Search results for: exploratory-factor-analysis

Exploratory Factor Analysis

Author : Leandre R. Fabrigar
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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.

Exploratory Factor Analysis

Author : W. Holmes Finch
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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.

Exploratory Factor Analysis

Author : William Holmes Finch
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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.

Exploratory Factor Analysis

Author : Diana Mindrila
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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.

An Easy Guide to Factor Analysis

Author : Paul Kline
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Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, and concludes with a discussion of the use of the technique with various examples. An Easy Guide to Factor Analysis is the clearest, most comprehensible introduction to factor analysis for students. All those who need to use statistics in psychology and the social sciences will find it invaluable. Paul Kline is Professor of Psychometrics at the University of Exeter. He has been using and teaching factor analysis for thirty years. His previous books include Intelligence: the psychometric view (Routledge 1990) and The Handbook of Psychological Testing (Routledge 1992).

Exploratory Factor Analysis with SAS

Author : Jason W Osborne
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Explore the mysteries of Exploratory Factor Analysis (EFA) with SAS with an applied and user-friendly approach. Exploratory Factor Analysis with SAS focuses solely on EFA, presenting a thorough and modern treatise on the different options, in accessible language targeted to the practicing statistician or researcher. This book provides real-world examples using real data, guidance for implementing best practices in the context of SAS, interpretation of results for end users, and it provides resources on the book's author page. Faculty teaching with this book can utilize these resources for their classes, and individual users can learn at their own pace, reinforcing their comprehension as they go. Exploratory Factor Analysis with SAS reviews each of the major steps in EFA: data cleaning, extraction, rotation, interpretation, and replication. The last step, replication, is discussed less frequently in the context of EFA but, as we show, the results are of considerable use. Finally, two other practices that are commonly applied in EFA, estimation of factor scores and higher-order factors, are reviewed. Best practices are highlighted throughout the chapters. A rudimentary working knowledge of SAS is required but no familiarity with EFA or with the SAS routines that are related to EFA is assumed.

Best Practices in Exploratory Factor Analysis

Author : Jason W. Osborne
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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

Exploratory Factor Analysis Tutorial

Author : David Modic
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Multivariate Exploratory Data Analysis

Author : Allen Yates
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In an exciting return to the roots of factor analysis, Allen Yates reviews its early history to clarify original objectives created by its discoverers and early developers. He then shows how computers can be used to accomplish the goals established by these early visionaries, while taking into account modern developments in the field of statistics that legitimize exploratory data analysis as a technique of discovery. The book presents a unique perspective on all phases of exploratory factor analysis. In doing so, the popular objectives of the method are literally turned upside down both at the stage where the model is being fitted to data and in the subsequent stage of simple structure transformation for meaningful interpretation. What results is a fully integrated approach to exploratory analysis of associations among observed variables, revealing underlying structure in a totally new and much more invariant manner than ever before possible.

A Step by Step Guide to Exploratory Factor Analysis with R and RStudio

Author : Marley W. Watkins
File Size : 83.19 MB
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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.

EFAP II

Author : K. G. Jöreskog
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Visualizing Exploratory Factor Analysis Models

Author : Sigbert Klinke
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Factor Analysis and Related Methods

Author : Roderick P. McDonald
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Factor Analysis is a genetic term for a somewhat vaguely delimited set of techniques for data processing, mainly applicable to the social and biological sciences. These techniques have been developed for the analysis of mutual relationships among a number of measurements made on a number of measurable entities. In the broad sense, factor analysis comprises a number of statistical models which yield testable hypotheses -- hypotheses that may confirm or disconfirm in terms of the usual statistical procedures for making tests of significance. It also comprises a number of simplifying procedures for the approximate description of data, which do not in any sense constitute disconfirmable hypotheses, except in the loose sense that they supply approximations to the data. In literature, the two types of analysis have often been confused. This book clarifies the concepts of factor analysis for students or professionals in the social sciences who wish to know the technique, rather than the mathematics, of factor theory. Mathematical concepts are described to have an intuitive meaning for the non-mathematical reader. An account of the elements of matrix algebra, in the appendix, and the (mathematical) notes following each chapter will help the reader who wishes to receive a more advanced treatment of the subject. Factor Analysis and Related Methods should prove a useful text for graduate and advanced undergraduate students in economics, the behavioral sciences, and education. Researchers and practitioners in those fields will also find this book a handy reference.

Exploratory Factor Analysis with SAS

Author : Erin S Banjanovic
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Explore the mysteries of Exploratory Factor Analysis (EFA) with SAS with an applied and user-friendly approach.Exploratory Factor Analysis with SAS focuses solely on EFA, presenting a thorough and modern treatise on the different options, in accessible language targeted to the practicing statistician or researcher. This book provides real-world examples using real data, guidance for implementing best practices in the context of SAS, interpretation of results for end users, and it provides resources on the book's author page. Faculty teaching with this book can utilize these resources for their classes, and individual users can learn at their own pace, reinforcing their comprehension as they go.Exploratory Factor Analysis with SAS reviews each of the major steps in EFA: data cleaning, extraction, rotation, interpretation, and replication. The last step, replication, is discussed less frequently in the context of EFA but, as we show, the results are of considerable use. Finally, two other practices that are commonly applied in EFA, estimation of factor scores and higher-order factors, are reviewed. Best practices are highlighted throughout the chapters.A rudimentary working knowledge of SAS is required but no familiarity with EFA or with the SAS routines that are related to EFA is assumed.

EFAP 2

Author : Karl G. Jöreskog
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Exploratory and Confirmatory Factor Analysis

Author : Bruce Thompson
File Size : 82.88 MB
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"Investigation of the structure underlying variables (or people, or time) has intrigued social scientists since the early origins of psychology. Conducting one's first factor analysis can yield a sense of awe regarding the power of these methods to inform judgment regarding the dimensions underlying constructs. This book presents the important concepts required for implementing two disciplines of factor analysis: exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The book may be unique in its effort to present both analyses within the single rubric of the general linear model. Throughout the book canons of best factor analytic practice are presented and explained. The book has been written to strike a happy medium between accuracy and completeness versus overwhelming technical complexity. An actual data set, randomly drawn from a large-scale international study involving faculty and graduate student perceptions of academic libraries, is presented in Appendix A. Throughout the book different combinations of these variables and participants are used to illustrate EFA and CFA applications"--Preface. (PsycINFO Database Record (c) 2005 APA, all rights reserved).

A Step by Step Guide to Exploratory Factor Analysis with Stata

Author : Marley W. Watkins
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This is a concise, easy to use, step-by-step guide for applied researchers conducting exploratory factor analysis (EFA) using Stata. In this book, Dr. Watkins systematically reviews each decision step in EFA with screen shots of Stata 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 when 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.

Exploratory Factor Analysis

Author : W. Holmes Finch
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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.

Confirmatory Factor Analysis

Author : J. Scott Long
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A statistical method that will appeal to two groups in particular - those who are currently using the more traditional technique of exploratory factor analysis and those who are interested in the analysis of covariance structures, commonly known as the LISREL model. The first group will find that this technique may be more appropriate to the analysis of their research problems while the second group will find that confirmatory factor analysis is a useful first step to understanding the LISREL model. This book, and its companion volume, Covariance Structure Models, are designed to be read consecutively. The proofs presented are simple, but the reader must feel comfortable with matrix algebra in order to understand the model.

Generalized Maximum Entropy Estimator for Exploratory Factor Analysis

Author : Yen Lee
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Exploratory factor analysis (EFA) has been considered to be a large-sample technique for a long period of time. When the sample size is small, the obtained solutions are subject to poor factor recovery and high risks of Heywood cases. In this dissertation, a generalized maximum estimator for EFA (GME-EFA) is proposed, which can circumvent the occurrence of Heywood cases and provide reliable solutions when the sample size is relatively small and the data are not well conditioned. This estimator adopts the concept of the generalized maximum entropy estimator, and is tailored to fit the nature of EFA. Four Monte Carlo studies were carried out to evaluate its performance under various conditions. The results showed that the GME-EFA has a higher probability to recover the factor structure than maximum likelihood and principal axis factoring when a proper shrinkage parameter was selected.