Correlation and Regression Analysis

Overview

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 ...

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Overview

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.

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Product Details

Meet the Author

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 (co-edited with Walter Stephan, Teachers College Press, 2004). He is also editor of three four-volume sets in the series, Sage Benchmarks in Social Research Methods: Selecting Research Methods (2008); Data Collection (2010); and Quantitative Research Methods (2011).

Burke Johnson (Ph D, College of Education, University of Georgia) is a Professor in the Professional Studies Department at the University of South Alabama. He also has graduate degrees in psychology, sociology, and public administration, which give him a multidisciplinary perspective on research methodology. He has published in the Educational Researcher; Evaluation and Program Planning; Evaluation Review; Journal of Mixed Methods Research; Journal of Educational Psychology; and Quantity and Quality. He was guest editor for a special issue of Research in the Schools (http://www.msera.org/rits_131.htm) and a special issue of American Behavioral Scientist both focusing on mixed methods research. He was on the editorial board of the Educational Researcher and was an Associate Editor of the Journal of Mixed Methods Research. Burke was also coauthor of Research Methods, Design, and Analysis; Dictionary of Statistics and Methodology: A Nontechnical Guide for the Social Sciences; and Oxford Handbook of Multiple and Mixed Methods Research.

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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 Le Breton
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 Mc Williams 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 De Maris
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 Le Breton
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 Mac Callum 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

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