2005 TheEncyclopediaofStatisticsinBe

• (Everitt & Howell, 2005) ⇒ Brian S. Everitt, and D. Howell (editors). (2005). “Encyclopedia of Statistics in Behavioral Science." Wiley. ISBN:9780470860809

Subject Headings: Statistics Encyclopedia.

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Book Overview

The Encyclopedia of Statistics in Behavioral Science encompasses the refined statistical concepts and techniques that are essential to the advancement in psychology and its ever-widening fields of application.

• Providing comprehensive accounts drawn from the huge expansion of statistical methodologies in the behavioral sciences, highlighting new techniques and developments from the past decade.
• Up-to-date coverage of developing fields including neuroscience, behavioral genetics, decision learning and cognitive science.
• Containing over 600 articles and over 400 contributions from eminent psychologists and statisticians world-wide.
• Emphasis on practical, non-technical methods with wide ranging application.
• Extensively cross-referenced to ensure fast and accurate access to available information

Introduction

Over the past four decades there has been a huge change in statistical methods in the behavioral sciences. New fields, with their own techniques, have developed, and the traditional fields have broadened their approach to the point that the classic methods of the past have been supplanted by more refined and useful techniques. Forty years ago there was hardly a field called "behavioral science", psychology largely was the behavioral sciences. Now, psychology has expanded and developed in a myriad of ways, to the point where "behavioral science" is often the more useful term. Physiological psychology has become neuroscience, covering areas not previously part of psychology. Decision making has become decision science, learning theory has become cognitive science, again exploring problems that were not even considered 40 years ago. Advances in technology have also brought forth a host of new techniques that were not possible in the days of manual or electronic calculators. And with all these changes, there have been corresponding changes in the appropriate statistical methodologies.

Encyclopedia of Statistics in Behavioral Science represents an invaluable addition to both the psychological and statistical literature, forming an essential reference work for researchers, educators and students in the fields of applied psychology, sociology, market research, consumer behavior, management science, decision making and human resource management.

The Encyclopedia of Statistics in Behavioral Science encompasses the refined statistical concepts and techniques that are essential to the advancement of psychology and its everwidening fields of application.

• Providing comprehensive accounts drawn from the huge expansion of statistical methodologies in the behavioral sciences, highlighting new techniques and recent developments
• Up-to-date coverage of developing fields including neuroscience, behavioral genetics, decision science and cognitive science.
• Containing over 600 articles from over 400 authors.

Contributions are from eminent psychologists and statisticians world-wide.

• Theoretical methods fully supported by practical examples
• Extensively cross-referenced to ensure fast and accurate access to authoritative information

Table of Contents

VOLUME 1

• A Priori v Post Hoc TestingACE Model.
• Adaptive Random Assignment.
• Adaptive Sampling.
• Additive Constant Problem.
• Additive Genetic Variance.
• Additive Models.
• Additive Tree.
• Additivity Tests.
• Adoption Studies.
• Age-Period-Cohort Analysis.
• Akaike's Criterion.
• Allelic Association.
• All-X Models.
• All-Y Models.
• Alternating Treatment Designs.
• Analysis of Covariance.
• Analysis of Covariance: Nonparametric.
• Analysis of Variance.
• Analysis of Variance and Multiple Regression Approaches.
• Analysis of Variance: Cell Means Approach.
• Analysis of Variance: Classification.
• Ansari-Bradley Test.
• Arbuthnot, John.
• Area Sampling.
• Arithmetic Mean.
• Ascertainment Corrections.
• Assortative Mating.
• Asymptotic Relative Efficiency.
• Attitude Scaling.
• Attrition.
• Average Deviation.
• Axes in Multivariate Analysis.
• Bagging.
• Balanced Incomplete Block Designs.
• Bar Chart.
• Battery Reduction.
• Bayes, Thomas.
• Bayesian Belief Networks.
• Bayesian Item Response Theory Estimation.
• Bayesian Methods for Categorical Data.
• Bayesian Statistics.
• Bernoulli Family.
• Binomial Confidence Interval.
• Binomial Distribution: Estimating and Testing parameters.
• Binomial Effect Size Display.
• Binomial Test.
• Biplot.
• Block Random Assignment.
• Boosting.
• Bootstrap Inference.
• Box Plots.
• Bradley-Terry Model.
• Breslow-Day Statistic.
• Brown, William.
• Bubble Plot.
• Burt, Cyril Lodowic.
• Bush, Robert R.
• Calculating Covariance.
• Campbell, Donald T.
• Canonical Correlation Analysis.
• Carroll-Arabie Taxonomy.
• Carryover and Sequence Effects.
• Case Studies.
• Case-Cohort Studies.
• Case-Control Studies.
• Catalogue of Parametric Tests.
• Catalogue of Probability Density Functions.
• Catastrophe Theory.
• Categorizing Data.
• Cattell, Raymond Bernard.
• Censored Observations.
• Census.
• Centering in Multivariate Linear Models.
• Central Limit Theory.
• Children of Twins Design.
• Chi-Square Decomposition.
• Cholesky Decomposition.
• Classical Statistical Inference: Practice versus Presentation.
• Classical Statistical Inference Extended: Split-Tailed Tests.
• Classical Test Models.
• Classical Test Score Equating.
• Classification and Regression Trees.
• Clinical Psychology.
• Clinical Trials and Intervention Studies. by Emmanuel LeSaffre and Geert Verbeke. http://www.wiley.com/legacy/wileychi/eosbs/pdfs/bsa099.pdf
• Cluster Analysis: Overview.
• Clustered Data.
• Cochran, William Gemmell.
• Cochran's C Test.
• Coefficient of Variation.
• Cohen, Jacob.
• Cohort Sequential Design.
• Cohort Studies.
• Coincidences.
• Collinearity.
• Combinatorics for Categorical Variables.
• Common Pathway Model.
• Community Intervention Studies.
• Comorbidity.
• Compensatory Equalization.
• Compensatory Rivalry.
• Completely Randomized Design.
• Computational Models.
• Computer-Adaptive Testing.
• Computer-Based Test Designs.
• Computer-Based Testing.
• Concordance Rates.
• Conditional Independence.
• Conditional Standard Errors of Measurement.
• Confidence Intervals.
• Confidence Intervals: Nonparametric.
• Configural Frequency Analysis.
• Confounding in the Analysis of Variance.
• Confounding Variable.
• Contingency Tables.
• Coombs, Clyde Hamilton.
• Correlation.
• Correlation and Covariance Matrices.
• Correlation Issues in Genetics Research.
• Correlation Studies.
• Correspondence Analysis.
• Co-Twin Control Methods.
• Counter Null Value of an Effect Size.
• Counterbalancing.
• Counterfactual Reasoning.
• Covariance.
• Covariance Matrices: Testing Equality of.
• Covariance Structure Models.
• Covariance/variance/correlation.
• Cox, Gertrude Mary.
• Cramer-Von Mises Test.
• Criterion-Referenced Assessment.
• Critical Region.
• Cross Sectional Design.
• Cross-Classified and Multiple Membership Models.
• Cross-Lagged Panel Design.
• Crossover Design.
• Cross-validation.
• Cultural Transmission.
• Data Mining.
• de Finetti, Bruno.
• de Moivre, Abraham.
• Decision Making Strategies.
• Deductive Reasoning and Statistical Inference.
• DeFries-Fulker Analysis.
• Demand Characteristics.
• Deming, Edwards William.
• Design Effects.
• Development of Statistical Theory in the 20th Century.
• Differential Item Functioning.
• Direct and Indirect Effects.
• Direct Maximum Likelihood Estimation.
• Directed Alternatives in Testing.
• Direction of Causation Models.
• Discriminant Analysis.
• Distribution Free Inference, an Overview.
• Dominance.
• Dot chart.
• Dropouts in Longitudinal Data.
• Dropouts in Longitudinal Studies: Methods of Analysis.
• Dummy Variables.

VOLUME 2

• Ecological Fallacy.
• Educational Psychology: Measuring Change Over Time.
• Effect Size Measures.
• Eigenvalue/Eigenvector.
• Empirical Quantile-Quantile Plots.
• Epistasis.
• Equivalence Trials.
• Error Rates.
• Estimation.
• ETA and ETA Squared.
• Ethics in Research.
• Evaluation Research.
• Event History Analysis.
• Exact Methods for Categorical Data.
• Expectancy Effect.
• Expectation.
• Experimental Design.
• Exploratory Data Analysis.
• External Validity.
• Facet Theory.
• Face-to-Face Surveys.
• Factor Analysis of Personality Measures.
• Factor Analysis: Confirmatory.
• Factor Analysis: Exploratory.
• Factor Analysis: Multiple Groups.
• Factor Analysis: Multitrait-Multimethod.
• Factor Score Estimation.
• Factorial Designs.
• Family History Versus Family Study Methods in Genetics.
• Family Study and Relative Risk.
• Fechner, Gustav T.
• Field Experiment.
• Finite Mixture Distributions.
• Fisher, Sir Ronald Aylmer.
• Fisherian Tradition in Behavioral Genetics.
• Fixed and Random Effects.
• Fixed Effect Models.
• Focus Group Techniques.
• Free Response Data Scoring.
• Friedman's Test.
• Functional Data Analysis.
• Fuzzy Cluster Analysis.
• Galton, Francis.
• Game Theory.
• Gauss, Johann Carl Friedrich.
• Gene-Environment Correlation.
• Gene-Environment Interaction.
• Generalizability.
• Generalizability Theory: Basics.
• Generalizability Theory: Estimation.
• Generalizability Theory: Overview.
• Generalized Additive Model.
• Generalized Estimating Equations (GEE).
• Generalized Linear Mixed Models.
  • ([[Hedecker, 2005) ⇒ Donald Hedeker. (2005). “Generalized Linear Mixed Models.” In: (Everitt & Howell, 2005).
• Generalized Linear Models (GLM).
• Genotype.
• Geometric Mean.
• Goodness of Fit.
• Goodness of Fit for Categorical Variables.
• Gosset, William Sealy.
• Graphical Chain Models.
• Graphical Methods Pre-twentieth Century.
• Graphical Presentation of Longitudinal Data.
• Growth Curve Modeling.
• Guttman, Louise (Eliyahu).
• Harmonic Mean.
• Hawthorne Effect.
• Heritability.
• Heritability: Overview.
• Heteroscedasticity and Complex Variation.
• Heuristics.
• Heuristics: Fast and Frugal.
• Hierarchical Clustering.
• Hierarchical Item Response Theory Modeling.
• Hierarchical Models.
• High-Dimensional Regression.
• Hill's Criteria of Causation.
• Histogram.
• Historical Controls.
• History of Analysis of Variance.
• History of Behavioral Statistics.
• History of Correlational Measurement.
• History of Discrimination and Clustering.
• History of Factor Analysis: British Perspective.
• History of Factor Analysis: Statistical Perspective.
• History of Intelligence Measurement.
• History of Mathematical Learning Theory.
• History of Multivariate Analysis of Variance.
• History of Path Analysis.
• History of Psychometrics.
• History of Surveys of Sexual Behavior.
• History of the Control Group.
• Hodges-Lehman Estimator.
• Horseshoe Pattern.
• Hotelling, Howard.
• Hull, Clark L.
• Identification.
• Inbred Strain Study.
• Incidence.
• Incomplete Contingency Tables.
• Incompleteness of Probability Models.
• Independence: Chi-square and likelihood Ratio Tests.
• Independent Components Analysis.
• Independent Pathway Model.
• Index Plots.
• Industrial/Organizational Psychology.
• Influential Observations.
• Information Matrix.
• Information Theory.
• Instrumental Variable.
• Intention-to-Treat.
• Interaction Effects.
• Interaction Plot.
• Internal Consistency.
• Internal Validity.
• Internet Research Methods.
• Interquartile Range.
• Interrupted Time Series Design.
• Intervention Analysis.
• Intraclass Correlation.
• Intrinsic Linearity.
• Introduction to Probability.
• INUS Conditions.
• Item Analysis.
• Item Bias Detection: Classical Approaches.
• Item Bias Detection: Modern Approaches.
• Item Exposure Detection.
• Item Response Theory (IRT) Models for Dichotomous Data.
• Item Response Theory Models for Rating Scale Data.
• Item Response Theory: Cognitive Models.
• Item Response Theory Models for Polytomous Response Data.
• Jackknife.
• Jonckheere-Terpstra Test.
• Kendall, Maurice George.
• Kendall's Coefficient of Concordance.
• Kendall's Tau - t.
• Kernel Smoothing.
• K-Means Analysis.
• Kolmogorov, Andrey Nikolaevich.
• Kolmogorov-Smirnov Tests.
• Kruskal-Wallis Test.
• Kurtosis.
• Laplace, Pierre Simon (Marquis de).
• Latent Class Analysis.
• Latent Transition Models.
• Latent Variable.
• Latin Squares Designs.
• Laws of Large Numbers.
• Least Squares Estimation.
• Leverage Plot.
• Liability Threshold Models.
• Linear Model.
• Linear Models: Permutation Methods.
• Linear Multilevel Models.
• Linkage Analysis.
• Logistic Regression.
• Log-linear Models.
• Log-linear Rasch Models for Stability and Change.
• Longitudinal Data Analysis.
• Longitudinal Designs in Genetic Research.
• Lord, Frederic Mather.
• Lord's Paradox.

Volume 3

• M Estimators of Location.
• Mahalanobis Distance.
• Mahalanobis, Prasanta Chandra.
• Mail Surveys.
• Mallows Cp Statistic.
• Mantel-Haenszel Methods.
• Marginal Independence.
• Marginal Models for Clustered Data.
• Markov Chain Monte Carlo and Bayesian Statistics.
• Markov Chain Monte-Carlo Item Response Theory Estimation.
• Markov Chains.
• Markov, Andrei Andreevich..
• Martingales.
• Matching.
• Mathematical Psychology.
• Maximum Likelihood Estimation.
• Maximum Likelihood Item Response Theory Estimation.
• Maxwell, Albert Ernest.
• Measurement: Overview.
• Measures of Association.
• Median.
• Median Absolute Deviation.
• Median Test.
• Mediation.
• Mendelian Genetics Rediscovered.
• Mendelian Inheritance and Segregation Analysis.
• Meta-Analysis.
• Microarrays.
• Mid-p Values.
• Minimum Spanning Tree.
• Misclassification Rates.
• Missing Data.
• Model Based Cluster Analysis.
• Model Evaluation.
• Model Fit: Assessment of.
• Model Identifiability.
• Model Selection.
• Models for Matched Pairs.
• Moderation.
• Moments.
• Monotonic Regression.
• Monte Carlo Goodness of Fit Tests.
• Monte Carlo Simulation.
• Multidimensional Item Response Theory Models.
• Multidimensional Scaling.
• Multidimensional Unfolding.
• Multigraph Modeling.
• Multilevel and SEM Approaches to Growth Curve Modeling.
• Multiple Baseline Designs.
• Multiple Comparison Procedures.
• Multiple Comparison Tests: Nonparametric and Resampling Approaches.
• Multiple Imputation.
• Multiple Informants.
• Multiple Linear Regression.
• Multiple Testing.
• Multi-trait Multi-method Analyses.
• Multivariate Analysis of Variance.
• Multivariate Analysis: Bayesian.
• Multivariate Analysis: Overview.
• Multivariate Genetic Analysis.
• Multivariate Multiple Regression.
• Multivariate Normality Tests.
• Multivariate Outliers.
• Artificial Neural Networks.
• Neuropsychology.
• New Item Types and Scoring.
• Neyman, Jerzy.
• Neyman-Pearson Inference.
• Nightingale, Florence.
• Nonequivalent Control Group Design.
• Nonlinear Mixed Effects Models.
  • (Lindstrom & Vorperian, 2005) ⇒ Mary J. Lindstrom, and Houri K. Vorperian. (2005). “Nonlinear Mixed Effects Models.” In: (Everitt & Howell, 2005).
• Nonlinear Models.
• Nonparametric Correlation (rs).
• Nonparametric Correlation (tau).
• Nonparametric Item Response Theory Models.
• Nonparametric Regression.
• Nonrandom Samples.
• Nonresponse in Sample Surveys.
• Nonshared Environment.
• Normal Scores & Expected Order Statistics.
• Nuisance Variables.
• Number Needed to Treat.
• Number of Clusters.
• Number of Matches and Magnitude of Correlation.
• Observational Study.
• Odds and Odds Ratios.
• One Way Designs:Nonparametric and Resampling Approaches.
• Optimal Design for Categorical Variables.
• Optimal Scaling.
• Optimization Methods.
• Ordinal Regression Models.
• Outlier Detection.
• Outliers.
• Overlapping Clusters.
• P Values.
• Page's 0rdered Alternatives Test.
• Paired Observations, Distribution Free Methods.
• Panel Study.
• Paradoxes.
• Parsimony/Occham's Razor.
• Partial Correlation Coefficients.
• Partial Least Squares.
• Path Analysis and Path Diagrams.
• Pattern Recognition.
• Pearson Product Moment Correlation.
• Pearson, Egon Sharpe.
• Pearson, Karl.
• Percentiles.
• Permutation Based Inference.
• Person Misfit.
• Pie Chart.
• Pitman Test.
• Placebo Effect.
• Point Biserial Correlation.
• Polychoric Correlation.
• Polynomial Model.
• Population Stratification.
• Power.
• Power Analysis for Categorical Methods.
• Power and Sample Size in Multilevel Linear Models.
• Prediction Analysis of Cross-Classifications.
• Prevalence.
• Principal Component Analysis.
• Principal Components and Extensions.
• Probability Plots.
• Probability: Foundations of.
• Probits.
• Procrustes Analysis.
• Projection Pursuit.
• Propensity Score.
• Proscriptive and Retrospective Studies.
• Proximity Measures.
• Psychophysical Scaling.
• Qualitative Research.
• Quantiles.
• Quantitative Methods in Personality Research.
• Quartiles.
• Quasi-Experimental Designs.
• Quasi-Independence.
• Quasi-Symmetry in Contingency Tables.
• Quetelet, Adolphe.

VOLUME 4

• R & Q Analysis.
• R Squared, Adjusted R Squared.
• Random Effects and Fixed Effects Fallacy.
• Random Effects in Multivariate Linear Models: Prediction.
• Random Forests.
• Random Walks.
• Randomization.
• Randomization Based Tests.
• Randomized Block Design: Nonparametric Analyses.
• Randomized Block Designs.
• Randomized Response Technique.
• Range.
• Rank Based Inference.
• Rasch Modeling.
• Rasch Models for Ordered Response Categories.
• Rater Agreement.
• Rater Agreement - Kappa.
• Rater Agreement - Weighted Kappa.
• Rater Bias Models.
• Reactivity.
• Receiver Operating Characteristics Curves.
• Recursive Models.
• Regression Artifacts.
• Regression Discontinuity Design.
• Regression Model Coding for the Analysis of Variance.
• Regression Models.
• Regression to the Mean.
• Relative Risk.
• Reliability: Definitions and Estimation.
• Repeated Measures Analysis of Variance.
• Replicability of Results.
• Reproduced Matrix.
• Resentful Demoralization.
• Residual Plot.
• Residuals.
• Residuals in Structural Equation, Factor Analysis, and Path Analysis Models.
• Resistant Line Fit.
• Retrospective Studies.
• Reversal Design.
• Risk Perception.
• Robust Statistics for Multivariate Methods.
• Robust Testing Procedures.
• Robustness of Standard Tests.
• Runs Test.
• Sample Size and Power Calculation.
• Sampling Distributions.
• Sampling Issues in Categorical Data.
• Saturated Model.
• Savage, Leonard Jimmie.
• Scales of Measurement.
• Scaling Asymmetric Matrices.
• Scaling of Preferential Choice.
• Scatterplot Matrices.
• Scatterplot Smoothers.
• Scatterplots.
• Scheffe, Henry.
• Second order Factor Analysis: Confirmatory.
• Selection Study (Mouse Genetics).
• Sensitivity Analysis.
• Sensitivity Analysis in Observational Studies.
• Sequential Decision Making.
• Sequential Testing.
• Setting Performance Standards - Issues, Methods.
• Sex-Limitation Models.
• Shannon, Claude.
• Shared Environment.
• Shepard Diagram.
• Sibling Interaction Effects.
• Sign Test.
• Signal Detection Theory.
• Signed Ranks Test.
• Simple Random Assignment.
• Simple Random Sampling.
• Simple V Composite Tests.
• Simulation Methods for Categorical Variables.
• Simultaneous Confidence Interval.
• Single and Double-Blind Procedures.
• Single-Case Designs.
• Skewness.
• Slicing Inverse Regression.
• Snedecor, George Waddell.
• Social Interaction Models.
• Social Networks.
• Social Psychology.
• Social Validity.
• Software for Behavioral Genetics.
• Software for Statistical Analyses.
• Spearman, Charles Edward.
• Spearman's Rho.
• Sphericity Test.
• Standard Deviation.
• Standard Error.
• Standardized Regression Coefficients.
• Stanine Scores.
• Star and Profile Plots.
• State Dependence.
• Statistical Models.
• Statistical Models for Causation.
  • (Freedman, 2005b) ⇒ David A. Freedman. (2005). “Linear Statistical Models for Causation.” In: (Everitt & Howell, 2005).
• Stem and Leaf Plot.
• Stephenson, William.
• Stevens, S S.
• Stratification.
• Structural Equation Modeling and Test Validation.
• Structural Equation Modeling: Categorical Variables.
• Structural Equation Modeling: Checking Substantive Plausibility.
• Structural Equation Modeling: Mixture Models.
• Structural Equation Modeling: Multilevel.
• Structural Equation Modeling: Nontraditional Alternatives.
• Structural Equation Modeling: Overview.
• Structural Equation Modeling: Software.
• Structural Equation Modeling:Latent Growth Curve Analysis.
• Structural Equation Modeling:Nonstandard Cases.
• Structural Zeros.
• Subjective Probability and Human Judgement.
• Summary Measure Analysis of Longitudinal Data.
• Survey Questionnaire Design.
• Survey Sampling Procedures.
• Survival Analysis.
• Symmetry Plot.
• Symmetry: Distribution Free Tests for.
• Tau-Equivalent and Congeneric Measurements.
• Teaching Statistics to Psychologists.
• Teaching Statistics: Sources.
• Telephone Surveys.
• Test Bias Detection.
• Test Construction.
• Test Construction: Automated.
• Test Dimensionality: Assessment of.
• Test Translation.
• Tetrachoric Correlation.
• Theil Slope Estimate.
• Thomson, Godfrey Hilton.
• Three dimensional (3D) Scatterplots.
• Three-Mode Component and Scaling Methods.
• Thurstone, Louis Leon.
• Time Series Analysis.
• Tolerance and Variance Inflation Factor.
• Transformation.
• Tree Models.
• Trellis Graphics.
• Trend Tests for Counts and Proportions.
• Trimmed Means.
• T-Scores.
• Tukey Quick Test.
• Tukey, John Wilder.
• Tversky, Amos.
• Twin Designs.
• Twins Reared Apart Design.
• Two by Two Contingency Tables.
• Two-mode Clustering.
• Two-way Factorial: Distribution-Free Methods.
• Type I, Type II and Type III Sums of Squares.
• Ultrametric Inequality.
• Ultrametric Trees.
• Unidimensional Scaling.
• Urban, F M.
• Utility Theory.
• Validity Theory and Applications.
• Variable Selection.
• Variance.
• Variance Components.
• Walsh Averages.
• Wiener, Norbert.
• Wilcoxon, Frank.
• Wilcoxon-Mann-Whitney test.
• Winsorized Robust Measures.
• Within Case Designs: Distribution Free Methods.
• Yates' Correction.
• Yates, Frank.
• Yule, George Udny.
• Z-Scores.

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Index

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STATISTICAL MODELS

• Additive Models.
• Akaike’s Criterion.
• All-X Models.
• All-Y Models.
• Calculating Covariance.
• Collinearity.
• Computational models.
• Dummy Variables.
• Fixed Effect Models.
• Generalizability.
• Generalized Additive Model.
• Generalized Linear Models (GLM).
• Goodness of Fit.
• Hierarchical Models.
• Instrumental Variable.
• Internal Consistency.
• Intrinsic Linearity.
• Linear Model.
• Mallows Cp Statistic.
• Mediation.
• Model Selection.
• Moderation.
• Multiple Linear Regression.
• Nonlinear Models.
• Parsimony/Occham’s Razor.
• Polynomial Model.
• Recursive Models.
• Regression Models.
• Reproduced Matrix.
• Standardized Regression.
• Coefficients.
• Statistical Models.
• Transformation.
• Tree Models.
• Variable Selection

SCALING

• Additive Constant Problem.
• Attitude Scaling.
• Bagging.
• Boosting.
• Bradley-Terry Model.
• Carroll-Arabie Taxonomy.
• Correspondence Analysis.
• Facet Theory.
• High-Dimensional Regression.
• Horseshoe Pattern.
• Minimum Spanning Tree.
• Monotonic Regression.
• Multidimensional Scaling.
• Multidimensional Unfolding.
• Artificial Neural Networks.
• Optimal Scaling.
• Optimization Methods
• Principal Components and Extensions.
• Procrustes Analysis.
• Psychophysical Scaling.
• Random Forests.
• Scaling Asymmetric.
• Matrices
• Scaling of Preferential.
• Choice.
• Shepard Diagram.
• Slicing Inverse Regression.
• Threeway and Multiway.
• Scaling.
• Ultrametric Inequality.
• Ultrametric Trees.
• Unidimensional Scaling

CLASSICAL TEST THEORY/ITEM RESPONSE THEORY/ PSYCHOLOGICAL MEASUREMENT

• Bayesian Item Response.
• Theory Estimation.
• Classical Test Models.
• Classical Test Score Equating.
• Computer-Adaptive Testing.
• Computer-Based Test Designs.
• Computer-Based Testing.
• Conditional Standard Errors of Measurement.
• Criterion-Referenced Assessment.
• Differential Item.
• Functioning.
• Free Response Data Scoring.
• Generalizability Theory: Basics.
• Generalizability Theory: Estimation.
• Hierarchical Item Response Theory Modeling.
• Item Analysis.
• Item Bias Detection: Classical Approaches.
• Item Bias Detection: Modern Approaches.
• Item Exposure Detection.
• Item Response Theory (IRT) Models for Dichotomous Data.
• Item Response Theory Models for Rating Scale Data.
• Item Response Theory: Cognitive Models.
• Item Response Theory Models for Polytomous Response Data.
• Latent Class Analysis.
• Markov Chain Monte-Carlo Item Response Theory Estimation.
• Maximum Likelihood Item Response Theory Estimation.
• Model Fit: Assessment of Multidimensional Item Response Theory Models.
• Multi-trait Multi-method Analyses.
• New Item Types and Scoring.
• Nonparametric Item.
• Response Theory Models.
• Person Misfit.
• Reliability: Definitions and Estimation.
• Sequential Decisio Making.
• Setting Performance.
• Standards - Issues, Methods
• Structural Equation Modeling and Test Validation.
• Tau-Equivalent and Congeneric Measurements.
• Test Bias Detection.
• Test Construction.
• Test Construction: Automated.
• Test Dimensionality: Assessment of Test Translation.
• Validity Theory and Applications

DESIGN OF EXPERIMENTS AND SURVEYS

• Analysis of Covariance.
• Analysis of Variance.
• Analysis of Variance and Multiple Regression Approaches.
• Analysis of Variance: Cell Means Approach.
• Analysis of Variance: Classification.
• Balanced Incomplete Block Designs.
• Carryover and Sequence Effects.
• Completely Randomized Design.
• Confounding in the Analysis of Variance.
• Effect Size Measures.
• Error Rates.
• Ethics in Research.
• Experimental Design.
• Face-to-Face Surveys.
• Factorial Designs.
• Focus Group Techniques
• Interaction Effects.
• Internet Research Methods
• Latin Squares Designs.
• Mail Surveys.
• Missing Data.
• Multiple Comparison Procedures.
• Nonresponse in Sample Surveys.
• Nuisance Variables.
• One Way Designs: Nonparametric and Resampling Approaches.
• Proscriptive and Restrospective Studies.
• Qualitative Research.
• Randomization.
• Randomized Block Designs.
• Regression Model Coding for the Analysis of Variance.
• Regression to the Mean.
• Sample Size and Power Calculation.
• Single and Double-Blind Procedures.
• Survey Questionnaire Design.
• Survey Sampling Procedures.
• Telephone Surveys.
• Type I, Type II and Type III Sums of Squares

MULTIVARIATE METHODS

• Additive Tree.
• Axes in Multivariate Analysis.
• Battery Reduction.
• Biplot.
• Canonical Correlation Analysis.
• Cluster Analysis: Overview
• Correlation and Covariance.
• Matrices
• Covariance Matrices: Testing Equality of Data Mining.
• Discriminant Analysis.
• Eigenvalue/Eigenvector.
• Finite Mixture Distributions.
• Fuzzy Cluster Analysis.
• Hierarchical Clustering.
• Independence: Chi-square and likelihood ratio tests.
• K-means Algorithm.
• Mahalanobis Distance.
• Microarrays.
• Misclassification Rates.
• Model Based Cluster Analysis.
• Multiple Imputation.
• Multivariate Analysis of Variance.
• Multivariate Analysis, Bayesian.
• Multivariate Analysis: Overview
• Multivariate Multiple Regression.
• Multivariate Normality Tests
• Multivariate Outliers.
• Number of Clusters.
• Overlapping Clusters.
• Partial Correlation.
• Coefficients.
• Pattern Recognition.
• Principal Component Analysis.
• Projection Pursuit.
• Proximity Measures.
• R & Q Analysis.
• Robust Statistics for Multivariate Methods
• Two-mode Clustering

INSTITUTIONAL/HISTORICAL

• Arbuthnot, John.
• Bayes, Thomas.
• Bernoulli Family.
• Brown, William.
• Burt, Cyril Lodowic.
• Bush, Robert R.
• Campbell, Donald T.
• Cattell, Raymond Bernard.
• Cochran, William Gemmell.
• Cohen, Jacob.
• Coombs, Clyde Hamilton.
• Cox, Gertrude Mary.
• de Finetti, Bruno.
• de Moivre, Abraham.
• Deming, Edwards William.
• Development of Statistical Theory in the 20th Century.
• Fechner, Gustav T.
• Fisher, Sir Ronald Aylmer.
• Fisherian Tradition in Behavioral Genetics.
• Galton, Francis.
• Gauss, Johann Carl Friedrich.
• Gosset, William Sealy.
• Graphical Methods Pretwentieth Century.
• Guttman, Louise (Eliyahu).
• History of Analysis of Variance.
• History of Behavioral Statistics.
• History of Correlational Measurement.
• History of Discrimination and Clustering.
• History of Factor Analysis: British Perspective.
• History of Mathematical Learning Theory.
• History of Multivariate Analysis of Variance.
• History of Psychometrics.
• History of the Control Group.
• Hotelling, Howard.
• Hull, Clark L.
• Kendall, Maurice George.
• Kolmogorov, Andrey Nikolaevich.
• Laplace, Pierre Simon (Marquis de).
• Lord, Frederic Mather.
• Mahalanobis, Prasanta Chandra.
• Markov, Andrei Andreevich..
• Maxwell, Albert Ernest.
• Mendelian Genetics Rediscovered.
• Neyman, Jerzy.
• Nightingale, Florence.
• Pearson, Egon Sharpe.
• Pearson, Karl.
• Quetelet, Adolphe.
• Savage, Leonard Jimmie.
• Scheffe, Henry.
• Shannon, Claude.
• Snedecor, George Waddell.
• Spearman, Charles Edward.
• Stephenson, William.
• Stevens, S S.
• Thomson, Godfrey Hilton.
• Thurstone, Louis Leon.
• Tukey, John Wilder.
• Tversky, Amos.
• Urban, F M.
• Wiener, Norbert.
• Wilcoxon, Frank.
• Yates, Frank.
• Yule, George Udny

DESCRIPTIVE STATISTICS/ GRAPHICAL PROCEDURES

• Arithmetic Mean.
• Average Deviation.
• Bar Chart.
• Box Plots.
• Bubble Plot.
• Coefficient of Variation Correlation.
• Covariance.
• Dot chart.
• Empirical Quantile-Quantile Plots.
• ETA and ETA Squared.
• Exploratory Data Analysis.
• Geometric Mean.
• Harmonic Mean.
• Histogram.
• Index Plots.
• Interaction Plot.
• Interquartile Range.
• Intraclass Correlation.
• Kendall’s Coefficient of Concordance.
• Kendall’s Tau - t.
• Kurtosis.
• Leverage Plot.
• M Estimators of Location.
• Median.
• Median Absolute Deviation.
• Pearson Product Moment Correlation.
• Percentiles.
• Pie Chart.
• Point Biserial Correlation.
• Probability Plots.
• Quantiles.
• Quartiles.
• R Squared, Adjusted R Squared.
• Range.
• Receiver Operating Characteristics Curves.
• Residual Plot.
• Residuals.
• Resistant Line Fit.
• Scales of Measurement.
• Scatterplot Matrices
• Scatterplot Smoothers.
• Scatterplots.
• Skewness.
• Spearman’s Rho.
• Standard Deviation.
• Standard Error.
• Stanine Scores.
• Star and Profile Plots.
• Stem and Leaf Plot.
• Symmetry Plot.
• Three dimensional (3D) Scatterplots.
• Tolerance and Variance Inflation Factor.
• Trellis Graphics.
• Trimmed Means.
• T-Scores.
• Variance.
• Winsorized Robust.
• Measures.
• Z-Scores

NONPARAMETRICS AND COMPUTATIONALLY INTENSIVE METHODS

• Analysis of Covariance: Nonparametric.
• Ansari-Bradley Test.
• Asymptotic Relative Efficiency.
• Binomial Confidence Interval.
• Binomial Distribution: Estimating and Testing parameters.
• Bootstrap Inference.
• Confidence Intervals: Nonparametric.
• Cramer-Von Mises Test.
• Cross-validation.
• Distribution Free Inference, an Overview
• Friedman’s Test.
• Functional Data Analysis.
• Graphical Chain Models.
• Hodges-Lehman Estimator.
• Independent Components Analysis.
• Influential Observations.
• Jackknife.
• Jonckheere-Terpstra Test.
• Kernel Smoothing.
• Kolmogorov-Smirnov Tests
• Kruskal-Wallis Test.
• Linear Models: Permutation Methods
• Median Test.
• Mid-p Values.
• Monte Carlo Goodness of Fit Tests
• Monte Carlo Simulation.
• Multiple Comparison Tests: Nonparametric and Resampling Approaches.
• Nonparametric correlation (Rs).
• Nonparametric correlation (tau).
• Nonparametric Regression.
• Normal Scores & Expected.
• Order Statistics.
• Page’s 0rdered Alternatives Test.
• Paired Observations, Distribution Free Methods
• Partial Least Squares.
• Permutation Based Inference.
• Pitman Test.
• Randomization Based Tests
• Randomized Block Design: Nonparametric Analyses.
• Rank Based Inference.
• Replicability of Results.
• Runs Test.
• Sign Test.
• Signed Ranks Test.
• Symmetry: Distribution Free Tests for Theil Slope Estimate.
• Tukey Quick Test.
• Two-way Factorial: Distribution-Free Methods
• Walsh Averages.
• Wilcoxon-Mann-Whitney test.
• Within Case Designs: Distribution Free Methods\

LONGITUDINAL/ MULTILEVEL MODELS

• Age-Period-Cohort Analysis Centering in Multivariate Linear Models.
• Classification and Regression Trees.
• Clustered Data.
• Conditional Independence.
• Cross-Classified and Multiple Membership Models.
• Dropouts in Longitudinal Data.
• Dropouts in Longitudinal Studies: Methods of Analysis.
• Fixed and Random Effects.
• Generalized Estimating Equations (GEE).
• Generalized Linear Mixed Models.
• Graphical Presentation of Longitudinal Data.
• Growth Curve Modeling.
• Heteroscedasticity and Complex Variation.
• Linear Multilevel Models.
• Longitudinal Data Analysis.
• Lord’s Paradox.
• Marginal Models for Clustered Data.
• Multilevel and SEM Approaches to Growth Curve Modeling.
• Nonlinear Mixed Effects Models.
• Power and Sample Size in Multivariate Linear Models.
• Random Effects in Multivariate Linear Models: Prediction.
• Randomized Response Technique.
• Repeated Measures Analysis of Variance.
• Sphericity Test.
• State Dependence.
• Summary Measure Analysis of Longitudinal Data.
• Variance Components

INTERVENTION STUDIES/ OBSERVATIONAL STUDIES

• Adaptive Random.
• Assignment.
• Adaptive Sampling.
• Alternating Treatment Designs.
• Area Sampling.
• Attrition.
• Binomial Effect Size Display.
• Block Random Assignment.
• Case Studies.
• Case-Cohort Studies.
• Case-Control Studies.
• Censored Observations.
• Census.
• Clinical Trials and Intervention Studies.
• Cohort Sequential Design.
• Cohort Studies.
• Community Intervention.
• Studies.
• Compensatory Equalization.
• Compensatory Rivalry.
• Confounding Variable.
• Correlation Studies.
• Counter Null Value of an Effect Size.
• Counterbalancing.
• Counterfactual Reasoning.
• Cross Sectional Design.
• Cross-Lagged Panel Design.
• Crossover Design.
• Demand Characteristics.
• Design Effects.
• Ecological Fallacy.
• Equivalence Trials.
• Expectancy Effect.
• External Validity.
• Field Experiment.
• Generalizability Theory: Overview
• Hawthorne Effect.
• Hill’s Criteria of Causation.
• Historical Controls.
• Incidence.
• Intention-to-Treat.
• Internal Validity.
• Interrupted Time Series Design.
• Intervention Analysis.
• INUS Conditions.
• Matching.
• Meta-Analysis.
• Multiple Baseline Designs.
• Nonequivalent Control Group Design.
• Nonrandom Samples.
• Number Needed to Treat.
• Observational Study.
• Panel Study.
• Placebo Effect.
• Prevalence.
• Propensity Score.
• Quasi-Experimental Designs.
• Reactivity.
• Regression Artifacts.
• Regression Discontinuity Design.
• Resentful Demoralization.
• Retrospective Studies.
• Reversal Design.
• Sensitivity Analysis in Observational Studies.
• Simple Random.
• Assignment.
• Simple Random Sampling.
• Single-Case Designs.
• Social Validity.
• Stratification.
• Time Series Analysis

BEHAVIORAL GENETICS

• ACE Model.
• Additive Genetic Variance.
• Adoption Studies.
• Allelic Association.
• Ascertainment Corrections.
• Assortative mating.
• Children of Twins Design.
• Cholesky Decomposition.
• Common Pathway Model.
• Comorbidity.
• Concordance Rates.
• Correlation Issues in Genetics Research.
• Co-Twin Control Methods
• Cultural Transmission.
• DeFries-Fulker Analysis.
• Direction of Causation Models.
• Dominance.
• Epistasis.
• Family History Versus.
• Family Study Methods in Genetics.
• Family Study and Relative Risk.
• Gene-Environment Correlation.
• Gene-Environment.
• Interaction.
• Genotype.
• Heritability.
• Heritability: Overview
• Inbred Strain Study.
• Independent Pathway Model.
• Liability Threshold Models.
• Linkage Analysis.
• Longitudinal Designs in Genetic Research.
• Mendelian Inheritance and Segregation Analysis.
• Multiple Informants.
• Multivariate Genetic Analysis.
• Nonshared Environment.
• Population Stratification.
• Rater Bias Models.
• Selection Study (Mouse Genetics).
• Sex-Limitation Models.
• Shared Environment.
• Sibling Interaction Effects.
• Social Interaction Models.
• Software for Behavioral Genetics.
• Twin Designs.
• Twins Reared Apart Design

FACTOR ANALYSIS/ STRUCTURAL EQUATION MODELS

• Covariance Structure Models.
• Covariance/variance/correlation.
• Direct and Indirect Effects.
• Factor Analysis of Personality Measures.
• Factor Analysis: Confirmatory.
• Factor Analysis: Exploratory.
• Factor Analysis: Multiple Groups.
• Factor Analysis: Multitrait- Multimethod.
• Factor Score Estimation.
• History of Factor Analysis: Statistical Perspective.
• History of Path Analysis.
• Identification.
• Latent Variable Path Analysis and Path Diagram.
• Residuals in Structural Equation, Factor Analysis, and Path Analysis Models.
• Second order Factor Analysis: Confirmatory.
• Statistical Models for Causation: A Critical Review.
• Structural Equation Modeling: Categorical Variables.
• Structural Equation Modeling: Checking.
• Substantive Plausibility.
• Structural Equation Modeling: Mixture Models.
• Structural Equation Modeling: Multilevel.
• Structural Equation Modeling: Nontraditional Alternatives.
• Structural Equation Modeling: Overview
• Structural Equation Modeling: Software.
• Structural Equation Modeling: Latent Growth Curve Analysis.
• Structural Equation Modeling: Nonstandard Cases

CATEGORICAL DATA ANALYSIS

• Binomial Test.
• Breslow-Day Statistic.
• Catastrophe Theory.
• Categorizing Data.
• Chi-Square Decomposition.
• Cochran’s C Test.
• Combinatorics for Categorical Variables.
• Configural Frequency Analysis.
• Contingency Tables.
• Exact Methods for Categorical Data.
• Goodness of Fit for Categorical Variables.
• Incomplete Contigency Tables.
• Information Theory.
• Latent Transition Models.
• Logistic Regression.
• Log-linear models.
• Log-linear Rasch Models.
• for Stability and Change.
• Mantel-Haenszel Methods
• Marginal Independence.
• Measures of Association.
• Models for Matched Pairs.
• Multigraph Modeling.
• Number of Matches and Magnitude of Correlation.
• Odds and Odds Ratios.
• Optimal Design for Categorical Variables.
• Ordinal Regression Models.
• Paradoxes.
• Polychoric Correlation.
• Power Analysis for Categorical Methods
• Prediction Analysis of Cross-Classifications.
• Probits.
• Quasi-Independence.
• Quasi-Symmetry in Contingency Tables.
• Rasch Modeling.
• Rasch Models for Ordered.
• Response Categories.
• Rater Agreement.
• Rater Agreement - Kappa.
• Rater Agreement - Weighted Kappa.
• Relative Risk.
• Sampling Issues in Categorical Data.
• Saturated Model.
• Simulation Methods for Categorical Variables.
• Structural Zeros.
• Tetrachoric Correlation.
• Trend Tests for Counts and Proportions.
• Two by Two Contingency Tables.
• Yates’ Correction

STATISTICAL THEORY

• A Priori v Post Hoc Testing.
• Additivity Tests
• Bayesian Belief Networks.
• Bayesian Methods for Categorical Data.
• Bayesian Statistics.
• Catalogue of Parametric Tests
• Catalogue of Probability.
• Density Functions.
• Central Limit Theory.
• Classical Statistical Inference: Practice versus Presentation.
• Classical Statistical Inference Extended: Split- Tailed Tests
• Confidence Intervals.
• Critical Region.
• Decision Making Strategies.
• Deductive Reasoning and Statistical Inference.
• Direct Maximum Likelihood Estimation.
• Directed Alternatives in Testing Estimation.
• Event History Analysis.
• Expectation.
• Game Theory.
• Heuristics.
• Hueristics: Fast and Frugal.
• Incompleteness of Probability Models.
• Information Matrix.
• Introduction to Probability.
• Laws of Large Numbers.
• Least Squares Estimation.
• Markov Chain Monte Carlo and Bayesian Statistics.
• Markov Chains.
• Martingales.
• Maximum Likelihood Estimation.
• Measurement: Overview
• Model Evaluation.
• Model Identifiability.
• Moments.
• Multiple Testing.
• Neyman-Pearson Inference.
• Outlier Detection.
• Outliers.
• P Values.
• Power.
• Probability: Foundations of Random Effects and Fixed Effects Fallacy.
• Random Walks.
• Robust Testing Procedures.
• Robustness of Standard Tests
• Sampling Distributions.
• Sensitivity Analysis.
• Sequential Testing.
• Signal Detection Theory.
• Simple V Composite Tests
• Simultaneous Confidence Interval.
• Social Networks.
• Subjective Probability and Human Judgement.
• Survival Analysis.
• Utility Theory.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 TheEncyclopediaofStatisticsinBe	Brian S. Everitt D. Howell			Encyclopedia of Statistics in Behavioral Science						2005