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# Correlation and Regression Analysis

Four Volume Set

**Edited by:**

- W Paul Vogt - Illinois State University, USA
- Burke Johnson - University of South Alabama, USA

September 2012 | 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 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

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VOLUME ONE: REGRESSION AND ITS CORRELATIONAL FOUNDATIONS AND CONCOMITANTS

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

LINEAR REGRESSION DESIGNS AND MODEL-BUILDING

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

VOLUME TWO: FACTOR ANALYSIS, REGRESSION DIAGNOSTICS, AND MODEL BUILDING

INHERENTLY NON-LINEAR MODELS: LOG-LINEAR MODELS AND PROBIT AND LOGISTIC REGRESSION

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

MULTILEVEL REGRESSION MODELING (MLM)

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

VOLUME THREE: DATA TRANSFORMATIONS, CURVILINEAR REGRESSION, AND LOGISTIC AGGRESSION

EXPLORATORY AND CONFIRMATORY FACTOR ANALYSIS

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

VOLUME FOUR: MULTI-LEVEL REGRESSION MODELING, STRUCTURAL EQUATION MODELING AND MIXED REGRESSION

STRUCTURAL EQUATION MODELING (SEM) AND LATENT CLASS MODELING

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