Correlation and Regression Analysis

It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression analysis (CRA) and their siblings and offspring. CRA are fundamental analytic tools in fields like sociology, economics and political science as well as applied disciplines such as marketing, nursing, education and social work. The subject is of great substantive importance; therefore, distinguished editors, W. Paul Vogt and R. Burke Johnson, have ordered the growing research literature on the use of CRA according to its natural steps. Each step in this logical progression constitutes a volume in this collection: Volume One: Regression and Its Correlational Foundations and Concomitants Volume Two: Factor Analysis, Regression Diagnostics, and Model Building Volume Three: Data Transformations, Curvilinear Regression, and Logistic Regression Volume Four: Multi-Level Regression Modeling, Structural Equation Modeling and Mixed Regression

Table of Contents:
VOLUME ONE: REGRESSION AND ITS CORRELATIONAL FOUNDATIONS AND CONCOMITANTS Report 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 Paradox Sample 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 Forest VIF Regression - Dongyu Lin, Dean Foster and Lyle Ungar A Fast Regression Algorithm for Large Data Graphical 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-BUILDING Multiple Regression as a General Data-Analytic System - Jacob Cohen Multiple Regression Analyses in Clinical Child and Adolescent Psychology - James Jaccard et al Methodologist as Arbitrator - Stephen Morgan Five Models for Black-White Differences in the Causal Effect of Expectations on Attainment Multivariate 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 BUILDING INHERENTLY NON-LINEAR MODELS: LOG-LINEAR MODELS AND PROBIT AND LOGISTIC REGRESSION Confronting Sociological Theory with Data - Bernice Pescosolido and Jonathan Kelley Regression Analysis, Goodman′s Log-Linear Models and Comparative Research Suppression and Confounding in Action - Henry Lynn Explained Variance in Logistic Regression - Alfred DeMaris A Monte Carlo Study of Proposed Measures Co-Efficients of Determination in Logistic Regression Models - A New Proposal - Tue Tjur The Co-Efficient of Discrimination A 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 Database Modeling 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 Analysis MULTILEVEL 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 Psychology Acceptance of Other Religions in the United States - Buster Smith An HLM Analysis of Variability across Congregations Multilevel Modeling of Social Segregation - George Leckie et al A New Approach for Estimating a Non-Linear Growth Component in Multilevel Modeling - Asko Tolvanen et al Addressing Data Sparseness in Contextual Population Research - Philippa Clarke and Blair Wheaton Using Cluster Analysis to Create Synthetic Neighborhoods Effect Sizes in Three-Level Cluster-Randomized Experiments - Larry Hedges VOLUME THREE: DATA TRANSFORMATIONS, CURVILINEAR REGRESSION, AND LOGISTIC AGGRESSION EXPLORATORY AND CONFIRMATORY FACTOR ANALYSIS Multiple 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 Practice The Quality of Factor Solutions in Exploratory Factor Analysis - Kristine Hogarty et al The Influence of Sample Size, Communality and Over-Determination Monte 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 Correlations Current 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 Recommendations The Desirability of Using Confirmatory Factor Analysis on Published Scales - Timothy Levine et al Measurement Invariance of Personality Traits from a Five-Factor Model Perspective - J Petter Gustavsson et al Multigroup Confirmatory Factor Analyses of the HP5 Inventory Comparing Groups on Latent Variables - Dimiter Dimitrov A Structural Equation Modeling Approach Higher Order Factor Structure of a Self-Control Test - David Flora, Eli Finkel and Vangie Foshee Evidence from Confirmatory Factor Analysis with Polychoric Correlations Confirmatory 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 REGRESSION STRUCTURAL EQUATION MODELING (SEM) AND LATENT CLASS MODELING Correlation 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 Considerations Structural 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 Problem Missing Data Techniques for Structural Equation Modeling - Paul Allison Working with Missing Values - Alan Acock Modeling Strategies - Kenneth Bollen In Search of the Holy Grail On 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 Analysis Latent Variable Interaction Modeling - Randall Schumaker Structural Equation Models of Latent Interactions - Guan-Chyun Lin et al Clarification of Orthogonalizing and Double-Mean-Centering Strategies Parenting 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