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Analysis of Variance Designs

Analysis of Variance Designs Author Glenn Gamst
ISBN-10 9780521874816
Release 2008-09-01
Pages 578
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This textbook explains ANOVA designs for advanced undergraduates and graduate students in the behavioural sciences.



Performing Data Analysis Using IBM SPSS

Performing Data Analysis Using IBM SPSS Author Lawrence S. Meyers
ISBN-10 9781118357019
Release 2013-08-12
Pages 736
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Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis.



Introduction to Analysis of Variance Design Analyis Interpretation

Introduction to Analysis of Variance  Design  Analyis   Interpretation Author J. Rick Turner
ISBN-10 9780803970755
Release 2001-04-13
Pages 180
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Organized so that the reader moves from the simplest type of design to more complex ones, the authors introduce five different kinds of ANOVA techniques and explain which design//analysis is appropriate to answer specific questions.



Statistical Survey Design and Evaluating Impact

Statistical Survey Design and Evaluating Impact Author Tarun Kumar Roy
ISBN-10 9781107146457
Release 2016-03-14
Pages 400
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Statistical designs, sample surveys and evaluation designs are fundamental tools for solving queries related to population parameters and the effects of public programs and policies. This book explores the concepts of effective sampling and evaluation techniques in a cohesive and concise manner. Sampling design techniques, including simple random sampling, stratified sampling, systematic sampling and cluster sampling, are presented in detail. These techniques play a vital role when choosing an appropriate sample survey design. The concepts of multistage design, non-sampling errors and evaluation techniques including before-after design, one-time treatment and control design are discussed extensively. The book focuses on different methods of estimation, including multiple regression analysis and logistic regression. It covers the issue of bias in a design, the source of such bias and ways to overcome it. Clear guidelines with remedial measures are outlined to facilitate choosing a suitable sampling design.



Data Analysis Using SAS Enterprise Guide

Data Analysis Using SAS Enterprise Guide Author Lawrence S. Meyers
ISBN-10 9780521112680
Release 2009-08-17
Pages 378
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This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.



Reporting Research in Psychology

Reporting Research in Psychology Author Harris M. Cooper
ISBN-10 UOM:39076002914138
Release 2011
Pages 137
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"An educational guide based on the Publication manual of the American Psychological Association"--Cover.



An Introduction to Statistical Methods and Data Analysis

An Introduction to Statistical Methods and Data Analysis Author R. Lyman Ott
ISBN-10 9781111779986
Release 2008-12-30
Pages 1296
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Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



Applied Multivariate Research

Applied Multivariate Research Author Lawrence S. Meyers
ISBN-10 9781506329789
Release 2016-10-28
Pages 1016
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Using a conceptual, non-mathematical approach, the updated Third Edition of Applied Multivariate Research: Design and Interpretation 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.



Introduction to Mediation Moderation and Conditional Process Analysis Second Edition

Introduction to Mediation  Moderation  and Conditional Process Analysis  Second Edition Author Andrew F. Hayes
ISBN-10 9781462534661
Release 2017-10-30
Pages 692
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Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website (www.afhayes.com), along with links to download PROCESS. New to This Edition *Chapters on using each type of analysis with multicategorical antecedent variables. *Example analyses using PROCESS v3, with annotated outputs throughout the book. *More tips and advice, including new or revised discussions of formally testing moderation of a mechanism using the index of moderated mediation; effect size in mediation analysis; comparing conditional effects in models with more than one moderator; using R code for visualizing interactions; distinguishing between testing interaction and probing it; and more. *Rewritten Appendix A, which provides the only documentation of PROCESS v3, including 13 new preprogrammed models that combine moderation with serial mediation or parallel and serial mediation. *Appendix B, describing how to create customized models in PROCESS v3 or edit preprogrammed models.



Introduction to Design and Analysis of Experiments

Introduction to Design and Analysis of Experiments Author George W. Cobb
ISBN-10 047041216X
Release 2008-06-10
Pages 832
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Introduction to Design and Analysis of Experiments explains how to choose sound and suitable design structures and engages students in understanding the interpretive and constructive natures of data analysis and experimental design. Cobb's approach allows students to build a deep understanding of statistical concepts over time as they analyze and design experiments. The field of statistics is presented as a matrix, rather than a hierarchy, of related concepts. Developed over years of classroom use, this text can be used as an introduction to statistics emphasizing experimental design or as an elementary graduate survey course. Widely praised for its exceptional range of intelligent and creative exercises, and for its large number of examples and data sets, Introduction to Design and Analysis of Experiments--now offered in a convenient paperback format--helps students increase their understanding of the material as they come to see the connections between diverse statistical concepts that arise from the experiments around which the text is built.



Multivariate Analysis for the Biobehavioral and Social Sciences

Multivariate Analysis for the Biobehavioral and Social Sciences Author Bruce L. Brown
ISBN-10 9781118131619
Release 2011-11-01
Pages 496
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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.



Global Development of Community Colleges Technical Colleges and Further Education Programs

Global Development of Community Colleges  Technical Colleges  and Further Education Programs Author Paul A. Elsner
ISBN-10 9780871173867
Release 2008
Pages 295
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"Describes the similarities and differences between the community colleges and their equivalent in 23 countries around the world"--Provided by publisher.



Mathematical Statistics with Applications in R

Mathematical Statistics with Applications in R Author Kandethody M. Ramachandran
ISBN-10 9780124171329
Release 2014-09-14
Pages 826
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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner. This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students. Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. Step-by-step procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, real-world chapter projects Provides an optional section in each chapter on using Minitab, SPSS and SAS commands Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods



Designing Experiments and Analyzing Data

Designing Experiments and Analyzing Data Author Scott E. Maxwell
ISBN-10 9781317284550
Release 2017-09-11
Pages 1056
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Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features?further facilitate understanding:?examples of published research demonstrate the applicability of each chapter’s content; flowcharts?assist in choosing the most appropriate procedure;?end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and?extensive sets of exercises?help develop a deeper understanding of the subject.?Detailed solutions?for some of the exercises and?realistic data sets?are included on the website (DesigningExperiments.com). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.



ANOVA and ANCOVA

ANOVA and ANCOVA Author Andrew Rutherford
ISBN-10 9781118491690
Release 2012-08-29
Pages 360
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Provides an in-depth treatment of ANOVA and ANCOVA techniques from a linear model perspective ANOVA and ANCOVA: A GLM Approach provides a contemporary look at the general linear model (GLM) approach to the analysis of variance (ANOVA) of one- and two-factor psychological experiments. With its organized and comprehensive presentation, the book successfully guides readers through conventional statistical concepts and how to interpret them in GLM terms, treating the main single- and multi-factor designs as they relate to ANOVA and ANCOVA. The book begins with a brief history of the separate development of ANOVA and regression analyses, and then goes on to demonstrate how both analyses are incorporated into the understanding of GLMs. This new edition now explains specific and multiple comparisons of experimental conditions before and after the Omnibus ANOVA, and describes the estimation of effect sizes and power analyses leading to the determination of appropriate sample sizes for experiments to be conducted. Topics that have been expanded upon and added include: Discussion of optimal experimental designs Different approaches to carrying out the simple effect analyses and pairwise comparisons with a focus on related and repeated measure analyses The issue of inflated Type 1 error due to multiple hypotheses testing Worked examples of Shaffer's R test, which accommodates logical relations amongst hypotheses ANOVA and ANCOVA: A GLM Approach, Second Edition is an excellent book for courses on linear modeling at the graduate level. It is also a suitable reference for researchers and practitioners in the fields of psychology and the biomedical and social sciences.



Statistical Methods for Psychology

Statistical Methods for Psychology Author David Howell
ISBN-10 9781111835484
Release 2012-01-01
Pages 792
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STATISTICAL METHODS FOR PSYCHOLOGY surveys the statistical techniques commonly used in the behavioral and social sciences, particularly psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasizes conceptual understanding. This Eighth Edition continues to focus students on two key themes that are the cornerstones of this book’s success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment. New and expanded topics--reflecting the evolving realm of statistical methods--include effect size, meta-analysis, and treatment of missing data. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



Data Analysis Using SAS Enterprise Guide

Data Analysis Using SAS Enterprise Guide Author Lawrence S. Meyers
ISBN-10 9780521112680
Release 2009-08-17
Pages 378
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This book presents the basic procedures for utilizing SAS Enterprise Guide to analyze statistical data. SAS Enterprise Guide is a graphical user interface (point and click) to the main SAS application. Each chapter contains a brief conceptual overview and then guides the reader through concrete step-by-step examples to complete the analyses. The eleven sections of the book cover a wide range of statistical procedures including descriptive statistics, correlation and simple regression, t tests, one-way chi square, data transformations, multiple regression, analysis of variance, analysis of covariance, multivariate analysis of variance, factor analysis, and canonical correlation analysis. Designed to be used either as a stand-alone resource or as an accompaniment to a statistics course, the book offers a smooth path to statistical analysis with SAS Enterprise Guide for advanced undergraduate and beginning graduate students, as well as professionals in psychology, education, business, health, social work, sociology, and many other fields.