Applied Statistics for the Social and Health Sciences provides graduate students in the social and health sciences with the basic skills that they need to estimate, interpret, present, and publish statistical models using contemporary standards. The book targets the social and health science branches such as human development, public health, sociology, psychology, education, and social work in which students bring a wide range of mathematical skills and have a wide range of methodological affinities. For these students, a successful course in statistics will not only offer statistical content but will also help them develop an appreciation for how statistical techniques might answer some of the research questions of interest to them. This book is for use in a two-semester graduate course sequence covering basic univariate and bivariate statistics and regression models for nominal and ordinal outcomes, in addition to covering ordinary least squares regression. Key features of the book include: interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature thorough integration of teaching statistical theory with teaching data processing and analysis teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set. This book is for a two-semester course. For a one-semester course, see http://www.routledge.com/9780415991544/
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Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications This book identifies connections between statistical ...
Provides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSSr and Excelr applications This book identifies connections between statistical ...
Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or ...
Features include: • multiple choice questions for both student and lecturer use • full Powerpoint slides for lecturers • practical exercises using SPSS • additional practical exercises using SAS and R This is an essential textbook ...
Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data.
Features new to this edition include: NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers ...
The revised second edition includes new tables that illustrate effect sizes for t-tests. Additionally, the second edition includes small text corrections throughout and updated interior design to increase readability.
(1995), “Fractional Bayes Factors for Model Comparisons,”Journal of the Royal Statistical Society, Ser. B, 57, 99–138. ́O Ruanaidh, J. J. K., and Fitzgerald, W. J. (1996), Numerical Bayesian Methods Applied to Signal Processing, ...
The design-adjusted Rao–Scott Pearson and likelihood ratio χ2 test statistics are computed as follows: XR2 − S ... the generalized design effect is defined as the mean of the eigenvalues of the following matrix, D: D = VDesign VSRS−1 ...