Applied Multiple Regression Analysis

Note: A laptop (with either SPSS, Stata or SAS installed/licensed) will be required for this course. If you have difficulty obtaining a laptop with suitable software, please contact us at info@acspri.org.au.

The course is 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. In addition, the course provides the necessary background in statistical methods for those seeking to advance to courses dealing with structural equation modelling (SEM).

 

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.

 

 

 

 

While this course will take place in a computer lab with most relParticipants are welcome to bring their own laptops pre-loaded with whatever statistical software package they use (SPSS, SAS, Stata, or R) and, where appropriate, their own datasets.   Participants will have an opportunity to analyse their own data and discuss the output.  

 

 

 

 
Level 3 - runs over 5 days
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 dates: Monday 10 February 2014 - Friday 14 February 2014
Course status: Course completed (no new applicants)
Week: 
Week 3
Recommended Background: 

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).  

Recommended Texts: 

The instructor’s course notes, which will be distributed to all participants, serve as the course text.   An additional, optional resource is Timothy Keith, Multiple Regression and Beyond (Allyn & Bacon, 2005), which covers multiple regression and basic structural equation modeling.

 

Course fees
Member: 
$1,710
Non Member: 
$3,130
Full time student Member: 
$1,710