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Exploratory Factor Analysis
Exploratory factor analysis (EFA) is a multivariate statistical technique to model the covariance structure of the observed variables by three sets of parameters: (a) factor loadings associated with latent (i.e., unobserved) variables called factors, (b) residual variances called unique variances, and (c) factor correlations. EFA aims at explaining the relationship of many observed variables by a relatively small number of factors. Thus, EFA is considered one of the data reduction techniques. Historically, EFA dates back to Charles Spearman's work in 1904, and the theory behind EFA has been developed along with the psychological theories of intelligence, such as L. L. Thurstone's multiple factor model. Today, EFA is among the most frequently used statistical techniques by researchers in the social sciences and education.
It is well-known ...
- Descriptive Statistics
- Distributions
- Graphical Displays of Data
- Hypothesis Testing
- p Value
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- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
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- One-Tailed Test
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- Significance Level, Concept of
- Significance Level, Interpretation and Construction
- Significance, Statistical
- Two-Tailed Test
- Type I Error
- Type II Error
- Type III Error
- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
- Probabilistic Models for Some Intelligence and Attainment Tests
- Statistical Power Analysis for the Behavioral Sciences
- Teoria Statistica Delle Classi e Calcolo Delle Probabilità
- Inferential Statistics
- Q-Statistic
- R2
- Association, Measures of
- Coefficient of Concordance
- Coefficient of Variation
- Coefficients of Correlation, Alienation, and Determination
- Confidence Intervals
- Margin of Error
- Nonparametric Statistics
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- Parameters
- Parametric Statistics
- Partial Correlation
- Pearson Product-Moment Correlation Coefficient
- Polychoric Correlation Coefficient
- Randomization Tests
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- Spearman Rank Order Correlation
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- Collinearity
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- Error Rates
- Expected Value
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- Inclusion Criteria
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- Intraclass Correlation
- Latent Variable
- Likelihood Ratio Statistic
- Loglinear Models
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- Multilevel Modeling
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- Analysis of Covariance (ANCOVA)
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- Barycentric Discriminant Analysis
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- Confirmatory Factor Analysis
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- Greenhouse–Geisser Correction
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- Fisher's Least Significant Difference Test
- Friedman Test
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- Kolmogorov-Smirnov Test
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- Mann–Whitney U Test
- Mauchly Test
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- Newman–Keuls Test and Tukey Test
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- Sign Test
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- Wilcoxon Rank Sum Test
- Theories, Laws, and Principles
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- Theory
- Theory of Attitude Measurement
- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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