W. Paul Vogt is Emeritus Professor of Research Methods and Evaluation at Illinois State University where he won both teaching and research awards. He specializes in methodological choice and program evaluation and is particularly interested in ways to integrate multiple methods. His other books include: Tolerance & Education: Learning to Live with Diversity and Difference (Sage Publications, 1998); Quantitative Research Methods for Professionals (Allyn & Bacon, 2007); Education Programs for Improving Intergroup Relations (coedited with Walter Stephan, Teachers College Press, 2004). He is also editor of four 4-volume sets in the series, Sage Benchmarks in Social Research Methods: Selecting Research Methods (2008); Data Collection (2010); Quantitative Research Methods (2011); and, with Burke Johnson, Correlation and Regression Analysis (2012).His most recent publications include the coauthored When to Use What Research Design (2012) and Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods Approaches (2014).Burke Johnson is a professor in the Professional Studies Department at the University of South Alabama. His PhD is from the REMS (research, evaluation, measurement, and statistics) program in the College of Education at the University of Georgia. He also has graduate degrees in psychology, sociology, and public administration, which have provided him with a multidisciplinary perspective on research methodology. He was guest editor for a special issue of Research in the Schools focusing on mixed research (available online at www.msera.org/rits_131.htm) and completed a similar guest editorship for the American Behavioral Scientist. He was an associate editor of the Journal of Mixed Methods Research. Burke is first author of Educational Research: Quantitative, Qualitative, and Mixed Approaches (Sage, 2014, 5th edition); second author of Research Methods, Design, and Analysis (Pearson, 2014, 12th edition); coeditor (with Sharlene Hesse-Biber) of The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (2015); coeditor (with Paul Vogt) of Correlation and Regression Analysis (2012); and associate editor of The SAGE Glossary of the Social and Behavioral Sciences (2009).

• VOLUME ONE: REGRESSION AND ITS CORRELATIONAL FOUNDATIONS AND CONCOMITANTSReport on Certain Enteric Fever Inoculation Statistics - Karl Pearson A Statistical Note on Karl Pearson′s 1904 Meta-Analysis - Harry Shannon An Historical Note on Zero Correlation and Independence - Herbert David Spurious Correlation - Herbert Simon A Causal Interpretation r equivalent, Meta-Analysis and Robustness - Andrew Gilpin An Empirical Examination of Rosenthal and Rubin′s Effect-Size Indicator Multiple Correlation versus Multiple Regression - Carl Huberty Regression to the Mean, Murder Rates and Shall-Issue Laws - Patricia Grambsch A Regression Paradox for Linear Models - Aiyou Chen, Thomas Bengtsson and Tin Kam Ho Sufficient Conditions and Relation to Simpson′s ParadoxSample Sizes When Using Multiple Linear Regression for Prediction - Gregory Knofczynski and Daniel Mundfrom Confidence Intervals for and Effect Size Measures in Multiple Linear Regression - James Algina, H Joanne Keselman and Randall Penfield History and Use of Relative Importance Indices in Organizational Research - Jeff Johnson and James LeBreton Variable Importance Assessment in Regression - Ulrike Grömping Linear Regression versus the Random ForestVIF Regression - Dongyu Lin, Dean Foster and Lyle Ungar A Fast Regression Algorithm for Large DataGraphical Views of Suppression and Multicollinearity in Multiple Linear Regression - Lynn Friedman and Melanie Wall Modern Insights about Pearson′s Correlation and Least Squares Regression - Rand Wilcox LINEAR REGRESSION DESIGNS AND MODEL-BUILDINGMultiple Regression as a General Data-Analytic System - Jacob Cohen Multiple Regression Analyses in Clinical Child and Adolescent Psychology - James Jaccard et alMethodologist as Arbitrator - Stephen Morgan Five Models for Black-White Differences in the Causal Effect of Expectations on AttainmentMultivariate Regression Analysis for the Item-Count Technique - Kosuke Imai Testing for Threshold Effects in Regression Models - Sokbae Lee, Myung Hwan Seo and Youngki Shin Robust Inference with Multiway Clustering - A Colin Cameron, Jonah Gelbach and Douglas Miller An Introduction to Ensemble Methods for Data Analysis - Richard Berk Sparse Partial Least Squares Regression for Online Variable Selection with Multivariate Data Streams - Brian McWilliams and Giovanni Montana VOLUME TWO: FACTOR ANALYSIS, REGRESSION DIAGNOSTICS, AND MODEL BUILDINGINHERENTLY NON-LINEAR MODELS: LOG-LINEAR MODELS AND PROBIT AND LOGISTIC REGRESSIONConfronting Sociological Theory with Data - Bernice Pescosolido and Jonathan Kelley Regression Analysis, Goodman′s Log-Linear Models and Comparative ResearchSuppression and Confounding in Action - Henry Lynn Explained Variance in Logistic Regression - Alfred DeMaris A Monte Carlo Study of Proposed MeasuresCo-Efficients of Determination in Logistic Regression Models - A New Proposal - Tue Tjur The Co-Efficient of DiscriminationA Graphical Method for Assessing the Fit of a Logistic Regression Model - Iain Pardoe and R Dennis Cook Determining the Relative Importance of Predictors in Logistic Regression - Scott Tonidandel and James LeBreton An Extension of Relative Weight Analysis Loss of Power in Logistic, Ordinal Logistic and Probit Regression When an Outcome Variable Is Coarsely Categorized - Aaron Taylor, Stephen West and Leona Aiken Using Heterogeneous Choice Models to Compare Logit and Probit Co-Efficients across Groups - Richard Williams Large-Scale Regression-Based Pattern Discovery - Ola Caster et al The Example of Screening the WHO Global Drug Safety DatabaseModeling Local Non-Linear Correlations Using Subspace Principal Curves - Chandan Reddy and Mohammad Aziz A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression - Carrie Petrucci The Effect of Childhood Maltreatment on Adult Criminality - Andrew Grogan-Kaylor and Melanie Otis A Tobit Regression AnalysisMULTILEVEL REGRESSION MODELING (MLM)Multiple-Level Regression Analysis of Survey and Ecological Data - Theodor Harder and Franz Urban Pappi Broadening the Scope of Regression Analysis - Robert Bickel Multilevel Modeling - Jeffrey Kahn Overview and Applications to Research in Counseling PsychologyAcceptance of Other Religions in the United States - Buster Smith An HLM Analysis of Variability across CongregationsMultilevel Modeling of Social Segregation - George Leckie et alA New Approach for Estimating a Non-Linear Growth Component in Multilevel Modeling - Asko Tolvanen et alAddressing Data Sparseness in Contextual Population Research - Philippa Clarke and Blair Wheaton Using Cluster Analysis to Create Synthetic NeighborhoodsEffect Sizes in Three-Level Cluster-Randomized Experiments - Larry Hedges VOLUME THREE: DATA TRANSFORMATIONS, CURVILINEAR REGRESSION, AND LOGISTIC AGGRESSIONEXPLORATORY AND CONFIRMATORY FACTOR ANALYSISMultiple Factor Analysis - L L Thurstone Use of Exploratory Factor Analysis in Published Research - Robin Henson and J Kyle Roberts Common Errors and Some Comment on Improved PracticeThe Quality of Factor Solutions in Exploratory Factor Analysis - Kristine Hogarty et al The Influence of Sample Size, Communality and Over-DeterminationMonte Carlo Experiments - Pamela Paxton et al Design and Implementation Rotation Criteria and Hypothesis-Testing for Exploratory Factor Analysis - Thomas Schmitt and Daniel Sass Implications for Factor Pattern Loadings and Inter-Factor CorrelationsCurrent Methodological Considerations in Exploratory and Confirmatory Factor Analysis - Thomas Schmitt Reporting Practices in Confirmatory Factor Analysis - Dennis Jackson, J Arthur Gillaspy and Rebecca Purc-Stephenson An Overview and Some RecommendationsThe Desirability of Using Confirmatory Factor Analysis on Published Scales - Timothy Levine et alMeasurement Invariance of Personality Traits from a Five-Factor Model Perspective - J Petter Gustavsson et alMultigroup Confirmatory Factor Analyses of the HP5 Inventory Comparing Groups on Latent Variables - Dimiter Dimitrov A Structural Equation Modeling ApproachHigher Order Factor Structure of a Self-Control Test - David Flora, Eli Finkel and Vangie Foshee Evidence from Confirmatory Factor Analysis with Polychoric CorrelationsConfirmatory Factor Analysis with Different Correlation Types and Estimation Methods - Randall Schumacker and Susan Beyerlein Latent Class Models in Social Work - Susan Neely-Barnes Regression Mixture Models of Alcohol Use and Risky Sexual Behavior among Criminally Involved Adolescents - Sarah Schmiege, Michael Levin and Angela Bryan VOLUME FOUR: MULTI-LEVEL REGRESSION MODELING, STRUCTURAL EQUATION MODELING AND MIXED REGRESSIONSTRUCTURAL EQUATION MODELING (SEM) AND LATENT CLASS MODELINGCorrelation and Causation - Sewall Wright Can Scientifically Useful Hypotheses Be Tested with Correlations? - Peter Bentler Latent Variables in Psychology and the Social Sciences - Kenneth Bollen A General Method for Analysis of Covariance Structures - Karl Gustav Joreskog Estimation in SEM - John Ferron and Melinda Hess A Concrete Example The General Linear Model as Structural Equation Modeling - James Graham Advanced Applications of Structural Equation Modeling in Counseling Psychology Research - Matthew Martens and Richard Haase Applications of Structural Equation Modeling in Psychological Research - Robert MacCallum and James Austin Using Structural Equation Modeling with Forensic Samples - Jeffrey Meehan and Gregory Stuart Introduction to Structural Equation Modeling - Pul-Wa Lei and Qiong Wu Issues and Practical ConsiderationsStructural Equation Modeling - Jodie Ullman Reviewing the Basics and Moving forward The Presence of Equivalent Models in Strategic Management Research Using Structural Equation Modeling - Amy Henley, Christopher Shook and Mark Peterson Assessing and Addressing the ProblemMissing Data Techniques for Structural Equation Modeling - Paul Allison Working with Missing Values - Alan Acock Modeling Strategies - Kenneth Bollen In Search of the Holy GrailOn Tests and Indices for Evaluating Structural Models - Peter Bentler The Reliability Paradox in Assessing Structural Relations within Covariance Structure Models - Gregory R Hancock and Ralph O Mueller Moderation and Mediation in Structural Equation Modeling - Christopher Hopwood Applications for Early Intervention Research Methods for Integrating Moderation and Mediation - Jeffrey Edwards and Lisa Lambert A General Analytical Framework Using Moderated Path AnalysisLatent Variable Interaction Modeling - Randall Schumaker Structural Equation Models of Latent Interactions - Guan-Chyun Lin et al Clarification of Orthogonalizing and Double-Mean-Centering StrategiesParenting Efficacy and the Early School Adjustment of Poor and Near-Poor Black Children - Aurora Jackson, Jeong-Kyun Choi and Peter Bentler Neighborhood Social Disorganization, Families and the Educational Behavior of Adolescents - Natasha Bowen, Gary Bowen and William Ware Intervention Effects on College Performance and Retention as Mediated by Motivational, Emotional and Social Control Factors - Steven Robbins et al Integrated Meta-Analytic Path Analyses

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W. Paul Vogt is Emeritus Professor of Research Methods and Evaluation at Illinois State University where he won both teaching and research awards. He specializes in methodological choice and program evaluation and is particularly interested in ways to integrate multiple methods. His other books include: Tolerance & Education: Learning to Live with Diversity and Difference (Sage Publications, 1998); Quantitative Research Methods for Professionals (Allyn & Bacon, 2007); Education Programs for Improving Intergroup Relations (coedited with Walter Stephan, Teachers College Press, 2004). He is also editor of four 4-volume sets in the series, Sage Benchmarks in Social Research Methods: Selecting Research Methods (2008); Data Collection (2010); Quantitative Research Methods (2011); and, with Burke Johnson, Correlation and Regression Analysis (2012).His most recent publications include the coauthored When to Use What Research Design (2012) and Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods Approaches (2014).Burke Johnson is a professor in the Professional Studies Department at the University of South Alabama. His PhD is from the REMS (research, evaluation, measurement, and statistics) program in the College of Education at the University of Georgia. He also has graduate degrees in psychology, sociology, and public administration, which have provided him with a multidisciplinary perspective on research methodology. He was guest editor for a special issue of Research in the Schools focusing on mixed research (available online at www.msera.org/rits_131.htm) and completed a similar guest editorship for the American Behavioral Scientist. He was an associate editor of the Journal of Mixed Methods Research. Burke is first author of Educational Research: Quantitative, Qualitative, and Mixed Approaches (Sage, 2014, 5th edition); second author of Research Methods, Design, and Analysis (Pearson, 2014, 12th edition); coeditor (with Sharlene Hesse-Biber) of The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (2015); coeditor (with Paul Vogt) of Correlation and Regression Analysis (2012); and associate editor of The SAGE Glossary of the Social and Behavioral Sciences (2009).

• VOLUME ONE: REGRESSION AND ITS CORRELATIONAL FOUNDATIONS AND CONCOMITANTSReport on Certain Enteric Fever Inoculation Statistics - Karl Pearson A Statistical Note on Karl Pearson′s 1904 Meta-Analysis - Harry Shannon An Historical Note on Zero Correlation and Independence - Herbert David Spurious Correlation - Herbert Simon A Causal Interpretation r equivalent, Meta-Analysis and Robustness - Andrew Gilpin An Empirical Examination of Rosenthal and Rubin′s Effect-Size Indicator Multiple Correlation versus Multiple Regression - Carl Huberty Regression to the Mean, Murder Rates and Shall-Issue Laws - Patricia Grambsch A Regression Paradox for Linear Models - Aiyou Chen, Thomas Bengtsson and Tin Kam Ho Sufficient Conditions and Relation to Simpson′s ParadoxSample Sizes When Using Multiple Linear Regression for Prediction - Gregory Knofczynski and Daniel Mundfrom Confidence Intervals for and Effect Size Measures in Multiple Linear Regression - James Algina, H Joanne Keselman and Randall Penfield History and Use of Relative Importance Indices in Organizational Research - Jeff Johnson and James LeBreton Variable Importance Assessment in Regression - Ulrike Grömping Linear Regression versus the Random ForestVIF Regression - Dongyu Lin, Dean Foster and Lyle Ungar A Fast Regression Algorithm for Large DataGraphical Views of Suppression and Multicollinearity in Multiple Linear Regression - Lynn Friedman and Melanie Wall Modern Insights about Pearson′s Correlation and Least Squares Regression - Rand Wilcox LINEAR REGRESSION DESIGNS AND MODEL-BUILDINGMultiple Regression as a General Data-Analytic System - Jacob Cohen Multiple Regression Analyses in Clinical Child and Adolescent Psychology - James Jaccard et alMethodologist as Arbitrator - Stephen Morgan Five Models for Black-White Differences in the Causal Effect of Expectations on AttainmentMultivariate Regression Analysis for the Item-Count Technique - Kosuke Imai Testing for Threshold Effects in Regression Models - Sokbae Lee, Myung Hwan Seo and Youngki Shin Robust Inference with Multiway Clustering - A Colin Cameron, Jonah Gelbach and Douglas Miller An Introduction to Ensemble Methods for Data Analysis - Richard Berk Sparse Partial Least Squares Regression for Online Variable Selection with Multivariate Data Streams - Brian McWilliams and Giovanni Montana VOLUME TWO: FACTOR ANALYSIS, REGRESSION DIAGNOSTICS, AND MODEL BUILDINGINHERENTLY NON-LINEAR MODELS: LOG-LINEAR MODELS AND PROBIT AND LOGISTIC REGRESSIONConfronting Sociological Theory with Data - Bernice Pescosolido and Jonathan Kelley Regression Analysis, Goodman′s Log-Linear Models and Comparative ResearchSuppression and Confounding in Action - Henry Lynn Explained Variance in Logistic Regression - Alfred DeMaris A Monte Carlo Study of Proposed MeasuresCo-Efficients of Determination in Logistic Regression Models - A New Proposal - Tue Tjur The Co-Efficient of DiscriminationA Graphical Method for Assessing the Fit of a Logistic Regression Model - Iain Pardoe and R Dennis Cook Determining the Relative Importance of Predictors in Logistic Regression - Scott Tonidandel and James LeBreton An Extension of Relative Weight Analysis Loss of Power in Logistic, Ordinal Logistic and Probit Regression When an Outcome Variable Is Coarsely Categorized - Aaron Taylor, Stephen West and Leona Aiken Using Heterogeneous Choice Models to Compare Logit and Probit Co-Efficients across Groups - Richard Williams Large-Scale Regression-Based Pattern Discovery - Ola Caster et al The Example of Screening the WHO Global Drug Safety DatabaseModeling Local Non-Linear Correlations Using Subspace Principal Curves - Chandan Reddy and Mohammad Aziz A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression - Carrie Petrucci The Effect of Childhood Maltreatment on Adult Criminality - Andrew Grogan-Kaylor and Melanie Otis A Tobit Regression AnalysisMULTILEVEL REGRESSION MODELING (MLM)Multiple-Level Regression Analysis of Survey and Ecological Data - Theodor Harder and Franz Urban Pappi Broadening the Scope of Regression Analysis - Robert Bickel Multilevel Modeling - Jeffrey Kahn Overview and Applications to Research in Counseling PsychologyAcceptance of Other Religions in the United States - Buster Smith An HLM Analysis of Variability across CongregationsMultilevel Modeling of Social Segregation - George Leckie et alA New Approach for Estimating a Non-Linear Growth Component in Multilevel Modeling - Asko Tolvanen et alAddressing Data Sparseness in Contextual Population Research - Philippa Clarke and Blair Wheaton Using Cluster Analysis to Create Synthetic NeighborhoodsEffect Sizes in Three-Level Cluster-Randomized Experiments - Larry Hedges VOLUME THREE: DATA TRANSFORMATIONS, CURVILINEAR REGRESSION, AND LOGISTIC AGGRESSIONEXPLORATORY AND CONFIRMATORY FACTOR ANALYSISMultiple Factor Analysis - L L Thurstone Use of Exploratory Factor Analysis in Published Research - Robin Henson and J Kyle Roberts Common Errors and Some Comment on Improved PracticeThe Quality of Factor Solutions in Exploratory Factor Analysis - Kristine Hogarty et al The Influence of Sample Size, Communality and Over-DeterminationMonte Carlo Experiments - Pamela Paxton et al Design and Implementation Rotation Criteria and Hypothesis-Testing for Exploratory Factor Analysis - Thomas Schmitt and Daniel Sass Implications for Factor Pattern Loadings and Inter-Factor CorrelationsCurrent Methodological Considerations in Exploratory and Confirmatory Factor Analysis - Thomas Schmitt Reporting Practices in Confirmatory Factor Analysis - Dennis Jackson, J Arthur Gillaspy and Rebecca Purc-Stephenson An Overview and Some RecommendationsThe Desirability of Using Confirmatory Factor Analysis on Published Scales - Timothy Levine et alMeasurement Invariance of Personality Traits from a Five-Factor Model Perspective - J Petter Gustavsson et alMultigroup Confirmatory Factor Analyses of the HP5 Inventory Comparing Groups on Latent Variables - Dimiter Dimitrov A Structural Equation Modeling ApproachHigher Order Factor Structure of a Self-Control Test - David Flora, Eli Finkel and Vangie Foshee Evidence from Confirmatory Factor Analysis with Polychoric CorrelationsConfirmatory Factor Analysis with Different Correlation Types and Estimation Methods - Randall Schumacker and Susan Beyerlein Latent Class Models in Social Work - Susan Neely-Barnes Regression Mixture Models of Alcohol Use and Risky Sexual Behavior among Criminally Involved Adolescents - Sarah Schmiege, Michael Levin and Angela Bryan VOLUME FOUR: MULTI-LEVEL REGRESSION MODELING, STRUCTURAL EQUATION MODELING AND MIXED REGRESSIONSTRUCTURAL EQUATION MODELING (SEM) AND LATENT CLASS MODELINGCorrelation and Causation - Sewall Wright Can Scientifically Useful Hypotheses Be Tested with Correlations? - Peter Bentler Latent Variables in Psychology and the Social Sciences - Kenneth Bollen A General Method for Analysis of Covariance Structures - Karl Gustav Joreskog Estimation in SEM - John Ferron and Melinda Hess A Concrete Example The General Linear Model as Structural Equation Modeling - James Graham Advanced Applications of Structural Equation Modeling in Counseling Psychology Research - Matthew Martens and Richard Haase Applications of Structural Equation Modeling in Psychological Research - Robert MacCallum and James Austin Using Structural Equation Modeling with Forensic Samples - Jeffrey Meehan and Gregory Stuart Introduction to Structural Equation Modeling - Pul-Wa Lei and Qiong Wu Issues and Practical ConsiderationsStructural Equation Modeling - Jodie Ullman Reviewing the Basics and Moving forward The Presence of Equivalent Models in Strategic Management Research Using Structural Equation Modeling - Amy Henley, Christopher Shook and Mark Peterson Assessing and Addressing the ProblemMissing Data Techniques for Structural Equation Modeling - Paul Allison Working with Missing Values - Alan Acock Modeling Strategies - Kenneth Bollen In Search of the Holy GrailOn Tests and Indices for Evaluating Structural Models - Peter Bentler The Reliability Paradox in Assessing Structural Relations within Covariance Structure Models - Gregory R Hancock and Ralph O Mueller Moderation and Mediation in Structural Equation Modeling - Christopher Hopwood Applications for Early Intervention Research Methods for Integrating Moderation and Mediation - Jeffrey Edwards and Lisa Lambert A General Analytical Framework Using Moderated Path AnalysisLatent Variable Interaction Modeling - Randall Schumaker Structural Equation Models of Latent Interactions - Guan-Chyun Lin et al Clarification of Orthogonalizing and Double-Mean-Centering StrategiesParenting Efficacy and the Early School Adjustment of Poor and Near-Poor Black Children - Aurora Jackson, Jeong-Kyun Choi and Peter Bentler Neighborhood Social Disorganization, Families and the Educational Behavior of Adolescents - Natasha Bowen, Gary Bowen and William Ware Intervention Effects on College Performance and Retention as Mediated by Motivational, Emotional and Social Control Factors - Steven Robbins et al Integrated Meta-Analytic Path Analyses

Select Guide Rating

W. Paul Vogt is Emeritus Professor of Research Methods and Evaluation at Illinois State University where he won both teaching and research awards. He specializes in methodological choice and program evaluation and is particularly interested in ways to integrate multiple methods. His other books include: Tolerance & Education: Learning to Live with Diversity and Difference (Sage Publications, 1998); Quantitative Research Methods for Professionals (Allyn & Bacon, 2007); Education Programs for Improving Intergroup Relations (coedited with Walter Stephan, Teachers College Press, 2004). He is also editor of four 4-volume sets in the series, Sage Benchmarks in Social Research Methods: Selecting Research Methods (2008); Data Collection (2010); Quantitative Research Methods (2011); and, with Burke Johnson, Correlation and Regression Analysis (2012).His most recent publications include the coauthored When to Use What Research Design (2012) and Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods Approaches (2014).Burke Johnson is a professor in the Professional Studies Department at the University of South Alabama. His PhD is from the REMS (research, evaluation, measurement, and statistics) program in the College of Education at the University of Georgia. He also has graduate degrees in psychology, sociology, and public administration, which have provided him with a multidisciplinary perspective on research methodology. He was guest editor for a special issue of Research in the Schools focusing on mixed research (available online at www.msera.org/rits_131.htm) and completed a similar guest editorship for the American Behavioral Scientist. He was an associate editor of the Journal of Mixed Methods Research. Burke is first author of Educational Research: Quantitative, Qualitative, and Mixed Approaches (Sage, 2014, 5th edition); second author of Research Methods, Design, and Analysis (Pearson, 2014, 12th edition); coeditor (with Sharlene Hesse-Biber) of The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry (2015); coeditor (with Paul Vogt) of Correlation and Regression Analysis (2012); and associate editor of The SAGE Glossary of the Social and Behavioral Sciences (2009).

• VOLUME ONE: REGRESSION AND ITS CORRELATIONAL FOUNDATIONS AND CONCOMITANTSReport on Certain Enteric Fever Inoculation Statistics - Karl Pearson A Statistical Note on Karl Pearson′s 1904 Meta-Analysis - Harry Shannon An Historical Note on Zero Correlation and Independence - Herbert David Spurious Correlation - Herbert Simon A Causal Interpretation r equivalent, Meta-Analysis and Robustness - Andrew Gilpin An Empirical Examination of Rosenthal and Rubin′s Effect-Size Indicator Multiple Correlation versus Multiple Regression - Carl Huberty Regression to the Mean, Murder Rates and Shall-Issue Laws - Patricia Grambsch A Regression Paradox for Linear Models - Aiyou Chen, Thomas Bengtsson and Tin Kam Ho Sufficient Conditions and Relation to Simpson′s ParadoxSample Sizes When Using Multiple Linear Regression for Prediction - Gregory Knofczynski and Daniel Mundfrom Confidence Intervals for and Effect Size Measures in Multiple Linear Regression - James Algina, H Joanne Keselman and Randall Penfield History and Use of Relative Importance Indices in Organizational Research - Jeff Johnson and James LeBreton Variable Importance Assessment in Regression - Ulrike Grömping Linear Regression versus the Random ForestVIF Regression - Dongyu Lin, Dean Foster and Lyle Ungar A Fast Regression Algorithm for Large DataGraphical Views of Suppression and Multicollinearity in Multiple Linear Regression - Lynn Friedman and Melanie Wall Modern Insights about Pearson′s Correlation and Least Squares Regression - Rand Wilcox LINEAR REGRESSION DESIGNS AND MODEL-BUILDINGMultiple Regression as a General Data-Analytic System - Jacob Cohen Multiple Regression Analyses in Clinical Child and Adolescent Psychology - James Jaccard et alMethodologist as Arbitrator - Stephen Morgan Five Models for Black-White Differences in the Causal Effect of Expectations on AttainmentMultivariate Regression Analysis for the Item-Count Technique - Kosuke Imai Testing for Threshold Effects in Regression Models - Sokbae Lee, Myung Hwan Seo and Youngki Shin Robust Inference with Multiway Clustering - A Colin Cameron, Jonah Gelbach and Douglas Miller An Introduction to Ensemble Methods for Data Analysis - Richard Berk Sparse Partial Least Squares Regression for Online Variable Selection with Multivariate Data Streams - Brian McWilliams and Giovanni Montana VOLUME TWO: FACTOR ANALYSIS, REGRESSION DIAGNOSTICS, AND MODEL BUILDINGINHERENTLY NON-LINEAR MODELS: LOG-LINEAR MODELS AND PROBIT AND LOGISTIC REGRESSIONConfronting Sociological Theory with Data - Bernice Pescosolido and Jonathan Kelley Regression Analysis, Goodman′s Log-Linear Models and Comparative ResearchSuppression and Confounding in Action - Henry Lynn Explained Variance in Logistic Regression - Alfred DeMaris A Monte Carlo Study of Proposed MeasuresCo-Efficients of Determination in Logistic Regression Models - A New Proposal - Tue Tjur The Co-Efficient of DiscriminationA Graphical Method for Assessing the Fit of a Logistic Regression Model - Iain Pardoe and R Dennis Cook Determining the Relative Importance of Predictors in Logistic Regression - Scott Tonidandel and James LeBreton An Extension of Relative Weight Analysis Loss of Power in Logistic, Ordinal Logistic and Probit Regression When an Outcome Variable Is Coarsely Categorized - Aaron Taylor, Stephen West and Leona Aiken Using Heterogeneous Choice Models to Compare Logit and Probit Co-Efficients across Groups - Richard Williams Large-Scale Regression-Based Pattern Discovery - Ola Caster et al The Example of Screening the WHO Global Drug Safety DatabaseModeling Local Non-Linear Correlations Using Subspace Principal Curves - Chandan Reddy and Mohammad Aziz A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression - Carrie Petrucci The Effect of Childhood Maltreatment on Adult Criminality - Andrew Grogan-Kaylor and Melanie Otis A Tobit Regression AnalysisMULTILEVEL REGRESSION MODELING (MLM)Multiple-Level Regression Analysis of Survey and Ecological Data - Theodor Harder and Franz Urban Pappi Broadening the Scope of Regression Analysis - Robert Bickel Multilevel Modeling - Jeffrey Kahn Overview and Applications to Research in Counseling PsychologyAcceptance of Other Religions in the United States - Buster Smith An HLM Analysis of Variability across CongregationsMultilevel Modeling of Social Segregation - George Leckie et alA New Approach for Estimating a Non-Linear Growth Component in Multilevel Modeling - Asko Tolvanen et alAddressing Data Sparseness in Contextual Population Research - Philippa Clarke and Blair Wheaton Using Cluster Analysis to Create Synthetic NeighborhoodsEffect Sizes in Three-Level Cluster-Randomized Experiments - Larry Hedges VOLUME THREE: DATA TRANSFORMATIONS, CURVILINEAR REGRESSION, AND LOGISTIC AGGRESSIONEXPLORATORY AND CONFIRMATORY FACTOR ANALYSISMultiple Factor Analysis - L L Thurstone Use of Exploratory Factor Analysis in Published Research - Robin Henson and J Kyle Roberts Common Errors and Some Comment on Improved PracticeThe Quality of Factor Solutions in Exploratory Factor Analysis - Kristine Hogarty et al The Influence of Sample Size, Communality and Over-DeterminationMonte Carlo Experiments - Pamela Paxton et al Design and Implementation Rotation Criteria and Hypothesis-Testing for Exploratory Factor Analysis - Thomas Schmitt and Daniel Sass Implications for Factor Pattern Loadings and Inter-Factor CorrelationsCurrent Methodological Considerations in Exploratory and Confirmatory Factor Analysis - Thomas Schmitt Reporting Practices in Confirmatory Factor Analysis - Dennis Jackson, J Arthur Gillaspy and Rebecca Purc-Stephenson An Overview and Some RecommendationsThe Desirability of Using Confirmatory Factor Analysis on Published Scales - Timothy Levine et alMeasurement Invariance of Personality Traits from a Five-Factor Model Perspective - J Petter Gustavsson et alMultigroup Confirmatory Factor Analyses of the HP5 Inventory Comparing Groups on Latent Variables - Dimiter Dimitrov A Structural Equation Modeling ApproachHigher Order Factor Structure of a Self-Control Test - David Flora, Eli Finkel and Vangie Foshee Evidence from Confirmatory Factor Analysis with Polychoric CorrelationsConfirmatory Factor Analysis with Different Correlation Types and Estimation Methods - Randall Schumacker and Susan Beyerlein Latent Class Models in Social Work - Susan Neely-Barnes Regression Mixture Models of Alcohol Use and Risky Sexual Behavior among Criminally Involved Adolescents - Sarah Schmiege, Michael Levin and Angela Bryan VOLUME FOUR: MULTI-LEVEL REGRESSION MODELING, STRUCTURAL EQUATION MODELING AND MIXED REGRESSIONSTRUCTURAL EQUATION MODELING (SEM) AND LATENT CLASS MODELINGCorrelation and Causation - Sewall Wright Can Scientifically Useful Hypotheses Be Tested with Correlations? - Peter Bentler Latent Variables in Psychology and the Social Sciences - Kenneth Bollen A General Method for Analysis of Covariance Structures - Karl Gustav Joreskog Estimation in SEM - John Ferron and Melinda Hess A Concrete Example The General Linear Model as Structural Equation Modeling - James Graham Advanced Applications of Structural Equation Modeling in Counseling Psychology Research - Matthew Martens and Richard Haase Applications of Structural Equation Modeling in Psychological Research - Robert MacCallum and James Austin Using Structural Equation Modeling with Forensic Samples - Jeffrey Meehan and Gregory Stuart Introduction to Structural Equation Modeling - Pul-Wa Lei and Qiong Wu Issues and Practical ConsiderationsStructural Equation Modeling - Jodie Ullman Reviewing the Basics and Moving forward The Presence of Equivalent Models in Strategic Management Research Using Structural Equation Modeling - Amy Henley, Christopher Shook and Mark Peterson Assessing and Addressing the ProblemMissing Data Techniques for Structural Equation Modeling - Paul Allison Working with Missing Values - Alan Acock Modeling Strategies - Kenneth Bollen In Search of the Holy GrailOn Tests and Indices for Evaluating Structural Models - Peter Bentler The Reliability Paradox in Assessing Structural Relations within Covariance Structure Models - Gregory R Hancock and Ralph O Mueller Moderation and Mediation in Structural Equation Modeling - Christopher Hopwood Applications for Early Intervention Research Methods for Integrating Moderation and Mediation - Jeffrey Edwards and Lisa Lambert A General Analytical Framework Using Moderated Path AnalysisLatent Variable Interaction Modeling - Randall Schumaker Structural Equation Models of Latent Interactions - Guan-Chyun Lin et al Clarification of Orthogonalizing and Double-Mean-Centering StrategiesParenting Efficacy and the Early School Adjustment of Poor and Near-Poor Black Children - Aurora Jackson, Jeong-Kyun Choi and Peter Bentler Neighborhood Social Disorganization, Families and the Educational Behavior of Adolescents - Natasha Bowen, Gary Bowen and William Ware Intervention Effects on College Performance and Retention as Mediated by Motivational, Emotional and Social Control Factors - Steven Robbins et al Integrated Meta-Analytic Path Analyses

Select Guide Rating