Fundamentals of Multiple Regression: Online

This course provides an introduction to, and the fundamentals of multiple regression, covering enough of the statistical material for the intelligent use of the technique.  The approach is informal and applied rather than emphasising proofs of relevant theorems. 

 

This course will be run over 5 days in three sessions per day:

  • 10.00 am - 11.30 am - Session 1
  • 12.00 pm - 1.30 pm - Session 2
  • 2.30 pm - 4.00 pm - Session 3

 

Exercises will be provided, and 20-30 minute individual consultation sessions can be scheduled for each participant by appointment.

 

Courses will run on Australian Eastern Daylight Time (GMT +11)

(ie Melbourne, Sydney, Canberra daylight savings time)

 

 
Level 2 - runs over 5 days
Instructor: 
David Gow is a consultant in social sciece research methods.   He has taught in the ACSPRI Summer and Winter Programs for 30 years.
About this course: 

Particular attention is given to the application of multiple regression to substantive problems in the social sciences. By the end of the course, you will have a knowledge of the principles of multiple regression, and the ability to conduct regression analyses, interpret the results, and to use regression diagnostics to test the underlying model assumptions, and write-up the results for publication.

 

Participants who are considering regression analysis of their own data are encouraged and there will be time for individual consultations.
This course provides the foundations necessary for progression to ‘Applied Multiple Regression Analysis’, and to subsequent advanced-level courses in structural equation modelling and log-linear modelling.

 

The course is designed for those who have limited knowledge and experience with multivariate statistical techniques, and seeking the knowledge and skills to use multiple regression for research at a post-graduate level / publication standard.  The course notes provide instructions for all the major statistical packages (SPSS, SAS, Stata and R.)

 

Course syllabus: 

The  Fundamentals of Multiple Regression  course covers the following 14 topics.

 

Module A:  Fundamentals

1.    Bivariate Regression

2.    Review of Classical Hypothesis Testing  (“Null-Hypothesis Significance Testing”), including the role and un-importance of p-values and the importance of effect size.

3.    Review of Regression Output for SPSS, SAS, Stata and R  -- what it means, how it is calculated, how it is interpreted, and how researchers might use it.

 

Module B:  Multiple Regression

4.    Multiple Regression --  detailed examination of how it works and the interpretation of output.

5.    Multiple Regression with Nominal-Level Variables.  Nominal–level variables (also known as unordered categorical variables) – creating and using "dummy variables" in regression (such as gender, country of birth, suburb of residence, etc).

6.    Multiple Regression with Ordinal-level Variables  -- for example, incorporating responses on a 5 or 7-point Likert-like rating scale in a regression model

 

Module C:  Extensions of OLS: Logistic Regression and Polynomial Regression

7.    Regression with Binary (Dichotomous) Dependent Variables -- an introduction to (binary) logistic regression with binary (dichotomous) dependent variables

8.    Non-linearity with Polynomial Regression

 

Module D:  Regression Diagnostics, Transformations and Polynomial Regression

9.    Variable Transformations to Re-Express Data – transformations towards normality and assessing normality.

10.  Regression Diagnostics & Functional Form – detecting outliers,  non-linearity

11.  Missing Values – treatment of missing values using deletion methods (pairwise and list wise deletion), single and multiple imputation

12.  Multicollinearity -  impact on regression results and measures of multicollinearity

 

Module E:   Applying Multiple Regression

13.  Model Building with Regression – a step-by-step guide to analysing data using regression

14.  Writing up Regression Analyses – presenting your results in theses and academic journals.

Course format: 

This course is being offered 'live' online using Zoom.

 

You will need a computer and your preferred software, SPSS, SAS, Stata or R.

 

Course Program—Session by Session

Day 1    
Session 1:    Bivariate regression
Session 2:    Significance testing and effect sizes
Session 3:    Regression Output with SPSS, SAS, Stata and R

 

Day 2    
Session 1:    Multiple regression
Session 2:    Regression with nominal (categorical) variables
Session 3:    Regression with ordinal-level variables

 

Day 3
Session 1:    Logistic regression for binary dependent variables
Session 2:    Non-linearity with polynomial regression
Session 3:    Variable transformations to re-express data

 

Day 4    
Session 1:    Regression diagnostics
Session 2:    Missing values
Session 3:    Multicollinearity

 

Day 5    
Session 1:    Model building with regression
Session 2:    Writing-up regression for publication
Session 3:    Unplanned Session (questions, queries, conundrums?)

Recommended Background: 

Participants should have completed an introductory statistics course covering at least some of the syllabus of Introduction to Statistics. A significant part of the course is the translation of the principles of multiple regression to practical data analysis using a statistical package. Some experience with a statistical package, such as SPSS, Stata or SAS, or the spreadsheet Excel is desirable.

Recommended Texts: 

Nearly all good social statistics texts treat regression analysis and thus constitute suitable reference material.  The following short monographs provide short, clear and technically sound coverage.

 

  • Lewis-Beck, M., Applied Regression: An Introduction, Sage, 1980
  • Achen, C., Interpreting and Using Regression, Sage, 1982.
  • Berry, William and Stanley Feldman, Multiple Regression in Practice, Sage, 1985.
FAQ: 

Q: Are there any prerequisites for this course?

A: You should have completed an introductory statistics course – see recommended background

Participant feedback: 

Would thoroughly recommend the course. (Winter 2021)

 

Will help me read & understand research. On my may to being able to conduct my research (Winter 2017)

 

To be honest it opened my mind to contain things that will help throughout my research (Summer 2017)

 

David was excellent. Honestly, I have had many stats teachers in the past and David was so clear great communication skills. (Winter 2016)

 

Gave me a sense of confidence in the statistical methods, and some helpful tips in the procedures to help in my work (Summer 2016)

 

Helps me understand the foundation to build my model and my next phase of study. (Summer 2015)

 

The course was true to label; it covered the fundamentals of regression and provided opportunities to learn how to interpret software outputs. (Winter 2014)

 

David Gow is a consultant in social sciece research methods.   He has taught in the ACSPRI Summer and Winter Programs for 30 years.
makes learning fun. (Winter 2014)

Program where course next likely to be offered: 
Online Summer Program 2022
Notes: 

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