Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

Name: Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations
ISBN: 9781446286104

doi:10.4135/9781446286104

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Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

Edited by:W. Paul Vogt & R. Burke Johnson
Publisher: SAGE Publications Ltd
Series: SAGE Benchmarks in Social Research Methods
- Publication year: 2012
- Online pub date: February 05, 2015
Discipline: Regression & Correlation
DOI: https://doi.org/10.4135/9781446286104
Keywords:
attitudes, confirmatory factor analysis, correlation, counseling psychology, effect size, evaluation, exploratory factor analysis, factor analysis, five-factor model of personality, hypothesis testing, latent variables, logistic regression, measurement, mediation, missing data, multilevel modeling, multiple regression, personality, regression, regression analysis, research design, sample size, scaling, simulations, social work, sociology, structural equation modeling, structural equation modelling, structural models, testing
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Online ISBN: 9781446286104

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Exploratory factor analysis (EFA) has long been used in the social sciences to depict the relationships between variables/items and latent traits. Researchers face many choices when using EFA, including the choice of rotation criterion, which can be difficult given that few research articles have discussed and/or demonstrated their differences. The goal of the current study is to help fill this gap by reviewing and demonstrating the utility of several rotation criteria. Furthermore, this article discusses and demonstrates the importance of using factor pattern loading standard errors for hypothesis testing. The choice of a rotation criterion and the use of standard errors in evaluating factor loadings are essential so researchers can make informed decisions concerning the factor structure. This study demonstrates that depending on the rotation criterion selected, and the complexity of the factor pattern matrix, the interfactor correlations and factor pattern loadings can vary substantially. It is also illustrated that the magnitude of the factor loading standard errors can result in different factor structures. Implications and future directions are discussed.

Full text

‘Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations’, ThomasA.Schmitt and DanielA.SassEducational and Psychological Measurement, 71 (1) (2011): 95–113. Published by SAGE Publications, Inc. Reprinted with permission.

Chronology of Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

Volume I
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    1904
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    Educational and Psychological Measurement
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    Carl Huberty
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    Educational and Psychological Measurement
  - 7. Regression to the Mean, Murder Rates, and Shall-Issue Laws
    
    Patricia Grambsch
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    The American Statistician
  - 8. A Regression Paradox for Linear Models: Sufficient Conditions and Relation to Simpson's Paradox
    
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    2009
    The American Statistician
  - 9. Sample Sizes When Using Multiple Linear Regression for Prediction
    
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    Educational and Psychological Measurement
  - 10. Confidence Intervals for an Effect Size Measure in Multiple Linear Regression
    
    James Algina | H. Keselman | Randall Penfield
    2007
    Educational and Psychological Measurement
  - 11. History and Use of Relative Importance Indices in Organizational Research
    
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    Organizational Research Methods
  - 12. Variable Importance Assessment in Regression: Linear Regression versus Random Forest
    
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  - 13. Modern Insights about Pearson's Correlation and Least Squares Regression
    
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    2001
    International Journal of Selection and Assessment
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    Stephen Morgan
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    Sociological Methods & Research
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    2006
    Sociological Methods & Research
  - 18. Sparse Partial Least Squares Regression for On-Line Variable Selection with Multivariate Data Streams
    
    Brian McWilliams | Giovanni Montana
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    Statistical Analysis and Data Mining
Volume II
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- Inherently Nonlinear Models – Log-Linear Models and Probit and Logistic Regression
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  - 19. Confronting Sociological Theory with Data: Regression Analysis, Goodman's Log-Linear Models and Comparative Research
    
    Bernice Pescosolido | Jonathan Kelley
    1983
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  - 20. Suppression and Confounding in Action
    
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  - 21. Explained Variance in Logistic Regression: A Monte Carlo Study of Proposed Measures
    
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  - 22. Coefficients of Determination in Logistic Regression Models – a New Proposal: The Coefficient of Discrimination
    
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  - 24. Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weight Analysis
    
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    2010
    Organizational Research Methods
  - 25. Loss of Power in Logistic, Ordinal Logistic, and Probit Regression When an Outcome Variable Is Coarsely Categorized
    
    Aaron Taylor | Stephen West | Leona Aiken
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    Educational and Psychological Measurement
  - 26. Using Heterogeneous Choice Models to Compare Logit and Probit Coefficients across Groups
    
    Richard Williams
    2009
    Sociological Methods & Research
  - 27. Large-scale Regression-based Pattern Discovery: The Example of Screening the WHO Global Drug Safety Database
    
    Ola Caster | G. Niklas | M. Norén | David Madigan | Andrew Bate
    2010
    Statistical Analysis and Data Mining
  - 28. Modeling Local Nonlinear Correlations Using Subspace Principal Curves
    
    Chandan Reddy | Mohammad Aziz
    2010
    Statistical Analysis and Data Mining
  - 29. A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression
    
    Carrie Petrucci
    2009
    Journal of Social Service Research
  - 30. The Effect of Childhood Maltreatment on Adult Criminality: A Tobit Regression Analysis
    
    Andrew Grogan-Kaylor | Melanie Otis
    2003
    Child Maltreatment
- Multi-Level Regression Modeling (MLM)
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  - 31. Multiple-level Regression Analysis of Survey and Ecological Data
    
    Theodor Harder | Franz Pappi
    1969
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  - 32. Broadening the Scope of Regression Analysis
    
    Robert Bickel
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    Multilevel Analysis for Applied Research: It's Just Regression
  - 33. Multilevel Modeling: Overview and Applications to Research in Counseling Psychology
    
    Jeffrey Kahn
    2011
    Journal of Counseling Psychology
  - 34. Acceptance of Other Religions in the United States: An HLM Analysis of Variability across Congregations
    
    Buster Smith
    2010
    Social Compass
  - 35. Multilevel Modeling of Social Segregation
    
    George Leckie | Rebecca Pillinger | Kelvyn Jones | Harvey Goldstein4
    2012
    Journal of Educational and Behavioral Statistics
  - 36. A New Approach for Estimating a Nonlinear Growth Component in Multilevel Modeling
    
    Asko Tolvanen | Noona Kiuru | Esko Leskinen | Kai Hakkarainen | Mikko Inkinen | Kirsti Lonka | Katariina Salmela-Aro
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    International Journal of Behavioral Development
  - 37. Addressing Data Sparseness in Contextual Population Research: Using Cluster Analysis to Create Synthetic Neighborhoods
    
    Philippa Clarke | Blair Wheaton
    2007
    Sociological Methods & Research
  - 38. Effect Sizes in Three-level Cluster-Randomized Experiments
    
    Larry Hedges
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    Journal of Educational and Behavioral Statistics
Volume III
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- Exploratory and Confirmatory Factor Analysis and Latent Class Modeling
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  - 39. Multiple Factor Analysis
    
    L. Thurstone
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  - 40. Use of Exploratory Factor Analysis in Published Research: Common Errors and Some Comment on Improved Practice
    
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  - 41. The Quality of Factor Solutions in Exploratory Factor Analysis: The Influence of Sample Size, Communality, and Overdetermination
    
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  - 43. Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations
    
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  - 44. Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis
    
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  - 45. Reporting Practices in Confirmatory Factor Analysis: An Overview and Some Recommendations
    
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  - 52. Regression Mixture Models of Alcohol Use and Risky Sexual Behavior among Criminally-Involved Adolescents
    
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Volume IV
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- Structural Equation Modeling (SEM)
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  - 53. Correlation and Causation
    
    Sewall Wright
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    Journal of Agricultural Research
  - 54. Can Scientifically Useful Hypotheses Be Tested with Correlations?
    
    Peter Bentler
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    Annual Review of Psychology
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    John Ferron | Melinda Hess
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    Journal of Educational and Behavioral Statistics
  - 58. The General Linear Model as Structural Equation Modeling
    
    James Graham
    2008
    Journal of Educational and Behavioral Statistics
  - 59. Advanced Applications of Structural Equation Modeling in Counseling Psychology Research
    
    Matthew Martens | Richard Haase
    2006
    The Counseling Psychologist
  - 60. Applications of Structural Equation Modeling in Psychological Research
    
    Robert MacCallum | James Austin
    2000
    Annual Review of Psychology
  - 61. Introduction to Structural Equation Modeling: Issues and Practical Considerations
    
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  - 62. Structural Equation Modeling: Reviewing the Basics and Moving Forward
    
    Jodie Ullman
    2006
    Journal of Personality Assessment
  - 63. Missing Data Techniques for Structural Equation Modeling
    
    Paul Allison
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    Journal of Abnormal Psychology
  - 64. Working with Missing Values
    
    Alan Acock
    2005
    Journal of Marriage and Family
  - 65. Modeling Strategies: In Search of the Holy Grail
    
    Kenneth Bollen
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    Structural Equation Modeling
  - 66. On Tests and Indices for Evaluating Structural Models
    
    Peter Bentler
    2007
    Personality and Individual Differences
  - 67. The Reliability Paradox in Assessing Structural Relations within Covariance Structure Models
    
    Gregory Hancock | Ralph Mueller
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    Educational and Psychological Measurement
  - 68. Moderation and Mediation in Structural Equation Modeling: Applications for Early Intervention Research
    
    Christopher Hopwood
    2007
    Journal of Early Intervention
  - 69. Latent Variable Interaction Modeling
    
    Randall Schumacker
    2002
    Structural Equation Modeling
  - 70. Parenting Efficacy and the Early School Adjustment of Poor and Near-Poor Black Children
    
    Aurora Jackson | Jeong-Kyun Choi | Peter Bentler
    2009
    Journal of Family Issues
  - 71. Neighborhood Social Disorganization, Families, and the Educational Behavior of Adolescents
    
    Natasha Bowen | Gary Bowen | William Ware
    2002
    Journal of Adolescent Research
  - 72. Intervention Effects on College Performance and Retention as Mediated by Motivational, Emotional, and Social Control Factors: Integrated Meta-Analytic Path Analyses
    
    Steven Robbins | In-Sue Oh | Huy Le | Christopher Button
    2009
    Journal of Applied Psychology

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Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

Key Reading

All Readings

Entry

Table of Contents

Volume I

Regression and Its Correlational Foundations and Concomitants

Linear Regression Designs and Model Building

Volume II

Inherently Nonlinear Models – Log-Linear Models and Probit and Logistic Regression

Multi-Level Regression Modeling (MLM)

Volume III

Exploratory and Confirmatory Factor Analysis and Latent Class Modeling

Volume IV

Structural Equation Modeling (SEM)

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Rotation Criteria and Hypothesis Testing for Exploratory Factor Analysis: Implications for Factor Pattern Loadings and Interfactor Correlations

Key Reading

All Readings

Entry

Table of Contents

Volume I

Regression and Its Correlational Foundations and Concomitants

Linear Regression Designs and Model Building

Volume II

Inherently Nonlinear Models – Log-Linear Models and Probit and Logistic Regression

Multi-Level Regression Modeling (MLM)

Volume III

Exploratory and Confirmatory Factor Analysis and Latent Class Modeling

Volume IV

Structural Equation Modeling (SEM)

Sign in to access this content

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