Applied Statistical Procedures

This is an intermediate, applied course covering a range of the most commonly used statistical procedures. It aims to provide participants with an ability to understand, run and interpret these procedures.This course will further enhance your ability to understand research based literature where these procedures were employed.


Level 2 - runs over 5 days

Dr Mark Griffin is the Director of Insight Research Services Associated (, where Insight consists of Insight Business Analytics, and Insight Training and Events. Insight Business Analytics providing training and consulting across the areas of business, statistics and IT. Insight Business Analytics is a Business Partner of Microsoft and a member of the IBM's Partner World and Google's Partner Program. Insight Training and Events teaches 30 qualifications from Certificate I to Graduate Diploma level across the areas of business, information technology, health and social services, and civil and environmental engineering.

Mark is also an Industry Fellow with the School of Business, University of Queensland, and has established and written training materials for several of their courses in Business Analytics. Mark serves on the Executive Committee for the Statistical Society of Australia, was the Co-Chair of their National Conference in 2021, and is the Founding Chair of their Section for Business Analytics. Mark is also the Founding Chair of the Business Analytics Special Interest Group within the International Institute of Business Analysis. To date he has presented over 100 two-day and 40 five-day workshops in statistics around Australia

Course dates: Monday 25 September 2017 - Friday 29 September 2017
Course status: Course completed (no new applicants)
Week 1
About this course: 

This is an intermediate level course that covers a range of the most used statistical procedures. The level falls between ‘Fundamentals of Statistics’, and the more detailed single procedure based courses covering topics such as SEM, Multiple Regression, and Factor Analysis. This course is taught from an applied prospective, with questions encouraged. The statistical package employed will be SPSS. No prior knowledge of SPSS is required.


You will be exposed to a variety of research scenarios and to the logic of statistical procedure selection and application. This course will provide a good foundation for progression to the more detailed courses in (Multiple) Regression, Factor Analysis, SEM and Latent Variables using Mplus. On completing this course you should be able to read and understand literature where these procedures are reported, select the appropriate statistical procedure for research, run the procedure, and report the results from an informed base of understanding.


The target audience for this course range from Qualitative researchers wanting to gain Quantitative skills, to Quantitative researchers wanting to broaden their understanding across procedures, or to become more comfortable with covariance prior to taking SEM.

Course syllabus: 

Day 1
The context of quantative research in relation to qualitative research. The language of quantitative research, and the required fundamentals of SPSS.


Day 2
Reliability, Correlations, Controlling for Confounding Variables, Chi Squares, and T-Tests.


Day 3
ANOVAS, ANCOVAS, Factoral ANOVAS, MANOVAS and Non-Parametric Tests.


Day 4
Simple Regression, Multiple Regression, Discriminate Analysis and Factor Analysis.


Day 5
Testing Normality, Data Transformations, Validity, Reporting and Ethics; plus individual sessions.



The procedures that will be covered will include the following:

  • The frequency based statistics of Chi Square Goodness of Fit and Chi Square Test of Association.
  • The Parametric test of difference statistics of T-tests, ANOVA, ANCOVA, MANOVA, and MANCOVA. Factorial analysis with multiple independent variables will also be covered along with Repeated Measures ANOVA.
  • The Non-Parametric test of difference statistics of Mann-Whitney, Wilcoxon, Friedmans Analysis of Variance, and Kruskal Wallis.
  • The statistics to predict and to explain variance of Simple Regression, Multiple Regression, Discriminant Analysis and Multiple Discriminant Analysis.
  • The data reduction technique of Factor Analysis
  • Power, the data and statistics that are most powerful, and techniques for increasing statistical power
  • How to determine the best procedure for the demands of the research
  • Data transformation to increase power and allow parametric procedures to be employed when data can be appropriately adjusted.
  • The important interplay between effect size and significance.
  • The integration of statistical results into reports.
Course format: 

This course will take place in a computer lab unless otherwise notified.

Recommended Background: 

Participants should have an understanding of elementary statistics equivalent to the syllabus of Fundamentals of Statistics. This lab based course requires students to follow instruction in running and interpreting a range of statistical procedures.  It is assumed that participants will have little or no familiarity with at least some of the procedures presented.

The statistical package employed will be SPSS. No prior knowledge of SPSS is required.

Course fees
Non Member: 
Full time student Member: 

Q: Do I have to have had prerequisites to do this course?

A: Yes, you will have been expected to have some knowledge of elementary statistics equivalent to ACSPRI's Fundamentals of Statistics Course.


Q: Do I need to know SPSS?

A: No prior knowledge of SPSS is required.


Participant feedback: 

It has filled in a lot of gaps in my knowledge. I had used most of the techniques and stats before but never had an understanding of why I used them for different data sets. (Summer, 2017)


Able to apply to practice immediately (Summer 2017)


The instructor was great the best I have had by far. Great pacing and cover of important tests. (Spring 2016)


Filled in lots of gaps in my knowledge in a systematic way. Use of SPSS on the screen by the lecturer meant it was a hands on course. (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)


It covered a variety of statistical techniques that I wanted to learn. It's highly practical and applied and plenty of real life examples were given by Gordon. (Spring 2015)


Great idea to have consultation available at the end as well. (Winter 2015)


Great course - the practical examples & exercises were especially useful, as was Gordan’s common sense advice on research design and analysis. (Summer 2015)

Spring Program 2017

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

Supported by: