EDUCATION Exploratory factor analysis: A five-step guide for novices
01 Jan 2010-
TL;DR: In this article, an exploratory factor analysis (EFA) protocol is proposed to simplify the many guidelines and options associated with completing EFA, which can be used in education and clinical contexts by paramedics.
Abstract: Factor analysis is a multivariate statistical approach commonly used in psychology, education, and more recently in the health-related professions. This paper will attempt to provide novice researchers with a simplified approach to undertaking exploratory factor analysis (EFA). As the paramedic body of knowledge continues to grow, indeed into scale and instrument psychometrics, it is timely that an uncomplicated article such as this be offered to the paramedic readership both nationally and internationally. Factor analysis is an important tool that can be used in the development, refinement, and evaluation of tests, scales, and measures that can be used in education and clinical contexts by paramedics. The objective of the paper is to provide an exploratory factor analysis protocol, offering potential researchers with an empirically-supported systematic approach that simplifies the many guidelines and options associated with completing EFA.
Citations
More filters
TL;DR: The objective is to offer the interested applied researcher updated guidance on how to perform an Exploratory Item Factor Analysis, according to the "post-Little Jiffy" psychometrics.
Abstract: Exploratory Factor analysis is one of the techniques used in the development, validation and adaptation of psychological measurement instruments Its use spread during the 1960s and has been growing exponentially thanks to the advancement of information technology The criteria used, of course, have also evolved But the applied researchers, who use this technique as a routine, remain often ignorant of all this In the last few decades numerous studies have denounced this situation There is an urgent need to update the classic criteria The incorporation of the most suitable criteria will improve the quality of our research In this work we review the classic criteria and, depending on the case, we also propose current criteria to replace or complement the former Our objective is to offer the interested applied researcher updated guidance on how to perform an Exploratory Item Factor Analysis, according to the “post-Little Jiffy” psychometrics This review and the guide with the corresponding recommendations have been articulated in four large blocks: 1) the data type and the matrix of association, 2) the method of factor estimation, 3) the number of factors to be retained, and 4) the method of rotation and allocation of items An abridged version of the complete guide is provided at the end of the article
738 citations
TL;DR: The pilot version of the cyberchondria severity scale (CSS) demonstrated good psychometric properties; the subscales had high internal consistency, along with good concurrent and convergent validity.
179 citations
TL;DR: In this article, the authors present a model for the measurement of corporate sustainability -complex performance indicator (CPI) which integrates the environmental, social, economic and corporate governance performance of the company.
142 citations
TL;DR: In this article, a study has been conducted to assess the vulnerabilities of the coastal region of Bangladesh by considering the IPCC framework of vulnerability studies and using multivariate statistical techniques and a total of 31 indicators have been used of which 24 are socio-economic and 7 are natural (exposure) indicators and these indicators were retrieved from the secondary source.
129 citations
TL;DR: In this article, the authors used mixed methods of qualitative and quantitative research approaches to determine effective approaches to eliminate and/or minimise waste generation in construction projects and found that both technologies and attitudinal approaches require improvement to eliminate or minimize waste generation.
Abstract: Construction waste generation has been identified as one of the major issues in the construction industry due to its direct impacts on the environment as well as the efficiency of the construction industry. As the industry cannot continue to practice if the environmental resources on which it depends are depleted, the significance of waste management needs to be understood in order to encourage stakeholders to achieve related goals. Therefore, this research aims to determine effective approaches to eliminate and/or minimise waste generation in construction projects. Mixed methods were adopted by combining qualitative and quantitative research approaches. Interviews and a questionnaire survey were conducted as the primary data collection methods. The findings reveal twenty six critical solutions for waste management. Five factors of solutions for waste management were extracted from the exploratory factor analysis. These factors were: team building and supervision; strategic guidelines in waste management; proper design and documentation; innovation in waste management decisions; and lifecycle management. The evidence from this study suggests that both technologies and attitudinal approaches require improvement to eliminate/minimise waste generation in construction projects. Similarly, attention should be paid to being mindful of the environmental effects of waste generation and avoiding waste generation as early as possible in construction projects.
122 citations
References
More filters
Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations
TL;DR: This book deals with probability distributions, discrete and continuous densities, distribution functions, bivariate distributions, means, variances, covariance, correlation, and some random process material.
Abstract: Chapter 3 deals with probability distributions, discrete and continuous densities, distribution functions, bivariate distributions, means, variances, covariance, correlation, and some random process material. Chapter 4 is a detailed study of the concept of utility including the psychological aspects, risk, attributes, rules for utilities, multidimensional utility, and normal form of analysis. Chapter 5 treats games and optimization, linear optimization, and mixed strategies. Entropy is the topic of Chapter 6 with sections devoted to entropy, disorder, information, Shannon’s theorem, demon’s roulette, Maxwell– Boltzmann distribution, Schrodinger’s nutshell, maximum entropy probability distributions, blackbodies, and Bose–Einstein distribution. Chapter 7 is standard statistical fare including transformations of random variables, characteristic functions, generating functions, and the classic limit theorems such as the central limit theorem and the laws of large numbers. Chapter 8 is about exchangeability and inference with sections on Bayesian techniques and classical inference. Partial exchangeability is also treated. Chapter 9 considers such things as order statistics, extreme value, intensity, hazard functions, and Poisson processes. Chapter 10 covers basic elements of risk and reliability, while Chapter 11 is devoted to curve fitting, regression, and Monte Carlo simulation. There is an ample number of exercises at the ends of the chapters with answers or comments on many of them in an appendix in the back of the book. Other appendices are on the common discrete and continuous distributions and mathematical aspects of integration.
19,893 citations
TL;DR: The Scree Test for the Number Of Factors this paper was first proposed in 1966 and has been used extensively in the field of behavioral analysis since then, e.g., in this paper.
Abstract: (1966). The Scree Test For The Number Of Factors. Multivariate Behavioral Research: Vol. 1, No. 2, pp. 245-276.
12,228 citations
TL;DR: This paper is concerned with the construction of planes of closest fit to systems of points in space and the relationships between these planes and the planes themselves.
Abstract: (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science: Vol. 2, No. 11, pp. 559-572.
10,656 citations