Advanced Statistics

Module titleAdvanced Statistics
Module codePSYM201
Academic year2017/8
Module staff

Dr Tim Fawcett (Convenor)

Duration: Term123
Duration: Weeks



Number students taking module (anticipated)


Description - summary of the module content

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 the packages SPSS and 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 seminars 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 stuff 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 with SPSS and 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

Syllabus plan

We will cover the following topics:

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
  • Repeated-measures designs

Relationships between Variables

  • Correlation
  • Linear regression
  • Multiple linear regression

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 and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching40Lectures
Scheduled Learning and Teaching40Supervised practical workshops
Guided Independent Study8Revision of basic statistics (1 week, prior to start of lectures)
Guided Independent Study25Preparation for lectures (e.g. readings)
Guided Independent Study80Review of lecture materials each week
Guided Independent Study12Preparation of fortnightly computer assignments
Guided Independent Study3Preparation for group-based oral presentation
Guided Independent Study12Analysis of large data set and write-up
Guided Independent Study80Revision for final examination


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Practical exercises2 hours per weekAllModel answers, oral feedback

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Computer assignments164 fortnightly assessments, requiring approximately 3 hours eachAllWritten
Group-based oral presentation45 minutes (plus preparation time)AllOral
Analysis of large data set2010 hoursAllAnnotated script
Examination603 hoursAllAnnotated script


Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Computer assignmentsAlternative assessments will be arranged as requiredAllBy end of August
Group-based oral presentationAlternative assessments will be arranged as requiredAllBy end of August
Analysis of large data setAnalysis of large data setAllBy end of August

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 computer assignments are not deferrable because they take place during the module; the group-based oral presentation is not deferrable because of the group nature of the assessment. Alternative assessments will be arranged as required. 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 sit a further examination. The module mark given for a re-assessment taken as a result of referral will count for 100% of the mark and will be capped at 50%.


Indicative learning resources - Basic reading

Other useful books:

  • Haslam SA & McGarty C, 2003. Research Methods and Statistics in Psychology. London: Sage.
  • Howell DC, 2007. Statistical Methods for Psychology (6th edition). Belmont, CA: Thomson Wadsworth.
  • Tabachnik B & Fidell LS, 2013. Using Multivariate Statistics (6th edition). London: Pearson.
  • Everitt BS & Dunn GD, 2000. Applied Multivariate Data Analysis (2nd edition). London: Arnold.
  • Cramer D, 2003. Advanced Quantitative Data Analysis. Maidenhead, Berks: Open University Press.
  • Crawley MJ, 2013. The R Book (2nd Ed). Chichester: Wiley.
  • Gelman A & Hill J, 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York: Cambridge University Press.

Indicative learning resources - Web based and electronic resources

  • ELE Module Homepage– resources posted on ELE include Powerpoint slides of lectures, additional papers for reading, practical exercises with model answers, and video tutorials

Module has an active ELE page

Key words search

Advanced statistics, SPSS, R

Credit value30
Module ECTS


Module pre-requisites


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NQF level (module)


Available as distance learning?


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Last revision date