Read more
Informationen zum Autor W. Paul Vogt is Professor Emeritus of Research Methods and Evaluation at Illinois State University where he has won both teaching and research awards. He is the editor of SAGE Quantitative Research Methods (2011) also in this series. His other books include: Dictionary of Statistics and Methodology (Fourth Edition, SAGE, 2011); Tolerance & Education: Learning to Live with Diversity and Difference (SAGE, 1997); Education Programs for Improving Intergroup Relations (co-edited with Walter Stephan, Teachers College Press, 2004) and Quantitative Research Methods for Professionals (Allyn & Bacon, 2006). Klappentext 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 Zusammenfassung CRA is of great substantive importance; 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 exciting new collection. Inhaltsverzeichnis 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-Whit...
