Fundamentals of Multiple Regression

Fundamentals of Multiple Regression covers the use of multiple regression in social science research.  The course covers enough of the underlying statistical material for the intelligent use of the technique.  The approach is informal and applied rather than emphasizing algebraic manipulation or proofs.  Participants can use their preferred statistical software (SPSS, SAS, Stata, or R) for the data analysis exercises.


This course will run over 4¾ days, in four sessions per day.  


Level 2 - runs over 5 days

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 29 January 2024 - Friday 2 February 2024
Course status: Course completed (no new applicants)
Week 2
About this course: 

This course is designed for those who have limited knowledge and experience with multivariate statistical techniques and are seeking the knowledge and skills to use multiple regression for research at a post-graduate level, and/or to publish in professional research journals.  Particular attention is given to the application of multiple regression to substantive problems in the social and behavioral sciences.  (See Course Program below.)


By the end of the course, you will understand the principles of multiple regression, and be able to conduct regression analyses, interpret the results, obtain regression diagnostics to test the underlying model assumptions, and write-up the results for publication.  The course notes provide instructions for undertaking regression analysis with the major statistical packages (SPSS, SAS, Stata).


Data sets are provided; however, participants may also use their own data.  There will be time for individual consultations about research projects in which regression is to be used.

The course provides the foundations necessary to progress to Applied Multiple Regression Analysis, and to subsequent advanced-level courses in structural equation modelling  (SEM) and multi-level analysis.

Course syllabus: 

A typical class session entails an interactive lecture with an exercise in applied data analysis.  In addition, there are three class sessions which are designated for applied data analysis.


Course Program—Session by Session


Day 1
Session 1: Introduction, Review of statistical basics and stats software.
Session 2: Bivariate regression
Session 3: Statistical inferences: null-hypothesis significance testing, confidence intervals and effect size.Regression output from SPSS, Stata, SAS and R.
Session 4: Multiple regression (ordinary least-squares – OLS)


Day 2
Session 1: Model building strategies (model specification and variable selection).
Session 2: Applied data analysis exercises
Session 3: Regression with nominal (“categorical”) independent variables.
Session 4: Regression with ordinal variables, including “ordered categorical” rating scales.


Day 3
Session 1: Non-linear (“curvilinear”) functional forms using polynomial regression.
Session 2: Variable transformations to re-express data (logs, “normal scores”)  
Session 3: Regression diagnostics
Session 4: Applied data analysis exercises


Day 4
Session 1: Multicollinearity
Session 2: Missing Data   
Session 3: Hierarchical Regression and Model Comparisons
Session 4: Applied data analysis exercises


Day 5
Session 1: Binary (Dichotomous) logistic regression
Session 2: Writing up and presenting research based on regression analysis.
Session 3: Unplanned session (questions, queries, conundrums, curiosities, quibbles)




Course format: 

This course will run in a classroom.


You will need to bring your own laptop with your preferred software preloaded (SPSS, SAS, Stata, or R)


Recommended Background: 

Participants should understand introductory statistics and have experience with a statistical software package.   More specifically, participants should have:

(a)    Completed an introductory statistics course, such as Fundamentals of Statistics, or have knowledge of descriptive statistics (central tendency, dispersion, Z-scores), bivariate analysis (cross tabs and/or bivariate correlations) and be familiar with the principles of null-hypothesis significance testing.

(b)    Knowledge and experience with a statistical package (such as SPSS, Stata, or SAS) sufficient to read (or import) data, obtain frequencies and descriptive statistics, recode variables and compute (“generate”) new variables.


Participants are encouraged to use the statistical package of their choice.  The course notes provide the commands necessary to use the techniques using the major software packages.


Recommended Texts: 

Detailed course notes are provided to all participants.  
No further reference material is required; however, a readable text is: Timothy Z. Keith, Multiple Regression and Beyond.

Course fees
Non Member: 
Full time student Member: 

Q: Are there any prerequisites for this course?
A: You should have completed an introductory statistics course – see recommended background


Q: My institution uses SAS software. Can I use it in the course?
A: You can use any statistical software package that you have on your laptop. The course notes provide instructions for three packages (SPSS, SAS and Stata); however, David is familiar with several other packages and will assist as best he can.


Participant feedback: 


I found the workshop extremely valuable. I am now confident in reading the results of regression analysis and knowing when to perform the tests. David is extremely knowledgeable and presented all of the information in ways I could understand.


It was incredibly helpful and very well explained - using a variety of methods which helped to properly learn the information and cater to different learning styles


Will help me read & understand research. On my may to being able to conduct my research


To be honest it opened my mind to contain things that will help throughout my research


David was excellent. Honestly, I have had many stats teachers in the past and David was so clear great communication skills.


Gave me a sense of confidence in the statistical methods, and some helpful tips in the procedures to help in my work


Helps me understand the foundation to build my model and my next phase of study.


The course was true to label; it covered the fundamentals of regression and provided opportunities to learn how to interpret software outputs.


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

makes learning fun.


The instructor’s bound, book-length course notes will serve as the course text.