In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"
How does one decide which statistical procedure is the most appropriate? What do all the sections of the output mean?
This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed.
Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.
A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course. You will need basic R proficiency to carry out the practical work; we will teach only the additional R commands needed for these analyses.
Do you need to have previous experience using the program R?
It is essential to have basic proficiency in using the R statistical package. Please read "Important Notes" above.
In this course, the statistical software package used is R exclusively. We do not use or demonstrate SPSS.
Summarising and Graphing Data
Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.
Hypothesis testing concepts - power, significance, P-value. Comparing two groups (t and Wilcoxon tests).
Comparing many groups - with one treatment factor (one-way ANOVA or Kruskal-Wallis test; multiple comparison tests).
Correlation, predicting relationships (regression - simple).
Other topics covered
Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.
Course Requirement: You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Basic proficiency in using the R statistical package is essential.
Date: Monday 24 to Thursday 27 August
Duration: 9.00am to 1.00pm each day
Location: Online (access to be advised)
You will receive a certificate of completion for the course.