Encyclopedia of Measurement and Statistics
Encyclopedias
Abstract
The Encyclopedia of Measurement and Statistics presents stateoftheart information and readytouse facts from the fields of measurement and statistics in an unintimidating style. The ideas and tools contained in these pages are approachable and can be invaluable for understanding our very technical world and the increasing flow of information. Although there are references that cover statistics and assessment in depth, none provides as comprehensive a resource in as focused and accessible a manner as the three volumes of this Encyclopedia. Through approximately 500 contributions, experts provide an overview and an explanation of the major topics in these two areas.
 Entries AZ
 Subject Index

 Biographies
 Charts, Graphs, and Visual Displays
 Computer Topics and Tools
 Concepts and Issues in Measurement
 Concepts and Issues in Statistics
 Data and Data Reduction Techniques
 Descriptive Statistics
 Evaluation
 Experimental Methods
 Inferential Statistics
 Organizations and Publications
 Prediction and Estimation
 Probability
 Qualitative Methods
 Samples, Sampling, and Distributions
 Statistical Techniques
 Statistical Tests
 Tests by Name

 A
 B
 C
 D
 E
 F
 G
 H
 I
 J
 K
 L
 M
 N
 O
 P
 Q
 R
 S
 T
 U
 V
 W
 X
 Y
 Z

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Copyright
Copyright © 2007 by SAGE Publications, Inc.
All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.
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Printed in the United States of America.
Library of Congress CataloginginPublication Data
Encyclopedia of measurement and statistics / editor Neil J. Salkind.
p. cm.
A SAGE Reference Publication.
Includes bibliographical references and index.
ISBN 1412916119 or 9781412916110 (cloth)
1. Social sciences—Statistical methods—Encyclopedias. 2. Social sciences—Research—Methodology—Encyclopedias. I. Salkind, Neil J.
HA29.S2363 2007
001.403—dc22
2006011888
This book is printed on acidfree paper.
06 07 08 09 10 10 9 8 7 6 5 4 3 2 1
Publisher: Rolf Janke
Acquisitions Editor: Lisa Cuevas Shaw
Reference Systems Coordinator: Leticia Gutierrez
Project Editor: Tracy Alpern
Copy Editors: Bonnie Freeman
Liann Lech
Carla Freeman
Typesetter: C&M Digitals (P) Ltd.
Indexer: David Luljak
Cover Designer: Michelle Kenny
Editorial Board
Editor
Neil J. Salkind
University of Kansas
Managing Editor
Kristin Rasmussen
University of Kansas
Advisory Board
Jeffrey Banfield
Department of Mathematical Sciences Montana State University
Bruce Frey
Department of Psychology and Research in Education University of Kansas
Wenxin Jiang
Department of Statistics Northwestern University
Galin L. Jones
School of Statistics University of Minnesota
Jianan Peng
Department of Mathematics and Statistics Acadia University
Jerome P. Reiter
Practice of Statistics and Decision Sciences Institute of Statistics and Decision Sciences Duke University
List of Entries
 Ability Tests
 Abstracts
 Acceptance Sampling
 Achievement Tests
 Active Life Expectancy
 Adaptive Sampling Design
 Adjective Checklist
 Age Norms
 Akaike Information Criterion
 Alcohol Use Inventory
 Alternate Assessment
 Alternative Hypothesis
 American Doctoral Dissertations
 American Psychological Association
 American Psychological Society. See
 American Statistical Association
 Americans with Disabilities Act
 Analysis of Covariance (ANCOVA)
 Analysis of Variance (ANOVA)
 Anthropometry
 Applied Research
 Aptitude Tests
 Area Chart
 Arithmetic Mean
 Armed Forces Qualification Test
 Armed Services Vocational Aptitude Battery
 Artificial Neural Network
 Assessment of Interactions in Multiple Regression
 Association for Psychological Science
 Asymmetry of G
 Attenuation, Correction for
 Attitude Tests
 Attributable Risk
 Attrition Bias
 Audit Trail
 Authenticity
 Autocorrelation
 Average
 Average Deviation
 Babbage, Charles
 Bar Chart
 Basal Age
 Basic Personality Inventory
 Basic Research
 Bayes Factors
 Bayesian Information Criterion
 Bayesian Statistics
 Bayley Scales of Infant Development
 Beck Depression Inventory
 Behavior Assessment System for Children
 BehrensFisher Test
 Bender Visual Motor Gestalt Test
 Bernoulli, Jakob
 Binomial Distribution/Binomial and Sign Tests
 Binomial Test
 Bioinformatics
 Biserial Correlation Coefficients
 Bivariate Distributions
 Bonferroni, Carlo Emilio
 Bonferroni Test
 Bowker Procedure
 Box Plot (Box and Whisker Plot)
 Bracken Basic Concept Scale–Revised
 Bruno, James Edward
 Buros Institute of Mental Measurements
 California Psychological Inventory
 Career Assessment Inventory
 Career Development Inventory
 Career Maturity Inventory
 Carroll Depression Scale
 Categorical Variable
 Causal Analysis
 Censored Data
 Centers for Disease Control and Prevention
 Central Limit Theorem
 Centroid
 Chance
 ChiSquare Test for Goodness of Fit
 ChiSquare Test for Independence
 Children's Academic Intrinsic Motivation Inventory
 Class Interval
 Classical Test Theory
 Classification and Regression Tree
 Clinical Assessment of Attention Deficit
 Clinical Assessment of Behavior
 Clinical Assessment of Depression
 Cluster Analysis
 Cluster Sampling
 Cochran Q Test
 Coefficient Alpha
 Coefficients of Correlation, Alienation, and Determination
 Cognitive Abilities Test
 Cognitive Psychometric Assessment
 Cohen's Kappa
 Complete Independence Hypothesis
 Completion Items
 Computational Statistics
 Computerized Adaptive Testing
 Comrey, Andrew L.
 Comrey Personality Scales
 Conditional Probability
 Confidence Intervals
 Construct Validity
 Content Validity
 Continuous Variable
 Contour Plot
 Convenience Sampling
 Coping Resources Inventory for Stress
 Correlation Coefficient
 Correspondence Analysis
 Covariance
 CriterionReferenced Test
 Criterion Validity
 Critical Value
 Cronbach, Lee J.
 Culture Fair Intelligence Test
 Cumulative Frequency Distribution
 CurriculumBased Measurement
 Curse of Dimensionality
 Curvilinear Regression
 Darwin, Charles
 Data Analysis ToolPak
 Data Collection
 Data Compression
 Data Mining
 Decision Boundary
 Decision Theory
 Delphi Technique
 Delta Method
 Deming, William Edwards
 Dependent Variable
 Descriptive Research
 Deviation Score
 Diagnostic Validity
 Difference Score
 Differential Aptitude Test
 DiggleKenward Model for Dropout
 Dimension Reduction
 Discriminant Analysis
 Discriminant Correspondence Analysis
 Dissimilarity Coefficient
 Distance
 DISTATIS
 Dixon Test for Outliers
 Dunn's Multiple Comparison Test
 Ecological Momentary Assessment
 Educational Testing Service
 Edwards Personal Preference Schedule
 Effect Size
 Eigendecomposition
 Eigenvalues
 EM Algorithm
 Embedded Figures Test
 Equivalence Testing
 Essay Items
 Estimates of the Population Median
 Ethical Issues in Testing
 Ethical Principles in the Conduct of Research With Human Participants
 EvidenceBased Practice
 Excel Spreadsheet Functions
 Exploratory Data Analysis
 Exploratory Factor Analysis
 Eyeball Estimation
 Face Validity
 Factor Analysis
 Factor Scores
 Factorial Design
 Fagan Test of Infant Intelligence
 Family Environment Scale
 File Drawer Problem
 Fisher, Ronald Aylmer
 Fisher Exact Probability Test
 FisherIrwin Test. See
 Fisher's LSD
 Fisher's Z Transformation
 Fourier Transform
 Fractal
 Fractional Randomized Block Design
 Frequency Distribution
 Friedman Test
 Galton, Sir Francis
 Gambler's Fallacy
 Gauss, Carl Friedrich
 Generalized Additive Model
 Generalized Estimating Equations
 Generalized Method of Moments
 Generalized Procrustes Analysis
 Gerontological Apperception Test
 GfGc Theory of Intelligence
 Goodenough Harris Drawing Test
 GoodnessofFit Tests
 Graduate Record Examinations
 Grand Mean
 Graphical Statistical Methods
 Gresham, Frank M.
 Grounded Theory
 Guttman Scaling
 Harmonic Mean
 Health Insurance Portability and Accountability Act
 HelloGoodbye Effect
 Heteroscedasticity and Homoscedasticity
 Hierarchical Linear Modeling
 HighStakes Tests
 Histogram
 Historiometrics
 Holden Psychological Screening Inventory
 Homogeneity of Variance
 Hypergeometric Distribution
 Hypothesis and Hypothesis Testing
 Illinois Test of Psycholinguistic Abilities
 Immediate and Delayed Memory Tasks
 Independent Variable
 Individuals with Disabilities Education Act
 Inferential Statistics
 Information Referenced Testing
 Information Systems Interaction Readiness Scales
 Informed Consent
 Instrumental Variables
 Intelligence Quotient
 Intelligence Tests
 Internal External Locus of Control Scale
 Internal Review Board
 International Assessment of Educational Progress
 Interrater Reliability
 Interval Level of Measurement
 Iowa Tests of Basic Skills
 Iowa Tests of Educational Development
 Ipsative Measure
 Item and Test Bias
 Item Response Theory
 Jackson, Douglas N.
 Jackson Personality Inventory–Revised
 Jackson Vocational Interest Survey
 Journal of the American Statistical Association
 Journal of Modern Applied Statistical Methods
 Journal of Statistics Education
 kMeans Cluster Analysis
 Kaufman Assessment Battery for Children
 Kendall Rank Correlation
 Kinetic Family Drawing Test
 Kingston Standardized Cognitive Assessment
 KolmogorovSmirnov Test for One Sample
 KolmogorovSmirnov Test for Two Samples
 KR20 and KR21
 KruskalWallis OneWay Analysis of Variance
 Kuder Occupational Interest Survey
 Kurtosis
 Laboratory Behavioral Measures of Impulsivity
 Latent Class Analysis
 Law of Large Numbers
 Law School Admissions Test
 Least Squares, Method of
 Life Values Inventory
 Likelihood Ratio Test
 Likert Scaling
 Lilliefors Test for Normality
 Line Chart
 Linear Regression
 Logistic Regression Analysis
 Loglinear Analysis
 Longitudinal/Repeated Measures Data
 Luria Nebraska Neuropsychological Battery
 Male Role Norms Inventory
 Malthus, Thomas
 MannWhitney U Test (Wilcoxon RankSum Test)
 Markov, Andrei Andreevich
 Markov Chain Monte Carlo Methods
 Matrix Analogies Test
 Matrix Operations
 Maximum Likelihood Method
 McNemar Test for Significance of Changes
 Mean
 Measurement
 Measurement Error
 Measures of Central Tendency
 Median
 Median Test
 MetaAnalysis
 Metric Multidimensional Scaling
 Millon Behavioral Medicine Diagnostic
 Millon Clinical Multiaxial InventoryIII
 Minnesota Clerical Test
 Minnesota Multiphasic Personality Inventory
 Missing Data Method
 Mixed Models
 Mixture Models
 Mixtures of Experts
 Mode
 Moderator Variable
 Monte Carlo Methods
 Mosaic Plots
 Moving Average
 Multicollinearity
 Multidimensional Aptitude Battery
 Multiple Affect Adjective Checklist–Revised
 MultipleChoice Items
 Multiple Comparisons
 Multiple Correlation Coefficient
 Multiple Correspondence Analysis
 Multiple Factor Analysis
 Multiple Imputation for Missing Data
 Multitrait Multimethod Matrix and Construct Validity
 Multivariate Analysis of Variance (MANOVA)
 Multivariate Normal Distribution
 MyersBriggs Type Indicator
 National Council on Measurement in Education
 National Science Foundation
 NEO Personality Inventory
 Neonatal Behavioral Assessment Scale
 NewmanKeuls Test
 Nominal Level of Measurement
 Nomothetic Versus Idiographic
 Nonparametric Statistics
 Nonprobability Sampling
 Normal Curve
 Null Hypothesis Significance Testing
 O'Brien Test for Homogeneity of Variance
 Observational Studies
 Ockham's Razor
 Ogive
 One and TwoTailed Tests
 OneWay Analysis of Variance
 Ordinal Level of Measurement
 Orthogonal Predictors in Regression
 Page's L Test
 Paired Samples t Test (Dependent Samples t Test)
 Pairwise Comparisons
 Parallel Coordinate Plots
 Parallel Forms Reliability
 Parameter
 Parameter Invariance
 Part and Partial Correlations
 Partial Least Square Regression
 Pascal, Blaise
 Path Analysis
 Peabody Picture Vocabulary Test
 Pearson, Karl
 Pearson ProductMoment Correlation Coefficient
 Percentile and Percentile Rank
 Performance IQ
 PerformanceBased Assessment
 Peritz Procedure
 Personal Projects Analysis
 Personality Assessment Inventory
 Personality Research Form
 Personality Tests
 Pie Chart
 PiersHarris Children's SelfConcept Scale
 Poisson, Siméon Denis
 Poisson Distribution
 Portfolio Assessment
 Post Hoc Comparisons
 Posterior Distribution
 Predictive Validity
 Preschool Language Assessment Instrument
 Principal Component Analysis
 Prior Distribution
 Probability Sampling
 Profile Analysis
 Projective Hand Test
 Projective Testing
 Propensity Scores
 Psychological Abstracts
 Psychometrics
 PsycINFO
 Q Methodology
 QQ Plot
 Quality of WellBeing Scale
 QuasiExperimental Method
 Questionnaires
 Quota Sampling
 Random Numbers
 Random Sampling
 Range
 Rasch Measurement Model
 Ratio Level of Measurement
 Raven's Progressive Matrices
 Record Linkage
 Regression Analysis
 Relative Risk
 Reliability Theory
 Repeated Measures Analysis of Variance
 Residuals
 Response to Intervention
 Reverse Scaling
 Reynolds, Cecil R.
 Roberts Apperception Test for Children
 Rorschach Inkblot Test
 RV and Congruence Coefficients
 Sample
 Sample Size
 Sampling Distribution of a Statistic
 Sampling Error
 Scaling
 Scan Statistic
 Scattergram
 Scree Plot
 Secondary Data Analysis
 Section 504 of the Rehabilitation Act of 1973
 SelfReport
 Semantic Differential
 Semantic Differential Scale
 SemiInterquartile Range
 ShapiroWilk Test for Normality
 Signal Detection Theory
 Significance Level
 Simple Main Effect
 Simpson's Paradox
 Simpson's Rule
 Simulated Annealing
 Simulation Experiments
 SingleSubject Designs
 Singular and Generalized Singular Value Decomposition
 Six Sigma
 Sixteen Personality Factor Questionnaire
 Skewness
 Smoothing
 Social Climate Scales
 Social Skills Rating System
 Society for Research in Child Development
 Sociological Abstracts
 Spatial Learning Ability Test
 Spatial Statistics
 Spearman's Rho
 Split Half Reliability
 Spreadsheet Functions
 Spurious Correlation
 Standard Deviation
 Standard Error of the Mean
 Standard Error of Measurement
 Standard Scores
 Standards for Educational and Psychological Testing
 Stanford Achievement Test
 StanfordBinet Intelligence Scales
 Stanine
 STATIS
 Statistical Significance
 StemandLeaf Display
 Stratified Random Sampling
 Strong Interest Inventory
 Stroop Color and Word Test
 Structural Equation Modeling
 Structured Clinical Interview for DSMIV
 Sunflower Plot
 Support Vector Machines
 Suppressor Variable
 Survey Weights
 Survival Analysis
 System of Multicultural Pluralistic Assessment
 T Scores
 t Test for Two Population Means
 TestRetest Reliability
 Tests of Mediating Effects
 Text Analysis
 Thematic Apperception Test
 ThreeCard Method
 Thurstone Scales
 Time Series Analysis
 Torrance, E. Paul
 Torrance Tests of Creative Thinking
 Torrance Thinking Creatively in Action and Movement
 Tree Diagram
 True/False Items
 True Score
 TukeyKramer Procedure
 Type I Error
 Type II Error
 Unbiased Estimator
 Universal Nonverbal Intelligence Test
 Validity Coefficient
 Validity Theory
 Variable
 Variable Deletion
 Variance
 Verbal IQ
 Vineland Adaptive Behavior Scales
 Vineland Social Maturity Scale
 Wechsler Adult Intelligence Scale
 Wechsler Individual Achievement Test
 Wechsler Preschool and Primary Scale of Intelligence
 West HavenYale Multidimensional Pain Inventory
 Wilcoxon, Frank
 Wilcoxon MannWhitney Test. See
 Wilcoxon Signed Ranks Test
 Woodcock Johnson Psychoeducational Battery
 Woodcock Reading Mastery Tests Revised
 z Scores
Reader's Guide
The purpose of the Reader's Guide is to provide you with a tool you can use to locate specific entries in the encyclopedia, as well as to find out what other related entries might be of interest to you. For example, if you are interested in the visual display of information and want to learn how to create a bar chart (under the general heading of Charts, Graphs, and Visual Displays in the Reader's Guide), you can also find reference to such entries as Histogram, Line Chart, and Mosaic Plots, all related to the same general topic.
The Reader's Guide is also a very direct and simple way to get an overview of which items are contained in the encyclopedia. Although each of the categories lists items in alphabetic order (as the encyclopedia is organized), you can glance through the main headings of the guide and then focus more on a particular area of interest. Then, just turn to any particular entry you want to locate. These are easily found because they appear in alphabetical order.
 Biographies
 Babbage, Charles
 Bernoulli, Jakob
 Bonferroni, Carlo Emilio
 Bruno, James Edward
 Comrey, Andrew L.
 Cronbach, Lee J.
 Darwin, Charles
 Deming, William Edwards
 Fisher, Ronald Aylmer
 Galton, Sir Francis
 Gauss, Carl Friedrich
 Gresham, Frank M.
 Jackson, Douglas N.
 Malthus, Thomas
 Markov, Andrei Andreevich
 Pascal, Blaise
 Pearson, Karl
 Poisson, Siméon Denis
 Reynolds, Cecil R.
 Torrance, E. Paul
 Wilcoxon, Frank
 Charts, Graphs, and Visual Displays
 Area Chart
 Bar Chart
 Box Plot (Box and Whisker Plot)
 Contour Plot
 Eyeball Estimation
 Frequency Distribution
 Histogram
 Line Chart
 Mosaic Plots
 Ogive
 Parallel Coordinate Plots
 Pie Chart
 QQ Plot
 Scattergram
 Scree Plot
 Smoothing
 StemandLeaf Display
 Sunflower Plot
 Tree Diagram
 Computer Topics and Tools
 Babbage, Charles
 Computational Statistics
 Computerized Adaptive Testing
 Curvilinear Regression
 Data Analysis ToolPak
 Data Compression
 DISTATIS
 Excel Spreadsheet Functions
 Linear Regression
 Residuals
 Spatial Statistics
 Spreadsheet Functions
 STATIS
 Concepts and Issues in Measurement
 Ability Tests
 Achievement Tests
 Alternate Assessment
 Americans with Disabilities Act
 Anthropometry
 Aptitude Tests
 Artificial Neural Network
 Asymmetry of G
 Attitude Tests
 Basal Age
 Categorical Variable
 Classical Test Theory
 Coefficient Alpha
 Completion Items
 Computerized Adaptive Testing
 Construct Validity
 Content Validity
 CriterionReferenced Test
 Criterion Validity
 Cronbach, Lee J.
 CurriculumBased Measurement
 Diagnostic Validity
 Educational Testing Service
 Equivalence Testing
 Essay Items
 Ethical Issues in Testing
 Face Validity
 GfGc Theory of Intelligence
 Guttman Scaling
 Health Insurance Portability and Accountability Act
 HighStakes Tests
 Immediate and Delayed Memory Tasks
 Individuals with Disabilities Education Act
 Information Referenced Testing
 Informed Consent
 Intelligence Quotient
 Intelligence Tests
 Internal Review Board
 Interrater Reliability
 Interval Level of Measurement
 Ipsative Measure
 Item and Test Bias
 Item Response Theory
 KR20 and KR21
 Likert Scaling
 Measurement
 Measurement Error
 Metric Multidimensional Scaling
 MultipleChoice Items
 Multitrait Multimethod Matrix and Construct Validity
 Nomothetic Versus Idiographic
 Ordinal Level of Measurement
 Parallel Forms Reliability
 Performance IQ
 PerformanceBased Assessment
 Personality Tests
 Portfolio Assessment
 Predictive Validity
 Projective Testing
 Q Methodology
 Questionnaires
 Ratio Level of Measurement
 Reliability Theory
 Response to Intervention
 Reverse Scaling
 Scaling
 Section 504 of the Rehabilitation Act of 1973
 SelfReport
 Semantic Differential
 Semantic Differential Scale
 Six Sigma
 Spearman's Rho
 Split Half Reliability
 Standard Error of Measurement
 Standard Scores
 Standards for Educational and Psychological Testing
 T Scores
 TestRetest Reliability
 Thurstone Scaling
 Torrance, E. Paul
 True/False Items
 Validity Coefficient
 Validity Theory
 Verbal IQ
 z Scores
 Concepts and Issues in Statistics
 Artificial Neural Network
 Attenuation, Correction for
 Autocorrelation
 Bayesian Statistics
 Bioinformatics
 Central Limit Theorem
 Decision Theory
 DiggleKenward Model for Dropout
 DISTATIS
 Exploratory Factor Analysis
 Factorial Design
 Fourier Transform
 Generalized Additive Model
 Generalized Method of Moments
 Generalized Procrustes Analysis
 Graphical Statistical Methods
 Hierarchical Linear Modeling
 Historiometrics
 Logistic Regression Analysis
 Loglinear Analysis
 Markov Chain Monte Carlo Methods
 Matrix Operations
 Mean
 Measurement Error
 Mixtures of Experts
 Nonparametric Statistics
 Propensity Scores
 Rasch Measurement Model
 Regression Analysis
 Sampling Distribution of a Statistic
 Signal Detection Theory
 Simpson's Paradox
 Spurious Correlation
 Standard Error of the Mean
 Standard Scores
 Support Vector Machines
 Survival Analysis
 Type I Error
 Type II Error
 Data and Data Reduction Techniques
 Censored Data
 Data Compression
 Data Mining
 Discriminant Analysis
 Eigenvalues
 Exploratory Data Analysis
 Factor Analysis
 Factor Scores
 Missing Data Method
 Multiple Factor Analysis
 Record Linkage
 Secondary Analysis of Data
 Descriptive Statistics
 Arithmetic Mean
 Attenuation, Correction for
 Autocorrelation
 Average
 Average Deviation
 Bayley Scales of Infant Development
 Biserial Correlation Coefficient
 Class Interval
 Coefficients of Correlation, Alienation, and Determination
 Cognitive Psychometric Assessment
 Cohen's Kappa
 Correlation Coefficient
 Cumulative Frequency Distribution
 Deviation Score
 Difference Score
 Estimates of the Population Median
 Fisher's Z Transformation
 Frequency Distribution
 Galton, Sir Francis
 Grand Mean
 Harmonic Mean
 Histogram
 Kendall Rank Correlation
 Mean
 Measures of Central Tendency
 Median
 Mode
 Moving Average
 Parameter
 Parameter Invariance
 Part and Partial Correlations
 Pearson, Karl
 Pearson ProductMoment Correlation Coefficient
 Percentile and Percentile Rank
 RV and Congruence Coefficients
 Scattergram
 SemiInterquartile Range
 Spurious Correlation
 Standard Deviation
 Survey Weights
 Text Analysis
 Evaluation
 Achievement Tests
 EvidenceBased Practices
 Health Insurance Portability and Accountability Act
 HighStakes Tests
 Questionnaires
 Experimental Methods
 Alternative Hypothesis
 American Statistical Association
 Americans with Disabilities Act
 Association for Psychological Science
 Basic Research
 Bioinformatics
 Complete Independence Hypothesis
 Continuous Variable
 Critical Value
 Data Collection
 Data Mining
 Delphi Technique
 Dependent Variable
 Descriptive Research
 Ethical Issues in Testing
 Ethical Principles in the Conduct of Research With Human Participants
 Fractional Randomized Block Design
 HelloGoodbye Effect
 Hypothesis and Hypothesis Testing
 Independent Variable
 Informed Consent
 Instrumental Variables
 Internal Review Board
 Longitudinal/Repeated Measures Data
 MetaAnalysis
 Missing Data Method
 Mixed Models
 Mixture Models
 Moderator Variable
 Monte Carlo Methods
 Null Hypothesis Significance Testing
 Ockham's Razor
 Pairwise Comparisons
 Post Hoc Comparisons
 Projective Testing
 QuasiExperimental Method
 Sample Size
 Section 504 of the Rehabilitation Act of 1973
 Significance Level
 Simple Main Effect
 Simulation Experiments
 SingleSubject Designs
 Standards for Educational and Psychological Testing
 Statistical Significance
 Suppressor Variable
 Variable
 Variable Deletion
 Variance
 Inferential Statistics
 Akaike Information Criterion
 Analysis of Covariance (ANCOVA)
 Analysis of Variance (ANOVA)
 Bayes Factors
 Bayesian Information Criterion
 Binomial Test
 Bonferroni, Carlo Emilio
 Complete Independence Hypothesis
 Data Analysis ToolPak
 Exploratory Factor Analysis
 Factorial Design
 Fisher, Ronald Aylmer
 Hierarchical Linear Modeling
 Hypothesis and Hypothesis Testing
 Inferential Statistics
 Logistic Regression Analysis
 Markov, Andrei Andreevich
 Null Hypothesis Significance Testing
 Pairwise Comparisons
 Part and Partial Correlations
 Repeated Measures Analysis of Variance
 Type I Error
 Type II Error
 Wilcoxon, Frank
 Organizations and Publications
 Abstracts
 American Doctoral Dissertations
 American Psychological Association
 American Statistical Association
 Association for Psychological Science
 Buros Institute of Mental Measurements
 Centers for Disease Control and Prevention
 Educational Testing Service
 Journal of the American Statistical Association
 Journal of Modern Applied Statistical Methods
 Journal of Statistics Education
 National Science Foundation
 Psychological Abstracts
 Psychometrics
 PsycINFO
 Society for Research in Child Development
 Sociological Abstracts
 Prediction and Estimation
 Attributable Risk
 Bernoulli, Jakob
 Chance
 Conditional Probability
 Confidence Intervals
 Continuous Variable
 Curse of Dimensionality
 Decision Boundary
 Decision Theory
 File Drawer Problem
 Gambler's Fallacy
 Generalized Estimating Equations
 Law of Large Numbers
 Maximum Likelihood Method
 Nonprobability Sampling
 Pascal, Blaise
 Probability Sampling
 Random Numbers
 Relative Risk
 Signal Detection Theory
 Significance Level
 ThreeCard Method
 Probability
 Alternate Assessment
 Audit Trail
 Authenticity
 Categorical Variable
 Essay Items
 Grounded Theory
 Observational Studies
 Portfolio Assessment
 SelfReport
 Text Analysis
 Qualitative Methods
 Active Life Expectancy
 Assessment of Interactions in Multiple Regression
 Eyeball Estimation
 Orthogonal Predictors in Regression
 Regression Analysis
 Survival Analysis
 Samples, Sampling, and Distributions
 Acceptance Sampling
 Adaptive Sampling Design
 Age Norms
 Attrition Bias
 Career Maturity Inventory
 Central Limit Theorem
 Class Interval
 Cluster Sampling
 Confidence Intervals
 Convenience Sampling
 Cumulative Frequency Distribution
 Data Collection
 DiggleKenward Model for Dropout
 Gauss, Carl Friedrich
 Heteroscedasticity and Homoscedasticity
 Homogeneity of Variance
 Hypergeometric Distribution
 Kurtosis
 Malthus, Thomas
 Multicollinearity
 Multivariate Normal Distribution
 Nonprobability Sampling
 Normal Curve
 Ogive
 Parameter
 Percentile and Percentile Rank
 Poisson, Siméon Denis
 Poisson Distribution
 Posterior Distribution
 Prior Distribution
 Probability Sampling
 Quota Sampling
 Random Sampling
 Sample
 Sample Size
 SemiInterquartile Range
 Simpson's Rule
 Skewness
 Smoothing
 Stanine
 Stratified Random Sampling
 Unbiased Estimator
 Statistical Techniques
 Binomial Distribution/Binomial and Sign Tests
 Bivariate Distributions
 Bonferroni Test
 Bowker Procedure
 Causal Analysis
 Centroid
 Chance
 ChiSquare Test for Goodness of Fit
 ChiSquare Test for Independence
 Classification and Regression Tree
 Cochran Q Test
 Cohen's Kappa
 Delta Method
 Dimension Reduction
 Discriminant Analysis
 Dissimilarity Coefficient
 Dixon Test for Outliers
 Dunn's Multiple Comparison Test
 Eigendecomposition
 Eigenvalues
 EM Algorithm
 Exploratory Data Analysis
 Factor Analysis
 Factor Scores
 Fisher Exact Probability Test
 Fisher's LSD
 Friedman Test
 GoodnessofFit Tests
 Grounded Theory
 kMeans Cluster Analysis
 KolmogorovSmirnov Test for One Sample
 KolmogorovSmirnov Test for Two Samples
 KruskalWallis OneWay Analysis of Variance
 Latent Class Analysis
 Likelihood Ratio Test
 Lilliefors Test for Normality
 MannWhitney U Test (Wilcoxon RankSum Test)
 McNemar Test for Significance of Changes
 Median Test
 MetaAnalysis
 Multiple Comparisons
 Multiple Factor Analysis
 Multiple Imputation for Missing Data
 Multivariate Analysis of Variance (MANOVA)
 NewmanKeuls Test
 O'Brien Test for Homogeneity of Variance
 Observational Studies
 OneWay Analysis of Variance
 Page's L Test
 Paired Samples t Test (Dependent Samples t Test)
 Path Analysis
 Peritz Procedure
 Scan Statistic
 ShapiroWilk Test for Normality
 Structural Equation Modeling
 t Test for Two Population Means
 Tests of Mediating Effects
 ThreeCard Method
 TukeyKramer Procedure
 Wilcoxon Signed Ranks Test
 Statistical Tests
 Analysis of Covariance (ANCOVA)
 Analysis of Variance (ANOVA)
 BehrensFisher Test
 Binomial Distribution/Binomial and Sign Tests
 Binomial Test
 Bonferroni Test
 Bowker Procedure
 ChiSquare Test for Goodness of Fit
 ChiSquare Test for Independence
 Classification and Regression Tree
 Cochran Q Test
 Dixon Test for Outliers
 Dunn's Multiple Comparison Test
 Excel Spreadsheet Functions
 Fisher Exact Probability Test
 Fisher's LSD
 Friedman Test
 GoodnessofFit Tests
 KolmogorovSmirnov Test for One Sample
 KolmogorovSmirnov Test for Two Samples
 KruskalWallis OneWay Analysis of Variance
 Latent Class Analysis
 Likelihood Ratio Test
 Lilliefors Test for Normality
 MannWhitney U Test (Wilcoxon RankSum Test)
 McNemar Test for Significance of Changes
 Median Test
 Multiple Comparisons
 Multivariate Analysis of Variance (MANOVA)
 NewmanKeuls Test
 O'Brien Test for Homogeneity of Variance
 One and TwoTailed Tests
 OneWay Analysis of Variance
 Page's L Test
 Paired Samples t Test (Dependent Samples t Test)
 Peritz Procedure
 Repeated Measures Analysis of Variance
 ShapiroWilk Test for Normality
 t Test for Two Population Means
 Tests of Mediating Effects
 TukeyKramer Procedure
 Wilcoxon Signed Ranks Test
 Tests by Name
 Adjective Checklist
 Alcohol Use Inventory
 Armed Forces Qualification Test
 Armed Services Vocational Aptitude Battery
 Basic Personality Inventory
 Bayley Scales of Infant Development
 Beck Depression Inventory
 Behavior Assessment System for Children
 Bender Visual Motor Gestalt Test
 Bracken Basic Concept Scale–Revised
 California Psychological Inventory
 Career Assessment Inventory
 Career Development Inventory
 Career Maturity Inventory
 Carroll Depression Scale
 Children's Academic Intrinsic Motivation Inventory
 Clinical Assessment of Attention Deficit
 Clinical Assessment of Behavior
 Clinical Assessment of Depression
 Cognitive Abilities Test
 Cognitive Psychometric Assessment
 Comrey Personality Scales
 Coping Resources Inventory for Stress
 Culture Fair Intelligence Test
 Differential Aptitude Test
 Ecological Momentary Assessment
 Edwards Personal Preference Schedule
 Embedded Figures Test
 Fagan Test of Infant Intelligence
 Family Environment Scale
 Gerontological Apperception Test
 Goodenough Harris Drawing Test
 Graduate Record Examinations
 Holden Psychological Screening Inventory
 Illinois Test of Psycholinguistic Abilities
 Information Systems Interaction Readiness
 Internal External Locus of Control Scale
 International Assessment of Educational Progress
 Iowa Tests of Basic Skills
 Iowa Tests of Educational Development
 Jackson Personality Inventory–Revised
 Jackson Vocational Interest Survey
 Kaufman Assessment Battery for Children
 Kinetic Family Drawing Test
 Kingston Standardized Cognitive Assessment
 Kuder Occupational Interest Survey
 Laboratory Behavioral Measures of Impulsivity
 Law School Admissions Test
 Life Values Inventory
 Luria Nebraska Neuropsychological Battery
 Male Role Norms Inventory
 Matrix Analogies Test
 Millon Behavioral Medicine Diagnostic
 Millon Clinical Multiaxial InventoryIII
 Minnesota Clerical Test
 Minnesota Multiphasic Personality Inventory
 Multidimensional Aptitude Battery
 Multiple Affect Adjective Checklist–Revised
 MyersBriggs Type Indicator
 NEO Personality Inventory
 Neonatal Behavioral Assessment Scale
 Peabody Picture Vocabulary Test
 Personal Projects Analysis
 Personality Assessment Inventory
 Personality Research Form
 PiersHarris Children's SelfConcept Scale
 Preschool Language Assessment Instrument
 Profile Analysis
 Projective Hand Test
 Quality of WellBeing Scale
 Raven's Progressive Matrices
 Roberts Apperception Test for Children
 Rorschach Inkblot Test
 Sixteen Personality Factor Questionnaire
 Social Climate Scales
 Social Skills Rating System
 Spatial Learning Ability Test
 Stanford Achievement Test
 StanfordBinet Intelligence Scales
 Strong Interest Inventory
 Stroop Color and Word Test
 Structured Clinical Interview for DSMIV
 System of Multicultural Pluralistic Assessment
 Thematic Apperception Test
 Torrance Tests of Creative Thinking
 Torrance Thinking Creatively in Action and Movement
 Universal Nonverbal Intelligence Test
 Vineland Adaptive Behavior Scales
 Vineland Social Maturity Scale
 Wechsler Adult Intelligence Scale
 Wechsler Individual Achievement Test
 Wechsler Preschool and Primary Scale of Intelligence
 West HavenYale Multidimensional Pain Inventory
 Woodcock Johnson Psychoeducational Battery
 Woodcock Reading Mastery Tests Revised
About the Editor
Neil J. Salkind (PhD, University of Maryland, 1973) is a Professor of Psychology and Research in Education at the University of Kansas in Lawrence, Kansas. He completed postdoctoral training as a member of the Bush Child and Family Policy Institute at the University of North Carolina and has authored and coauthored more than 125 scholarly papers and books. Most recently, he is the author of Statistics for People Who (Think They) Hate Statistics: The Excel Edition (2007), Tests & Measurement for People Who (Think They) Hate Tests & Measurement (2006), the Encyclopedia of Human Development (2006), Theories of Human Development (2004), and Statistics for People Who (Think They) Hate Statistics (2004), all published by Sage. He was the editor of Child Development Abstracts and Bibliography, published by the Society for Research in Child Development (SRCD), from 1988 through 2001, and he is the treasurerelect of Division 7 of the American Psychological Association.
Contributors
Francisco J. Abad
Universidad Autonoma de Madrid
Inmaculada Aban
University of Alabama at Birmingham
Hervé Abdi
University of Texas at Dallas
Phillip L. Ackerman
Georgia Institute of Technology
Demetrios S. Alexopoulos
University of Patras, Greece
Audrey AmreinBeardsley
Arizona State University
Lauren E. Auld
DePauw University
Carrie R. Ball
University of Wisconsin–Madison
Kimberly A. Barchard
University of Nevada, Las Vegas
Jonathan Barzilai
Dalhousie University
Edward J. Bedrick
University of New Mexico
Mark L. Berenson
Montclair State University
Dongsheng Bi
University of Nebraska Lincoln
Damian P. Birney
University of Sydney
David M. Boynton
Saint Michael's College
Bruce A. Bracken
College of William & Mary
Jennifer Bragger
Montclair State University
Gary G. Brannigan
State University of New York–Plattsburgh
Ernest W. Brewer
University of Tennessee
Carolyn Brodbeck
Chapman University
Sarah Brookhart
American Psychological Society
Duane Brown
University of North Carolina, Chapel Hill
Jennifer Ann Brown
University of Canterbury
Shawn T. Bubany
University of Minnesota
Michael J. Burke
Tulane University
Mary Margaret Capraro
Texas A&M University
Robert M. Capraro
Texas A&M University
Joseph E. Cavanaugh
University of Iowa
HuaHua Chang
University of Illinois
Elaine Chapman
University of Western Australia
Bernard C. K. Choi
Public Health Agency of Canada
Siu L. Chow
University of Regina
Michelle D. Chudleigh
Alberta Hospital Edmonton
Moo K. Chung
University of Wisconsin
M. H. Clark
Southern Illinois University
Murray Clayton
University of Wisconsin–Madison
A. Jill Clemence
Austen Riggs Center
Roberto Colom
Universidad Autonoma de Madrid
John Colombo
University of Kansas
Andrew L. Comrey
University of California, Los Angeles
Dianne Cook
Iowa State University
R. Kelly Crace
College of William & Mary
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University of Georgia
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University of Auburn
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Queen's University
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Meredith College
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University of Houston
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Craig K. Enders
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University of Toronto
Leandre R. Fabrigar
Queen's University
Gail F. Fahoome
Wayne State University
Andy P. Field
University of Sussex
Barry Forer
University of British Columbia
Robert A. Forsyth
University of Iowa
Brian F. French
Purdue University
Kathryn H. Ganske
Georgia State University
Travis L. Gee
University of Queensland, Australia
Carole E. Gelfer
William Paterson University
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Morton A. Gernsbacher
University of Wisconsin–Madison
Maribeth Gettinger
University of Wisconsin–Madison
Marjan GhahramanlouHolloway
Uniformed Services University of the Health Sciences
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Kent State University
Charles Golden
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Naomi Grant
Queen's University
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Oklahoma State University
Erik J. Groessl
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Lyndsi M. Grover
University of North Texas
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California State University, San Bernardino
Anthony J. Guarino
University of Auburn
Mads Haahr
Trinity College Dublin
John W. Hagen
Society of Research in Child Development
Brian Haig
University of Canterbury
Thomas Haladyna
Arizona State University
YoungHoon Ham
University of Tennessee
Ronald K. Hambleton
University of Massachusetts
Chirok Han
Victoria University of Wellington
David Hann
University of Kansas
JoIda C. Hansen
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James W. Hardin
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Clay Helberg
SPSS Inc.
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Alberta Hospital Edmonton
Heike Hofmann
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Robert Hopkins
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Jennifer R. Hsu
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Jennifer Ivie
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University of Rochester
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Samuel Juni
New York University
Sema A. Kalaian
Eastern Michigan University
Matthew E. Kaler
University of Minnesota
Kristen M. Kalymon
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Robert M. Kaplan
University of California, Los Angeles
Michael A. Karchmer
Gallaudet Research Institute
Michael Karson
University of Denver
Rafa M. Kasim
Kent State University
Allison B. Kaufman
University of California, Riverside
James C. Kaufman
California State University, San Bernardino
Lisa Keller
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Lindy Kilik
Queen's University
Kyung Hee Kim
Eastern Michigan University
Roger E. Kirk
Baylor University
Steve Kirkland
University of Regina
Theresa Kline
University of Calgary
James Randolph Knaub, Jr.
U.S. Government, Energy Information Administration
John C. Kolar
Medical City Children's Hospital, Dallas
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Dawn M. Marsh
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Kenneth B. Matheny
Georgia State University
Charles W. Mathias
Wake Forest University Health Sciences
Sonia Matwin
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Mary Ann McCabe
American Psychological Association
Geoffrey McLachlan
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Franklin Mendivil
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Kevin E. Moore
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Bernice S. Moos
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Rudolf H. Moos
Stanford University
Mark Mostert
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Liqiang Ni
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Royal Military College
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Anthony J. Onwuegbuzie
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J. Shelly Paik
Queen's University
Anita W. P. Pak
University of Ottawa
Paul E. Panek
Ohio State University–Newark
Hans Anand Pant
Humboldt University of Berlin
DongHo Park
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Scott Parker
American University
Sandrine Pavoine
Muséum National d'Histoire Naturelle, Paris
Manohar Pawar
Charles Sturt University
Edsel Pena
University of South Carolina
Sarah Peterson
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Andrew M. Pomerantz
Southern Illinois University Edwardsville
Jennifer L. Porter
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Ronald D. Porter
Queen's University
Patricia Ramsey
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Philip H. Ramsey
Queens College of City University of New York
John Randal
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Kristin Rasmussen
University of Kansas
Marco Reale
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John R. Reddon
Alberta Hospital Edmonton
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Jerome Reiter
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Bixiang Ren
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Beth Rodgers
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Michael C. Rodriguez
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Ward Rodriguez
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Javier Rojo
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Thomas E. Rudy
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André A. Rupp
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Charles J. Russo
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Steve Saladin
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Neil J. Salkind
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Kyoungah See
Miami University
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Ramalingam Shanmugam
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Child and Family Psychology Centre
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University College Northampton
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Kansas University
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Alberta Hospital Edmonton
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Queens University
Suzanne WoodsGroves
Auburn University
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Karl L. Wuensch
East Carolina University
Hongwei Yang
University of Tennessee–Knoxville
Keming Yang
University of Reading
Zhiliang Ying
Columbia University
Vincent R. Zalcik
Alberta Hospital Edmonton
April L. Zenisky
University of Massachusetts, Amherst
Jin Zhang
University of Manitoba
Zhigang Zhang
Oklahoma State University
Shuangmei (Christine) Zhou
University of Minnesota
Marvin Zuckerman
University of Delaware
Bruno D. Zumbo
University of British Columbia
Preface
It's an interesting paradox when an important subject, which can help us make sense of our busy, everyday world, is considered very difficult to approach. Such is the case with measurement and statistics. However, this does not necessarily have to be the case, and we believe that the Encyclopedia of Measurement and Statistics will show you why.
These two areas of study encompass a very wide range of topics, and a knowledge of even the basic concepts and ideas allows us to be much better prepared as intelligent consumers of information.
Whether we are interested in knowing if there is a difference between two groups in their preference for a particular brand of cereal or how the Americans with Disabilities Act works, we need to know how to analyze and interpret information. And often, when that information is in the form of numbers, that's where statistics and measurement come into play. That basic stat course in college might have been a nightmare, but the material is no more difficult to grasp and apply than is any other discipline in the social and behavioral sciences.
Although hundreds of books have been written about the different topics that are contained in the Encyclopedia of Measurement and Statistics, and there are thousands upon thousands of studies that have been conducted in this area, what we offer here is something quite different—a comprehensive overview of important topics. What we hope we have accomplished are entries that comprise a comprehensive overview of the most important topics in the areas of measurement and statistics—entries that share this important information in a way that is informative; not too technical; and even, in some cases, entertaining.
Through almost 500 contributions and some special elements that will be described later in this preface, experts in each of the entries contained in these pages contribute an overview and an explanation of the major topics in these two fields.
The underlying rationale for the selection of particular topics and their presentation in this encyclopedia comes from the need to share with the educated reader topics that are rich, diverse, and deserving of closer inspection. Within these pages, we provide the overview and the detail that we feel is necessary to become well acquainted with these topics.
As in many other encyclopedias, the Encyclopedia of Measurement and Statistics is organized in alphabetical order, from A through Z. However, particular themes were identified early on that could be used to organize conceptually the information and the entries. These themes or major topic areas constitute the Reader's Guide, which appears on page xiii. Categories such as Experimental Methods, Qualitative Methods, and Organizations and Publications are only a few of the many that help organize the entire set of contributions.
The ProcessThe first task in the creation of a multivolume encyclopedia such as this is the development of a complete and thorough listing of the important topics in the disciplines of measurement and statistics. This process started with the identification of entries that the editor and advisory board thought were important to include. We tried to make sure that these entries included topics that would be of interest to a general readership, but we wanted to exclude terms and ideas that were too highly technical or too far removed from the interests of the everyday reader. This list was reviewed several times until we felt that it was a comprehensive set of topics that best fit the vision for the encyclopedia.
Like many other disciplines, there is a great deal of overlap between different important concepts and ideas in measurement and statistics. For example, although there is an entry titled Descriptive Statistics (which is a general overview), there is much greater detail in the entries titled Mean and Median. That overlap is fine because it provides two different, and compatible, views of the same topic and can only help reinforce one's knowledge. We hope that the crossreferences we provide will help the user understand this and get the most out of learning about any one idea, term, or procedure.
As expected, this list was edited and revised as we worked and as authors were recruited to write particular entries. Enthusiastic authors suggested new topics that might have been overlooked as well as removing topics that might have no appeal. All of these changes were taken into consideration as the final list was assembled.
The next step was to assign a length to a particular entry, which ranged from 500 words for simple definitions or biographies (such as the one for the Arithmetic Mean or Charles Babbage, respectively) to almost 4,000 words for longer, more indepth exploration for topics (such as the entry on Aptitude Tests). In between were articles that were 1,500 and 2,000 words in length. (At times, authors asked that the length be extended because they had so much information they wanted to share and they felt that the limitation on space was unwarranted. In most cases, it was not a problem to allow such an extension.)
The final step was the identification of authors. This took place through a variety of mechanisms, including the identification of individuals based on the advisory board recommendations and/or the editor's professional and personal experiences, authors of journal articles and books who focused on a particular area directly related to the entry, and referrals from other individuals who were well known in the field.
Once authors were identified and invited, and once they confirmed that they could participate, they were sent detailed instructions and given a deadline for the submission of their entry. The results, as you well know by now, after editing, layout, and other production steps, are in your hands.
How to use this ReferenceWe know the study of measurement and statistics can be less than inviting. But, as we mentioned at the beginning of this preface, and want to emphasize once again here, the ideas and tools contained in these pages are approachable and can be invaluable for understanding our very technical world and an increasing flow of information.
Although many of the ideas you read about in these pages are relatively recent, some are centuries old. Yet both kinds hold promise for your being able to better navigate the increasingly complex world of information we each face every day.
So, although many of us believe that this encyclopedia should only be consulted when a term or idea needs some clarification, why not take some time and just browse through the material and see what types of entries are offered and how useful you might find them?
As we wrote earlier, a primary goal of creating this set of volumes was to open up the broad discipline of measurement and statistics to a wider and more general audience than usual.
Take these books and find a comfortable seat in the library, browse through the topics, and read the ones that catch your eye. We're confident that you'll continue reading and looking for additional related entries, such as “Applying Ideas on Statistics and Measurement,” where you can find examples of how these ideas are applied and, in doing so, learn more about whatever interests you.
Should you want to find a topic within a particular area, consult the Reader's Guide, which organizes entries within this twovolume set into one general category. Using this tool, you can quickly move to an area or a specific topic that you find valuable and of interest.
Finally, there other elements that should be of interest.
Appendix A is a guide to basic statistics for those readers who might like a more instructional, stepbystep presentation of basic concepts in statistics and measurement. It also includes a table of critical values used in hypothesis testing and an important part of any reference in this area. These materials are taken from Statistics for People Who (Think They) Hate Statistics, written by the editor and also published by Sage.
Appendix B represents a collection of some important and useful sites on the Internet that have additional information about measurement and statistics. Although such sites tend to remain stable, there may be some changes in the Internet address. If you cannot find the Web page using the address that is provided, then search for the name of the Web site using Google or another search engine.
Finally, Appendix C is a glossary of terms and concepts you will frequently come across in these volumes.
AcknowledgmentsThis has been a challenging and rewarding project. It was ambitious in scope because it tried to corral a wide and diverse set of topics within measurement and statistics into a coherent set of volumes.
First, thanks to the Advisory Board, a group of scholars in many different areas that took the time to review the list of entries and make invaluable suggestions as to what the reader might find valuable and how that topic should be approached. The Advisory Board members are very busy people who took the time to help the editor develop a list that is broad in scope and represents the most important topics in human development. You can see a complete list of who these fine people are on page vi.
My editor and my publisher at Sage Publications, Lisa Cuevas Shaw and Rolf Janke, respectively, deserve a great deal of thanks for bringing this project to me and providing the chance to make it work. They are terrific people who provide support and ideas and are always there to listen. And perhaps best of all, they get things done.
Other people also helped make this task enjoyable and helped create the useful, informative, and approachable set of volumes you hold in your hands. Among these people are Tracy Alpern, Sage senior project editor, and Bonnie Freeman, Liann Lech, and Carla Freeman, copy editors.
I'll save one of the biggest thankyous for Kristin Rasmussen, the managing editor, who managed this project in every sense of the word, including the formidable tasks of tracking entries, submissions, reviews, and resubmissions. All of this was easily accomplished with enthusiasm, initiative, and perseverance when answering endless questions through thousands of emails to hundreds of authors. She is currently a doctoral student at the University of Kansas and has an exceptionally bright future. Thank you sincerely.
And, of course, how could anything of this magnitude ever have been done without the timely execution and accurate scholarship of the contributing authors? They understood that the task at hand was to introduce educated readers (such as you) to new areas of interest in a very broad field, and without exception, they did a wonderful job. You will see throughout that their writing is clear and informative—just what material like this should be for the intelligent reader. To them, a sincere thank you and a job well done.
Finally, as always, none of this would have happened or been worth undertaking without my comrade in (almost all) ups and down and ins and outs, and my truest and best friend, Leni. Sara and Micah, versions 1.1 and 1.2, didn't hurt either.

Appendix A
Ten Commandments of Data CollectionThe following text is taken from Neil J. Salkind's bestselling introduction to statistics text, Statistics for People Who (Think They) Hate Statistics, 2nd edition (2004).
Now that you know how to analyze data, you would be well served to hear something about collecting them. The data collection process can be a long and rigorous one, even if it involves only a simple, onepage questionnaire given to a group of students, parents, patients, or voters. The data collection process may very well be the most timeconsuming part of your project. But as many researchers do, this period of time is also used to think about the upcoming analysis and what it will entail.
Here they are: the ten commandments for making sure your data get collected in a way that they are usable. Unlike the original Ten Commandments, these should not be carved in stone (because they can certainly change), but if you follow them, you can avoid lots of aggravation.
Commandment 1. As you begin thinking about a research question, also begin thinking about the type of data you will have to collect to answer that question. Interview? Questionnaire? Paper and pencil? Find out how other people have done it in the past by reading the relevant journals in your area of interest and consider doing what they did.
Commandment 2. As you think about the type of data you will be collecting, think about where you will be getting the data. If you are using the library for historical data or accessing files of data that have already been collected, such as census data (available through the U.S. Census Bureau and some online), you will have few logistical problems. But what if you want to assess the interaction between newborns and their parents? The attitude of teachers toward unionizing? The age at which people over 50 think they are old? All of these questions involve needing people to provide the answers, and finding people can be tough. Start now.
Commandment 3. Make sure that the data collection forms you use are clear and easy to use. Practice on a set of pilot data so you can make sure it is easy to go from the original scoring sheets to the data collection form.
Commandment 4. Always make a duplicate copy of the data file, and keep it in a separate location. Keep in mind that there are two types of people: those who have lost their data and those who will. Keep a copy of data collection sheets in a separate location. If you are recording your data as a computer file, such as a spreadsheet, be sure to make a backup!
Commandment 5. Do not rely on other people to collect or transfer your data unless you have personally trained them and are confident that they understand the data collection process as well as you do. It is great to have people help you, and it helps keep the morale up during those long data collection sessions. But unless your helpers are competent beyond question, you could easily sabotage all your hard work and planning.
Commandment 6. Plan a detailed schedule of when and where you will be collecting your data. If you need to visit three schools and each of 50 children needs to be tested for a total of 10 minutes at each school, that is 25 hours of testing. That does not mean you can allot 25 hours from your schedule for this activity. What about travel from one school to another? What about the child who is in the bathroom when it is his turn, and you have to wait 10 minutes until he comes back to the classroom? What about the day you show up and Cowboy Bob is the featured guest… and on and on. Be prepared for anything, and allocate 25% to 50% more time in your schedule for unforeseen happenings.
Commandment 7. As soon as possible, cultivate possible sources for your subject pool. Because you already have some knowledge in your own discipline, you probably also know of people who work with the type of population you want or who might be able to help you gain access to these samples. If you are in a university community, it is likely that there are hundreds of other people competing for the same subject sample that you need. Instead of competing, why not try a more outoftheway (maybe 30 minutes away) school district or social group or civic organization or hospital, where you might be able to obtain a sample with less competition?
Commandment 8. Try to follow up on subjects who missed their testing session or interview. Call them back and try to reschedule. Once you get in the habit of skipping possible participants, it becomes too easy to cut the sample down to too small a size. And you can never tell—the people who drop out might be dropping out for reasons related to what you are studying. This can mean that your final sample of people is qualitatively different from the sample you started with.
Commandment 9. Never discard the original data, such as the test booklets, interview notes, and so forth. Other researchers might want to use the same database, or you may have to return to the original materials for further information.
And Number 10? Follow the previous 9. No kidding!
Tables of Critical ValuesThe following tables are taken from Neil J. Salkind's bestselling introduction to statistics text, Statistics for People Who (Think They) Hate Statistics, 2nd edition (2004).
Table 1 Areas Beneath the Normal Curve Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the Area Between the Mean and the zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore zscore 0.00 0.00 0.50 19.15 1.00 34.13 1.50 43.32 2.00 47.72 2.50 49.38 3.00 49.87 3.50 49.98 0.01 0.40 0.52 19.50 1.01 34.38 1.51 43.45 2.01 47.78 2.51 49.40 3.01 49.87 3.51 49.98 0.02 0.50 0.53 19.85 1.02 34.61 1.52 43.57 2.02 47.83 2.52 49.41 3.02 49.87 3.52 49.98 0.03 1.20 0.54 20.19 1.03 34.85 1.53 43.70 2.03 47.88 2.53 49.43 3.03 49.88 3.53 49.98 0.04 1.60 0.55 20.54 1.04 35.08 1.54 43.82 2.04 47.93 2.54 49.45 3.04 49.88 3.54 49.98 0.05 1.99 0.56 20.88 1.05 35.31 1.55 43.94 2.05 47.98 2.55 49.46 3.05 49.89 3.55 49.98 0.06 2.39 0.57 21.23 1.06 35.54 1.56 44.06 2.06 48.03 2.56 49.48 3.06 49.89 3.56 49.98 0.07 2.79 0.58 21.57 1.07 35.77 1.57 44.18 2.07 48.08 2.57 49.49 3.07 49.89 3.57 49.98 0.08 3.19 0.59 21.90 1.08 35.99 1.58 44.29 2.08 48.12 2.58 49.51 3.08 49.9 3.58 49.98 0.09 3.59 0.60 22.24 1.09 36.21 1.59 44.41 2.09 48.17 2.59 49.52 3.09 49.9 3.59 49.98 0.10 3.98 0.61 22.57 1.10 36.43 1.60 44.52 2.10 48.21 2.60 49.53 3.10 49.9 3.60 49.98 0.11 4.38 0.62 22.91 1.11 36.65 1.61 44.63 2.11 48.26 2.61 49.55 3.11 49.91 3.61 49.98 0.12 4.78 0.63 23.24 1.12 36.86 1.62 44.74 2.12 48.30 2.62 49.56 3.12 49.91 3.62 49.98 0.13 5.17 0.64 23.57 1.13 37.08 1.63 44.84 2.13 48.34 2.63 49.57 3.13 49.91 3.63 49.98 0.14 5.57 0.65 23.89 1.14 37.29 1.64 44.95 2.14 48.38 2.64 49.59 3.14 49.92 3.64 49.98 0.15 5.96 0.66 24.54 1.15 37.49 1.65 45.05 2.15 48.42 2.65 49.60 3.15 49.92 3.65 49.98 0.16 6.36 0.67 24.86 1.16 37.70 1.66 45.15 2.16 48.46 2.66 49.61 3.16 49.92 3.66 49.98 0.17 6.75 0.68 25.17 1.17 37.90 1.67 45.25 2.17 48.50 2.67 49.62 3.17 49.92 3.67 49.98 0.18 7.14 0.69 25.49 1.18 38.10 1.68 45.35 2.18 48.54 2.68 49.63 3.18 49.93 3.68 49.98 0.19 7.53 0.70 25.80 1.19 38.30 1.69 45.45 2.19 48.57 2.69 49.64 3.19 49.93 3.69 49.98 0.20 7.93 0.71 26.11 1.20 38.49 1.70 45.54 2.20 48.61 2.70 49.65 3.20 49.93 3.70 49.99 0.21 8.32 0.72 26.42 1.21 38.69 1.71 45.64 2.21 48.64 2.71 49.66 3.21 49.93 3.71 49.99 0.22 8.71 0.73 26.73 1.22 38.88 1.72 45.73 2.22 48.68 2.72 49.67 3.22 49.94 3.72 49.99 0.23 9.10 0.74 27.04 1.23 39.07 1.73 45.82 2.23 48.71 2.73 49.68 3.23 49.94 3.73 49.99 0.24 9.48 0.75 27.34 1.24 39.25 1.74 45.91 2.24 48.75 2.74 49.69 3.24 49.94 3.74 49.99 0.25 0.99 0.76 27.64 1.25 39.44 1.75 45.99 2.25 45.78 2.75 49.70 3.25 49.94 3.75 49.99 0.26 10.26 0.77 27.94 1.26 39.62 1.76 46.08 2.26 48.81 2.76 49.71 3.26 49.94 3.76 49.99 0.27 10.64 0.78 28.23 1.27 39.80 1.77 46.16 2.27 48.84 2.77 49.72 3.27 49.94 3.77 49.99 0.28 11.03 0.79 28.52 1.28 39.97 1.78 46.25 2.28 48.87 2.78 49.73 3.28 49.94 3.78 49.99 0.29 11.41 0.80 28.81 1.29 40.15 1.79 46.33 2.29 48.90 2.79 49.74 3.29 49.94 3.79 49.99 0.30 11.79 0.81 29.10 1.30 40.32 1.80 46.41 2.30 48.93 2.80 49.74 3.30 49.95 3.80 49.99 0.31 12.17 0.82 29.39 1.31 40.49 1.81 46.49 2.31 48.96 2.81 49.75 3.31 49.95 3.81 49.99 0.32 12.55 0.83 29.67 1.32 40.66 1.82 46.56 2.32 48.98 2.82 49.76 3.32 49.95 3.82 49.99 0.33 12.93 0.84 29.95 1.33 40.82 1.83 46.64 2.33 49.01 2.83 49.77 3.33 49.95 3.83 49.99 0.34 13.31 0.85 30.23 1.34 40.99 1.84 46.71 2.34 49.04 2.84 49.77 3.34 49.95 3.84 49.99 0.35 13.68 0.86 30.51 1.35 41.15 1.85 46.78 2.35 49.06 2.85 49.78 3.35 49.96 3.85 49.99 0.36 14.06 0.87 30.78 1.36 41.31 1.86 46.86 2.36 49.09 2.86 49.79 3.36 49.96 3.86 49.99 0.37 14.43 0.88 31.06 1.37 41.47 1.87 46.93 2.37 49.11 2.87 49.79 3.37 49.96 3.87 49.99 0.38 14.80 0.89 31.33 1.38 41.62 1.88 46.99 2.38 49.13 2.88 49.80 3.38 49.96 3.88 49.99 0.39 15.17 0.90 31.59 1.39 41.77 1.89 47.06 2.39 49.16 2.89 49.81 3.39 49.96 3.89 49.99 0.40 15.54 0.91 31.86 1.40 41.92 1.90 47.13 2.40 49.18 2.90 49.81 3.40 49.97 3.90 49.99 0.41 15.91 0.92 32.12 1.41 42.07 1.91 47.19 2.41 49.20 2.91 49.82 3.41 49.97 3.91 49.99 0.42 16.28 0.93 32.38 1.42 42.22 1.92 47.26 2.42 49.22 2.92 49.82 3.42 49.97 3.92 49.99 0.43 16.64 0.94 32.64 1.43 42.36 1.93 47.32 2.43 49.25 2.93 49.83 3.43 49.97 3.93 49.99 0.44 17.00 0.95 32.89 1.44 42.51 1.94 47.38 2.44 49.27 2.94 49.84 3.44 49.97 3.94 49.99 0.45 17.36 0.96 33.15 1.45 42.65 1.95 47.44 2.45 49.29 2.95 49.84 3.45 49.98 3.95 49.99 0.46 17.72 0.97 33.40 1.46 42.79 1.96 47.50 2.46 49.31 2.96 49.85 3.46 49.98 3.96 49.99 0.47 18.08 0.98 33.65 1.47 42.92 1.97 47.56 2.47 49.32 2.97 49.85 3.47 49.98 3.97 49.99 0.48 18.44 0.99 33.89 1.48 43.06 1.98 47.61 2.48 49.34 2.98 49.86 3.48 49.98 3.98 49.99 0.49 18.79 1.00 34.13 1.49 43.19 1.99 47.67 2.49 49.36 2.99 49.86 3.49 49.98 3.99 49.99 Table 2 t Values Needed for Rejection of the Null Hypothesis
How to use this table: Compute the t value test statistic.
 Compare the obtained t value to the critical value listed in this table. Be sure you have calculated the number of degrees of freedom correctly and you have selected an appropriate level of significance.
 If the obtained value is greater than the critical or tabled value, the null hypothesis (that the means are equal) is not the most attractive explanation for any observed differences.
 If the obtained value is less than the critical or table value, the null hypothesis is the most attractive explanation for any observed differences.
OneTailed Test TwoTailed Test df 0.10 0.05 0.01 df 0.10 0.05 0.01 1 3.078 6.314 31.821 1 6.314 12.706 63.657 2 1.886 2.92 6.965 2 2.92 4.303 9.925 3 1.638 2.353 4.541 3 2.353 3.182 5.841 4 1.533 2.132 3.747 4 2.132 2.776 4.604 5 1.476 2.015 3.365 5 2.015 2.571 4.032 6 1.44 1.943 3.143 6 1.943 2.447 3.708 7 1.415 1.895 2.998 7 1.895 2.365 3.5 8 1.397 1.86 2.897 8 1.86 2.306 3.356 9 1.383 1.833 2.822 9 1.833 2.262 3.25 10 1.372 1.813 2.764 10 1.813 2.228 3.17 11 1.364 1.796 2.718 11 1.796 2.201 3.106 12 1.356 1.783 2.681 12 1.783 2.179 3.055 13 1.35 1.771 2.651 13 1.771 2.161 3.013 14 1.345 1.762 2.625 14 1.762 2.145 2.977 15 1.341 1.753 2.603 15 1.753 2.132 2.947 16 1.337 1.746 2.584 16 1.746 2.12 2.921 17 1.334 1.74 2.567 17 1.74 2.11 2.898 18 1.331 1.734 2.553 18 1.734 2.101 2.879 19 1.328 1.729 2.54 19 1.729 2.093 2.861 20 1.326 1.725 2.528 20 1.725 2.086 2.846 21 1.323 1.721 2.518 21 1.721 2.08 2.832 22 1.321 1.717 2.509 22 1.717 2.074 2.819 23 1.32 1.714 2.5 23 1.714 2.069 2.808 24 1.318 1.711 2.492 24 1.711 2.064 2.797 25 1.317 1.708 2.485 25 1.708 2.06 2.788 26 1.315 1.706 2.479 26 1.706 2.056 2.779 27 1.314 1.704 2.473 27 1.704 2.052 2.771 28 1.313 1.701 2.467 28 1.701 2.049 2.764 29 1.312 1.699 2.462 29 1.699 2.045 2.757 30 1.311 1.698 2.458 30 1.698 2.043 2.75 35 1.306 1.69 2.438 35 1.69 2.03 2.724 40 1.303 1.684 2.424 40 1.684 2.021 2.705 45 1.301 1.68 2.412 45 1.68 2.014 2.69 50 1.299 1.676 2.404 50 1.676 2.009 2.678 55 1.297 1.673 2.396 55 1.673 2.004 2.668 60 1.296 1.671 2.39 60 1.671 2.001 2.661 65 1.295 1.669 2.385 65 1.669 1.997 2.654 70 1.294 1.667 2.381 70 1.667 1.995 2.648 75 1.293 1.666 2.377 75 1.666 1.992 2.643 80 1.292 1.664 2.374 80 1.664 1.99 2.639 85 1.292 1.663 2.371 85 1.663 1.989 2.635 90 1.291 1.662 2.369 90 1.662 1.987 2.632 95 1.291 1.661 2.366 95 1.661 1.986 2.629 100 1.29 1.66 2.364 100 1.66 1.984 2.626 Infinity 1.282 1.645 2.327 Infinity 1.645 1.96 2.576 Table 3 Critical Values for Analysis of Variance or F Test
How to use this table: Compute the F value.
 Determine the number of degrees of freedom for the numerator (k–1) and the number of degrees of freedom for the denominator (n–k).
 Locate the critical value by reading across to locate the degrees of freedom in the numerator and down to locate the degrees of freedom in the denominator. The critical value is at the intersection of this column and row.
 If the obtained value is greater than the critical or tabled value, the null hypothesis (that the means are equal to one another) is not the most attractive explanation for any observed differences.
 If the obtained value is less than the critical or tabled value, the null hypothesis is the most attractive explanation for any observed differences.
df for the Numerator df for the Denominator Type I Error Rate 1 2 3 4 5 6 1 .01 4052.00 4999.00 5403.00 5625.00 5764.00 5859.00 .05 162.00 200.00 216.00 225.00 230.00 234.00 .10 39.90 49.50 53.60 55.80 57.20 58.20 2 .01 98.50 99.00 99.17 99.25 99.30 99.33 05 18.51 19.00 19.17 19.25 19.30 19.33 10 8.53 9.00 9.16 9.24 9.29 9.33 3 .01 34.12 30.82 29.46 28.71 28.24 27.91 .05 10.13 9.55 9.28 9.12 9.01 8.94 10 5.54 5.46 5.39 5.34 5.31 5.28 4 .01 21.20 18.00 16.70 15.98 15.52 15.21 .05 7.71 6.95 6.59 6.39 6.26 6.16 .10 .55 4.33 4.19 4.11 4.05 4.01 5 .01 16.26 13.27 12.06 11.39 10.97 10.67 .05 6.61 5.79 5.41 5.19 5.05 4.95 .10 4.06 3.78 3.62 3.52 3.45 3.41 6 .01 13.75 10.93 9.78 9.15 8.75 8.47 .05 5.99 5.14 4.76 4.53 4.39 4.28 .10 3.78 3.46 3.29 3.18 3.11 3.06 7 .01 12.25 9.55 8.45 7.85 7.46 7.19 .05 5.59 4.74 4.35 4.12 3.97 3.87 .10 3.59 3.26 3.08 2.96 2.88 2.83 8 .01 11.26 8.65 7.59 7.01 6.63 6.37 .05 5.32 4.46 4.07 3.84 3.69 3.58 .10 3.46 3.11 2.92 2.81 2.73 2.67 9 .01 10.56 8.02 6.99 6.42 6.06 5.80 .05 5.12 4.26 3.86 3.63 3.48 3.37 .10 3.36 3.01 2.81 2.69 2.61 2.55 10 .01 10.05 7.56 6.55 6.00 5.64 5.39 .05 4.97 4.10 3.71 3.48 3.33 3.22 .10 3.29 2.93 2.73 2.61 2.52 2.46 11 .01 9.65 7.21 6.22 5.67 5.32 5.07 .05 4.85 3.98 3.59 3.36 3.20 3.10 .10 3.23 2.86 2.66 2.54 2.45 2.39 12 .01 9.33 6.93 5.95 5.41 5.07 4.82 .05 4.75 3.89 3.49 3.26 3.11 3.00 .10 3.18 2.81 2.61 2.48 2.40 2.33 13 .01 9.07 6.70 5.74 5.21 4.86 4.62 .05 4.67 3.81 3.41 3.18 3.03 2.92 .10 3.14 2.76 2.56 2.43 2.35 2.28 14 .01 8.86 6.52 5.56 5.04 4.70 4.46 .05 4.60 3.74 3.34 3.11 2.96 2.85 .10 3.10 2.73 2.52 2.40 2.31 2.24 15 .01 8.68 6.36 5.42 4.89 4.56 4.32 .05 4.54 3.68 3.29 3.06 2.90 2.79 .10 3.07 2.70 2.49 2.36 2.27 2.21 16 .01 8.53 6.23 5.29 4.77 4.44 4.20 .05 4.49 3.63 3.24 3.01 2.85 2.74 .10 3.05 2.67 2.46 2.33 2.24 2.18 17 .01 8.40 6.11 5.19 4.67 4.34 4.10 .05 4.45 3.59 3.20 2.97 2.81 2.70 .10 3.03 2.65 2.44 2.31 2.22 2.15 18 .01 8.29 6.01 5.09 4.58 4.25 4.02 .05 4.41 3.56 3.16 2.93 2.77 2.66 .10 3.01 2.62 2.42 2.29 2.20 2.13 19 .01 8.19 5.93 5.01 4.50 4.17 3.94 .05 4.38 3.52 3.13 2.90 2.74 2.63 .10 2.99 2.61 2.40 2.27 2.18 2.11 20 .01 8.10 5.85 4.94 4.43 4.10 3.87 .05 4.35 3.49 3.10 2.87 2.71 2.60 .10 2.98 2.59 2.38 2.25 2.16 2.09 21 .01 8.02 5.78 4.88 4.37 4.04 3.81 .05 4.33 3.47 3.07 2.84 2.69 2.57 .10 2.96 2.58 2.37 2.23 2.14 2.08 22 .01 7.95 5.72 4.82 4.31 3.99 3.76 .05 4.30 3.44 3.05 2.82 2.66 2.55 .10 2.95 2.56 2.35 2.22 2.13 2.06 23 .01 7.88 5.66 4.77 4.26 3.94 3.71 .05 4.28 3.42 3.03 2.80 2.64 2.53 .10 2.94 2.55 2.34 2.21 2.12 2.05 24 .01 7.82 5.61 4.72 4.22 3.90 3.67 .05 4.26 3.40 3.01 2.78 2.62 2.51 .10 2.93 2.54 2.33 2.20 2.10 2.04 25 .01 7.77 5.57 4.68 4.18 3.86 3.63 .05 4.24 3.39 2.99 2.76 2.60 2.49 .10 2.92 2.53 2.32 2.19 2.09 2.03 26 .01 7.72 5.53 4.64 4.14 3.82 3.59 .05 4.23 3.37 2.98 2.74 2.59 2.48 .10 2.91 2.52 2.31 2.18 2.08 2.01 27 .01 7.68 5.49 4.60 4.11 3.79 3.56 .05 4.21 3.36 2.96 2.73 2.57 2.46 .10 2.90 2.51 2.30 2.17 2.07 2.01 28 .01 7.64 5.45 4.57 4.08 3.75 3.53 .05 4.20 3.34 2.95 2.72 2.56 2.45 .10 2.89 2.50 2.29 2.16 2.07 2.00 29 .01 7.60 5.42 4.54 4.05 3.73 3.50 .05 4.18 3.33 2.94 2.70 2.55 2.43 .10 2.89 2.50 2.28 2.15 2.06 1.99 30 .01 7.56 5.39 4.51 4.02 3.70 3.47 .05 4.17 3.32 2.92 2.69 2.53 2.42 .10 2.88 2.49 2.28 2.14 2.05 1.98 35 .01 7.42 5.27 4.40 3.91 3.59 3.37 .05 4.12 3.27 2.88 2.64 2.49 2.37 .10 2.86 2.46 2.25 2.14 2.02 1.95 40 .01 7.32 5.18 4.31 3.91 3.51 3.29 .05 4.09 3.23 2.84 2.64 2.45 2.34 .10 2.84 2.44 2.23 2.11 2.00 1.93 45 .01 7.23 5.11 4.25 3.83 3.46 3.23 .05 4.06 3.21 2.81 2.61 2.42 2.31 .10 2.82 2.43 2.21 2.09 1.98 1.91 50 .01 7.17 5.06 4.20 3.77 3.41 3.19 .05 4.04 3.18 2.79 2.58 2.40 2.29 .10 2.81 2.41 2.20 2.08 1.97 1.90 55 .01 7.12 5.01 4.16 3.72 3.37 3.15 .05 4.02 3.17 2.77 2.56 2.38 2.27 .10 2.80 2.40 2.19 2.06 1.96 1.89 60 .01 7.08 4.98 4.13 3.68 3.34 3.12 .05 4.00 3.15 2.76 2.54 2.37 2.26 .10 2.79 2.39 2.18 2.05 1.95 1.88 65 .01 7.04 4.95 4.10 3.65 3.31 3.09 .05 3.99 3.14 2.75 2.53 2.36 2.24 .10 2.79 2.39 2.17 2.04 1.94 1.87 70 .01 7.01 4.92 4.08 3.62 3.29 3.07 .05 3.98 3.13 2.74 2.51 2.35 2.23 .10 2.78 2.38 2.16 2.03 1.93 1.86 75 .01 6.99 4.90 4.06 3.60 3.27 3.05 .05 3.97 3.12 2.73 2.50 2.34 2.22 .10 2.77 2.38 2.16 2.03 1.93 1.86 80 .01 3.96 4.88 4.04 3.56 3.26 3.04 .05 6.96 3.11 2.72 2.49 2.33 2.22 .10 2.77 2.37 2.15 2.02 1.92 1.85 85 .01 6.94 4.86 4.02 3.55 3.24 3.02 .05 3.95 3.10 2.71 2.48 2.32 2.21 .10 2.77 2.37 2.15 2.01 1.92 1.85 90 .01 6.93 4.85 4.02 3.54 3.23 3.01 .05 3.95 3.10 2.71 2.47 2.32 2.20 .10 2.76 2.36 2.15 2.01 1.91 1.84 95 .01 6.91 4.84 4.00 3.52 3.22 3.00 .05 3.94 3.09 2.70 2.47 2.31 2.20 .10 2.76 2.36 2.14 2.01 1.91 1.84 100 .01 6.90 4.82 3.98 3.51 3.21 2.99 .05 3.94 3.09 2.70 2.46 2.31 2.19 .10 2.76 2.36 2.14 2.00 1.91 1.83 Infinity .01 6.64 4.61 3.78 3.32 3.02 2.80 .05 3.84 3.00 2.61 2.37 2.22 2.10 .10 2.71 2.30 2.08 1.95 1.85 1.78 Table 4 Values of the Correlation Coefficient Needed for Rejection of the Null Hypothesis
How to use this table: Compute the value of the correlation coefficient.
 Compare the value of the correlation coefficient with the critical value listed in this table.
 If the obtained value is greater than the critical or tabled value, the null hypothesis (that the correlation coefficient is equal to 0) is not the most attractive explanation for any observed differences.
 If the obtained value is less than the critical or tabled value, the null hypothesis is the most attractive explanation for any observed differences.
OneTailed Test TwoTailed Test df .05 .01 df .05 .01 1 .9877 .9995 1 .9969 .9999 2 .9000 .9800 2 .9500 .9900 3 .8054 .9343 3 .8783 .9587 4 .7293 .8822 4 .8114 .9172 5 .6694 .832 5 .7545 .8745 6 .6215 .7887 6 .7067 .8343 7 .5822 .7498 7 .6664 .7977 8 .5494 .7155 8 .6319 .7646 9 .5214 .6851 9 .6021 .7348 10 .4973 .6581 10 .5760 .7079 11 .4762 .6339 11 .5529 .6835 12 .4575 .6120 12 .5324 .6614 13 .4409 .5923 13 .5139 .6411 14 .4259 .5742 14 .4973 .6226 15 .4120 .5577 15 .4821 .6055 16 .4000 .5425 16 .4683 .5897 17 .3887 .5285 17 .4555 .5751 18 .3783 .5155 18 .4438 .5614 19 .3687 .5034 19 .4329 .5487 20 .3598 .4921 20 .4227 .5368 25 .3233 .4451 25 .3809 .4869 30 .2960 .4093 30 .3494 .4487 35 .2746 .3810 35 .3246 .4182 40 .2573 .3578 40 .3044 .3932 45 .2428 .3384 45 .2875 .3721 50 .2306 .3218 50 .2732 .3541 60 .2108 .2948 60 .2500 .3248 70 .1954 .2737 70 .2319 .3017 80 .1829 .2565 80 .2172 .2830 90 .1726 .2422 90 .2050 .2673 100 .1638 .2301 100 .1946 .2540 Table 5 Critical Values for the ChiSquare Test
How to use this table: Compute the χ2 value.
 Determine the number of degrees of freedom for the rows (R–1) and the number of degrees of freedom for the columns (C–1). If it's a onedimensional table, then you have only columns.
 Locate the critical value by locating the degrees of freedom in the titled (df) column, and under the appropriate column for level of significance, read across.
 If the obtained value is greater than the critical or tabled value, the null hypothesis (that the frequencies are equal to one another) is not the most attractive explanation for any observed differences.
 If the obtained value is less than the critical or tabled value, the null hypothesis is the most attractive explanation for any observed differences.
Level of Significance df .10 .05 .01 1 2.71 3.84 6.64 2 4.00 5.99 9.21 3 6.25 7.82 11.34 4 7.78 9.49 13.28 5 9.24 11.07 15.09 6 10.64 12.59 16.81 7 12.02 14.07 18.48 8 13.36 15.51 20.09 9 14.68 16.92 21.67 10 16.99 18.31 23.21 11 17.28 19.68 24.72 12 18.65 21.03 26.22 13 19.81 22.36 27.69 14 21.06 23.68 29.14 15 22.31 25.00 30.58 16 23.54 26.30 32.00 17 24.77 27.60 33.41 18 25.99 28.87 34.80 19 27.20 30.14 36.19 20 28.41 31.41 37.57 21 29.62 32.67 38.93 22 30.81 33.92 40.29 23 32.01 35.17 41.64 24 33.20 36.42 42.98 25 34.38 37.65 44.81 26 35.56 38.88 45.64 27 36.74 40.11 46.96 28 37.92 41.34 48.28 29 39.09 42.56 49.59 30 40.26 43.77 50.89 Appendix B
Internet Sites about StatisticsWhat follows is a listing of Internet sites and a brief description of each that focus on the general areas of statistics and measurement. Also included are sites where data (on many different topics) have been collected and can be accessed.
As you use these, keep in mind the following:
 Internet addresses (known as URLs) often change, as does the content. If one of these Internet addresses does not work, search for the name of the site using any search engine.
 Any Internet site is only as good as its content. For example, N or N − 1 might be given as the correct denominator for a formula, and although that might be true, you should double check any information with another Internet resource or a book on the subject.
 If you find something that is inaccurate on a site, contact the Webmaster or the author of the site and let him or her know that a correction needs to be made.
Name: http://statistics.com
Where to find it: http://www.statistics.com/
If there is a queen of statistics sites, then http://statistics.com is it. It offers not only links to hundreds of other sites and an online introductory statistics course, but also online professional development courses. You can try statistics software, look at the free stuff available on the Web, get help if you're a teacher with quizzes and other teaching materials, and even participate in online discussions. This is the place to start your travels.
Name: U.S. Department of Labor, Bureau of Labor Statistics
Where to find it: http://www.bls.gov/
Local, state, and federal government agencies are data warehouses, full of information about everything from employment to demographics to consumer spending. This particular site (which is relatively old at 10 years on the Web) is for the Bureau of Labor Statistics, the principal factfinding agency for the federal government in the areas of labor economics and statistics. It is full of numbers and ideas. Some of the data can be downloaded as HTML or Excel files, and you can also get historical data going back 10 years in some instances.
Name: Probability and Quintile Applets Where to find it: http://www.stat.stanford.edu/~naras/jsm/FindProbability.html
Applets are small programs that can visually represent an idea or a process very effectively. These two, by Balasubramanian Narasimhan from Stanford University, do such things as compute the probability of a score under the normal curve (see Figure 1 on the following page) and calculate the quintiles (fifths) of a distribution. They are easy to use, fun to play with, and very instructional. You can find another similar applet by Gary McClelland at http://psych.colorado.edu/~mcclella/java/normal/handleNormal.html
Figure 1 Probability AppletName: FedStats
Where to find it: http://www.fedstats.gov/
Here's another huge storehouse of data that is the entry point for many different federal agencies. You can easily access data from individual states or from agencies by subjects (such as health), access published collections of statistics, and even get the kids involved in childoriented agency Web sites both entertaining and educational.
Name: Random Birthday Applet
Where to find it: http://wwwstat.stanford.edu/~susan/surprise/Birthday.html
This is an incredible illustration of how probability works. You enter the number of birthdays you want generated at random, and the laws of probability should operate so that in a group of 30 such random selections, the odds are very high that there will be at least two matches for the same birthday. Try it—it works.
Name: The Statistics Homepage
Where to find it: http://www.statsoftinc.com/textbook/stathome.html
Here you'll find a selfcontained course in basic statistics, brought to you by the people who developed and sell StatSoft, one of many statistical programs. On this site, you will find tutorials that take you from the elementary concepts of statistics through the more advanced topics, such as factor and discriminant analysis.
Name: National Center for Health Statistics
Where to find it: http://www.cdc.gov/nchs/
The National Center for Health Statistics compiles information that helps guide actions and policies to improve health in the United States. Among other things, these data are used to help identify health problems, evaluate the effectiveness of programs, and provide data for policymakers.
Name: The World Wide Web Virtual Library: Statistics
Where to find it: http://www.stat.ufl.edu/vlib/statistics.html
The good people at the University of Florida's Department of Statistics bring you this page, which contains links to statistics departments all over the world. It provides a great deal of information about graduate study in these areas as well as other resources.
Name: Social Statistics Briefing Room
Where to find it: http://www.whitehouse.gov/fsbr/ssbr.html
This service, which calls the White House home, provides access to current federal social statistics and links to information from a wide range of federal agencies. This is a very good, and broad, starting point to access data made available through different agencies.
Name: Statistics on the Web
Where to find it: http://my.execpc.com/~helberg/statistics.html
More groupings of URLs and Internet addresses from Clay Helberg. A bit like http://statistics.com, but full of listings of professional organizations, publications, and software packages (many of which you can download for a trial).
Name: Food and Agriculture Organization for the United Nations
Where to find it: http://faostat.fao.org/
If you want to go international, this is a site containing online information (in multilingual formats) and databases for more than 3 million time series records covering international statistics in areas such as production, population, and exports.
Name: Web Pages That Perform Statistical Calculations!
Where to find it: http://members.aol.com/johnp71/javastat.html
At the time of this writing, this site contains more than 600 links to books, tutorials, free software, and interactive tools, such as a guide to what statistical test to use to answer what questions, all assembled by John Pezzullo.
Name: Free Statistical Software
Where to find it: http://freestatistics.altervista.org/stat.php
An extensive collection of statistical analysis software packages that range from simple programs for students to advanced programs that do everything from statistical visualization to time series analysis. Many of these programs are freeware, and many are open source, available to be modified by users.
Name: Java Applets
Where to find it: http://www.stat.duke.edu/sites/java.html
The Institute of Statistics and Decision Sciences at Duke University and NWP Associates put together a collection of Java applets (Java is the language in which these small programs are written, and applets are small applications) that allows the user to demonstrate interactively various statistical techniques and tools, such as constructing histograms and illustrating how the central limit theorem works.
Name: HyperStat Online Textbook
Where to find it: http://davidmlane.com/hyperstat/
This site contains an entire online course in basic statistics from David Lane that covers every topic from simple descriptive statistics to effect size. The “Hyper” nature of the site allows the user to easily move from one topic to another through the extensive use of live links. And, as a bonus, each new screen has additional links to sites that focus on learning statistics.
Name: Rice Virtual Lab in Statistics
Where to find it: http://www.ruf.rice.edu/~lane/rvls.html
This is where the HyperStat Online Textbook has its home and is the main page (also done by David Lane) of Rice University's statistics program. In addition to the HyperStat link, it has links to simulations, case studies, and a terrific set of applets that are very useful for teaching and demonstration purposes.
Name: Reliability, Validity, and Fairness of Classroom Assessments
Where to find it: http://www.ncrel.org/sdrs/areas/issues/methods/assment/as5relia.htm
A discussion of the reliability, validity, and fairness of classroom testing from the North Central Educational Laboratory.
Name: The Multitrait Multimethod Matrix
Where to find it: http://www.socialresearchmethods.net/kb/mtmmmat.htm
A very good site for a discussion of validity issues in measurement in general and specific discussion about the multitrait multimethod brought to you by William M. K. Trochim.
Name: Content Validity, Face Validity, and Quantitative Face Validity
Where to find it: http://www.burns.com/wcbcontval.htm
Although a bit dated (around 1996), this Web site offers a detailed discussion by William C. Burns on content, face, quantitative, and other types of validity.
Name: The National Education Association
Where to find it: http://www.nea.org/parents/testingguide.html. also
This national organization of teaching professionals provides assistance to parents, teachers, and others in understanding test scores.
Name: The Learning Center
Where to find it: http://webster.commnet.edu/faculty/~simonds/tests.htm
It's a reality that other than through studying, test scores can be improved if test takers understand the different demands of different types of tests. This item contains information on using different strategies to increase test scores.
Name: The Advanced Placement
Where to find it: http://apbio.biosci.uga.edu/exam/Essays/
This is an old site, but people at the University of Georgia have posted items from a variety of different topic areas covered in the Advanced Placement (AP) exams that high school students can take in a step to qualify for college credit.
Name: Essay Question
Where to find it: http://www.salon.com/tech/feature/1999/05/25/computer_grading/
http://Salon.com offers a discussion of automated grading in general and specially, as well as essay question grading using computers.
Name: Matching Questions on Minerals and Rocks
Where to find it: http://www.usd.edu/esci/exams/matching.html
A good example of how easy it is to adapt matching questions to an interactive electronic format.
An increasingly large part of doing research, as well as other intensive, more qualitative projects, involves specially designed software. At http://www.scolari.com/, you can find a listing of several different types and explore which might be right for you if you intend to pursue this method (interviewing) and this methodology (qualitative).
FairTest—The National Center for Fair and Open Testing at http://www.fairtest.org/index.htm has as its mission to “end the misuses and flaws of standardized testing and to ensure that evaluation of students, teachers and schools is fair, open, valid and educationally beneficial.” A really interesting site to visit.
Preparing Students to Take Standardized Achievement Tests (at http://pareonline.net/getvn.asp?v=1&n=11) was written by William A. Mehrens (and first appeared in Practical Assessment, Research & Evaluation) for school administrators and teachers and discusses what test scores mean and how they can be most useful in understanding children's performance.
The Clifton StrengthsFinder™ at http://education.gallup.com/content/default.asp?ci=886 is a Webbased assessment tool published by the Gallup Organization (yep, the poll people) to help people better understand their talents and strengths by measuring the presence of 34 themes of talent. You might want to take it and explore these themes.
Find out just about everything you ever wanted to know (and more) about human intelligence at Human Intelligence: Historical Influences, Current Controversies and Teaching Resources at http://www.indiana.edu/~intell/
The following text is taken from Neil J. Salkind's bestselling introduction to statistics text, Statistics for People Who (Think They) Hate Statistics, 2nd edition (2004).
Pages and pages of every type of statistical resource you can want has been creatively assembled by Professor David W. Stockburger at http://www.psychstat.smsu.edu/scripts/dws148f/statisticsresourcesmain.asp. This site receives the gold medal of statistics sites. Don't miss it.
For example, take a look at Berrie's page (at http://www.huizen.dds.n~berrie/) and see some QuickTime (short movies) of the effects of changing certain data points on the value of the mean and standard deviation. Or, look at the different home pages that have been created by instructors for courses offered around the country. Or, look at all of the different software packages that can do statistical analysis.
Want to draw a histogram? How about a table of random numbers? A samplesize calculator? The Statistical Calculators page at http://www.stat.ucla.edu/calculators/ has just about every type (more than 15) of calculator and table you could need. Enough to carry you through any statistics course that you might take and even more.
For example, you can click on the Random Permutations link and complete the two boxes (as you see in Figure 2 for 2 random permutations of 100 integers), and you get the number of permutations you want. This is very handy when you need a table of random numbers for a specific number of participants so you can assign them to groups.
Figure 1 Generating a Set of Random NumbersThe History of Statistics page located at http://www.Anselm.edu/homepage/jpitocch/biostatshist.html contains portraits and bibliographies of famous statisticians and a time line of important contributions to the field of statistics. So, do names like Bernoulli, Galton, Fisher, and Spearman pique your curiosity? How about the development of the first test between two averages during the early 20th century? It might seem a bit boring until you have a chance to read about the people who make up this field and their ideas—in sum, pretty cool ideas and pretty cool people.
SurfStat Australia (at http://www.anu.edu.au/nceph/surfstat/surfstathome/surfstat.html) is the online component of a basic stat course taught at the University of Newcastle, Australia, but has grown far beyond just the notes originally written by Annette Dobson in 1987, and updated over several years' use by Anne Young, Bob Gibberd, and others. Among other things, SurfStat contains a complete interactive statistics text. Besides the text, there are exercises, a list of other statistics sites on the Internet, and a collection of Java applets (cool little programs you can use to work with different statistical procedures).
This online tutorial with 18 lessons, at http://www.davidmlane.com/hyperstat/index.html, offers nicely designed and userfriendly coverage of the important basic topics. What we really liked about the site was the glossary, which uses hypertext to connect different concepts to one another. For example, in Figure 3, you can see the definition of descriptive statistics also linked to other glossary terms, such as mean, standard deviation, and box plot. Click on any of those and zap! you're there.
Figure 1 Sample HyperStat ScreenThere are data all over the place, ripe for the picking. Here are just a few. What to do with these? Download them to be used as examples in your work or as examples of analysis that you might want to do, and you can use these as a model.
 Statistical Reference Datasets at http://www.itl.nist.gov/div898/strd/
 United States Census Bureau (a huge collection and a gold mine of data) at http://factfinder.census.gov/servlet/DatasetMainPageServlet?_lang=en
 The Data and Story Library (http://lib.stat.cmu.edu/DASL/) with great annotations about the data (look for the stories link)
 Tons of economic data sets at Growth Data Sets (at http://www.bris.ac.uk/Depts/Economics/Growth/datasets.htm)
Then there are all the data sets that are available through the federal government (besides the census). Your tax money supports it, so why not use it? For example, there's FEDSTATS (at http://www.fedstats.gov/), where more than 70 agencies in the U.S. federal government produce statistics of interest to the public. The Federal Interagency Council on Statistical Policy maintains this site to provide easy access to the full range of statistics and information produced by these agencies for public use. Here you can find country profiles contributed by the (boo!) CIA; public school student, staff, and faculty data (from the National Center for Education Statistics); and the Atlas of the United States Mortality (from the National Center for Health Statistics). What a ton of data!
The University of Michigan's Statistical Resources on the Web (at http://www.lib.umich.edu/govdocs/stats.html) has hundreds and hundreds of resource links, including those to banking, book publishing, the elderly, and, for those of you with allergies, pollen count. Browse, search for what exactly it is that you need—no matter, you are guaranteed to find something interesting.
At http://mathforum.org/workshops/sum96/data.collections/datalibrary/data.set6.html, you can find a data set including the 1994 National League Baseball Salaries or the data on TV, Physicians, and Life Expectancy. Nothing earthshaking, just fun to download and play with.
The World Wide Web Virtual Library: Statistics is the name of the page, but the oneword title is misleading because the site (from the good people at the University of Florida at http://www.stat.ufl.edu/vlib/statistics.html) includes information on just about every facet of the topic, including data sources, job announcements, departments, divisions and schools of statistics (a huge description of programs all over the world), statistical research groups, institutes and associations, statistical services, statistical archives and resources, statistical software vendors and software, statistical journals, mailing list archives, and related fields. Tons of great information is available here. Make it a stop along the way.
Statistics on the Web at http://www.maths.uq.edu.au/~gks/webguide/datasets.html is another location that's just full of information and references that you can easily access. Here, you'll find information on professional organizations, institutes and consulting groups, educational resources, Web courses, online textbooks, publications and publishers, statistics book lists, softwareoriented pages, mailing lists and discussion groups, and even information on statisticians and other statistical people.
Figure 1 Selecting the Correct Stat Technique to Use—Just a Few Clicks AwayIf you do ever have to teach statistics, or even tutor fellow students, this is one place you'll want to visit: http://noppa5.pc.helsinki.fi/links.html. It contains hundreds of resources on every topic that was covered in Statistics for People Who (Think They) Hate Statistics and more. You name it and it's here: regression, demos, history, Sila (a demonstration of inference), an interactive online tutorial, statistical graphics, handouts to courses, teaching materials, journal articles, and even quizzes! Whew, what a deal. There tends to be a lot of material that may not be suited to what you are doing in this class, but this wide net has certainly captured some goodies.
http://Statistics.com (http://www.statistics.com) has it all—a wealth of information on courses, software, statistical methods, jobs, books, and even a homework helper. For example, if you want to know about free Webbased stat packages, click on that link on the lefthand side of the page. Here's one (see Figure 4) from Dr. Bill Trochim…. You just click your way through answering questions to get the answer to what type of analysis should be used.
Appendix C
GlossaryThe following text is taken from Neil J. Salkind's bestselling introduction to statistics text, Statistics for People Who (Think They) Hate Statistics, 2nd edition (2004).
 A test for the difference between two or more means. A simple analysis of variance (or ANOVA) has only one independent variable, whereas a factorial analysis of variance tests the means of more than one independent variable. Oneway analysis of variance looks for differences between the means of more than two groups.
Analysis of variance A measure of central tendency that sums all the scores in the data sets and divides by the number of scores.
Arithmetic mean The quality of the normal curve such that the tails never touch.
Asymptotic The most representative score in a set of scores.
Average A distribution of scores that is symmetrical about the mean, median, and mode and has asymptotic tails.
Bellshaped curve The upper and lower boundaries of a set of scores used in the creation of a frequency distribution.
Class interval The amount of variance unaccounted for in the relationship between two variables.
Coefficient of alienation The amount of variance accounted for in the relationship between two variables.
Coefficient of determination See coefficient of alienation
Coefficient of nondetermination A type of validity that examines how well a test outcome is consistent with a criterion that occurs in the present.
Concurrent validity A type of validity that examines how well a test reflects an underlying construct.
Construct validity A type of validity that examines how well a test samples a universe of items.
Content validity A numerical index that reflects the relationship between two variables.
Correlation coefficient A set of correlation coefficients.
Correlation matrix Another term for the outcome variable.
Criterion A type of validity that examines how well a test reflects some criterion that occurs either in the present (concurrent) or in the future (predictive).
Criterion validity The value necessary for rejection (or nonacceptance) of the null hypothesis.
Critical value A frequency distribution that shows frequencies for class intervals along with the cumulative frequency for each.
Cumulative frequency distribution A record of an observation or an event such as a test score, a grade in math class, or a response time.
Data An observation.
Data point A set of data points.
Data set A value that is different for different statistical tests and approximates the sample size of number of individual cells in an experimental design.
Degrees of freedom The outcome variable or the predicted variable in a regression equation.
Dependent variable Values that describe the characteristics of a sample or population.
Descriptive statistics A positive correlation where the values of both variables change in the same direction.
Direct correlation A research hypothesis that includes a statement of inequality.
Directional research hypothesis A measure of the magnitude of a particular outcome.
Effect size The difference between the actual score (Y) and the predicted score (Y¯).
Error in prediction See error in prediction
Error of estimate The part of a test score that is random and contributes to the unreliability of a test.
Error score An analysis of variance with more than one factor or independent variable.
Factorial analysis of variance A research design where there is more than one treatment variable.
Factorial design A method for illustrating the distribution of scores within class intervals.
Frequency distribution A graphical representation of a frequency distribution.
Frequency polygon A graphical representation of a frequency distribution.
Histogram An ifthen statement of conjecture that relates variables to one another.
Hypothesis The treatment variable that is manipulated or the predictor variable in a regression equation.
Independent variable A negative correlation where the values of variables move in opposite directions.
Indirect correlation Tools that are used to infer the results based on a sample to a population.
Inferential statistics The outcome where the effect of one factor is differentiated across another factor.
Interaction effect A type of reliability that examines the onedimensional nature of an assessment tool.
Internal consistency reliability A type of reliability that examines the consistency of raters.
Interrater reliability A scale of measurement that is characterized by equal distances between points on some underlying continuum.
Interval level of measurement The quality of a distribution such that it is flat or peaked.
Kurtosis The quality of a normal curve that defines its peakedness.
Leptokurtic The regression line that best fits the actual scores and minimizes the error in prediction.
Line of best fit A correlation that is best expressed as a straight line.
Linear correlation In analysis of variance, when a factor or an independent variable has a significant effect upon the outcome variable.
Main effect A type of average where scores are summed and divided by the number of observations.
Mean The average deviation for all scores from the mean of a distribution.
Mean deviation The mean, median, and mode.
Measures of central tendency The point at which 50% of the cases in a distribution fall below and 50% fall above.
Median The central point in a class interval.
Midpoint The most frequently occurring score in a distribution.
Mode A statistical technique where several variables are used to predict one.
Multiple regression A scale of measurement that is characterized by categories with no order or difference in magnitude.
Nominal level of measurement A hypothesis that posits no direction, but a difference.
Nondirectional research hypothesis Distributionfree statistics.
Nonparametric statistics See bellshaped curve
Normal curve A statement of equality between a set of variables.
Null hypothesis The score that is recorded or observed.
Observed score The value that results from the application of a statistical test.
Obtained value A visual representation of a cumulative frequency distribution.
Ogive A directional test.
Onetailed test See analysis of variance
Oneway analysis of variance A scale of measurement that is characterized by an underlying continuum that is ordered.
Ordinal level of measurement Those scores in a distribution that are noticeably much more extreme than the majority of scores. Exactly what score is an outlier is usually an arbitrary decision made by the researcher.
Outliers A type of reliability that examines the consistency across different forms of the same test.
Parallel forms reliability Statistics used for the inference from a sample to a population.
Parametric statistics See correlation coefficient
Pearson productmoment correlation The point at or below where a score appears.
Percentile point The quality of a normal curve that defines its flatness.
Platykurtic All the possible subjects or cases of interest.
Population After the fact, referring to tests done to determine the true source of a difference between three or more groups.
Post hoc A type of validity that examines how well a test outcome is consistent with a criterion that occurs in the future.
Predictive validity The variable that predicts an outcome.
Predictor The highest minus the lowest score, and a gross measure of variability. Exclusive range is the highest score minus the lowest score. Inclusive range is the highest score minus the lowest score plus 1.
Range A scale of measurement that is characterized by an absolute zero.
Ratio level of measurement The equation that defines the points and the line that are closest to the actual scores.
Regression equation The line drawn based on the values in the regression equation.
Regression line The quality of a test such that it is consistent.
Reliability A statement of inequality between two variables.
Research hypothesis A subset of a population.
Sample The difference between sample and population values.
Sampling error Different ways of categorizing measurement outcomes.
Scales of measurement A plot of paired data points.
Scattergram, or scatterplot The risk set by the researcher for rejecting a null hypothesis when it is true.
Significance level See analysis of variance
Simple analysis of variance The quality of a distribution that defines the disproportionate frequency of certain scores. A longer right tail than left corresponds to a smaller number of occurrences at the high end of the distribution; this is a positively skewed distribution. A shorter right tail than left corresponds to a larger number of occurrences at the high end of the distribution; this is a negatively skewed distribution.
Skew, or skewness A listing of sources of variance in an analysis of variance summary table.
Source table The average deviation from the mean.
Standard deviation A measure of accuracy in prediction.
Standard error of estimate See z score
Standard score See significance level
Statistical significance A set of tools and techniques used to organize and interpret information.
Statistics A type of reliability that examines consistency over time.
Testretest reliability See obtained value
Test statistic value The unobservable part of an observed score that reflects the actual ability or behavior.
True score A test of a nondirectional hypothesis where the direction of the difference is of little importance.
Twotailed test The probability of rejecting a null hypothesis when it is true.
Type I error The probability of accepting a null hypothesis when it is false.
Type II error A conservative estimate of a population parameter.
Unbiased estimate The quality of a test such that it measures what it says it does.
Validity The amount of spread or dispersion in a set of scores.
Variability The square of the standard deviation, and another measure of a distribution's spread or dispersion.
Variance The predicted Y value.
Y′ or Y prime A raw score that is adjusted for the mean and standard deviation of the distribution from which the raw score comes.
z scoreMaster Bibliography
Aaron T. Beck Web page: http://mail.med.upenn.edu/~abeck/1987). Introduction au traitement statistique des données expérimentales. Grenoble, France: Presses Universitaires de Grenoble.(Additivetree representations. Lecture Notes in Biomathematics 84 43–59 (1990).2004). Least squares. In M. LewisBeck, A. Bryman, & T. F. Liao (Eds.), The SAGE encyclopedia of social science research methods. Thousand Oaks, CA: Sage. Retrieved April 11, 2006, from http://www.utdallas.edu/~herve/AbdiLeastSquarespretty.pdf http://dx.doi.org/10.4135/9781412950589(2004). Multivariate analysis. In M. LewisBeck, A. Bryman, & T. F. Liao (Eds.), The SAGE encyclopedia of social science research methods. Thousand Oaks, CA: Sage. http://dx.doi.org/10.4135/9781412950589(2004). PLSregression. In M. LewisBeck, A. Bryman, & T. F. Liao (Eds.), The SAGE encyclopedia of social science research methods. Thousand Oaks, CA: Sage. http://dx.doi.org/10.4135/9781412950589(2006). Mathématiques pour les sciences cognitives [Mathematics for cognitive sciences]. Grenoble, France: Presses Universitaires de Grenoble., & (2002). Experimental design and research methods. Unpublished manuscript, University of Texas at Dallas, Program in Cognition., , , , & (2002). Experimental design and research methods. Unpublished manuscript, University of Texas at Dallas, Program in Cognition., , , , & (1999). Neural networks. Thousand Oaks, CA: Sage., , & (2005). DISTATIS: The analysis of multiple distance matrices. Proceedings of the IEEE Computer Society: International Conference on Computer Vision and Pattern Recognition, pp. 42–47., , , & (Testretest reliability of computer based MCWAPM test scoring methods. Journal of ComputerBased Instruction 16 29–35 (1989)., and1983). Manual for the child behavior checklist and revised child behavior profile. Burlington: University of Vermont, Department of Psychiatry., & (Does face validity exist? Educational and Psychological Measurement 10 320–328 (1950).Projecting the next decade in safety management: A Delphi technique study. American Society of Safety Engineers 32 26–29 (2001, October).Adaptive testing Web site including SLAT: http://psychology.gatech.edu/cml/Adaptive/slat.htm1996). Designing and conducting health surveys ((2nd ed.). San Francisco: JosseyBass.ade4 package for R: http://pbil.univlyon1.fr/R/rplus/ade4dsR.html (enables you to enter data and compute dissimilarity coefficients, diversity coefficients, the Principal Coordinates Analysis and the double Principal Coordinates Analysis)1990). Categorical data analysis. New York: Wiley.(1996). An introduction to categorical data analysis. New York: Wiley.(1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage., & (AIMSweb Response to Intervention in a threetiered model: http://www.aimsweb.com/products/aimsweb_rti.htm1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov, and F. Csáki (Eds.), 2nd International Symposium on Information Theory (pp. 267–281). Budapest, Hungary: Akadémia Kiadó.((test developers) biographical information: http://www.mhhe.com/mayfieldpub/psychtesting/profiles/karfmann.htm, andCorrelations genuine and spurious in Pearson and Yule. Statistical Science 10 364–376 (1995).Alexander Luria biographies: http://www.marxists.org/archive/luria/comments/bio.htm and http://en.wikipedia.org/wiki/Alexander_Luria10871961). Elements of mathematical statistics. New York: Wiley.(Alfred Binet and his contributions: http://www.indiana.edu/~intell/binet.shtml1979). Introduction to measurement theory. Prospect Heights, IL: Waveland Press., & (1979). Introduction to test theory. Monterey, CA: BrooksCole., & (1997). Record linkagetechniques—1997, Proceedings of an International Workshop and Exposition. Federal Committee on Statistical Methodology, Office of Management and Budget. Retrieved from http://www.fcsm.gov/workingpapers/RLT_1997.html, & (American Academy of Pediatrics. Clinical practice guideline: Diagnosis and evaluation of the child with attentiondeficit/hyperactivity disorder. Pediatrics 105I 1158–1170 (2000).American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (1985). Standards for educational and psychological testing (2nd ed.). Washington, DC: Authors.American Educational Research Association. (1999). Standards for educational and psychological testing (3rd ed.). Washington, DC: Author.American Guidance Service: http://www.agsnet.comAmerican Medical Association. (2004). Code of medical ethics: Current opinions with annotations, 2004–2005. Chicago: Author.American National Standards Institute (ANSI)/American Society for Quality (ASQ). (2003). Sampling procedures and tables for inspection by attributes, ANSI/ASQ Z1.4. Milwaukee, WI: ASQ Quality Press.American National Standards Institute (ANSI)/American Society for Quality (ASQ). (2003). Sampling procedures and tables for inspection by variables for percent nonconforming, ANSI/ASQ Z1.9. Milwaukee, WI: ASQ Quality Press.American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.American Psychological Association. Ethical principles of psychologists and code of conduct. American Psychologist 57 1060–1073 (2002).American Psychological Association. (2002). Ethical principles of psychologists and code of conduct. Washington, DC: Author.American Statistical Association Web site: http://www.amstat.orgAmericans with Disabilities Act of 1990, 42 U.S.C. §§ 12101 et seq.2002). Highstakes testing, uncertainty, and student learning. Education Policy Analysis Archives, 10 (18). Retrieved July 18, 2005, from http://epaa.asu.edu/epaa/v10n18, & (1988). Psychological testing ((6th ed.). New York: Macmillan.1997). Part four: Personality testing. In Psychological testing (, & (8th ed., pp. 348–471). Upper Saddle River, NJ: Prentice Hall.1997). Psychological testing (, & (7th ed.). New York: Prentice Hall.Sufficient statistics and latent trait models. Psychometrika 42 69–81 (1977).2001). Empirical direction in design and analysis. Mahwah, NJ: Erlbaum.(1984). An introduction to multivariate statistical analysis ((2nd ed.). New York: Wiley.Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics 20 46–63 (1949)., and1979). Manual for the Minnesota Clerical Test. New York: Psychological Corporation., , & ( :A hyperbolic cosine latent trait model for unfolding dichotomous singlestimulus responses. Applied Psychological Measurement 17 (3) 253–276 (1993)., andAPA Ethics Code (2002): http://www.apa.org/ethicsAPA Ethics Code, including a section on assessment: http://www.apa.org/ethics/code2002.htmlAPA policy statement: http://www.apa.org/practice/ebpstatement.pdfAPA report: http://www.apa.org/practice/ebpreport.pdfAPA statement on test security in educational settings: http://www.apa.org/science/securetests.htmlAPA Web site: http://www.apa.orgThe Apoala project is an offspring of GeoVista and is a dynamic parallel coordinate plot, implemented in TCL, designed to show the relationships between multiple variables in large data sets: http://www.geovista.psu.edu/products/demos/edsall/Tclets072799/pcpdescription.htmlAn argument for divine providence, taken from the constant regularity observed in the births of both sexes. Philosophical Transaction of the Royal Society of London 27 186–190 (1710).Area under the curve and receiving operator characteristic curve description: http://www.anaesthetist.com/mnm/stats/roc/Armed Services Vocational Aptitude Battery: http://www.asvabprogram.com/Testing multiplicative models does not require ration scales. Organizational Behavior and Human Performance 24 214–224 (1979)., and, & (1995). Informed consent: Clinical aspects of consent in health care. In W. Reich (Ed.), Encyclopedia of bioethics (Vol. 3,Rev. ed., pp. 1250–1256). New York: Macmillan.2005). Statistics for psychology (, , & (4th ed.). Englewood Cliffs, NJ: Prentice Hall.Artificial neural networks technology: http://www.dacs.dtic.mil/techs/neural/neural_ToC.htmlRelations between prototype, exemplar, and decision bound models of categorization. Journal of Mathematical Psychology 37 372–400 (1993)., andThe grand tour: A tool for viewing multidimensional data. SIAM Journal on Scientific and Statistical Computing 6 128–143 (1985).Association for Psychological Science Web site: http://www.psychologicalscience.orgAverages and deviations: http://www.sciencebyjones.com/average_deviation.htmBabbage's machines: http://www.sciencemuseum.org.uk/online/babbage/index.aspTables of the Bonferroni t statistic. Journal of the American Statistical Association 72 469–478 (1977).Prospective randomized study of individual and group psychotherapy versus controls in recipients of renal transplants. Kidney International 65 1523–1755 (2004)., , andSimilarity as a factor effecting change in children's attitudes toward mentally retarded peers. American Journal of Mental Deficiency 91 (5) 524–531 (1987)., and2002). Validity issues for accountability systems. Los Angeles: University of California, Los Angeles, National Center for Research on Evaluation, Standards, and Student Testing., & (Policy and validity prospects for performancebased assessments. American Psychologist 48 (12) 1210–1218 (1993)., , and1960). A short account of the history of mathematics. New York: Dover.(Modelbased Gaussian and nonGaussian clustering. Biometrics 49 803–821 (1993)., and2005). Discreteevent system simulation (, , , & (4th ed.). Englewood Cliffs, NJ: Prentice Hall.The effects of sampling model on inference with coefficient alpha. Educational & Psychological Measurement 57 893–905 (1997)., andThe robustness of confidence intervals for coefficient alpha under violation of the assumption of essential parallelism. Multivariate Behavioral Research 32 169–191 (1997)., and1984). Single case experimental design: Strategies for studying behavior change (, & (2nd ed.). Needham Heights, MA: Allyn & Bacon.Small sample degrees of freedom with multiple imputation. Biometrika 86 948–955 (1999)., and1994). Outliers in statistical data (, & (3rd ed.). Chichester, UK: Wiley.Effects of stem and Likert response option reversals on survey internal consistency: If you feel the need, there is a better alternative to using those negativelyworded stems. Educational and Psychological Measurement 60 (3) 361–370 (2000).1988). Fractals everywhere. New York: Academic Press.(Barton, J., & Collins, A. (Eds.). (1997). Portfolio assessment: A handbook for educators. Menlo Park, CA: AddisonWesley.On the decomposition of value functions. Operations Research Letters 22 159–170 (1998).2001). Notes on the analytic hierarchy process. Proceedings of the NSF Design and Manufacturing Research Conference (pp. 1–6). Tampa, Florida.(2004). Notes on utility theory. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pp. 1000–1005.(Measurement and preference function modelling. International Transactions in Operational Research 12 173–183 (2005).2006). Preference modelling in engineering design. In K. Lewis, W. Chen, & L. Schmidt (Eds.), Decision making in engineering design. New York: ASME Press.(BASC2 information: http://www.agsnet.com/Group.asp?nGroupInfoID=a30000Basic Personality Inventory: http://www.sigmaassessmentsystems.com/assessments/bpi.aspBasic vs. applied research (discussion from the Ethical, Legal, and Social Issues in Science site at the University of California): http://www.lbl.gov/Education/ELSI/researchmain.htmlBivariate geometric distribution. Journal of Applied Statistical Science 2 33–44 (1995)., and1988). Nonlinear regression analysis and its applications. New York: Wiley., & (Correcting effect sizes for score reliability: A reminder that measurement and substantive issues are linked inextricably. Educational and Psychological Measurement 62 254–263 (2002).Specious causal attributions in the social sciences: The reformulated steppingstone theory of heroin use as exemplar. Journal of Personality and Social Psychology 45 1289–1298 (1983).Utility of the Vineland Adaptive Behavior Scales in diagnosis and research with adults who have mental retardation. Mental Retardation 41 (4) 286–289 (2003).Outcome variables for anorexic males and females one year after discharge from residential treatment. Journal of Addictive Diseases 23 83–94 (2004)., , , , , and , et al.Comparison of aligned Friedman rank and parametric methods for testing interactions in splitplot designs. Computational Statistics and Data Analysis 42 569–593 (2003)., and1994). Principles of biomedical ethics (, & (4th ed.). New York: Oxford University Press.Brushing scatterplots. Technometrics 29 127–142 (1987)., and1988). The use of brushing and rotation for data analysis. In W. S. Cleveland & M. E. McGill (Eds.), Dynamic graphics for statistics (pp. 247–275). Pacific Grove, CA: Wadsworth., , & (The significance of congruence coefficients: A comment and statistical test. Journal of Management 14 559–566 (1988)., , andA comparison of modified and generalized sample biserial correlation estimators. Psychometrika 57 183–201 (1992).Special article: Ethics and clinical research. New England Journal of Medicine 274 1354–1360 (1966).2004). Identifying likely duplicates by record linkage in a survey of prostitutes. In A. Gelman & X. L. Meng (Eds.), Applied Bayesian modeling and causal inference from incompletedata perspectives. New York: Wiley., , , , & (Interpretive issues with the Roberts Apperception Test for Children: Limitations of the standardization group. Psychology in the Schools 36 277–283 (1999)., andChildren's attitudes and behavioral intentions toward a peer presented as obese: Does a medical explanation for the obesity make a difference? Journal of Pediatric Psychology 25 (3) 137–145 (2000)., and1961). Adaptive control processes. Princeton, NJ: Princeton University Press.(The Belmont Report: http://www.hhs.gov/ohrp/humansubjects/guidance/belmont.htmBelmont Report. (1979). Ethical principles and guidelines for the protection of human subjects of research. Washington, DC: The National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, Department of Health, Education, and Welfare.Opening the box of a boxplot. American Statistician 42 257–262 (1988).Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57 289–300 (1995)., and1990). Toward a framework for constructedresponse items (RR90–7). Princeton, NJ: Educational Testing Service., , , & (1981, April). The effect of positive and negative item phrasing on the measurement of attitudes. Paper presented at the annual meeting of the National Council on Measurement in Education, Los Angeles. (ERIC Document Reproduction Service No. ED204404), & (1973). Analyse des données. Paris: Dunod.(Sur le calcul des taux d'inertie dans l'analyse d'un questionnaire. Cahiers de l'Analyse des Données 4 377–378 (1979).Some useful nonparametric tests for ordered alternatives in randomized block experiments. Communications in Statistics: Theory and Methods 11 1681–1693 (1982).A study of several useful nonparametric tests for ordered alternatives in randomized block experiments. Communications in Statistics: Simulation and Computation 11 563–581 (1982).Bioequivalence trials, intersectionunion tests and equivalence confidence sets. Statistical Science 11 283–319 (1996)., andClinical practice: How physicians make medical decisions and why medical decision making can help. Primary Care 22 (2) 167–180 (1995)., and2003). Regression analysis: A constructive critique. Thousand Oaks, CA: Sage.(Mythology and the American system of education. Phi Delta Kappan pp. 632–640 (1993, April).Bermuda Atlantic Time Series Study: http://coexploration.org/bbsr/classroombats/html/visualization.html (illustrates use of contour plots in oceanography)1994). Bayesian theory. Chichester, UK: Wiley., & (1991). The practice of econometrics. New York: AddisonWesley.(Effects of stimulus emotionality and sentence generation on memory for words in adults with unilateral brain damage. Neuropsychology 17 429–438 (2003)., , and2004). Data mining techniques: For marketing, sales, and customer relationship management (, & (2nd ed.). New York: Wiley.Bersoff, D. N. (Ed.). (2003). Ethical conflicts in psychology (3rd ed.). Washington, DC: American Psychological Association.2001). Markov Chain Monte Carlo for statistical inference. Center for Statistics and the Social Sciences Working Paper No. 9. Available at http://www.csss.washington.edu/Papers/(1967). The empty fortress. New York: Free Press.(Bezruczko, N. (Ed.). (2005). Rasch measurement in health sciences. Maple Grove, MN: JAM.1980). The development of intelligence in children (1916 limited ed.). Nashville, TN: Williams Printing., , & (1995). Neural networks for pattern recognition. Oxford, UK: Oxford University Press.(1999). Essentials of Bayley Scales of Infant Development II assessment. New York: Wiley.(Twosided equivalence testing of the difference between two means. Journal of Modern Applied Statistics 1 139–142 (2002)., andA comparison of the power of Wilcoxon's ranksum statistic to that of Student's t statistic under various nonnormal distributions. Journal of Educational Statistics 5 (4) 309–335 (1980)., and1964). Causal inference in nonexperimental research. Chapel Hill: University of North Carolina Press.(1978). The language of learning: The preschool years. New York: Grune & Stratton., , & (BLAST resources: http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/tut1.html, http://www.ncbi.nlm.nih.gov/2000). Fourier analysis of time series: An introduction. New York: Wiley.(Board of Education of the Hendrick Hudson Central School District v. Rowley, 458 U. S. 176 (1982).2000). Assessing personality with the Millon Behavioral Health Inventory, the Millon Behavioral Medicine Diagnostic, and the Millon Clinical Multiaxial Inventory. In R. J. Gatchel & J. N. Weisberg (Eds.), Personality characteristics of patients with pain (pp. 61–88). Washington, DC: American Psychological Association., , & (The notitia intuitiva of nonexistents according to William Ockham. Traditio 1 223–275 (1943).Interactions, partial interactions, and interaction contrasts in the analysis of variance. Psychological Bulletin 86 1084–1089 (1979).1989). Structural equations with latent variables. New York: Wiley.(Interactions of latent variables in structural equations models. Structural Equation Modeling 5 267–293 (1998)., and2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah, NJ: Erlbaum., & (Bonferroni correction: http://www.cmh.edu/stats/ask/bonferroni.aspBonferroni correction/adjustment: http://home.clara.net/sisa/bonhlp.htmPerformancebased assessment or multiple choice? Educational Horizons 72 (1) 50–56 (1993).Boole's inequality and Bonferroni's inequalities description: http://www.absoluteastronomy.com/encyclopedia/b/bo/booles_inequality.htmA survey of Monte Carlo algorithms for maximizing the likelihood of a twostage hierarchical model. Statistical Modelling 1 333–349 (2001)., , and1997). Modern multidimensional scaling. New York: Springer Verlag., & (An error ratio for scalogram analysis. Public Opinion Quarterly 19 96–100 (1955).The concept of validity. Psychological Review 111 (4) 1061 (2004)., , andProblems with instrumental variable estimation when the correlation between the instruments and endogenous explanatory variables is weak. Journal of the American Statistical Association 90 443–450 (1995)., , and1991). A history of mathematics ((2nd ed.). New York: Wiley.1992). Graph it! How to make, read, and interpret graphs. Upper Saddle River, NJ: Prentice Hall.(A test for symmetry in contingency tables. Journal of the American Statistical Association 43 572–574 (1948).1997). Applied smoothing techniques for data analysis: The Kernel Approach with SPlus illustrations. New York: Oxford University Press., & (1978). R. A. Fisher: The life of a scientist. New York: Wiley.(Minimizing respondent attrition in longitudinal research: Practical implications from a cohort study of adolescent drinking. Journal of Adolescence 26 363–373 (2003)., , , , , and1999). The Fourier transform and its applications ((3rd ed.). New York: McGrawHill.Incidence of basic concepts in five commonly used American tests of intelligence. School Psychology International 7 1–10 (1986).1992). Multidimensional self concept scale. Austin, TX: PROED.((1996). Clinical applications of a multidimensional, contextdependent model of selfconcept. In B. A. Bracken (Ed.), Handbook of self concept: Developmental, social, and clinical considerations (pp. 463–505). New York: Wiley.1996). Handbook of selfconcept: Developmental, social, and clinical considerations. New York: Wiley.(1998). Bracken Basic Concept Scale—Revised. San Antonio, TX: Psychological Corporation.(Multinational validation of the Bracken Basic Concept Scale. Journal of School Psychology 28 325–341 (1990)., , , , , and2005). Clinical assessment of attention deficit—adult. Lutz, FL: Psychological Assessment Resources., & (2005). Clinical assessment of depression. Lutz, FL: Psychological Assessment Resources., & (Ipsative subtest pattern stability of the Bracken Basic Concept Scale and the Kaufman Assessment Battery for Children in a preschool sample. School Psychology Review 20 309–324 (1991)., , , , and2004). Clinical assessment of behavior. Lutz, FL: Psychological Assessment Resources., & (2003). Assessing diverse populations with nonverbal tests of general intelligence. In C. R. Reynolds & R. W. Kamphaus (Eds.), Handbook of psychological and educational assessment of children (, & (2nd ed., pp. 243–274). New York: Guilford.The effects of cognitive rehabilitation therapy techniques for enhancing the cognitive/intellectual functioning of seventh and eighth grade children. International Journal of Cognitive Technology 4 19–27 (1999)., , , , , , andInformed decision making in outpatient practice: Time to get back to basics. Journal of the American Medical Association 282 (24) 2313–2320 (1999)., , , , and2005). Using the joint test standards to evaluate the validity evidence for intelligence tests. In D. F. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment (, & (2nd ed.). New York: Guilford.Type I error rate of the chi square test of independence in r × c tables that have small expected frequencies. Psychological Bulletin 86 1290–1297 (1979)., , , and1987). A guide to simulation (, , & (2nd ed.). New York: SpringerVerlag.1995). Neonatal Behavioral Assessment Scale (, & (3rd ed.). London: MacKeith Press.1994). Surface learning with applications to lipreading. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems. San Mateo, CA: Morgan Kaufman., & (1984). Classification and regression trees. Boca Raton, FL: Chapman & Hall/CRC.(Random forests. Machine Learning 45 (1) 5–32 (2001).1984). Classification and regression trees. Belmont, CA: Wadsworth., , , & (1999). Markov chains: Gibbs fields, Monte Carlo simulation, and queues. New York: Springer.(Covariance analysis of censored survival data. Biometrics 30 89–99 (1974).1980). Statistical methods in cancer research, Volume I. The analysis of casecontrol studies. Lyon, France: International Agency for Research on Cancer., & (2002). Combined survey sampling inference: Weighing Basu's elephants. London: Oxford University Press/Arnold.(2001). Managing Six Sigma: A practical guide to understanding, assessing, and implementing the strategy that yields bottomline success. New York: Wiley., , & (1986). Learning how to ask: A sociolinguistic appraisal of the role of the interview in social science research. Cambridge, UK: Cambridge University Press.(A Monte Carlo study of the sampling distribution of the congruence coefficient. Educational and Psychological Measurement 47 1–11 (1987)., andA new coefficient: Application to biserial correlation and to estimation of selective inefficiency. Psychometrika 14 169–182 (1949).2004). Extreme measures: The dark visions and bright ideas of Francis Galton. London: Bloomsbury.(Consideration of what may influence student outcomes on alternate assessment. Education and Training in Developmental Disabilities 38 255–270 (2003)., , , and2002). Facilitator's guide to the Life Values Inventory. Williamsburg, VA: Applied Psychology Resources., & (The forcedfree distinction in Qtechnique. Journal of Educational Measurement 8 283–287 (1971).1986). Q technique and method. In W. D. Berry & M. S. LewisBeck (Eds.), New tools for social scientists. Beverly Hills, CA: Sage.(Some experimental results in the correlation of mental abilities. British Journal of Psychology 3 296–322 (1910).Alternative ways of assessing model fit. Sociological Methods and Research 21 230–258 (1992)., andAssessing the knowledge base of students with admissible probability measurement (APM): A microcomputer based information theoretic approach to testing. Measurement and Evaluation in Counseling and Development 19 116–130 (1986).Monitoring the academic progress of low achieving students: A analysis of rightwrong (RW) versus Information Referenced (MCWAPM) formative and summative evaluation procedures. Journal of Research and Development in Education 23 51–61 (1989).Time perceptions and time allocation preferences among adolescent boys and girls. Journal of Adolescence 31 (121) 109–126 (1996).Determining the optimal number of alternatives to a multiplechoice test item: An information theoretic perspective. Educational & Psychological Measurement 55 959–966 (1995)., and1995). Practical data analysis: Case studies in business statistics. Homewood, IL: Richard D. Irwin., & (Average IQ values in various European countries. Personality and Individual Differences 2 169–170 (1981).KolmogorovSmirnov and Cramer von Mises type twosample tests with various weights. Communications in Statistics—Theory and Methods 30 847–866 (2001).Adult measures of pain: The McGill Pain Questionnaire (MPQ), Rheumatoid Arthritis Pain Scale (RAPS), ShortForm McGill Pain Questionnaire (SFMPQ), Verbal Descriptive Scale (VDS), Visual Analog Scale (VAS), and West HavenYale Multidisciplinary Pain Inventory (WHYMPI). Arthritis & Rheumatism (Arthritis Care & Research) 49 96–104 (2003)., and2004). Numerical analysis (, & (8th ed.). Pacific Grove, CA: Brooks/Cole.2002). Model selection and multimodel inference: A practical informationtheoretic approach (, & (2nd ed.). New York: Springer.1982). Selfgrowth in families: Kinetic Family Drawings (KFD) research and application. New York: Bruner/Mazel.(Buros, O. K. (Ed.). (1938). The nineteen thirty eight mental measurements yearbook. New Brunswick, NJ: Rutgers University Press.Buros Center for Testing: http://www.unl.edu/buros/Buros Institute for Mental Measurements: http://www.unl.edu/buros/Buros Institute Test Reviews Online: http://www.unl.edu/buros/Factor analysis and canonical correlations. British Journal of Psychology, Statistical Section 1 95–106 (1948).1997). The history of mathematics ((3rd ed.). New York: McGrawHill.Analysis of multiplicative causal rules when the causal variables are measured with error. Psychological Bulletin 93 549–562 (1983)., and2004). A beginner's guide to the MMPI2. Washington, DC: American Psychological Association.(2003). Measurement error. Institute of Statistics Mimeo Series No. 2544. Raleigh: North Carolina State University., , & (1996). Measuring selfconcept across the life span: Issues and instrumentation. Washington, DC: American Psychological Association.(2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, NJ: Erlbaum.(1919). A history of mathematics ((2nd ed.). New York: Macmillan.Convergent and discriminant validation by the multitrait multimethod matrix. Psychological Bulletin 56 81–105 (1959)., and1963). Experimental and quasiexperimental designs for research. Chicago: RandMcNally., & (Test reviews. Journal of Psychoeducational Assessment 16 334–338 (1998).Concurrent validity of the Peabody Picture Vocabulary TestThird Edition as an intelligence and achievement screener for low SES African American children. Assessment 8 (1) 85–94 (2001)., , andCombined descriptive and explanatory information improves peers' perceptions of autism. Research in Developmental Disabilities 25 (4) 321–339 (2004)., , , , andThe Campbell Collaboration: http://www.campbellcollaboration.org/1999). Variable deletion. In B. Thompson (Ed.), Advances in social science methodology, Vol. 5 (pp. 321–333). Greenwich, CT: JAI Press.(Bigger is not better: Seeking parsimony in canonical correlation analysis via variable deletion strategies. Multiple Linear Regression Viewpoints 27 (2) 24–33 (2001)., andTreatments of effect sizes and statistical significance tests in textbooks. Educational and Psychological Measurement 62 771–782 (2002)., andCarl Friedrich Gauss article: http://en.wikipedia.org/wiki/Carl_Friedrich_GaussCarlo Emilio Bonferroni biography and readings: http://www.aghmed.fsnet.co.uk/bonf/bonf.html2005). [Reviews of the Stanford Achievement Test, 10th Edition]. In R. A. Spies & B. S. Plake (Eds.), The sixteenth mental measurements yearbook (pp. 969–975). Lincoln, NE: Buros Institute of Mental Measurements., & (1997). ExplorN: Design considerations past and present. Technical Report 137, Center for Computational Statistics, George Mason University, Fairfax, VA., , & (The Carroll Rating Scale for Depression: I. Development, reliability, and validation. British Journal of Psychiatry 138 194–200 (1981).1993). Human cognitive abilities: A survey of factoranalytic studies. New York: Cambridge University Press.(1997). Mathematical tools for applied multivariate analysis. San Diego, CA: Academic Press., & (1988). Transformation and weighting in regression. New York: Chapman & Hall/CRC., & (2003). The skeptic's dictionary: A collection of strange beliefs, amusing deceptions, and dangerous delusions. Chichester, UK: Wiley. Retrieved August 7, 2005, from http://www.skepdic.com/placebo.html(2002) Statistical inference (, & (2nd ed.). Pacific Grove, CA: Duxbury.Special Olympics unified sports: Changes in male athletes during a basketball season. Adapted Physical Activity Quarterly 18 (2) 193–206 (2001).Sharp large deviations estimates for simulated annealing algorithms. Annales de l'institut Henri Poincaré, Probabilités et Statistiques 27 (3) 291–383 (1991).The time it takes to see and name objects. Mind 11 63–65 (1886).1971). Abilities: Their structure, growth, and action. Boston: HoughtonMifflin.(They talk of some strict testing of us—pish. Behavioral and Brain Sciences 3 336–337 (1980).1987). Intelligence: Its structure, growth, and action. Amsterdam: NorthHolland.(CattellHornCarroll Human Cognitive Abilities Project: http://www.iapsych.com/chchca.htmHuman needs and job satisfaction: A multidimensional approach. Human Relations 35 703–715 (1982)., , andCBM testing materials page: http://aimsweb.comCecil Reynolds and BASC2: http://www.agsnet.com/psych/oct04a.aspCenter for Creative Learning. (n.d.). Creativity assessment test number 72. Retrieved August 30, 2005, from http://www.creativelearning.com/Assess/test72.htmCenters for Disease Control and Prevention. (2003, September 5). Cigarette smokingattributable morbidity—United States, 2000. MMWR, 52(35), 842–844. Available from http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5235a4.htmDocumenting the world's sociological literature: Sociological Abstracts. Publishing Research Quarterly 11 (3) 83–95 (1995)., andChance magazine: http://www.amstat.org/publications/chance/Chance Web site devoted to the teaching of courses dealing with chance: http://www.dartmouth.edu/~chance/aStratified computerized adaptive testing. Applied Psychological Measurement 23 211–222 (1999)., andHypergeometric family and item overlap rates in computerized adaptive testing. Psychometrika 67 387–398 (2002)., and2000). Grounded theory: Constructivist and objectivist methods. In N. Denzin & Y. Lincoln (Eds.), Handbook of qualitative research ((2nd ed., pp. 509–535). Thousand Oaks, CA: Sage.2003). The analysis of time series: An introduction ((6th ed.). Toronto, Canada: Chapman & Hall.Use of the STATIS method to analyze timeintensity profiling data. Food Quality and Preference 15 3–12 (2003)., , , , andThe ade4 packageI: Onetable methods. R News 4 5–10 (2004)., , and1994). Radical behaviorism: The philosophy and the science. Sarasota, FL: Authors Cooperative.(Chisquare calculator: http://faculty.vassar.edu/lowry/csfit.html1998). Bias, overview. In P. Armitage & P. Colton (Eds.), Encyclopedia of biostatistics: Vol. 1 (pp. 331–338). Chichester, UK: Wiley., & (2005). A catalog of biases in questionnaires. Preventing Chronic Diseases, 2, 1–20. Retrieved August 6, 2005, from http://www.cdc.gov/pcd/issues/2005/jan/04_0050.htm, & (Some reservations about statistical power. American Psychologist 46 1088–1089 (1991).1996). Statistical significance: Rationale, validity and utility. London: Sage.(1974). Luria's neuropsychological investigation. Copenhagen: Munksgaard.(Citro, C. F., Ilgen, D. R., & Marrett, C. B. (Eds.). (2003). Protecting participants and facilitating social and behavioral sciences research. Washington, DC: National Academies Press.Interethnic group and intraethnic group racism: Perceptions and coping in Black university students. Journal of Black Psychology 30 506–526 (2004).Class interval discussion: Scale and impression: http://www.shodor.org/interactivate/discussions/sd2.htmlClassification and regression tree downloads: http://cran.au.rproject.org/src/contrib/Descriptions/tree.html1998). Applied correspondence analysis. Thousand Oaks, CA: Sage.(Delphi: A technique to harness expert opinion for critical decisionmaking tasks in education. Educational Psychology 17 (4) 373–386 (1997).Clericaltype work: http://www.bls.gov/oco/ocos130.htm#nature1985). The elements of graphing data. Monterey, CA: Wadsworth.(Cleveland, W. S., & McGill, M. E. (Eds.). (1988). Dynamic graphics for statistics. Pacific Grove, CA: Wadsworth.The many faces of the scatterplot. Journal of the American Statistical Association 79 807–822 (1984)., andSome cautions concerning the application of causal modeling methods. Multivariate Behavioral Research 18 115–126 (1983).Latent structureanalysis of a set of multidimensional contingencytables. Journal of the American Statistical Association 79 (388) 762–771 (1984)., andClustering algorithm tutorial: http://www.elet.polimi.it/upload/matteucc/Clustering/tutorial_html/kmeans.htmlBasic versus applied research in cognitive science: A view from industry. Ecological Psychology 6 131–135 (1994).The comparison of percentages in matched samples. Biometrika 37 256–266 (1950).1977). Sampling techniques ((3rd ed.). New York: Wiley.The Cochrane Collaboration: The reliable source of evidence in health care: http://www.cochrane.org/index0.htm2004). Essentials of statistics for the social and behavioral sciences. Hoboken, NJ: Wiley., & (A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 37–46 (1960).Multiple regression as a general dataanalytic system. Psychological Bulletin 70 (6) 426–443 (1968).Weighted kappa: Nominal scale agreement with provision for scale disagreement or partial credit. Psychological Bulletin 70 213–220 (1968).1977). Statistical power analysis for the behavioral sciences ((rev. ed.). New York: Academic Press.Partialed products are interactions; partialed powers are curve components. Psychological Bulletin 85 858–866 (1978).1988). Statistical power analysis for the behavioral sciences ((2nd ed.). Hillsdale, NJ: Erlbaum.Things I have learned (so far). American Psychologist 45 (12) 1304–1312 (1990).The earth is round (p < .05). American Psychologist 49 997–1003 (1994).1983). Applied multiple regression/correlation analysis for the behavioral sciences (, & (2nd ed.). Hillsdale, NJ: Erlbaum.2003). Applied multiple regression/correlation analysis for the behavioral sciences (, , , & (3rd ed.). Mahwah, NJ: Erlbaum.1995). Portfolios across the curriculum and beyond. Thousand Oaks, CA: Corwin., , & (Estimates of test size for several test procedures based on conventional variance ratios, in repeated measures designs. Psychometrika 32 339–353 (1967)., , , andSecular gains in fluid intelligence: Evidence from the Culture Fair Intelligence Test. Journal of Biosocial Science 35 33–39 (2003)., andComprehensive MetaAnalysis: A computer program for research synthesis: http://www.metaanalysis.com/1970). Manual for the Comrey Personality Scales. San Diego, CA: Educational and Industrial Testing Service.(1976). Mental testing and the logic of measurement. In W. L. Barnette (Ed.), Readings in psychological tests and measurement. Baltimore: Williams & Wilkins.(1995). Manual and handbook of interpretations for the Comrey Personality Scales. San Diego, CA: EDITS Publishers.(1992). A first course in factor analysis (, & (2nd ed.). Hillsdale, NJ: Erlbaum.1995). Elementary statistics: A problem solving approach (, & (3rd ed.). Dubuque, IA: KendallHunt.Conditional probability: http://en.wikipedia.org/wiki/Conditional_probabilityConfidence intervals, Rice Virtual Lab in Statistics page: http://davidmlane.com/hyperstat/confidence_intervals.htmlConnotative meaning information: http://www.writing.ws/reference/history.htm1980). Practical nonparametric statistics ((2nd ed.). New York: Wiley.1999). Practical nonparametric statistics ((3rd ed.). New York: Wiley.Consulting Psychologists Press, Inc.: http://www.cppdb.comCalibrate your eyes to recognize highdimensional shapes from their lowdimensional projections [Electronic version]. Journal of Statistical Software 2 (6) (1997).(2000, June). Using Arc for dimension reduction and graphical exploration in regression. Retrieved from http://www.stat.umn.edu/arc/InvReg/DimRed.pdfSufficient dimension reduction via inverse regression: A minimum discrepancy approach. Journal of the American Statistical Society 100 410–428 (2005)., and1982). Residuals and influence in regression. New York: Chapman & Hall., & (1979). Quasiexperimentation: Design and analysis issues for field settings. Chicago: RandMcNally., & (Cooper, H., & Hedges, L. V. (Eds.). (1994). The handbook of research synthesis. New York: Russell Sage.The STATIS method: Characterization of conformational states of flexible molecules from molecular dynamics simulation in solution. Journal of Molecular Graphics 14 206–212 (1996)., , and2002). Remaking the concept of aptitude: Extending the legacy of Richard E. Snow. Hillsdale, NJ: Erlbaum., , , , , and et al. (Correlation coefficient page: http://mathworld.wolfram.com/CorrelationCoefficient.htmlWhat is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology 78 98–104 (1993).1992). Revised NEO Personality Inventory and NEO Five FactorInventory Professional Manual. Odessa, FL: Psychological Assessment Resources., & (Council for Exceptional Children: http://www.cec.sped.org1989). Oxford advanced learner's dictionary. Oxford, UK: Oxford University Press.(The use of secondary data in business ethics research. Journal of Business Ethics 17 423–434 (1998).Regression models and lifetables (with discussion). Journal of Royal Statistical Society B 33 187–220 (1972).A general definition of residuals. Journal of the Royal Statistical Society, B 30 248–275 (1968)., and1985). Methods in behavioral research ((3rd ed.). Palo Alto, CA: Mayfield.2002). Understanding your values. Williamsburg, VA: Applied Psychology Resources., & (A report on the 40year followup of the Torrance Tests of Creative Thinking: Alive and well in the new millennium. Gifted Child Quarterly 49 283–291 (2005)., , , andCreative Research Systems. (2003). The survey system. Retrieved August 23, 2005, from http://www.surveysystem.com/sdesign.htm1993). Statistics for spatial data. New York: Wiley.(Determining validity in qualitative inquiry. Theory Into Practice 39 (3) 124–130 (2000)., andThe CRISPDM consortium: http://www.crispdm.org/2001). An introduction to support vector machines. Cambridge, UK: Cambridge University Press., & (1978). Theory and research handbook for the Career Maturity Inventory ((2nd ed.). Monterey, CA: CTB/McGrawHill.Revision of the Career Maturity Inventory. Journal of Career Assessment 4 131–138 (1996)., andMeasuring the affective and cognitive properties of attitudes: Conceptual and methodological issues. Personality and Social Psychology Bulletin 20 (6) 619–634 (1994)., , andCritical r values table: http://www.ecowin.org/aulas/resources/stats/correlation.htmTeaching for the test: Validity, fairness, and moral action. Educational Measurement: Issues and Practice 22 (3) 5–11 (2003).1986). Introduction to classical and modern test theory. New York: Holt, Rinehart & Winston., & (Coefficient alpha and the internal structure of tests. Psychometrika 16 297–334 (1951).1970). Essentials of psychological testing ((3rd ed.). New York: Harper & Row.1971). Test validation. In R. L. Thorndike (Ed.), Educational measurement ((2nd ed., pp. 443–507). Washington, DC: American Council on Education.1989). Lee J. Cronbach. In G. Lindzey (Ed.), A history of psychology in autobiography, Vol. 8 (pp. 62–93). Stanford, CA: Stanford University Press.(1990). Essentials of psychological testing ((5th ed.). New York: Harper & Row.1972). The dependability of behavioural measurements: Theory of generalizability for scores and profiles. New York: Wiley., , , & (Construct validity in psychological tests. Psychological Bulletin 52 (4) 281–302 (1955)., and1990). Analysis of repeated measures. London: Chapman & Hall., & (Detecting and statistically correcting sample selection bias. Journal of Social Service Research 30 19–33 (2004)., , , andA primer on the understanding, use and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement 61 532–575 (2001)., and(1981). Validation of a diagnostic interpretation technique for the Iowa Test of Basic Skills: Final report to the National Institute of Education. Grant Wood Area Education Agency.Rankbiserial correlation. Psychometrika 21 287–290 (1956).1996). Validity. In A. W. Ward, H. W. Stoker, & M. MurrayWard (Eds.), Educational measurement: Origins, theories, and explications: Vol. 1. Basic concepts and theories. Lanham, MD: University Press of America., , , , , & (How accurate are traditional quota opinion polls? Journal of the Market Research Society 39 (3) 433–438 (1997)., andCurvilinear bivariate regression: http://core.ecu.edu/psyc/wuenschk/MV/multReg/Curvi.docCurvilinear regression: http://www.vias.org/tmdatanaleng/cc_regress_curvilin.html1986). Tests for the normal distribution. In R. B. D'Agostino & M. A. Stephens (Eds.), Goodnessoffit techniques (pp. 367–419). New York: Marcel Dekker.(A suggestion for using powerful and informative tests of normality. American Statistician 44 316–321 (1990)., , andD'Agostino, R. B., & Stephens, M. A. (Eds.). (1986). Goodnessoffittechniques. New York: Marcel Dekker.A propos de l'emploi du test de KolmogorovSmirnov comme test de normalité. Biometrie–Praximetrie 9 (1) 3–13 (1968).DAMBE Windows95/98/NT executables: http://aix1.uottawa.ca/~xxia/software/software.htm1990). Regression and linear models. New York: McGrawHill.(Combining independent p values: Extensions of the Stouffer and binomial methods. Psychological Methods 4 496–515 (2000)., andPoint biserial correlation and its generalization. Psychometrika 25 393–408 (1960).Data compression information: http://datacompression.infoData compression principles and practices: http://www.datacompression.comData mining information clearinghouse: http://www.kdnuggets.com/Perseveration of checking thoughts and moodasinput hypothesis. Journal of Behavior Therapy & Experimental Psychiatry 34 141–160 (2003)., , , , and1997). Bootstrap methods and their application. Cambridge, UK: Cambridge University Press., & (1972). Fundamentals of attitude measurement. New York: Wiley.(1995). The “unusual episode” data revisited. Journal of Statistics Education, 3. Available: http://www.amstat.org/publications/jse/v3n3/datasets.dawson.html(1947). Intuitive cognition: A key to the significance of the late Scholastics. St. Bonaventure, NY: Franciscan Institute.(The psychometric properties of the Vineland Adaptive Behavior Scales in children and adolescents with mental retardation. Journal of Autism and Developmental Disorders 35 53–62 (2005)., , , andThe amount eaten in meals by humans is a power function of the number of people present. Physiology & Behavior 51 121–125 (1991)., andConfirmatory factor analysis of a 4factor model of chronic pain evaluation. Pain 60 195–202 (1995)., , andModelling progression of CD4lymphocyte count and its relationship to survival time. Biometrics 50 1003–1014 (1994)., andHigher order latent trait models for cognitive diagnosis. Psychometrika 69 333–353 (2004)., andOn the prehistory of correspondence analysis. Statistica Neerlandica 37 161–164 (1983).1959). A principalcomponents missing data method for multiple regression models (Technical Report SP86). Santa Monica, CA: Systems Development Corporation.(Intelligence and the differentiation hypothesis. Intelligence 23 105–132 (1996)., , , , , and1972). Techniques ordinales en analyse des données. Paris: Hachette.(Dekking, M., Lévy Véhel, J., Lutton, E., & Tricot, C. (Eds.). (1999). Fractals: Theory and applications in engineering. London: Springer.Poisson approximations for rscan processes. Annals of Applied Probability 2 (2) 329–357 (1992)., andMaximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39 1–38 (1977)., , andDepartment of Health and Human Services—Centers for Disease Control and Prevention: http://www.cdc.govDescriptive Statistics: http://www.physics.csbsju.edu/stats/descriptive2.html (application that computes the mean once data are entered)Correlates of mental tests with each other and with cognitive variables are highest for low IQ groups. Intelligence 13 349–359 (1989)., and, , & (2005). Intro stats (2nd ed.). Boston: AddisonWesley.1999). Grounding grounded theory. San Diego, CA: Academic Press.(Differential Aptitude Tests for Personnel and Career Assessment: http://www.pantesting.com/products/PsychCorp/DAT.aspDifferential Aptitudes Tests: http://www.psychcorpcenter.com2003). Statistical analysis of spatial point patterns. London: Hodder Arnold.(2002). Analysis of longitudinal data (, , , & (2nd ed.). Oxford, UK: Clarendon.Informative dropout in longitudinal data analysis (with discussion). Applied Statistics 43 49–93 (1994)., andThe interpretation of generalized procrustes analysis and allied methods. Food Quality and Preference 3 67–87 (1991/2)., and2001). Profile analysis: Multidimensional scaling approach. Practical Assessment, Research & Evaluation, 7(16). Retrieved October 1, 2005, from http://PAREonline.net/getvn.asp?v=7&n=16(Disabilities Education Improvement Act (IDEA) of 2004. resources: http://www.ed.gov/offices/OSERS/IDEA/Disease rates comparison: http://bmj.bmjjournals.com/epidem/epid.3.htmlRatios involving extreme values. Annals of Mathematical Statistics 22 (1) 68–78 (1951).Dixon test for outliers has been implemented in the R project, a free software environment for statistical computing and graphics: http://finzi.psych.upenn.edu/R/library/outliers/html/dixon.test.htmlPlotting with confidence: Graphical comparisons of two populations. Biometrika 63 421–434 (1976)., andMortality in relation to smoking: 40 years' observations on male British doctors. British Medical Journal 309 (6959) 901–911 (1994)., , , , and2005). Strong Interest Inventory manual. Palo Alto, CA: Consulting Psychologists Press., , , & (A comparison of procedures to detect item parameter drift. Applied Psychological Measurement 22 33–51 (1998)., and1970). Elaboration of Guttman scaling techniques. In G. F. Summers (Ed.), Attitude measurement (pp. 203–213). Chicago: Rand McNally., & (Immediate and Delayed Memory Tasks: A computerized measure of memory, attention, and impulsivity. Behavioral Research Methods, Instruments, and Computers 34 391–398 (2002)., , and2003). Laboratory measures of impulsivity. In E. F. Coccaro (Ed.), Aggression: Psychiatric assessment and treatment (Medical Psychiatric Series No. 22, pp. 247–265). New York: Marcel Dekker., , & (Laboratory behavioral measures of impulsivity. Behavior Research Methods 37 82–90 (2005)., , , andDowning, S. M., & Haladyna, T. M. (Eds.). (2006). Handbook of test development. Mahwah, NJ: Erlbaum.Dr. Levant: http://www.DrRonaldLevant.comDrawing tree diagrams: http://wwwstat.stanford.edu/~susan/surprise/ProbabilityTree.html2001). The three card method: Estimating sensitive survey items—with permanent anonymity of response. Proceedings of the American Statistical Association, Statistical Computing Section, CDROM. Retrieved from http://www.amstat.org/sections/srms/Proceedings/y2001/Proceed/00582.pdf, , & (DrugFree Workplace Act of 1988, 41 U.S.C. Sec. 701 et seq.Quality control in the development and use of performancebased assessments. Applied Measurement in Education 4 (4) 289–303 (1991)., , and1972). Socioeconomic background and achievement. New York: Seminar Press., , & (Education and occupational mobility: A regression analysis. American Journal of Sociology 68 629–644 (1963)., andAccurate tests of statistical significance for r sub (WG) and average deviation interrater agreement indexes. Journal of Applied Psychology 88 (2) 356–362 (2003)., , andMultiple comparisons of means. Journal of the American Statistical Association 56 52–64 (1961).Estimation of multiple contrasts using tdistributions. Journal of the American Statistical Association 60 573–583 (1965)., and2004). Scaling methods (, , , & (2nd ed.). Mahwah, NJ: Erlbaum.2004). Carl Friedrich Gauss: Titan of science. Washington, DC: Mathematical Association of America.(The Stroop phenomenon and its use in the study of perceptual, cognitive, and response processes. Memory & Cognition 1 (2) 106–120 (1973).E. Paul Torrance: http://www.coe.uga.edu/torrance/Some simple statistical procedures for classroom use. Diagnostique 4 3–12 (1979)., , andObtaining and reporting evidence for content validity. Educational and Psychological Measurement 16 269–282 (1956).Statistical inference and nonrandom samples. Psychological Bulletin 66 485–487 (1966).EdiTS/Educational and Industrial Testing Service: http://www.edits.netEducational Testing Service: http://www.ets.org1957). Techniques of attitude scale construction (pp. 172–199). New York: AppletonCenturyCrofts.(1959). Manual: Edwards Personal Preference Schedule. Washington, DC: The Psychological Corporation.(1993). An introduction to the bootstrap. New York: Chapman & Hall., & (Elementary and Secondary Education Act, 20 U.S.C. 6311(b)(3)(C)(ii) (2002).The utility of curriculumbased measurement and performance assessment as alternatives to traditional intelligence and achievement tests. School Psychology Review 26 224–233 (1997)., and1991). Social skills intervention guide: Practical strategies for social skills training. Circle Pines, MN: American Guidance Service., & (1654). Anthropometria. Padua: M. Cadorini.(1992). Technical manual for the Spatial Learning Ability Test (Tech. Rep. No. 9201). Lawrence: University of Kansas, Department of Psychology.(1994). Application of cognitive design systems to test development. In C. R. Reynolds (Ed.), Cognitive assessment: A multidisciplinary perspective. New York: Plenum.(A measurement model for linking individual learning to processes and knowledge: Application to mathematical reasoning. Journal of Educational Measurement 32 277–294 (1995).Cognitive design systems and the successful performer: A study on spatial ability. Journal of Educational Measurement 33 29–39 (1996).The factorial validity of scores from a cognitively designed test: The Spatial Learning Ability Test. Educational and Psychological Measurement 57 99–107 (1997).Improving construct validity with cognitive psychology principles. Journal of Educational Measurement 38 (4) 343 (2001)., and2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum., & (2000). Item response theory for psychologists. Mahwah, NJ: Erlbaum., & (1983). Stemandleaf displays. In D. C. Hoaglin, F. Mosteller, & J. W. Tukey (Eds.), Understanding robust and exploratory data analysis (pp. 7–32). New York: Wiley., & (2004). Applied econometric time series ((2nd ed.). Hoboken, NJ: Wiley.1994). Historical views of the concept of invariance in measurement theory. In M. Wilson (Ed.), Objective measurement: Theory into practice (2, pp. 73–99). Norwood, NJ: Ablex.(Analyse factorielle et distances répondant au principe d'équivalence distributionnelle. Revue de Statistiques Appliquées 26 29–37 (1978).1990). Analyses factorielles simples et multiples: Objectifs, méthodes, interprétation. Paris: Dunod., & (Multiple factor analysis. Computational Statistics & Data Analysis 18 121–140 (1994)., and1998). Analyses factorielles simples et multiples. Paris: Dunod., & (Le traitement des variables vectorielles. Biometrics 29 751–760 (1973).1980). L'analyse conjointe de plusieurs matrices de données. In M. Jolivet (Ed.), Biométrie et temps (pp. 59–76). Paris: Société Française de Biométrie.(1992). Sentinel for health: A history of the Centers for Disease Control. Berkeley: University of California Press.(1999). Nonparametric regression and spline smoothing ((2nd ed.). New York: Dekker.1985). Invitation to psychological research. New York: Holt, Rinehart, & Winston.(A MonteCarlo study of correlated error in moderated multiple regression analysis. Organizational Behavior and Human Decision Processes 36 305–323 (1985).The problem of analyzing multiplicative composites: Interactions revisited. American Psychologist 46 6–15 (1991).On the asymmetry of g. Psychological Reports 85 1059–1069 (1999).2002). The implications of the asymmetry of g for predictive validity. In W. AuerRizzi, E. Szabo, & C. InnreiterMoser (Eds.), Management in einer Welt der Globalisierung und Diversitaet: Europaeische und nordamerikanische Sichtweisen (pp. 433–441). Stuttgart, Germany: SchaefferPoeschel Verlag.(2004). Probability and statistics: The science of uncertainty. New York: Freeman., & (1999). Chance rules: An informal guide to probability, risk, and statistics. New York: Springer.(2001). Cluster analysis. London: Hodder Arnold., , & (2003). The Rorschach: A comprehensive system ((4th ed.). New York: Wiley.Exploratory factor analysis primer: http://www.upa.pdx.edu/IOA/newsom/semclass/ho_efa.docExploratory Software for Confidence Intervals: http://www.latrobe.edu.au/psy/esci/Exploring Careers: The ASVAB career exploration guide. (2005). DD Form 13045WB, July 2005. U.S. Government Printing Office.ExplorN software for touring through highdimensional data using parallel coordinate plots: http://www.galaxy.gmu.edu/pub/software/ (It is now replaced by its commercial evolution, CrystalVision)2005). Attitude measurement: Techniques for measuring the unobservable. In C. T. Brock & M. C. Green (Eds.), Persuasion: Psychological insights and perspectives (pp. 17–40). Thousand Oaks, CA: Sage., , & (Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods 4 272–299 (1999)., , , andFactor analysis article: http://en.wikipedia.org/wiki/Factor_analysisFactor analysis software sampling: http://quantrm2.psy.ohiostate.edu/browne/software.htmFactor scores computation and evaluation tools: http://psychology.okstate.edu/faculty/jgrice/factorscores/1986). A history and theory of informed consent. New York: Oxford University Press., & (The Fagan Test of Infant Intelligence: A technical summary. Journal of Applied Developmental Psychology 13 173–193 (1992)., andTwenty nonparametric statistics and their large sample approximations. Journal of Modern Applied Statistical Methods 1 (2) 248–268 (2002).2000, April). Twenty nonparametric statistics. Paper presented at the annual meeting of the American Educational Research Association, SIG/Educational Statisticians, New Orleans, LA., & (FairTest: http://www.fairtest.orgFalse negative: http://en.wikipedia.org/wiki/Type_II_errorFamily Education Rights and Privacy Act (29 U.S.C.A. § 1232g).Two approaches for correcting correlation attenuation caused by measurement error: Implications for research practice. Educational and Psychological Measurement 63 915–930 (2003).The effect Type I error and power of various methods of resolving ties for six distributionfree tests of location. Journal of Modern Applied Statistical Methods 5 (1) 50–67 (2006).1988). Fractals. New York: Plenum.(Federal regulations on the protection of human participants: http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.htmthe Carroll Rating Scale for Depression: III. Comparison with other rating instruments. British Journal of Psychiatry 138 205–209 (1981)., , and1989). Reliability. In R. L. Linn (Ed.), Educational measurement (, & (3rd ed., pp. 105–146). New York: American Council on Education; Macmillan.Approximating scale score standard error of measurement from the raw score standard error. Applied Measurement in Education 11 159–177 (1998)., andStatistical inference for coefficient alpha. Applied Psychological Measurement 11 93–103 (1987)., , andA theory for record linkage. Journal of the American Statistical Association 64 1183–1210 (1969)., andPublic concerns in the United Kingdom about general and specific applications of genetic engineering: Risk, benefit, and ethics. Science, Technology, & Human Values 22 98–124 (1997)., , and2005). Discovering statistics using SPSS ((2nd ed.). London: Sage.Development of a model for selfdetermination. Career Development for Exceptional Individuals 17 159–169 (1994)., and1995). SelfDetermination Knowledge Scale, Form A and Form B. Austin, TX: ProEd., , & (1994). The analysis of crossclassified categorical data ((2nd ed.). Cambridge: MIT Press.SOMPA and the psychological testing of Hispanic children. Metas 2 1–6 (1982).A longitudinal study of the predictive validity of the System of Multicultural Pluralistic Assessment (SOMPA) (ERIC Document Reproduction Service No. EJ391800). Psychology in the Schools 26 5–19 (1989)., andAn interruptible algorithm for perfect sampling via Markov chains. Annals of Applied Probability 8 131–162 (1988).1993). Projective techniques. In T. H. Ollendick & M. Hersen (Eds.), Handbook of child and adolescent assessment (pp. 224–238). Boston: Allyn & Bacon., & (The investigation of personality structure: Statistical models. Journal of Research in Personality 31 439–485 (1997)., and1960). Fundamentals of child psychiatry. New York: Norton.(Reporting of statistical inference in the journal of applied psychology: Little evidence of reform. Educational and Psychological Measurement 61 181–210 (2001)., , and, , , & (1997). Structured Clinical Interview for DSMIV® Axis I disorders (SCIDI), clinician version, user's guide. Washington, DC: American Psychiatric Press.Linear logistic test model as an instrument in educational research. Acta Psychologica 37 359–374 (1973).2000). Informed consent. In B. Sales & F. Susan (Eds.), Ethics in research with human participants (pp. 35–48). Washington, DC: American Psychological Association.(2003). Decoding the ethics code: A practical guide for psychologists. Thousand Oaks, CA: Sage.(Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10 507–521 (1915).1925). Statistical methods for research workers (1st–13th eds.). Edinburgh, UK: Oliver & Boyd.(The use of multiple measurements in taxonomic problems. Annals of Eugenics 7 179–188 (1936).1971). The design of experiments. New York: Hafner. (Original work published in 1935)(Fisher's exact test calculation: http://www.unc.edu/~preacher/fisher/fisher.htmFisher's Z description with formulas: http://davidmlane.com/hyperstat/A50760.htmlFisher's z'Transformation, from MathWorld—a Wolfram Web resource, by E. W. Weisstein: http://mathworld.wolfram.com/FisherszTransformation.htmlCitations do not solve problems. Psychological Bulletin 112 393–395 (1992)., and1971). Performance and product evaluation. In E. L. Thorndike (Ed.), Educational measurement (, & (2nd ed., pp. 237–270). Washington, DC: American Council on Education.1997). Contemporary intellectual assessment: Theories, tests, and issues. New York: Guilford., , & (2000). The Wechsler Intelligence Scales and GfGc theory: A contemporary approach to interpretation. Boston: Allyn & Bacon., , & (1973). Statistical methods for rates and proportions. New York: Wiley.(1988). Multivariate statistics: A practical approach. New York: Chapman & Hall., & (Fourier transform article: http://en.wikipedia.org/wiki/Fourier_transformFourier transformations information by Wolfram Research: http://mathworld.wolfram.com/FourierTransform.html1997). Applied regression analysis, linear models, and related methods. Thousand Oaks, CA: Sage.(Frank Gresham home page: http://www.behavioralinstitute.org/Frank%20Gresham.htmFrank Wilcoxon biographical essay: http://www.umass.edu/wsp/statistics/tales/wilcoxon.htmlA comparison of occupational stressors in selected allied health disciplines. Health Care Supervisor 13 (1) 53–65 (1994)., and1991). Statistics (, , , & (2nd ed.). New York: Norton.1996). Experiments with a new boosting algorithm. In Machine learning: Proceedings of the Thirteenth International Conference (L., Saitta, ed., pp. 148–156). San Francisco: Morgan Kaufmann., & (2001). Psychological assessment with the MMPI2. Mahwah, NJ: Erlbaum., , , & (A developmental eventrelated potential study of picture matching in children, adolescents, and young adults: A replication and extension. Psychophysiology 29 593–610 (1992)., , , , andThe use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32 675–701 (1937).Friedman's Test Applet (allows you to enter data and calculate the test statistic): http://www.fon.hum.uva.nl/Service/Statistics/Friedman.htmlMaking sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education 32 124–158 (2001)., , andConceptual and visual models for categorical data. American Statistician 49 153–160 (1995).Extending mosaic displays: Marginal, conditional, and partial views of categorical data. Journal of Computational and Graphical Statistics 8 373–395 (1999).The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences 41 103–130 (2005)., and2006). Milestones in the history of thematic cartography, statistical graphics, and data visualization. Retrieved from http://www.math.yorku.ca/SCS/Gallery/milestone/, & (Some implementations of the boxplot. American Statistician 43 50–54 (1989)., , andFTII description and other measures of infant cognitive assessment: http://ehp.niehs.nih.gov/members/2003/6205/6205.html#comp1972). Introduction to statistical pattern recognition. New York: Academic Press.(1996). Bonferronitype inequalities with applications. New York: Springer., & (Composite portraits. J Anthropol Inst Gr Brit & Ireland 8 132 (1878).An improved t table for simultaneous control on g contrasts. Journal of the American Statistical Association 72 531–534 (1977).Tests for homogeneity of variance in factorial designs. Psychological Bulletin 86 978–984 (1979)., , andThe Frankfort CraniometricConvention, with critical remarks thereon. J Anthropol Inst Gr Brit & Ireland 14 64–83 (1884).1821). Theoria combinationis observationum erroribus minimis obnoxiae. Göttingen, Germany: Royal Society of Göttingen.(1998). Individual and jointlevel properties of personal project matrices: An exploration of the nature of project spaces. Unpublished doctoral dissertation, Carleton University, Ottawa, Canada.(1996). Model determination using samplingbased methods. In W. R. Gilks, S. Richardson, & D. J. Spiegelhalter (Eds.), Markov chain Monte Carlo in practice. Boca Raton, FL: Chapman & Hall/CRC.(1995). Bayesian data analysis. Boca Raton, FL: Chapman & Hall., , , & (2004). Bayesian data analysis (, , , & (2nd ed.). Boca Raton, FL: Chapman & Hall/CRC.Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 721–741 (1984)., and2002). Elements of computational statistics. New York: Springer.(Gentle, J. E., Härdle, W., & Mori, Y. (Eds.). (2004). Handbook of computational statistics: Concepts and methods. New York: Springer.Contribution a la theorie des valeurs extremes. Publications de l'Institut de Statistique de l'Universite de Paris 7 37–121 8 123–184 (1958, 1959).2001). The econometric analysis of seasonal time series. New York: Cambridge University Press., & (1993). Nonparametric measures of association (Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07B091). Newbury Park, CA: Sage.(2003). Nonparametric statistical inference (, & (4th ed.). New York: Dekker.1999). Evocative images: The Thematic Apperception Test and the art of projection. Washington, DC: American Psychological Association., & (1990). Nonlinear multivariate analysis. Chichester, UK: Wiley.(Mindless statistics. Journal of SocioEconomics 33 587–606 (2004).“Sweeping” the library: Mapping the social activity space of the public library. Library & Information Science Research 25 (4) 365–385 (2003)., and2005). Computational statistics. New York: Wiley Interscience., & (1992). Emerging versus forcing: Basics of grounded theory analysis. Mill Valley, CA: Sociology Press.(1967). The discovery of grounded theory. Chicago: Aldine., & (1962). Proficiency measurements: Assessing human performance. In R. M. Gagne (Ed.), Psychological principles in systems development. New York: Holt, Rinehart & Winston., & (1995). Statistical methods in education and psychology (, & (3rd ed.). Boston: Allyn & Bacon.1981). Metaanalysis in social research. Beverly Hills, CA: Sage., , & (1970). Statistical methods in education and psychology. Englewood Cliffs, NJ: Prentice Hall., & (1998). Writing the winning dissertation: A stepbystep guide. Thousand Oaks, CA: Corwin Press.(1999). Scan statistics and applications. Boston: Birkhauser., & (A proposal for handling missing data. Psychometrika 40 (2) 229–252 (1975)., andMultivariate logistic models. Journal of the Royal Statistical Society–Series B 57 533–546 (1995)., andGoffin, R. D., & Helmes, E. (Eds.). (2000). Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy. Boston: Kluwer.1997). The gambler's fallacy. Unpublished doctoral dissertation, Carnegie Mellon University.(Occupational exposure and addictions for physicians: Case studies and theoretical implications. Psychiatric Clinics of North America 27 (4) 745–753 (2004)., , andThe structure of phenotypic personality traits. American Psychologist 48 26–34 (1993).The diagnosis of brain damage by the Stroop test. Journal of Clinical Psychology 32 654–658 (1976).2001). LuriaNebraska Neuropsychological Battery. In W. I. Dorfman & M. Hersen (Eds.), Understanding psychological assessment: Perspectives on individual differences. New York: Kluwer Academic/Plenum., & (2000). The LuriaNebraska Neuropsychological Battery. In G. GrothMarnat (Ed.), Neuropsychological assessment in clinical practice: A guide to test interpretation and integration. New York: Wiley., , & (1989). Matrix computations. Baltimore: Johns Hopkins University Press., & (GoodenoughHarris drawing test: http://gri.gallaudet.edu/~catraxle/INTELLEC.html#goodenoughAssessing the nonrandom sampling effects of subject attrition in longitudinal research. Journal of Management 22 627–652 (1996)., andExploratory latent structureanalysis using both identifiable and unidentifiable models. Biometrika 61 (2) 215–231 (1974).Understanding correlation: Factors that affect the size of r. The Journal of Experimental Education 74 (3) 251–266 (2006)., and1999). Classification. London: Chapman & Hall.(1983). Factor analysis ((2nd ed.). Hillsdale, NJ: Erlbaum.Academic intrinsic motivation in elementary and junior high school students. Journal of Educational Psychology 77 631–635 (1985).Academic intrinsic motivation in young elementary school children. Journal of Educational Psychology 82 525–538 (1990).Role of parental motivational practices in children's academic intrinsic motivation and achievement. Journal of Educational Psychology 86 104–113 (1994)., , andRole of cognitively stimulating home environment in children's academic intrinsic motivation: A longitudinal study. Child Development 69 1448–1460 (1998)., , andContinuity of academic intrinsic motivation from childhood through late adolescence: A longitudinal study. Journal of Educational Psychology 93 3–13 (2001)., , andEducational characteristics of adolescents with gifted academic intrinsic motivation: A longitudinal study from school entry through early adulthood. Gifted Child Quarterly 49 172–186 (2005)., , , and1952). The Adjective Check List. Palo Alto, CA: Consulting Psychologists Press.(1996). The California Psychological Inventory manual. Palo Alto, CA: Consulting Psychologists Press.(1996). The mismeasure of man ((revised & expanded ed.). New York: Norton.Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53 325–338 (1966).Generalized procrustes analysis. Psychometrika 40 33–51 (1975).Grace, C., Shores, E., & Charner, K. (Eds.). (1998). The portfolio book: A stepbystep guide for teachers. Beltsville, MD: Gryphon House.Graduate Record Examinations Web site: http://www.gre.orgGraduate Record Examinations Board. (1997). GRE 1997–98: Guide to the use of scores. Princeton, NJ: Educational Testing Service.Attitudes of primary school children toward the physical appearance and labels associated with Down syndrome. American Journal of Mental Retardation 93 (1) 28–35 (1988)., andEvaluating interventions with differential attrition: The importance of nonresponse mechanisms and use of follow up data. Journal of Applied Psychology 78 119–128 (1993)., and2005). Essentials of statistics for the behavioral sciences. Belmont, CA: Wadsworth., & (Great Britain. Parliament. (1998). Report on the loss of the S. S. Titanic: The official government enquiry. New York: Picador.1994). Pathologies of rational choice theory: A critique of applications in political science. New Haven, CT: Yale University Press., & (1966). Signal detection theory and psychophysics. New York: Wiley., & (1984). Theory and applications of correspondence analysis. London: Academic Press.(1993). Correspondence analysis in practice. London: Academic Press.(2000). The MMPI2: An interpretive manual ((2nd ed.). Boston: Allyn & Bacon.2003). Econometric analysis ((5th ed.). Upper Saddle River, NJ: Prentice Hall.1999). Foundations of intellectual assessment: The WAISIII and other tests in clinical practice. Boston: Allyn & Bacon.(1990). Social Skills Rating System. Circle Pines, MN: American Guidance Service., & (Computing and evaluating factor scores. Psychological Methods 6 430–450 (2001).1993). Learning and practicing econometrics. New York: Wiley., , & (2005). Effect sizes for research: A broad practical approach. Hillsdale, NJ: Erlbaum., & (1985). Measurement and evaluation in teaching ((5th ed.). New York: Macmillan.2003). Handbook of psychological assessment. New York: Wiley.(Grounded theory resources: http://dmoz.org/Science/Social_Sciences/Methodology/Grounded_Theory/1954). Psychometric methods ((2nd ed.). New York: McGrawHill.1950). Theory of mental tests. New York: Wiley.(Nonprobability sampling in social work research: Dilemmas, consequences, and strategies. Journal of Social Service Research 30 1–18 (2004)., andA basis for scaling qualitative data. American Sociological Review 9 139–150 (1944).The determinacy of factor score matrices with applications for five other problems of common factor theory. British Journal of Statistical Psychology 8 65–82 (1955).1970). The Cornell technique for scale and intensity analysis. In G. F. Summers (Ed.), Attitude measurement (pp. 187–203). Chicago: Rand McNally.(Do face validity items have more predictive validity than subtle items? Journal of Consulting and Clinical Psychology 47 295–300 (1979)., , andLoglinear models for frequency tables derived by indirect observation: Maximum likelihood equations. Annals of Statistics 2 (5) 911–924 (1974).Product models for frequency tables involving indirect observation. Annals of Statistics 5 (6) 1124–1147 (1977).Validity arguments for highstake testing: In search of the evidence. Educational Measurement: Issues and Practice 18 (4) 5–9 (1999).Hagenaars, J. A., & McCutcheon, A. L. (Eds.). (2002). Applied latent class analysis. Cambridge, UK: Cambridge University Press.1995). Grounded theory as scientific method (Philosophy of Education Society Yearbook 1995, pp. 281–290). Urbana: University of Illinois Press.(What is a spurious correlation? Understanding Statistics 2 125–132 (2003).1967). Handbook of the Poisson distribution. New York: Wiley.(1990). Spatial data analysis in the social and environmental sciences. Cambridge, UK: CambridgeUniversity Press.(1998). Multivariate data analysis (, , , & (5th ed., pp. 469–519). Upper Saddle River, NJ: Prentice Hall.1969). A course in nonparametric statistics. San Francisco: HoldenDay.(1997). Writing test items to evaluate higher order thinking. Boston: Allyn & Bacon.(2004). Developing and validating multiple choice test items ((3rd ed.). Mahwah, NJ: Erlbaum.A review of multiple choice item writing guidelines for classroom assessment. Applied Measurement in Education 15 (3) 309–334 (2002)., , andTesting for moderator variables in meta analysis: Issues and methods. Communication Monographs 58 437–448 (1991)., andSelecting statistical clerks with the Minnesota Clerical Test. Journal of Psychology 96 297–301 (1977)., and1991). Fundamentals of item response theory. Newbury Park, CA: Sage., , & (1994). Time series analysis. Princeton, NJ: Princeton University Press.(1992). Regression with graphics: A second course in applied statistics. Belmont, CA: Duxbury.(WPPSIIII: Wechsler Preschool and Primary Scale of Intelligence (, and3rd ed.). Applied Neuropsychology 10 (3) 188–190 (2003).1997). Construction and assessment of classification rules. New York: Wiley.(Statistics and the theory of measurement. Journal of the Royal Statistical Society, Series A (Statistics in Society) 159 445–492 (1996).1986). Educating the developmentally disabled: Meeting the needs of children and families. San Diego, CA: CollegeHill Press., & (The Kinetic Family Drawing technique: A review of the literature. Journal of Personality Assessment 62 440–464 (1994)., and2005). Business forecasting. Upper Saddle River, NJ: Pearson., & (1992). User's guide for the Strong Interest Inventory. Stanford, CA: Stanford University Press.(Large sample properties of generalized method of moments estimators. Econometrica 50 1029–1054 (1982).1993). Sample survey methods and theory(2 vols.). New York: Wiley. (Original work published 1953), , & (Harcourt Assessment: http://www.harcourtassessment.comHarcourt/PsychCorp: http://www.harcourt.com/bu_info/harcourt_assessment.html1976). Modern factor analysis ((3rd ed.). Chicago: University of Chicago Press.Harmonic mean article: http://en.wikipedia.org/wiki/Harmonic_meanThe method of least squares and some alternatives. Part I, II, II, IV, V, VI. International Statistical Review 42 147–174 42 235–264 43 1–44 43 125–190 43 269–272 44 113–159 (1974–1976).1975). Causal powers. Oxford, UK: Blackwell., & (A new distributionfree quantile estimator. Biometrika 69 635–640 (1982)., and1963). Children's drawings as measures of intellectual maturity: A revision and extension of the Goodenough Harris Draw a Man Test. New York: Harcourt, Brace & World.(Content analysis of secondary data: A study of courage in managerial decisionmaking. Journal of Business Ethics 34 (3/4) 191–208 (2001).Fat, four eyed, and female: Stereotypes of obesity, glasses, and gender. Journal of Applied Social Psychology 12 503–516 (1982)., , and1993). Multivariate analysis of variance. In L. Edwards (Ed.), Applied analysis of variance in behavioral science (pp. 255–296). New York: Marcel Dekker.(2001). A primer of multivariate statistics ((3rd ed.). Mahwah, NJ: Erlbaum.1985). Manual for the selfperception profile for children. Denver, CO: University of Denver.(A mosaic of television ratings. American Statistician 38 32–35 (1984)., and1998). An evaluation of structured abstracts in journals published by the British Psychological Society. Retrieved from http://cogprints.org/587/00/199801001.html(1979). Exploratory data analysis (pp. 16–19). Beverly Hills, CA: Sage., & (2002). A Bayesian guide for the Unified Model for assessing cognitive abilities: Blending theory with practicality. Unpublished doctoral dissertation, University of Illinois, UrbanaChampaign, Department of Statistics.(Principal curves. Journal of the American Statistical Association 84 502–516 (1989)., and1990). Generalized additive models. London: Chapman & Hall., & (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer., , & (Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 97–109 (1970).On the prediction of phenomena from qualitative data and the quantification of qualitative data from the mathematicostatistical point of view. Annals of the Institute of Statistical Mathematics 5 121–143 (1952).Issues in measuring reliability: Correlation versus percentage of agreement. Written Communication 16 (3) 354–367 (1999)., and1985). Review of Preschool Language Assessment Instrument, Experimental Edition. In J. V. Mitchell (Ed.), The ninth mental measurements yearbook (pp. 1190–1192). Lincoln: University of Nebraska Press.(1973). Statistics. New York: Holt, Rinehart and Winston.(1981). Statistics ((3rd ed.). New York: Holt, Rinehart and Winston.1994). Statistics ((5th ed.). Orlando, FL: Harcourt Brace.The development of a scoring system for the Gerontological Apperception Test. Journal of Clinical Psychology 58 471–478 (2002)., , , , andA proof of the conjecture that the TukeyKramer multiple comparisons procedure is conservative. Annals of Statistics 12 61–75 (1984).The maximum familywise error rate of Fisher's least significant difference test. Journal of the American Statistical Association 81 1000–1004 (1986).Multivariate normal plotting. Applied Statistics 17 157–161 (1968).The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5 475–492 (1976).Sample selection bias as a specification error. Econometrica 47 153–161 (1979).1985). Statistical methods for metaanalysis. Orlando, FL: Academic Press., & (2006). Basic statistics for the behavioral sciences. Boston: Houghton Mifflin.(1970). The semantic differential and attitude research. In G. Summers (Ed.), Attitude measurement (pp. 235–253). Chicago: Rand McNally.(1983). Practitioner's guide to the Edwards Personal Preference Schedule. Springfield, IL: Charles C Thomas.(1952). Fundamentals of concept formation in empirical science. Chicago: University of Chicago Press.(Adaptive and conventional versions of the DAT: The first complete test battery comparison. Applied Psychological Measurement 13 363–371 (1989)., , , and1990). Practical sampling. Newbury Park, CA: Sage.(Henry A. Murray and Christiana D. Morgan biographies: http://www.mhhe.com/mayfieldpub/psychtesting/profiles/Invariant tests for multivariate normality: A critical review. Statistical Papers 43 467–503 (2002).1991). Introduction to the theory of neural computation. Reading, MA: AddisonWesley., , & (Heubert, J. P., & Hauser, R. M. (Eds.). (1999). High stakes: Testing for tracking, promotion, and graduation. Washington, DC: National Academies Press.Heyde, C. C., & Seneta, C. (Eds.). (2001). Statisticians of the centuries. New York: Springer.1973). Iowa Test of Basic Skills: Manual for administrators, supervisors, and counselors. Chicago: Houghton Mifflin.(1986). Iowa Test of Basic Skills: Forms G and H. Chicago: Riverside.(1995). Concepts in probability and stochastic modeling. Belmont, CA: Duxbury., & (Hilsenroth, M. J., & Segal, D. L. (Eds.). (2004). Comprehensive handbook of psychological assessment (Volume 2): Personality assessment. Hoboken, NJ: Wiley.1993). The measurement of attitudes. In A. H. Eagly & S. Chaiken (Eds.), The psychology of attitudes (pp. 23–87). Orlando, FL: Harcourt Brace Jovanovich.(Violin plots: A box plotdensity trace synergism. American Statistician 52 181–184 (1998)., andLocal solutions in the estimation of growth mixture models. Psychological Methods 11 36–53 (2006)., andEstimation of causal effects using propensity score weighting: An application to data on right heart catheterization. Health Services and Outcomes Research Methodology 2 259–278 (2001)., andHistoriometrics articles: http://psychology.ucdavis.edu/Simonton/dkspdf.html (especially publications 204 and 257, which provide overviews)Hoaglin, D. C., Mosteller, F., & Tukey, J. W. (Eds.). (1983). Understanding robust and exploratory data analysis. New York: Wiley.A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75 800–803 (1988).1987). Multiple comparison procedures. New York: Wiley., & (The efficiency of some nonparametric competitors of the ttest. Annals of Mathematical Statistics 27 324–335 (1956)., and1970). Basic concepts of probability and statistics (, & (2nd ed.). San Francisco: HoldenDay.Averages on the move. Mathematics Magazine 58 151–156 (1985)., andThe abuse of power: The pervasive fallacy of power calculations for data analysis. American Statistician 55 19–24 (2001)., and1995). SelfDetermination Knowledge Scale: Forms A & B. Austin, TX: ProEd., , & (Constructing and reading mosaicplots. Computational Statistics and Data Analysis 43 565–580 (2003).2001). Probability and statistical inference (, & (6th ed.). Upper Saddle River, NJ: Prentice Hall.Scan statistics to scan markers for susceptibility genes. Proceedings of the National Academy of Sciences USA 97 (17) 9615–9617 (2000)., and1996). Holden Psychological Screening Inventory (HPSI). North Tonawanda, NY: MultiHealth Systems.(2000). Application of the construct heuristic to the screening of psychopathology: The Holden Psychological Screening Inventory (HPSI). In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy (pp. 97–121). Boston: Kluwer.(1992). Assessing psychopathology using the Basic Personality Inventory: Rationale and applications. In J. Rosen & P. McReynolds (Eds.), Advances in psychological assessment (8, pp. 165–199). New York: Plenum., & (1973). Nonparametric statistical methods. New York: Wiley., & (1999). Nonparametric statistical methods (, & (2nd ed.). New York: Wiley., and