Advanced Statistics
| Module title | Advanced Statistics |
|---|---|
| Module code | PSYM201 |
| Academic year | 2024/5 |
| Credits | 30 |
| Module staff | Dr Tim Fawcett (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 9 | 9 |
| Number students taking module (anticipated) | 50 |
|---|
Module description
Statistics are at the heart of quantitative research across the social and life sciences. This module explores the theoretical foundations of modern statistical approaches and their application in software packages for quantitative data analysis. We start by discussing fundamental statistical concepts before covering a wide range of techniques for detecting differences between groups and relationships among variables. We consider how to deal with outliers, missing values, non-normal distributions and other features of the complex, messy data sets often encountered in real scientific research (such as your Research Apprenticeship). We look at how statistical thinking is critical for designing powerful experiments and discuss ways to present results in the most meaningful and impactful way. Throughout the module we emphasise that the ability to apply statistical techniques properly relies on an in-depth understanding of the fundamental concepts.
Module aims - intentions of the module
This module will equip you with the knowledge and practical skills to plan quantitative research, prepare data for analysis and evaluate those data with the most appropriate statistical techniques using specialised software. You will develop knowledge and understanding of the assumptions that underlie all statistical techniques and how to test those assumptions. Importantly, these techniques will also enable you to evaluate reported evidence in the research literature critically and draw appropriate conclusions.
In the weekly lectures you will develop an understanding of the theory and core concepts of statistics. In the practical classes and assignments you will put this knowledge into practice, by carrying out a wide range of statistical analyses using modern software packages such as R. Alongside these activities you will develop a broader set of academic and professional skills in problem solving, task planning, time management, collaboration, critical analysis of published information, self-directed study and active participation in open discussions.
Through attending the weekly classes and completing the assessments, you will further develop the following academic and professional skills:
- problem solving (linking theory to practice, developing your own ideas with confidence, showing entrepreneurial awareness, being able to respond to novel and unfamiliar problems)
- managing structure (identifying key demands of the task, setting clearly defined goals, responding flexibly to changing priorities)
- time management (managing time effectively individually and within a group)
- collaboration (respecting the views and values of others, taking initiative and leading others
- supporting others in their work, maintaining group cohesiveness and purpose), and audience awareness (presenting ideas effectively in multiple formats, persuading others of the importance and relevance of your views, responding positively and effectively to questions).
The module also emphasises the modern statistical approaches used by the module staff in their own research, such as general linear modelling, that are likely to be used in your own Research Apprenticeship.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Describe in detail the central role of statistical analysis in drawing meaningful conclusions from psychological data
- 2. Competently use a range of standard statistical techniques and appreciate their strengths and limitations
- 3. Analyse quantitative datasets using R
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Apply appropriate statistical techniques in conducting your own research
- 5. Critically evaluate statistical approaches used in published research
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 6. Identify complex statistical problems and apply appropriate knowledge and methods for their solution with confidence and flexibility
- 7. Produce detailed and coherent written results of statistical analysis
- 8. Manage your own learning using the full range of resources of the discipline and with minimum guidance
- 9. Interact effectively and supportively within a learning group
Syllabus plan
Example topics that will be covered are:
Fundamental Concepts
- Probability distributions and descriptive statistics
- Statistical modelling and significance testing
- Type I and II errors
- The central limit theorem
- Visualising your data
- Assumptions of parametric tests
- Outliers, transformations and missing values
- Fixed and random factors
- Problems with P values
- Frequentist versus Bayesian statistics
Differences between Groups
- t tests
- Confidence intervals
- Non-parametric tests and randomisation tests
- ANOVA (one-way and factorial designs)
- Planned contrasts and post-hoc tests
- MANOVA
- Repeated-measures designs
Relationships between Variables
- Correlation
- Linear regression
- Multiple linear regression
- ANCOVA
Advanced Techniques
- Data reduction: factor analysis and PCA
- Moderation and mediation analysis
- Linear mixed models
- Generalised linear models: logistic regression and Poisson regression
- Generalised linear mixed models (GLMMs)
Statistical Power, Effect Size and Experimental Design
- Power analysis
- Effect-size measures
- Meta-analysis
- Pseudoreplication
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 80 | 220 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 40 | Lectures |
| Scheduled Learning and Teaching | 40 | Supervised practical workshops |
| Guided Independent Study | 8 | Revision of basic statistics (1 week, prior to start of lectures) |
| Guided Independent Study | 25 | Preparation for lectures (e.g. readings) |
| Guided Independent Study | 80 | Review of lecture materials each week |
| Guided Independent Study | 12 | Preparation of fortnightly computer assignments |
| Guided Independent Study | 3 | Preparation for group-based oral presentation |
| Guided Independent Study | 12 | Analysis of large data set and write-up |
| Guided Independent Study | 80 | Revision for final examination |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Practical exercises | 2 hours per week | All | Model answers, oral feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 36 | 60 | 4 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Computer assignments | 16 | 4 fortnightly assessments, requiring approximately 3 hours each | All | Written |
| Group-based oral presentation | 4 | 5 minutes (plus preparation time) | All | Oral |
| Analysis of large data set | 20 | 10 hours | All | Annotated script |
| Examination | 60 | 3 hours | All | Annotated script |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Computer assignments | Computer assignments | All | By end of August |
| Group-based oral presentation | Individual oral presentation (or video-recording of this) | All | By end of August |
| Analysis of large data set | Analysis of large data set | All | By end of August |
| Examination | Examination | All | August/September |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The module mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to redo the assessment components you have failed. The higher of the two marks for any referred component will contribute to the final module mark, which will be calculated using the same weightings given above but capped at 50%.
Indicative learning resources - Basic reading
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-
Winter B, 2019. Statistics for Linguists: An Introduction Using R. New York: Routledge.
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Other useful books:
- Crawley MJ, 2013. The R Book (2nd Ed). Chichester: Wiley.
- Revelle W & The Personality Project, 2017. Using R for Psychological Research. URL: http://www.personality-project.org/r/r.guide.html.
- Wickham H & Grolemund G, 2017. R for Data Science. Sebastopol, CA: O’Reilly.
Indicative learning resources - Web based and electronic resources
Other web-based and electronic resources:
- ELE page: http://vle.exeter.ac.uk/course/view.php?id=1803 (resources posted on ELE include Powerpoint slides of lectures, additional papers for reading, practical exercises with model answers, and video tutorials)
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 01/09/2011 |
| Last revision date | 08/09/2021 |
