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Applied Multivariate Statistics for the Social Sciences

Applied Multivariate Statistics for the Social Sciences
Author: Keenan A. Pituch
Publisher: Routledge
Total Pages: 814
Release: 2015-12-07
Genre: Psychology
ISBN: 1317805925

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Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. 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 understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises) Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.


Applied Multivariate Statistics for the Social Sciences, Fifth Edition

Applied Multivariate Statistics for the Social Sciences, Fifth Edition
Author: James P. Stevens
Publisher: Routledge
Total Pages: 666
Release: 2012-11-12
Genre: Education
ISBN: 1136910697

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This best-selling text is written for those who use, rather than develop statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving results. Helpful narrative and numerous examples enhance understanding and a chapter on matrix algebra serves as a review. Annotated printouts from SPSS and SAS indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use these packages, the author stresses the importance of checking the data, assessing the assumptions, and ensuring adequate sample size by providing guidelines so that the results can be generalized. The book is noted for its extensive applied coverage of MANOVA, its emphasis on statistical power, and numerous exercises including answers to half. The new edition features: New chapters on Hierarchical Linear Modeling (Ch. 15) and Structural Equation Modeling (Ch. 16) New exercises that feature recent journal articles to demonstrate the actual use of multiple regression (Ch. 3), MANOVA (Ch. 5), and repeated measures (Ch. 13) A new appendix on the analysis of correlated observations (Ch. 6) Expanded discussions on obtaining non-orthogonal contrasts in repeated measures designs with SPSS and how to make the identification of cell ID easier in log linear analysis in 4 or 5 way designs Updated versions of SPSS (15.0) and SAS (8.0) are used throughout the text and introduced in chapter 1 A book website with data sets and more. Ideal for courses on multivariate statistics found in psychology, education, sociology, and business departments, the book also appeals to practicing researchers with little or no training in multivariate methods. Prerequisites include a course on factorial ANOVA and covariance. Working knowledge of matrix algebra is not assumed.


Applied Multivariate Statistics for the Social Sciences

Applied Multivariate Statistics for the Social Sciences
Author: James Stevens
Publisher:
Total Pages: 0
Release: 1996
Genre: Multivariate analysis
ISBN: 9780805834710

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This book was written for those who will be using, rather than developing, advanced statistical methods. It focuses on a conceptual understanding of the material rather than proving results. It is a graduate level textbook with abundant examples.


Applied Multivariate Statistics for the Social Sciences

Applied Multivariate Statistics for the Social Sciences
Author: James Paul Stevens
Publisher: Lawrence Erlbaum Associates
Total Pages: 656
Release: 1992
Genre: Mathematics
ISBN:

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This book was written for those who will be using, rather than developing, advanced statistical methods. It focuses on a conceptual understanding of the material rather than proving results. It is a graduate level textbook with abundant examples.


Multivariate Analysis for the Biobehavioral and Social Sciences

Multivariate Analysis for the Biobehavioral and Social Sciences
Author: Bruce L. Brown
Publisher: John Wiley & Sons
Total Pages: 404
Release: 2011-11-01
Genre: Mathematics
ISBN: 1118131614

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An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question "how do you do that?" is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages. Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.


Handbook of Applied Multivariate Statistics and Mathematical Modeling

Handbook of Applied Multivariate Statistics and Mathematical Modeling
Author: Howard E.A. Tinsley
Publisher: Academic Press
Total Pages: 751
Release: 2000-05-22
Genre: Mathematics
ISBN: 0080533566

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Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.


Applied Multivariate Statistics for the Social Sciences

Applied Multivariate Statistics for the Social Sciences
Author: James Stevens
Publisher: Psychology Press
Total Pages: 718
Release: 1996
Genre: Education
ISBN:

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Of Important Points -- Two-Group Multivariate Analysis Of Variance -- Four Statistical Reasons for Preferring a Multivariate Analysis -- The Multivariate Test Statistic as a Generalization of Univariate t -- Numerical Calculations for a Two-Group Problem -- Three Post Hoc Procedures -- SAS and SPSS Control Lines for Sample Problem and Selected Printout -- Multivariate Significance but No Univariate Significance -- Multivariate Regression Analysis for the Sample Problem -- Power Analysis -- Ways of Improving Power -- Power Estimation on SPSS MANOVA -- Multivariate Estimation of Power -- K-Group Manova: A Priori And Post Hoc Procedures -- Multivariate Regression Analysis for a Sample Problem -- Traditional Multivariate Analysis of Variance -- Multivariate Analysis of Variance for Sample Data -- Post Hoc Procedures -- The Tukey Procedure -- Planned Comparisons -- Test Statistics for Planned Comparisons -- Multivariate Planned Comparisons on SPSS MANOVA -- Correlated Contrasts -- Studies Using Multivariate Planned Comparisons -- Stepdown Analysis -- Other Multivariate Test Statistics -- How Many Dependent Variables for a MANOVA? -- Power Analysis--A Priori Determination of Sample Size -- Novince (1977) Data for Multivariate Analysis of Variance Presented in Tables 5.3 and 5.4 -- Assumptions In Manova -- ANOVA and MANOVA Assumptions -- Independence Assumption -- What Should Be Done With Correlated Observations? -- Normality Assumption -- Multivariate Normality -- Assessing Univariate Normality -- Homogeneity of Variance Assumption.


Statistics for the Social Sciences

Statistics for the Social Sciences
Author: R. Mark Sirkin
Publisher: SAGE
Total Pages: 636
Release: 2006
Genre: Mathematics
ISBN: 9781412905466

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Do your students lack confidence in their ability to handle quantitative work? Do they get confused about how to enter statistical data on SAS, SPSS, and Excel programs? The new Third Edition of the bestselling Statistics for the Social Sciences is the solution to these dilemmas Popular in previous editions, this Third Edition continues to help build students' confidence and ability in doing statistical analysis by slowly moving from concepts that require little computational work to those that require more. Author R. Mark Sirkin once again demonstrates how statistics can be used so that students come to appreciate their usefulness rather than fearing them. Statistics for the Social Sciences emphasizes the analysis and interpretation of data to give students a feel for how data interpretation is related to the methods by which the information was obtained. The book includes lists of key concepts, chapter exercises, topic boxes, and more


Applied Multivariate Statistics for the Social Sciences

Applied Multivariate Statistics for the Social Sciences
Author: Keenan A. Pituch
Publisher: Routledge
Total Pages: 827
Release: 2015-12-07
Genre: Psychology
ISBN: 1317805917

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Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. 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 understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) A free online resources site at www.routledge.com/9780415836661 with data sets and syntax from the text, additional data sets, and instructor’s resources (including PowerPoint lecture slides for select chapters, a conversion guide for 5th edition adopters, and answers to exercises) Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.


Applied Multivariate Research

Applied Multivariate Research
Author: Lawrence S. Meyers
Publisher: SAGE Publications
Total Pages: 938
Release: 2016-10-28
Genre: Social Science
ISBN: 1506329780

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Using a conceptual, non-mathematical approach, the updated Third Edition provides full coverage of the wide range of multivariate topics that graduate students across the social and behavioral sciences encounter. Authors Lawrence S. Meyers, Glenn Gamst, and A. J. Guarino integrate innovative multicultural topics in examples throughout the book, which include both conceptual and practical coverage of: statistical techniques of data screening; multiple regression; multilevel modeling; exploratory factor analysis; discriminant analysis; structural equation modeling; structural equation modeling invariance; survival analysis; multidimensional scaling; and cluster analysis.