Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.

Contributors thoroughly survey the most important statistical models used in empirical reserch in the social and behavioral sciences. Following a common format, each chapter introduces a model, illustrates the types of problems and data for which the model is best used, provides numerous examples that draw upon familiar models or procedures, and includes material on software that can be used to estimate the models studied. This handbook will aid researchers, methodologists, graduate students, and statisticians to understand and resolve common modeling problems.

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.

Multiple regression analysis has been widely used by researchers to analyze complex social problems since the 1950s. A specialization in economics, known as econometrics, developed out of a recognition that multiple regression is based upon a large number of assumptions—many of which are commonly violated in specific applications. Econometricians developed tests for violations of the regression model assumptions, as well as a variety of corrective measures for estimating regression models in the presence of many of the violations. Unfortunately, the mathematical sophistication required to understand the econometrics literature started out high and has continued to rise over the years. As a consequence, an understanding of the assumptions of the regression model, tests for violations, and corrective estimation approaches have failed to permeate widely many other policy-related disciplines such as political science, social work, public administration, and sociology. One of the key objectives of this book is to translate the results from the econometrics literature into language that policy analysts from other disciplines can understand easily. A second objective is to present a discussion of so-called limited-dependent variable models. One of the assumptions of the regression model is that the dependent variable is measured on an interval scale. But often the dependent variable of interest is discrete or categorical. Whether someone is in poverty or, whether they are working full-time, part-time, or out of the labor force, marital status—all are examples of categorical variables that might be of policy interest. Moreover, the growing availability of large-scale public use data sets containing information on individuals and families has heightened the relevance of categorical variables in policy analysis. The mathematical preparation required to understand procedures for estimating categorical models is, however, even more daunting than that for fully understanding and using the regression model. As with the theoretical development of the regression model, most presentations of categorical models, such as Logit and Probit, are to be found in econometric literature. Moreover, this literature offers little in the way of practical advice on how to estimate and interpret model results. This book is the first to present a detailed and accessible discussion of multiple regression and limited-dependent variable models in the context of policy analysis. As such it will be an invaluable resource for most scholars, researchers, and students in the social and behavioral sciences.

This is the first book to demonstrate the application of power analysis to the newer more advanced statistical techniques that are increasingly used in the social and behavioral sciences. Both basic and advanced designs are covered. Readers are shown how to apply power analysis to techniques such as hierarchical linear modeling, meta-analysis, and structural equation modeling. Each chapter opens with a review of the statistical procedure and then proceeds to derive the power functions. This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS. Readers can verify the power computation using the computer programs on the book's website. There is a growing requirement to include power analysis to justify sample sizes in grant proposals. Most chapters are self-standing and can be read in any order without much disruption.This book will help readers do just that. Sample computer code in R, SPSS, and SAS at www.routledge.com/9781848729810 are written to tabulate power values and produce power curves that can be included in a grant proposal. Organized according to various techniques, chapters 1 – 3 introduce the basics of statistical power and sample size issues including the historical origin, hypothesis testing, and the use of statistical power in t tests and confidence intervals. Chapters 4 - 6 cover common statistical procedures -- analysis of variance, linear regression (both simple regression and multiple regression), correlation, analysis of covariance, and multivariate analysis. Chapters 7 - 11 review the new statistical procedures -- multi-level models, meta-analysis, structural equation models, and longitudinal studies. The appendixes contain a tutorial about R and show the statistical theory of power analysis. Intended as a supplement for graduate courses on quantitative methods, multivariate statistics, hierarchical linear modeling (HLM) and/or multilevel modeling and SEM taught in psychology, education, human development, nursing, and social and life sciences, this is the first text on statistical power for advanced procedures. Researchers and practitioners in these fields also appreciate the book‘s unique coverage of the use of statistical power analysis to determine sample size in planning a study. A prerequisite of basic through multivariate statistics is assumed.

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences shows students how to apply statistical methods to behavioral science data in a sensible manner. Assuming some familiarity with introductory statistics, the book analyzes a host of real-world data to provide useful answers to real-life issues.The author begins by exploring

Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.

Designed to engage students and lower their "fear factor", Integrative Statistics for the Social and Behavioral Sciences is a concise, user-friendly text that prepares students to use statistics in the real world. Providing depth and breadth of statistical tests, the text focuses on choosing the appropriate statistical analysis, and shows how to interpret the output and present the results.

Description: Incorporating a hands-on pedagogical approach, Nonparametric Statistics for Social and Behavioral Sciences presents the concepts, principles, and methods used in performing many nonparametric procedures. It also demonstrates practical applications of the most common nonparametric procedures using IBM's SPSS software. This text is the only current nonparametric book written specifically for students in the behavioral and social sciences. Emphasizing sound research designs, appropriate statistical analyses, and accurate interpretations of results, the text: Explains a conceptual framework for each statistical procedure Presents examples of relevant research problems, associated research questions, and hypotheses that precede each procedure Details SPSS paths for conducting various analyses Discusses the interpretations of statistical results and conclusions of the research With minimal coverage of formulas, the book takes a nonmathematical approach to nonparametric data analysis procedures and shows students how they are used in research contexts. Each chapter includes examples, exercises, and SPSS screen shots illustrating steps of the statistical procedures and resulting output.