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Research Methods in Practice


  • Credit value: 15 credits at Level 4
  • Convenor: Dr Michael Mallaghan
  • Assessment: a 1500-word and a 2000-word laboratory report (20% and 25%), two one-hour timed assessments (10% and 40%) and a worksheet portfolio (5%)

Module description

This module deepens and broadens your understanding of core concepts and tests, helping you evaluate theory and research in psychology, and preparing you for potential progression onto the second year of the BSc Psychology.

Indicative module content

Quantitative methods

  • Recap selecting statistical tests based on experimental design and level of measurement
  • Describing the circumstances under which specific tests are appropriate
  • Differences between parametric and nonparametric tests
  • Parametric assumptions (normal distribution, homogeneity of variance, interval or ratio data)
  • Overview of parametric test tests to be covered (related t-test, unrelated t-tests, Pearson correlation)
  • Probability theory (simultaneous and sequential events)
  • Central limits theorem (samples and populations)
  • Planned (a priori) and unplanned (post hoc) tests
  • Experimental reliability, internal and external validity (test-retest and split-half techniques)
  • Extraneous and confounding variables
  • Carry-over effects and Latin Square designs
  • Type I and Type II errors
  • Floor and ceiling effects
  • Experimenter and participant effects, experimenter bias and demand characteristics

Qualitative methods

  • Sampling Methods (eg situation sampling, time sampling, event sampling)
  • External and internal validity of observational methods
  • Qualitative analysis techniques (grounded theory, thematic analysis, Interpretative Phenomenological Analysis - IPA)
  • Converting verbal data to numeric codes (content analysis)
  • Ensuring rigor in observational methods (eg observer bias, observer influence, inter-observer reliability, blind  and double blind techniques)
  • Single case studies with clinical examples, converging evidence from single cases

Data collection and analysis

  • Recap frequency distribution
  • Understanding the principles of inferential tests
  • Drawing and interpreting box plots, identifying outliers
  • Worked examples of parametric tests of difference (Related t-test, unrelated t-test)
  • Worked example of F-test for variance
  • Making multiple comparisons, family-wise error rates (Bonferroni t-test/Dunn’s test)
  • Worked example of parametric test of association (Pearson correlation)
  • Normal distribution and Z-scores (comparing individual scores with sample, comparing sample with population)
  • Interpreting box plots and stem and leaf diagrams
  • Normalising data distributions: trimming scores and transforming data
  • Worked examples of Chi Square test for nominal data (goodness of fit, 2 by 2 and larger contingency tables) observed and expected values, small sample sizes

Advanced ethical practices

  • Recap BPS ethical guidelines work working with humans
  • The work of ethics committees
  • Designing a consent form
  • Considering physical and psychological harm when briefing and debriefing participants

Advanced SPSS

  • Recap defining variables and entering data
  • Producing and interpreting boxplots and stem and leaf diagrams
  • Customising tables, graphs and charts
  • Carrying out parametric tests of difference (related and unrelated t-tests)
  • Recap producing scatterplots and requesting ‘best fit’ line
  • Carrying out parametric test of association (Pearson)
  • Examining and interpreting output tables

    Advanced maths and study skills for research methods

    • Recap feeling confident with numeric data
    • Understanding statistical notation and formulae
    • Using brackets in calculations
    • Squaring numbers and square roots
    • Understanding probabilities and significance levels
    • Recap consulting statistical tables
    • Recap writing laboratory reports
    • Recap referencing
    • Recap ownership and plagiarism
    • Carrying out critical analysis (orienting and critical questions)
    • Practical critique of published journal article(s)
    • Revision for timed test

    Learning objectives

    By the end of this module, you should be able to:

    • feel confident using and interpreting numeric data
    • conduct statistical analysis using SPSS and interpret output tables
    • understand probabilities and significance levels
    • compare quantitative with qualitative methods
    • conduct critical analysis of published psychology articles
    • understand methodological issues concerned with experimental design and procedure and selection of participants
    • understand ethical issues connected with carrying out research including consent and British Psychological Society guidelines
    • think critically about the topics covered and contribute to class discussion
    • understand the nature of scientific research
    • understand the use of qualitative methods and carry out Interpretative Phenomenological Analysis (IPA).