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Applied Multiple Regression Analysis
A course designed for those with previous knowledge and experience with regression analysis, who seek to enhance their current knowledge and extend their proficiency in applying the technique.
Instructor
David John Gow is a consultant in research methods and statistics and their application in the social sciences. He has taught in many ACSPRI Summer and Winter Programs
Course Level
The main focus of the course is on the practical side of using regression to address common methodological, statistical and data issues that arise in the social sciences. The course begins with a brief review of the principles of multiple regression. It then examines the specific issues and problems that arise from its application, considers appropriate approaches to address the problems, and how to interpret the results that it produces.
These issues include:
• regression diagnostics to detect potential violations of multiple regression's assumptions
• how to identify the presence of multicollinearity and what to do about it
• establishing the existence of non-linear relationships and identifying the transformation procedures necessary to apply to the data
• how to treat missing data, particularly the use of multiple imputation (MI) approaches
• how to incorporate and interpret interactions and moderator variables
• how to model and test for mediating variables, including the classic Baron-Kenny approach, the Sobel test, and more modern approaches
• how to estimate model with binary and ordinal dependent variables (including binary logistic regression and ordered probit)
• how to estimate models with simultaneity (“two-way” causation) using two-stage least squares.
The course concludes by examining the relationship between multiple regression and structural equation modeling (SEM) with latent variables and compares the results of regression and SEM analyses.The course provides the necessary background in statistical methods for those seeking to advance to courses dealing with structural equation modelling (SEM).
Knowledge of elementary statistical techniques and some knowledge of the principles of multiple regression at a level comparable to that provided by the Fundamentals of Multiple Regression course, and basic knowledge and skills of a statistical package (SPSS, SAS, Stata or R).
An additional, optional resource which covers multiple regression and basic structural equation modeling is:
- Timothy Keith, Multiple Regression and Beyond (Allyn & Bacon, 2005),
The instructor's bound, book length course notes will serve as the course texts.
David did an absolutely wonderful job of explaining regression
The course was very thorough and the course teacher very passionate. I found it useful and the notes really helpful.
The course content is very useful for developing my PHD research work analysis models and also gave me ideas for more new analysis on existing data sets.
Nice balance of activities.
Course notes provided were very well written and comprehensive. David gave us a good mix of theoretical and practical information.
It is just what I needed at this time, formulate models for my research, got the idea now – happy.