Statistical Methods For The Social Sciences

The book presents an introduction to statistical methods for students majoring in social science disciplines. No previous knowledge of statistics is assumed, and mathematical background is assumed to be minimal (lowest-level high-school algebra). The book contains sufficient material for a two-semester sequence of courses. Such sequences are commonly required of social science graduate students in sociology, political science, and psychology. Students in geography, anthropology, journalism, and speech also are sometimes required to take at least one statistics course.

For courses in Statistical Methods for the Social Sciences. Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition uses examples and exercises with a variety of "real data." It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics--often a stumbling block for students--while avoiding reliance on an overly simplistic recipe-based approach to statistics.

Assuming no prior knowledge of statistics, these authors combine their varied backgrounds—one a statistician, the other a social scientist—to introduce statistical methods with a high degree of statistical accuracy and a wealth of examples that are interesting and relevant to social scientists.TheThird Editionhas been both updated and improved to integrate real-world data into examples and exercises, and make coverage more accessible throughout.

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.

The fourth edition has an even stronger emphasis on concepts and applications, with greater attention to "real data" both in the examples and exercises. The mathematics is still downplayed, in particular probability, which is all too often a stumbling block for students. On the other hand, the text is not a cookbook. Reliance on an overly simplistic recipe-based approach to statistics is not the route to good statistical practice. Changes in the Fourth Edition: Since the first edition, the increase in computer power coupled with the continued improvement and accessibility of statistical software has had a major impact on the way social scientists analyze data. Because of this, this book does not cover the traditional shortcut hand-computational formulas and approximations. The presentation of computationally complex methods, such as regression, emphasizes interpretation of software output rather than the formulas for performing the analysis. Teh text contains numerous sample printouts, mainly in the style of SPSS and occasionaly SAS, both in chapter text and homework problems. This edition also has an appendix explaining how to apply SPSS and SAS to conduct the methods of each chapter and a website giving links to information about other software.

In Using Statistical Methods, Soleman Abu-Bader detects and addresses the gaps between the research and data analysis of the classroom environment and the practitioner's office. This book not only guides social scientists through different tests, but also provides students and researchers alike with information that will help them in their own practice. With focus on the purpose, rationale, and assumptions made by each statistical test, and a plethora of research examples that clearly display their applicability and function in real-world practice, Professor Abu-Bader creates a step-by-step description of the process needed to clearly organize, choose a test or statistical technique, analyze, interpret, and report research findings.

This unique volume addresses the inadequacies of basic statistical methods that standard textbooks tend to ignore. The author introduces new procedures with accompanying tables that illustrate the practicality of the methods. Concentrating on basic experimental designs that are central to research in the social sciences, Wilcox describes new nonparametric techniques, two-way ANOVA designs, and new results related to the analysis of covariance and repeated measure design. This book serves as the ideal reference and supplement to standard texts by making the statistical advances of the last thirty years accessible to graduate students and researchers.

This textbook offers an essential introduction to survey research and quantitative methods. Building on the premise that statistical methods need to be learned in a practical fashion, the book guides students through the various steps of the survey research process and helps to apply those steps toward a real example. In detail, the textbook introduces students to the four pillars of survey research and quantitative analysis: (1) the importance of survey research, (2) preparing a survey, (3) conducting a survey and (4) analyzing a survey. Students are shown how to create their own questionnaire based on some theoretically derived hypotheses to achieve empirical findings for a solid dataset. Lastly, they use said data to test their hypotheses in a bivariate and multivariate realm. The book explains the theory, rationale and mathematical foundations of these tests. In addition, it provides clear instructions on how to conduct the tests in SPSS and Stata. Given the breadth of its coverage, the textbook is suitable for introductory statistics, survey research or quantitative methods classes in the social sciences.