why data analysis plays an important role in the fields of criminal justice and criminology. The book is divided into three units. Unit 1 discusses how data analysis is us
The Foundations of Statistics in Criminology and Criminal Justice underscores the purpose and role of statistics in criminal justice practice, emphasizes major analytical techniques, and covers two key types of statistics--descriptive and inferential. The text helps students build a fundamental background in analytical methods within the context of criminal justice. Part I demonstrates the importance of statistics and its practical application within criminology and criminal justice. Part II introduces students to descriptive statistics. They learn about measures of central tendency and measures of variability within empirical research in the criminal justice system. In Part III, students examine the issues of probability and the normal curve, which explain how criminal justice data sources and research can be used for decision-making tools with an adequate degree of scientific validation. Part IV explores inferential statistics as a decision-making aid that plays a pivotal roles in criminal justice policy development and planning. The final section demonstrates practical applications within the discipline and summarizes analytical methods. Accessible and engaging, The Foundations of Statistics in Criminology and Criminal Justice is an exemplary resource for courses in statistics in criminology and criminal justice.
Designed for students with little or no familiarity with statistics, this text presents clear explanations for underlying concepts of statistical inference and hypothesis testing, and features step-by- step demonstrations of common statistical procedures used in criminal justice research. Excel application exercises allow students to visualize the processes of statistical calculations and to perform advanced statistical techniques using the function wizard and data analysis tool. Other learning features include boxes introducing statistical techniques for addressing specific research problems, boxes requiring students to perform different statistical tests, key terms, and problem sets. Proctor teaches statistics at Indiana University. Badzinski teaches statistics and communications. Annotation copyrighted by Book News, Inc., Portland, OR.
The spread of sophisticated computer packages and the machinery on which to run them has meant that procedures which were previously only available to experienced researchers with access to expensive machines and research students can now be carried out in a few seconds by almost every undergraduate. Understanding and Using Advanced Statistics provides the basis for gaining an understanding of what these analytic procedures do, when they should be used, and what the results provided signify. This comprehensive textbook guides students and researchers through the transition from simple statistics to more complex procedures with accessible language and illustration.
This book is concept-based and focuses on the building blocks of statistical ideas. Covering the essential techniques—univariate tools, Chi-square, t-test, analysis of variance, and Pearson's r—in a simple conversational style, the text explains the concepts behind each technique and how results are interpreted. Its emphasis is on understanding over mathematical calculations, and its goal is to give students a grasp of the role of variance and error. A chapter on graphical statistics complements the normal quantitative approaches and each technique is set in the context of how it is used to answer research questions.