Methods and Statistics in Psychology II

Module titleMethods and Statistics in Psychology II
Module codePSY2206
Academic year2014/5
Module staff

Dr Cris Burgess (Convenor)

Duration: Term123
Duration: Weeks



Number students taking module (anticipated)


Description - summary of the module content

Module description

Most psychological research involves quantitative analysis of numerical data. The purpose of this module is to introduce you to techniques that represent extensions of the General Linear Model (GLM), introduced in PSY1205. Hence, it is important that you have an understanding of the basic concepts in research statistics delivered in PSY1205; the distinction between descriptive and inferential statistics, statistical significance and significance testing, as well as practical experience of using relevant statistical software for carrying out between-subjects analysis of variance (ANOVA) procedures.

The module continues the discussion of what is almost certainly the most widely used statistical method within Psychology, analysis of variance (ANOVA), describing between-subjects ANOVA, contrasts and multi-factorial designs, and goes on to introduce two further GLM-based statistical methods commonly used in the social sciences; linear regression and exploratory factor analysis. The module discusses conceptual issues and provides hands-on experience of using statistical software for carrying out such analyses in the practical classes.

The statistical techniques covered in this module are widely used within Psychology, but also within other disciplines, such as Business, Biosciences, Geography and Sports Science.

Module aims - intentions of the module

The central objective of this module is to provide you with the skills to analyse relevant datasets using statistical software to carry out within-subjects ANOVA, linear regression and factor analysis, and interpret the results, allowing you to then report your findings using relevant reporting conventions. These skills will assist you in any subsequent practical work, including the Final Year Research Project.

A broader objective is to equip you with the skills to understand published research papers that employ these methods, allowing you to understand the Results sections of such papers and provide opportunities for critical appraisal of the methods used to analyse data and to critically assess the conclusions drawn by the authors.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Describe the conceptual basis and the purpose of analysis of variance, multiple regression and exploratory factor analysis
  • 2. Carry out ANOVA, regression and exploratory factor analyses quickly and without error using the most widely available computer statistical software, Statistical Package for the Social Sciences (SPSS)
  • 3. Interpret ANOVA, regression and exploratory factor analysis results correctly and report the results using journal conventions
  • 4. Interpret the results of research using these methods
  • 5. Decide when it is appropriate to use these techniques for purposes of analysing data for any projects they are planning and collect data in an appropriate form so that the analysis can be used properly

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 6. Learn quickly how to use new or more advanced forms of analysis should the need arise
  • 7. Evaluate and analyse critically empirical evidence
  • 8. Identify and evaluate critically the strengths and weaknesses of published work
  • 9. Apply detailed knowledge and understanding of critical principles in designing research

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 10. Use and interpret statistical data with a scope extending well beyond the coverage of the module itself
  • 11. Master a sophisticated software package on a largely self-taught basis (including use of Help facility provided with the software)
  • 12. Use electronic information including resources posted on the module ELE page and in PowerPoint handouts from lectures
  • 13. Work in small groups, tackling and completing complex tasks effectively

Syllabus plan

Syllabus plan

Week 1. Revision of basic concepts in ANOVA including SPSS methods for running 1-, 2- and 3-way univariate analyses and instructions for running planned and post-hoc comparisons.

Week 2. Introduction to repeated measures designs. SPSS methods for running repeated-measures ANOVAs; Sphericity assumption; Greenhouse-Geisser and Huynh-Feldt procedures for dealing with sphericity violations.

Week 3. SPSS procedures for 1 and 2-way repeated-measures and mixed designs; dealing with planned and unplanned contrasts on repeated measures factors.

Week 4. More complex repeated-measures and mixed designs; overview, including interpretation of error terms and of 3-way interactions, use of decision-tree to select analyses, review of treatment of fixed and random effects etc.

Week 5. Assumptions and robustness of Anova. When not to use it. Power of ANOVA designs. Explanation of use of Monte Carlo methods for assessing robustness; Introduction to multivariate techniques: Relevance of the General Linear Model in linking ANOVA with multivariate statistical techniques.

Week 6. From ANOVA to Regression: aims to show how Regression relates to ANOVA, and gives some general rules for Multiple Regression;

Week 7. From Simple to Multiple Regression: describes regression with more than one regressor and explains how to assess a model's goodness of fit. Multiple Regression in Practice: uses ‘real life’ examples to demonstrate utility of technique, and uses SPSS demonstrations to show how to carry out analyses.

Week 8. Model Checking: Explains how to report Multiple Regression analyses, and how to check the model using residuals analysis. Reporting conventions and interpretation: how to correctly report analyses and how to interpret such analyses in published research.

Week 9. Choosing Between Regression Models: How to choose regressors for a regression model, and how to choose between models.

Week 10. Regression with Categorical Variables: How to deal with unordered category (nominal) variables as regressors in a regression model using dummy variables.

Week 11. Introduction to Factor Analysis: Introduces conceptual basis for factor analysis. Discusses when to use such a technique, and when not to. Includes discussion of correlational associations and some fundamental considerations of psychometric methods.

Week 12. Factor analysis in practice I: specifically focuses on exploratory factor analysis (EFA) method of ‘principal axis factoring’. Rotation and Interpretation: Describes utility of rotated solutions; orthogonal versus oblique, and relevant theoretical considerations.

Week 13. Factor Analysis in Practice II: uses SPSS to analyse and interpret data using the principal axis factoring method. Discussion of interpretation of factor solutions, using ‘real life’ examples.

Week 14. Revision class: ANOVA topics to be covered dependent on student requests.

Week 15. Revision class: Multiple regression and factor analysis topics to be covered dependent on student requests.

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 & Teaching15Lectures
Scheduled Learning & Teaching15Examples classes
Guided Independent Study15Completing the weekly assignments using online resources and support via ELE
Guided Independent Study105Revision and wider reading


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Weekly exercise1 hour per week1-5Personal contact with module convenor and demonstrators within practical classes
Formative online tests (via module ELE page)30 minutes per week1-5Automatic feedback provided on-screen in response to correct/incorrect student responses

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
Unseen, open-book examination1003 hours1-5Generic feedback posted on module ELE page.


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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
ExaminationExaminationSame as aboveAug Ref/Def

Re-assessment notes

One assessment is required for this module. For the examination, the reassessment will be the same as the original assessment. Where you have been referred/deferred in the examination you will have the opportunity to take a second examination in the August/September re-assessment period. This will constitute 100% of the module. Deferred marks are not capped; referred marks are capped at 40%.


Indicative learning resources - Basic reading

Core reading:

Field, A, (2009) Discovering Statistics Using SPSS (3rd Edition) London: Sage.

Pallant, J. (2005) SPSS survival manual (2nd Edition) Maidenhead, Berks.: OUP.


Recommended reading:

Harris, P. (2002) Designing and Reporting Experiments in Psychology (2nd Edition) Buckingham: OUP.

Howell, D. C. (1997). Statistical Methods for Psychology (4th Edition) Belmont, Calif.: Duxbury Press.

(Earlier editions also include most of what you need, as does Howell’s book on: “Fundamental statistics

for the behavioral sciences”).

Howell, D.C. (1995). Fundamental Statistics for the Behavioural Sciences (3rd Edition). Duxbury.

Howitt, D. & Cramer, D. (2008) Introduction to Statistics in Psychology (4th Edition) Harlow, Essex:


Kirk, P.E. (1968). Experimental Design. Procedures for the Behavioural Sciences. Brooks/Cole.

Kline, P. (1994). An Easy Guide to Factor Analysis. Routledge: London.

Myers, J.L. (1972). Fundamentals of Experimental Design. Second Edition. Allyn and Bacon.

Myers, J.L, & Well, A.D. (1991) Research Design and Statistical Analysis. HarperCollins: New York.

Indicative learning resources - Web based and electronic resources

Exeter Learning Environment Module Homepage provides lecture slides, lecture notes, podcast lectures,weekly assignment exercises, online SPSS instructions, screen-capture videos of SPSS procedures and links to further online resources.

Module has an active ELE page

Indicative learning resources - Other resources

Statistics and Computing Helpdesk in the computer room (220 WSL) is available for help and advice.

Key words search

Psychology, Research statistics, SPSS, ANOVA, linear regression, factor analysis, multivariate

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date