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Structural Equation Modeling
Structural equation modeling (SEM) belongs to the class of statistical analyses that examines the relations among multiple variables (both exogenous and endogenous). The methodology can be viewed as a combination of three statistical techniques: multiple regression, path analysis, and factor analysis. It has the purpose of determining the extent to which a proposed theoretical model, which is often expressed by a set of relations among different constructs, is supported by the collected data. SEM is, therefore, a technique for confirmatory instead of exploratory analysis. This entry represents a nonmathematical introduction to SEM, with an emphasis on its benefits, usage, and basic underlying assumptions. Principal concepts related to the methodology and summarized comparisons with other multivariate analyses are also presented. Then, the entry outlines and ...
- Descriptive Statistics
- Distributions
- Graphical Displays of Data
- Hypothesis Testing
- p Value
- Alternative Hypotheses
- Beta
- Critical Value
- Decision Rule
- Hypothesis
- Nondirectional Hypotheses
- Nonsignificance
- Null Hypothesis
- One-Tailed Test
- Power
- Power Analysis
- 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
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- 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
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- Sample Size
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- Collinearity
- Correlation
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- Data Mining
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- Error Rates
- Expected Value
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- Inclusion Criteria
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- Latent Variable
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- Loglinear Models
- Main Effects
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- Multilevel Modeling
- Odds
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- Outlier
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- Restriction of Range
- Robust
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- Rosenthal Effect
- Serial Correlation
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- Simple Main Effects
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- Sums of Squares
- Statistical Procedures
- Accuracy in Parameter Estimation
- Analysis of Covariance (ANCOVA)
- Analysis of Variance (ANOVA)
- Barycentric Discriminant Analysis
- Bivariate Regression
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- Bootstrapping
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- Categorical Data Analysis
- Confirmatory Factor Analysis
- Contrast Analysis
- Descriptive Discriminant Analysis
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- Dummy Coding
- Effect Coding
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- Jackknife
- Latent Growth Modeling
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- Logistic Regression
- Mean Comparisons
- Missing Data, Imputation of
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- Pairwise Comparisons
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- Survival Analysis
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- Fisher's Least Significant Difference Test
- Friedman Test
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- Kolmogorov-Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar's Test
- Multiple Comparison Tests
- Newman–Keuls Test and Tukey Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey's Honestly Significant Difference (HSD)
- Welch's t Test
- Wilcoxon Rank Sum Test
- Theories, Laws, and Principles
- Bayes's Theorem
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Falsifiability
- Game Theory
- Gauss–Markov Theorem
- Generalizability Theory
- Grounded Theory
- Item Response Theory
- Occam's Razor
- Paradigm
- Positivism
- Probability, Laws of
- Theory
- Theory of Attitude Measurement
- Weber–Fechner Law
- Types of Variables
- Validity of Scores
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