Exploratory Factor Analysis PDF Download

Author: Leandre R. Fabrigar
Publisher: Oxford University Press
ISBN: 0199734178
Category : Medical
Languages : en
Pages : 170

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Book Description
This book provides a non-mathematical introduction to the theory and application of Exploratory Factor Analysis. Among the issues discussed are the use of confirmatory versus exploratory factor analysis, the use of principal components analysis versus common factor analysis, and procedures for determining the appropriate number of factors.

Author: Marley W. Watkins
Publisher: Routledge
ISBN: 1000336824
Category : Psychology
Languages : en
Pages : 227

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Book Description
This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using the open source software R. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of R and RStudio code, and recommends evidence-based best practice procedures. This is an eminently applied, practical approach with few or no formulas and is aimed at readers with little to no mathematical background. Dr. Watkins maintains an accessible tone throughout and uses minimal jargon and formula to help facilitate grasp of the key issues users will face while applying EFA, along with how to implement, interpret, and report results. Copious scholarly references and quotations are included to support the reader in responding to editorial reviews. This is a valuable resource for upper-level undergraduate and postgraduate students, as well as for more experienced researchers undertaking multivariate or structure equation modeling courses across the behavioral, medical, and social sciences.

Author: W. Holmes Finch
Publisher: SAGE Publications
ISBN: 1544339879
Category : Social Science
Languages : en
Pages : 133

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Book Description
A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. Exploratory Factor Analysis by W. Holmes Finch provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data. It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.

Author: Howard E.A. Tinsley
Publisher: Academic Press
ISBN: 9780080533568
Category : Mathematics
Languages : en
Pages : 721

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Book Description
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.

Author: Jason W. Osborne
Publisher: Createspace Independent Publishing Platform
ISBN: 9781500594343
Category : Factor analysis
Languages : en
Pages : 0

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Book Description
Best Practices in Exploratory Factor Analysis (EFA) is a practitioner-oriented look at this popular and often-misunderstood statistical technique. We avoid formulas and matrix algebra, instead focusing on evidence-based best practices so you can focus on getting the most from your data.Each chapter reviews important concepts, uses real-world data to provide authentic examples of analyses, and provides guidance for interpreting the results of these analysis. Not only does this book clarify often-confusing issues like various extraction techniques, what rotation is really rotating, and how to use parallel analysis and MAP criteria to decide how many factors you have, but it also introduces replication statistics and bootstrap analysis so that you can better understand how precisely your data are helping you estimate population parameters. Bootstrap analysis also informs readers of your work as to the likelihood of replication, which can give you more credibility. At the end of each chapter, the author has recommendations as to how to enhance your mastery of the material, including access to the data sets used in the chapter through his web site. Other resources include syntax and macros for easily incorporating these progressive aspects of exploratory factor analysis into your practice. The web site will also include enrichment activities, answer keys to select exercises, and other resources. The fourth "best practices" book by the author, Best Practices in Exploratory Factor Analysis continues the tradition of clearly-written, accessible guides for those just learning quantitative methods or for those who have been researching for decades.NEW in August 2014! Chapters on factor scores, higher-order factor analysis, and reliability. Chapters: 1 INTRODUCTION TO EXPLORATORY FACTOR ANALYSIS 2 EXTRACTION AND ROTATION 3 SAMPLE SIZE MATTERS 4 REPLICATION STATISTICS IN EFA 5 BOOTSTRAP APPLICATIONS IN EFA 6 DATA CLEANING AND EFA 7 ARE FACTOR SCORES A GOOD IDEA? 8 HIGHER ORDER FACTORS 9 AFTER THE EFA: INTERNAL CONSISTENCY 10 SUMMARY AND CONCLUSIONS

Author: John R. Nesselroade
Publisher: Springer Science & Business Media
ISBN: 1461308933
Category : Psychology
Languages : en
Pages : 977

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Book Description
When the first edition of this Handbook was fields are likely to be hard reading, but anyone who wants to get in touch with the published in 1966 I scarcely gave thought to a future edition. Its whole purpose was to growing edges will find something to meet his inaugurate a radical new outlook on ex taste. perimental psychology, and if that could be Of course, this book will need teachers. As accomplished it was sufficient reward. In the it supersedes the narrow conceptions of 22 years since we have seen adequate-indeed models and statistics still taught as bivariate staggering-evidence that the growth of a new and ANOV A methods of experiment, in so branch of psychological method in science has many universities, those universities will need become established. The volume of research to expand their faculties with newly trained has grown apace in the journals and has young people. The old vicious circle of opened up new areas and a surprising increase obsoletely trained members turning out new of knowledge in methodology. obsoletely trained members has to be The credit for calling attention to the need recognized and broken. And wherever re for new guidance belongs to many members search deals with integral wholes-in per of the Society of Multivariate Experimental sonalities, processes, and groups-researchers Psychology, but the actual innervation is due will recognize the vast new future that to the skill and endurance of one man, John multivariate methods open up.

Author: Diana Mindrila
Publisher:
ISBN: 9781536124866
Category : Exploratory factor analysis
Languages : en
Pages : 0

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Book Description
In education, researchers often work with complex data sets that include a multitude of variables. One question that often arises in such contexts is whether the structure of associations that underlies the data is accounted for by a latent construct. Exploratory factor analysis is a multivariate correlational procedure that helps researchers overcome such challenges. It helps reduce large data sets into main components or identify distinct constructs that account for the pattern of correlations among observed variables. These unobservable constructs are referred to as common factors, latent variables, or internal attributes, and they exert linear influences on more than one observed variable. Although exploratory factor analysis is widely used, many applied educational researchers and practitioners are not yet familiar with this procedure and are intimidated by the technical terminology. This book provides a conceptual description of this method and includes a collection of applied research studies that illustrates the application of exploratory factor analysis in school improvement research. The first chapter provides a theoretical overview of exploratory factor analysis. It explains the purposes for which this procedure can be used, the related terminology, the distinction between key concepts, the steps that must be taken, and the criteria for making the decisions. This information can serve as a starting point for researchers who need a brief, conceptual introduction to this topic. The following chapters present a series of research studies in which exploratory factor analysis was employed either by itself or in conjunction with other statistical procedures. The studies presented in this book address a variety of research problems in the field of school improvement. They specify how the factor analytic procedure was applied, and explain the theoretical contributions and the practical applications of the factor analytic results. In most studies, results from factor analysis were used for subsequent statistical procedures, thus helping researchers address more complex research questions and enriching the results.

Author: Timothy A. Brown
Publisher: Guilford Publications
ISBN: 146251779X
Category : Science
Languages : en
Pages : 482

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Book Description
This accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA) for its emphasis on practical and conceptual aspects rather than mathematics or formulas. Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities ...

Author: Dennis Howitt
Publisher: Pearson Education
ISBN: 9780132051613
Category : CD-ROMs
Languages : en
Pages : 564

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Book Description
Introduction to Statistics in Psychology4th edition is the complete guide to statistics for psychology students. Its range is exceptional in order to meet student needs throughout their undergraduate degree and beyond. By keeping to simple mathematics, step by step explanations of all the important statistical concepts, tests and procedures ensure that students understand data analysis properly. Pedagogical features such as ‘research design issues’, ‘calculations’ and the advice boxes help structure study into manageable sections whilst the overview and key points help with revision. Plus this 4th edition includes even more examples to bring to life how different statistical tests can be used in different areas of psychology.

Author:
Publisher: Elsevier
ISBN: 0080471269
Category : Mathematics
Languages : en
Pages : 458

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Book Description
This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.