• Social Science

Multivariate Analysis Techniques in Social Science Research

From Problem to Analysis
Author: Jacques Tacq
Publisher: SAGE
ISBN: 9780761952732
Category: Social Science
Page: 411
View: 9314
Unlike most statistical texts, this book breathes real life into multivariate analysis. Starting with a range of actual research examples in the social sciences, it demonstrates how to make the most appropriate choice of technique. The examples are drawn from a broad spectrum of disciplines including: sociology, psychology, economics, political science and international comparative research.

    • Psychology

Longitudinal Data Analysis

A Practical Guide for Researchers in Aging, Health, and Social Sciences
Author: Jason Newsom,Richard N. Jones,Scott M. Hofer
Publisher: Routledge
ISBN: 1136705465
Category: Psychology
Page: 405
View: 9347
This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

    • Mathematics

Handbook of Applied Multivariate Statistics and Mathematical Modeling


Author: Howard E.A. Tinsley,Steven D. Brown
Publisher: Academic Press
ISBN: 9780080533568
Category: Mathematics
Page: 721
View: 8982
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.

Advanced and Multivariate Statistical Methods for Social Science Research


Author: Soleman H. Abu-Bader
Publisher: N.A
ISBN: 9780190616397
Category:
Page: 350
View: 8584
Unlike other advanced statistical texts, this book combines the theory and practice behind a number of statistical techniques which students of the social sciences need to evaluate, analyze, and test their research hypotheses.Each chapter discusses the purpose, rationale, and assumptions for using each statistical test, rather than focusing on the memorization of formulas. The tests are further elucidated throughout the text by real examples of analysis. Of particular value to students is the book's detailed discussionof how to utilize SPSS to run each test, read its output, interpret, and write the results.Advanced and Multivariate Statistical Methods for Social Science Research is an indispensable resource for students of disciplines as varied as social work, nursing, public health, psychology, and education.Electronic database files are available for student and instructor use.http://lyceumbooks.com/StudentResources.htm

    • Psychology

Applied Multivariate Statistics for the Social Sciences

Analyses with SAS and IBM’s SPSS, Sixth Edition
Author: Keenan A. Pituch,James P. Stevens
Publisher: Routledge
ISBN: 1317805917
Category: Psychology
Page: 814
View: 1631
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.

    • Computers

JMP for Basic Univariate and Multivariate Statistics

Methods for Researchers and Social Scientists, Second Edition
Author: Ann Lehman,Norm O'Rourke,Larry Hatcher,Edward Stepanski
Publisher: SAS Institute
ISBN: 1612906036
Category: Computers
Page: 558
View: 6172
Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition. Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way that is suitable for publication, perform a wide range of JMP analyses, interpret the results, and more. Topics include screening data for errors selecting subsets computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale performing bivariate analyses for all types of variables performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA) Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation. This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.

    • Social Science

Using R With Multivariate Statistics


Author: Randall E. Schumacker
Publisher: SAGE Publications
ISBN: 1483377989
Category: Social Science
Page: 408
View: 2532
Using R with Multivariate Statistics by Randall E. Schumacker is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis. Designed to serve as a companion to a more comprehensive text on multivariate statistics, this book helps students and researchers in the social and behavioral sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.

    • Mathematics

Multivariate Analysis for the Biobehavioral and Social Sciences

A Graphical Approach
Author: Bruce L. Brown,Suzanne B. Hendrix,Dawson W. Hedges,Timothy B. Smith
Publisher: John Wiley & Sons
ISBN: 1118131614
Category: Mathematics
Page: 496
View: 8879
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.

    • Social Science

Applied Multivariate Statistics for the Social Sciences


Author: James Paul Stevens
Publisher: Taylor & Francis
ISBN: 0805859012
Category: Social Science
Page: 651
View: 6089
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.

    • Social Science

Applied Statistics Using Stata

A Guide for the Social Sciences
Author: Mehmet Mehmetoglu,Tor Georg Jakobsen
Publisher: SAGE
ISBN: 1473987903
Category: Social Science
Page: 376
View: 5614
Clear, intuitive and written with the social science student in mind, this book represents the ideal combination of statistical theory and practice. It focuses on questions that can be answered using statistics and addresses common themes and problems in a straightforward, easy-to-follow manner. The book carefully combines the conceptual aspects of statistics with detailed technical advice providing both the ‘why’ of statistics and the ‘how’. Built upon a variety of engaging examples from across the social sciences it provides a rich collection of statistical methods and models. Students are encouraged to see the impact of theory whilst simultaneously learning how to manipulate software to meet their needs. The book also provides: Original case studies and data sets Practical guidance on how to run and test models in Stata Downloadable Stata programmes created to work alongside chapters A wide range of detailed applications using Stata Step-by-step notes on writing the relevant code. This excellent text will give anyone doing statistical research in the social sciences the theoretical, technical and applied knowledge needed to succeed.

    • Mathematics

Making Sense of Multivariate Data Analysis

An Intuitive Approach
Author: John Spicer
Publisher: SAGE
ISBN: 9781412904018
Category: Mathematics
Page: 233
View: 3054
'This book is a helpful guide to reading and understanding multivariate data analysis results in social and psychological research' --C. Y. Joanne Peng, Indiana University at Bloomington 'This book serves as a resource for readers who want to have an overall view of what encompasses multivariate analyses. The author has discussed some important issues rather philosophically (e.g., theory vs. data analysis). These points are valuable even for readers who have extensive training with multivariate analyses' --Jenn-Yun Tein, Arizona State University

    • Social Science

Basic Statistics in Multivariate Analysis


Author: Karen A. Randolph,Laura L. Myers
Publisher: Oxford University Press
ISBN: 0199764042
Category: Social Science
Page: 213
View: 5610
This pocket guide introduces readers to linear regression analysis, analysis of variance and covariance, and path analysis with an emphasis on the basic statistics. It prepares doctoral students and early career social work researchers with limited statistics exposure in the use of multivariate methods by providing an easy-to-understand presentation.

    • Psychology

Applied Meta-Analysis for Social Science Research


Author: Noel A. Card
Publisher: Guilford Publications
ISBN: 1462525008
Category: Psychology
Page: 377
View: 2627
Offering pragmatic guidance for planning and conducting a meta-analytic review, this book is written in an engaging, nontechnical style that makes it ideal for graduate course use or self-study. The author shows how to identify questions that can be answered using meta-analysis, retrieve both published and unpublished studies, create a coding manual, use traditional and unique effect size indices, and write a meta-analytic review. An ongoing example illustrates meta-analytic techniques. In addition to the fundamentals, the book discusses more advanced topics, such as artifact correction, random- and mixed-effects models, structural equation representations, and multivariate procedures. User-friendly features include annotated equations; discussions of alternative approaches; and "Practical Matters" sections that give advice on topics not often discussed in other books, such as linking meta-analytic results with theory and the utility of meta-analysis software programs. ÿ

    • Reference

The Chicago Guide to Writing about Multivariate Analysis, Second Edition


Author: Jane E. Miller
Publisher: University of Chicago Press
ISBN: 022603819X
Category: Reference
Page: 560
View: 8710
Many different people, from social scientists to government agencies to business professionals, depend on the results of multivariate models to inform their decisions. Researchers use these advanced statistical techniques to analyze relationships among multiple variables, such as how exercise and weight relate to the risk of heart disease, or how unemployment and interest rates affect economic growth. Yet, despite the widespread need to plainly and effectively explain the results of multivariate analyses to varied audiences, few are properly taught this critical skill. The Chicago Guide to Writing about Multivariate Analysis is the book researchers turn to when looking for guidance on how to clearly present statistical results and break through the jargon that often clouds writing about applications of statistical analysis. This new edition features even more topics and real-world examples, making it the must-have resource for anyone who needs to communicate complex research results. For this second edition, Jane E. Miller includes four new chapters that cover writing about interactions, writing about event history analysis, writing about multilevel models, and the “Goldilocks principle” for choosing the right size contrast for interpreting results for different variables. In addition, she has updated or added numerous examples, while retaining her clear voice and focus on writers thinking critically about their intended audience and objective. Online podcasts, templates, and an updated study guide will help readers apply skills from the book to their own projects and courses. This continues to be the only book that brings together all of the steps involved in communicating findings based on multivariate analysis—finding data, creating variables, estimating statistical models, calculating overall effects, organizing ideas, designing tables and charts, and writing prose—in a single volume. When aligned with Miller’s twelve fundamental principles for quantitative writing, this approach will empower readers—whether students or experienced researchers—to communicate their findings clearly and effectively.

    • Business & Economics

Multivariable Modeling and Multivariate Analysis for the Behavioral Sciences


Author: Brian S. Everitt
Publisher: CRC Press
ISBN: 1439807701
Category: Business & Economics
Page: 320
View: 762
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 the types and design of behavioral studies. He also explains how models are used in the analysis of data. After describing graphical methods, such as scatterplot matrices, the text covers simple linear regression, locally weighted regression, multiple linear regression, regression diagnostics, the equivalence of regression and ANOVA, the generalized linear model, and logistic regression. The author then discusses aspects of survival analysis, linear mixed effects models for longitudinal data, and the analysis of multivariate data. He also shows how to carry out principal components, factor, and cluster analyses. The final chapter presents approaches to analyzing multivariate observations from several different populations. Through real-life applications of statistical methodology, this book elucidates the implications of behavioral science studies for statistical analysis. It equips behavioral science students with enough statistical tools to help them succeed later on in their careers. Solutions to the problems as well as all R code and data sets for the examples are available at www.crcpress.com

    • Psychology

Data Analysis with Mplus


Author: Christian Geiser
Publisher: Guilford Press
ISBN: 1462507824
Category: Psychology
Page: 305
View: 351
A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (http://crmda.ku.edu/guilford/geiser) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.

    • Social Science

Theory-Based Data Analysis for the Social Sciences


Author: Carol S. Aneshensel
Publisher: SAGE
ISBN: 1412994357
Category: Social Science
Page: 446
View: 8037
This book presents a method for bringing data analysis and statistical technique into line with theory. The author begins by describing the elaboration model for analyzing the empirical association between variables. She then introduces a new concept into this model, the focal relationship. Building upon the focal relationship as the cornerstone for all subsequent analysis, two analytic strategies are developed to establish its internal validity: an exclusionary strategy to eliminate alternative explanations, and an inclusive strategy which looks at the interconnected set of relationships predicted by theory. Using real examples of social research, the author demonstrates the use of this approach for two common forms of analysis, multiple linear regression and logistic regression. Whether learning data analysis for the first time or adding new techniques to your repertoire, this book provides an excellent basis for theory-based data analysis.

    • Social Science

Applied Multivariate Research

Design and Interpretation
Author: Lawrence S. Meyers,Glenn Gamst,A.J. Guarino
Publisher: SAGE Publications
ISBN: 1506329780
Category: Social Science
Page: 1016
View: 5557
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.

    • Technology & Engineering

Research Methodology: A Guide for Researchers In Agricultural Science, Social Science and Other Related Fields


Author: Pradip Kumar Sahu
Publisher: Springer Science & Business Media
ISBN: 8132210204
Category: Technology & Engineering
Page: 432
View: 5490
This book is the outcome of more than 20 years of experience of the author in teaching and research field. The wider scope and coverage of the book will help not only the students/ researchers/professionals in the field of agriculture and allied disciplines, but also the researchers and practitioners in other fields. Written in simple and lucid language, the book would appeal to all those who are meant to be benefitted out of it. All efforts have been made to present "RESEARCH", its meaning, intention and usefulness. The book reflects current methodological techniques used in interdisciplinary research, as illustrated with many relevant worked out examples. Designing of research programme, selection of variables, collection of data and their analysis to interpret the data are discussed extensively. Statistical tools are complemented with real-life examples, making the otherwise complicated subject like statistics seem simpler. Attempts have been made to demonstrate how a user can solve the problems using simple computer-oriented programme. Emphasis is placed not only on solving the problems in various fields but also on drawing inferences from the problems. The importance of instruments and computers in research processes and statistical analyses along with their misuse/incorrect use is also discussed to make the user aware about the correct use of specific technique. In all the chapters, theories are combined with examples, and steps are enumerated to follow the correct use of the available packages like MSEXCELL, SPSS, SPAR1, SAS etc. Utmost care has been taken to present varied range of research problems along with their solutions in agriculture and allied fields which would be of immense use to readers.

Analyzing Social Science Data

50 Key Problems in Data Analysis
Author: Professor David de Vaus
Publisher: SAGE
ISBN: 9781446226100
Category:
Page: 402
View: 319
In this novel and refreshing textbook, David de Vaus directs students to the core of data analysis. The book is an authoritative guide to the problems facing beginners in the field. Analyzing Social Science Data guides students in: problems with the initial data; problems with the initial variables; how to handle too much data; how to generalize; problems of analyzing single variables; problems examining bivariate relationships; and problems examining multivariate relationships The book is a "tour de force" in making data analysis manageable and rewarding for today's undergraduate studying research methods. I'm full of admiration for this book. Once again, David de Vaus has come up with a superb book that is well written and organized and which will be a boon to a wide range of students. He has taken a vast array of problems that users of quantitative data analysis procedures are likely to encounter. The selection of issues and problems ... reflects the experience of a true practitioner with a grasp of his field and of the intricacies of the research process. The selection of issues clearly derives also from experience of teaching students how to do research and analyse data....A large number of practitioners will want the book. I was surprised at how much I learned from this. This will be a vital book for the bookshelves of practitioners of the craft of quantitative data analysis' - "Alan Bryman, Professor of Social Research, Loughborough University