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.
Dr Gordon Emmerson is a specialist in quantitative research. He taught undergraduate and postgraduate statistics programs at Victoria University within the Psychology Department, where he currently holds the position of Honory Fellow. He coordinated a major in Social Research Methods. Gordon was employed as a statistical/methods advisor to university staff in the US at Kansas State University in the late 1980s. He is an experienced group facilitator and regularly conducts workshops across a range of topic areas. He is an experienced user of data management and statistical packages including SPSSwin, Excel and Access. He has also undertaken a number of consultancies in quantitative research in the health and education sector.
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.
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.
This course will take place in a computer lab. Equipment will be provided.
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.
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.
Gave me a sense of confidence in the statistical methods, and some helpful tips in the procedures to help in my work. (Summer 2016)
This course was exactly what I needed and pitched at the right level for me. Gordon is a patient and interesting lecturer. (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)
The course has exceeded my expectations + will be incredibly useful (Spring 2015)
I know how to do quite a lot of the analysis techniques already but i had gaps in knowledge. The course was great for me, it answered and filled in all those gaps. (Winter 2015)
It was designed with lots of examples which helped to understand the theory better. (Winter 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)
He covered underlying principles how they look in practice, the ”art” of doing statistics as well as the techniques. (Summer 2015)
Has shown me why certain tests are better than others with different research design. (Summer 2015)
Balance between theory & practice was excellent. Theory was contextualised with real life examples. (Winter 2014)
The instructor's bound, book length course notes will serve as the course text.