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Description

Introduction to Statistics

Module titleIntroduction to Statistics
Module codePSYM221
Academic year2021/2
Credits15
Module staff
Duration: Term123
Duration: Weeks

11

Number students taking module (anticipated)

40

Description - summary of the module content

Module description

This module will give you training on a variety of different statistical techniques commonly used in research conducted by psychologists. As such, it provides core skills required for the research project that you will take as part of your Masters. The aim is to provide you with knowledge of commonly used tests such as analysis of variance (ANOVA) and linear regression. The module discusses conceptual issues and provides hands-on experience of using statistical software (SPSS) for carrying out such analyses in the practical classes accompanying the lectures. Whilst basic mathematical skills are required, the module does not assume Mathematics A level or degree level experience.

Module aims - intentions of the module

The central objective of this module is to provide you with the skills to carry out research in psychological domains, analyse relevant datasets using statistical software to carry out between and within-subjects ANOVA and multiple linear regression, and interpret the results, allowing you to then report your findings using relevant reporting conventions. These skills will assist you in your Research Project.

A broader objective is to equip you with the skills to understand published research papers that employ these methods and forms of analysis, allowing you to understand the Methods and Results sections of such papers and provide opportunities for critical appraisal of the methods used to collect and 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. Identify weaknesses in specific methodologies and understand the relative merits of quantitative approaches
  • 2. Describe the conceptual basis and the purpose of analysis of variance and multiple linear regression
  • 3. Carry out ANOVA and regression quickly and without error using the most widely used computer statistical software, Statistical Package for the Social Sciences (SPSS)
  • 4. Interpret ANOVA and regression results correctly and report the results using journal conventions
  • 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

ILO: Personal and key skills

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

  • 8. Manage information, collect appropriate information from a range of sources and undertake essential study tasks under guidance
  • 9. Use and interpret statistical data with a scope extending well beyond the coverage of the module itself

Syllabus plan

Syllabus plan

Statistics topics covered on this module will include:

Analysis of Variance (ANOVA):

  • Learning how to run ANOVA including SPSS methods for conducting 1-, 2- and 3-way univariate analyses as well as planned and post-hoc comparisons.
  • Introduction to between and repeated measures designs. SPSS methods for running repeated-measures ANOVAs.
  • SPSS procedures for 1 and 2-way repeated-measures and mixed designs; dealing with planned and unplanned contrasts on repeated measures factors.
  • More complex repeated-measures and mixed designs; overview, including interpretation of error terms and of 3-way interactions.
  • Assumptions and robustness of ANOVA. When not to use it. Power of ANOVA designs.

Linear regression

  • From ANOVA to Regression: aims to show how Regression relates to ANOVA, and gives some general rules for Multiple Regression; 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.
  • Model Checking: explains how to report Multiple Regression analyses.
  • Reporting conventions and interpretation: how to correctly report analyses and how to interpret such analyses in published research.
  • Choosing Between Regression Models: how to choose regressors for a regression model, and how to choose between models.
  • Regression with Categorical Variables: how to deal with unordered category (nominal) variables as regressors in a regression model using dummy variables.

Learning and teaching

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
221280

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching11Lectures
Scheduled Learning and Teaching11Practical classes
Guided Independent Study22Completing the weekly assignments using online resources and support via ELE
Guided Independent Study106Revision and further practice

Assessment

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Participation in practical classes11 x 1 hourAllPersonal contact with module convenor within practical classes

Summative assessment (% of credit)

CourseworkWritten examsPractical exams
15850

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Examination 853 hoursAllGeneric written feedback posted on module ELE page
Weekly practical exercises1511 x 2 hoursAllSelf-graded and moderated by module convenor

Re-assessment

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Examination ExaminationAllAug Ref/Def
Weekly practical exercisesWeekly practical exercisesAllAug Ref/Def

Re-assessment notes

Two assessments are required for this module. Where you have been referred/deferred in either you will have the opportunity to retake in the August re-assessment period. If you are successful on referral, your overall module mark will be capped at 50%; deferred marks are not capped.

Resources

Indicative learning resources - Basic reading

Indicative basic reading list:

Core reading:

  • Dancey, C. & Reidy, J. (2017). Statistics without Maths for Psychology (seventh edition). Pearson education.
  • Field, A. P. (2013). Discovering statistics using SPSS (fourth edition). London: Sage publications. There are many hard copies of this in the library.

Indicative learning resources - Other resources

  • Statistics and Computing Helpdesk in the computer room is available for help and advice.

Key words search

Psychology, research statistics, SPSS, ANOVA, linear regression, multivariate, methods

Credit value15
Module ECTS

7.5

Module pre-requisites

None

Module co-requisites

None

NQF level (module)

7

Available as distance learning?

No

Origin date

05/03/2020

Last revision date

29/03/2021