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Correlation and Regression Analysis

Correlation and Regression Analysis

Four Volume Set
Edited by:

1 632 pages | SAGE Publications Ltd
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 part in this collection:

Part I. Regression and Its Correlational Foundations and Concomitants
Part II. Linear Regression Designs and Model Building
Part III. Inherently Nonlinear Models: Log-Linear Models And Probit And Logistic Regression
Part IV. Multi-Level Regression Modeling (MLM)
Part V. Exploratory and Confirmatory Factor Analysis and Latent Class Modeling
Part VI. Structural Equation Modeling (SEM)
Karl Pearson
Report on Certain Enteric Fever Inoculation Statistics
Harry Shannon
A Statistical Note on Karl Pearson's 1904 Meta-Analysis
Herbert David
An Historical Note on Zero Correlation and Independence
Herbert Simon
Spurious Correlation
A Causal Interpretation

Andrew Gilpin
r equivalent, Meta-Analysis and Robustness
An Empirical Examination of Rosenthal and Rubin's Effect-Size Indicator

Carl Huberty
Multiple Correlation versus Multiple Regression
Patricia Grambsch
Regression to the Mean, Murder Rates and Shall-Issue Laws
Aiyou Chen, Thomas Bengtsson and Tin Kam Ho
A Regression Paradox for Linear Models
Sufficient Conditions and Relation to Simpson's Paradox

Gregory Knofczynski and Daniel Mundfrom
Sample Sizes When Using Multiple Linear Regression for Prediction
James Algina, H Joanne Keselman and Randall Penfield
Confidence Intervals for and Effect Size Measures in Multiple Linear Regression
Jeff Johnson and James LeBreton
History and Use of Relative Importance Indices in Organizational Research
Ulrike Grömping
Variable Importance Assessment in Regression
Linear Regression versus the Random Forest

Dongyu Lin, Dean Foster and Lyle Ungar
VIF Regression
A Fast Regression Algorithm for Large Data

Lynn Friedman and Melanie Wall
Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression
Rand Wilcox
Modern Insights about Pearson's Correlation and Least Squares Regression
Jacob Cohen
Multiple Regression as a General Data-Analytic System
James Jaccard et al
Multiple Regression Analyses in Clinical Child and Adolescent Psychology
Stephen Morgan
Methodologist as Arbitrator
Five Models for Black-White Differences in the Causal Effect of Expectations on Attainment

Kosuke Imai
Multivariate Regression Analysis for the Item-Count Technique
Sokbae Lee, Myung Hwan Seo and Youngki Shin
Testing for Threshold Effects in Regression Models
A Colin Cameron, Jonah Gelbach and Douglas Miller
Robust Inference with Multiway Clustering
Richard Berk
An Introduction to Ensemble Methods for Data Analysis
Brian McWilliams and Giovanni Montana
Sparse Partial Least Squares Regression for Online Variable Selection with Multivariate Data Streams
Bernice Pescosolido and Jonathan Kelley
Confronting Sociological Theory with Data
Regression Analysis, Goodman's Log-Linear Models and Comparative Research

Henry Lynn
Suppression and Confounding in Action
Alfred DeMaris
Explained Variance in Logistic Regression
A Monte Carlo Study of Proposed Measures

Tue Tjur
Co-Efficients of Determination in Logistic Regression Models - A New Proposal
The Co-Efficient of Discrimination

Iain Pardoe and R Dennis Cook
A Graphical Method for Assessing the Fit of a Logistic Regression Model
Scott Tonidandel and James LeBreton
Determining the Relative Importance of Predictors in Logistic Regression
An Extension of Relative Weight Analysis

Aaron Taylor, Stephen West and Leona Aiken
Loss of Power in Logistic, Ordinal Logistic and Probit Regression When an Outcome Variable Is Coarsely Categorized
Richard Williams
Using Heterogeneous Choice Models to Compare Logit and Probit Co-Efficients across Groups
Ola Caster et al
Large-Scale Regression-Based Pattern Discovery
The Example of Screening the WHO Global Drug Safety Database

Chandan Reddy and Mohammad Aziz
Modeling Local Non-Linear Correlations Using Subspace Principal Curves
Carrie Petrucci
A Primer for Social Worker Researchers on How to Conduct a Multinomial Logistic Regression
Andrew Grogan-Kaylor and Melanie Otis
The Effect of Childhood Maltreatment on Adult Criminality
A Tobit Regression Analysis

Theodor Harder and Franz Urban Pappi
Multiple-Level Regression Analysis of Survey and Ecological Data
Robert Bickel
Broadening the Scope of Regression Analysis
Jeffrey Kahn
Multilevel Modeling
Overview and Applications to Research in Counseling Psychology

Buster Smith
Acceptance of Other Religions in the United States
An HLM Analysis of Variability across Congregations

George Leckie et al
Multilevel Modeling of Social Segregation
Asko Tolvanen et al
A New Approach for Estimating a Non-Linear Growth Component in Multilevel Modeling
Philippa Clarke and Blair Wheaton
Addressing Data Sparseness in Contextual Population Research
Using Cluster Analysis to Create Synthetic Neighborhoods

Larry Hedges
Effect Sizes in Three-Level Cluster-Randomized Experiments
L L Thurstone
Multiple Factor Analysis
Robin Henson and J Kyle Roberts
Use of Exploratory Factor Analysis in Published Research
Common Errors and Some Comment on Improved Practice

Kristine Hogarty et al
The Quality of Factor Solutions in Exploratory Factor Analysis
The Influence of Sample Size, Communality and Over-Determination

Pamela Paxton et al
Monte Carlo Experiments
Design and Implementation

Thomas Schmitt and Daniel Sass
Rotation Criteria and Hypothesis-Testing for Exploratory Factor Analysis
Implications for Factor Pattern Loadings and Inter-Factor Correlations

Thomas Schmitt
Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis
Dennis Jackson, J Arthur Gillaspy and Rebecca Purc-Stephenson
Reporting Practices in Confirmatory Factor Analysis
An Overview and Some Recommendations

Timothy Levine et al
The Desirability of Using Confirmatory Factor Analysis on Published Scales
J Petter Gustavsson et al
Measurement Invariance of Personality Traits from a Five-Factor Model Perspective
Multigroup Confirmatory Factor Analyses of the HP5 Inventory

Dimiter Dimitrov
Comparing Groups on Latent Variables
A Structural Equation Modeling Approach

David Flora, Eli Finkel and Vangie Foshee
Higher Order Factor Structure of a Self-Control Test
Evidence from Confirmatory Factor Analysis with Polychoric Correlations

Randall Schumacker and Susan Beyerlein
Confirmatory Factor Analysis with Different Correlation Types and Estimation Methods
Susan Neely-Barnes
Latent Class Models in Social Work
Sarah Schmiege, Michael Levin and Angela Bryan
Regression Mixture Models of Alcohol Use and Risky Sexual Behavior among Criminally Involved Adolescents
Sewall Wright
Correlation and Causation
Peter Bentler
Can Scientifically Useful Hypotheses Be Tested with Correlations?
Kenneth Bollen
Latent Variables in Psychology and the Social Sciences
Karl Gustav Joreskog
A General Method for Analysis of Covariance Structures
John Ferron and Melinda Hess
Estimation in SEM
A Concrete Example

James Graham
The General Linear Model as Structural Equation Modeling
Matthew Martens and Richard Haase
Advanced Applications of Structural Equation Modeling in Counseling Psychology Research
Robert MacCallum and James Austin
Applications of Structural Equation Modeling in Psychological Research
Jeffrey Meehan and Gregory Stuart
Using Structural Equation Modeling with Forensic Samples
Pul-Wa Lei and Qiong Wu
Introduction to Structural Equation Modeling
Issues and Practical Considerations

Jodie Ullman
Structural Equation Modeling
Reviewing the Basics and Moving forward

Amy Henley, Christopher Shook and Mark Peterson
The Presence of Equivalent Models in Strategic Management Research Using Structural Equation Modeling
Assessing and Addressing the Problem

Paul Allison
Missing Data Techniques for Structural Equation Modeling
Alan Acock
Working with Missing Values
Kenneth Bollen
Modeling Strategies
In Search of the Holy Grail

Peter Bentler
On Tests and Indices for Evaluating Structural Models
Gregory R Hancock and Ralph O Mueller
The Reliability Paradox in Assessing Structural Relations within Covariance Structure Models
Christopher Hopwood
Moderation and Mediation in Structural Equation Modeling
Applications for Early Intervention Research

Jeffrey Edwards and Lisa Lambert
Methods for Integrating Moderation and Mediation
A General Analytical Framework Using Moderated Path Analysis

Randall Schumaker
Latent Variable Interaction Modeling
Guan-Chyun Lin et al
Structural Equation Models of Latent Interactions
Clarification of Orthogonalizing and Double-Mean-Centering Strategies

Aurora Jackson, Jeong-Kyun Choi and Peter Bentler
Parenting Efficacy and the Early School Adjustment of Poor and Near-Poor Black Children
Natasha Bowen, Gary Bowen and William Ware
Neighborhood Social Disorganization, Families and the Educational Behavior of Adolescents
Steven Robbins et al
Intervention Effects on College Performance and Retention as Mediated by Motivational, Emotional and Social Control Factors
Integrated Meta-Analytic Path Analyses


W. Paul Vogt

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... More About Author

R. Burke Johnson

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 and completed a similar guest editorship for the American... More About Author