To introduce students to the main classical statistical methods that are commonly applied in psychology and other social sciences and to give hands-on experience of using more advanced techniques for exploring multivariate data.
In social sciences, such as psychology, experiments and surveys typically yield large quantities of high dimensional data (e.g. in the form of questionnaire responses) from which we wish to extract simpler underlying relationships, or evidence of differences in subgroups in a population. The course will give students a grounding in the most common classical statistical methods used in analysing psychological data, the correct interpretation of results, and the application of methods to real data sets using the computer package SPSS.
Topics covered will include: confidence intervals, hypothesis testing, parametric t-tests and non-parametric tests, correlation and regression, analysis of variance, multivariate data analysis.
The style of the course will be practical and it will involve computer-laboratory based work, including four assessed practical projects.
Course Description: Link to Official Course Descriptor.
Location: Edinburgh, Malaysia.
- Basic background
- Data & descriptive summaries
- Random variables & distributions; correlation and covariance
- Parametric methods
- Estimation & confidence intervals for means and proportions
- Hypothesis testing for means – z and t tests
- Comparisons – tests for independent samples and paired samples
- Non-parametric methods
- Assumptions for t tests and the need for non-parametric alternatives
- Mann-Whitney U test; Wilcoxon Signed-Rank test; Sign test
- Correlation & Regression
- Scatterplots; correlation coefficients; linear regression
- Analysis of variance
- One-way ANOVA; F tests; checking validity
- A-priori & Post-hoc comparisons; linear contrasts & Bonferroni t
- Effect size & Power; Non-parametric Kruskal-Wallis test
- Two-way ANOVA & Interactions; extension to more tha two factors
- Multivariate data analysis
- Introduction & graphical representation
- Principal components analysis
- Factor analysis
Learning Outcomes: Subject Mastery
After studying this course, students should be able to:
- know when and how to apply appropriate statistical methods in practical situations involving social sciences data.
- Howell, D C (2002) Statistical Methods for Psychology (5th ed.), Duxbury.
[A good general text with full detail at a basic level.]
- Brace, Kemp & Snelgar (2006) SPSS for Psychologists (3rd ed.), Palgrave Macmillan.
[A good text for applying SPSS to data sets.]
- Coolican, H (1999) Research Methods and Statistics in Psychology, Hodder & Stoughton.
[Less detail than Howell but good for pyschologists in later work. ]
Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22
SCQF Level: 9.
Help: If you have any problems or questions regarding the course, you are encouraged to contact the course leader.
Canvas: further information and course materials are available on Canvas