Understanding and Using Advanced Statistics by Foster, J. J. ; Barkus, E. ; Yavorsky, C.Understanding and Using Advanced Statistics is a comprehensive, practical guide for postgraduate students advising how and when to use more advanced statistical methods. Perfect for students without a mathematical background, the authors refresh important basics such as descriptive statistics and research design as well as introducing essential upper-level techniques to cater for the advanced student. Key Features: - Comprehensive guide informing how to use a range of advanced statistical methods such as MANOVA, path analysis and logistical regression; - Inter-disciplinary: ideal for students studying upper level statistical methods in any subject across the social sciences; - Practical guide: case studies, further reading, key terms explained in order to help the non-mathematically orientated student get ahead with their research. Building on undergraduate statistical grounding, Understanding and Using Advanced Statistics provides the upper-level researcher with the knowledge of what advanced statistics do, how they should be used, and what their output means.
Call Number: eBook
Publication Date: 2006
Handbook of Univariate and Multivariate Data Analysis with IBM SPSS by Ho, R.Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book's CRC Press web page.
Call Number: eBook
Publication Date: 2013
Multivariate Statistical Methods by Manly, B. F. J. ; Navarro Alberto, J. A.Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used. The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.
Call Number: 519.535 MAN
Publication Date: 2016
Exploring data : an introduction to data analysis for social scientists by Marsh, C. ; Elliott, J.The updated edition of this classic text introduces a range of techniques for exploring quantitative data. Beginning with an emphasis on descriptive statistics and graphical approaches, it moves on in later chapters to simple strategies for examining the associations between variables using inferential statistics such as chi squared. The book has been substantially revised to include the most recent approaches to data analysis, and includes step-by-step instructions on using SPSS. All these techniques are illustrated with intriguing real examples, drawn from important social research over the past three decades, designed to illuminate significant sociological and political debates. The book shows how students can use quantitative data to answer various questions: Is it true that the rich are getting richer and the poor are getting poorer? Are crime rates really going down, and how can we tell? How much alcohol do men and women really drink in an average week? Which country in Europe has the highest average working hours? Readers are encouraged to explore data for themselves, and are carefully guided through the opportunities and pitfalls of using statistical packages, as well as the numerous data sources readily available online. Suitable for those with no previous experience of quantitative data analysis, the second edition of Exploring Data will be invaluable to students across the social sciences. Download answers to exercises in book.