Research Methods in Practice

Course details

ENTRY REQUIREMENTS

Students must have passed Research Methods for Psychology (Introduction) before commencing the course.

AIMS

The overall aim of this module is to build on and advance the understanding and knowledge gained on the 'Research methods for psychology (Introduction)' module and to enable students to develop a deeper understanding of the challenges of the research process including ethical considerations and the strengths and weaknesses of each approach. The module will build on students understanding of research design to provide students with an understanding of data collection and statistical analysis.

OBJECTIVES

On successful completion of this course students should be able to:

• Feel confident using and interpreting numeric data

• Conduct statistical analysis using SPSS and interpret output tables

• Develop an understanding of probabilities and significance levels

• Compare quantitative with qualitative methods

• Conduct critical analysis of published psychology articles

• Demonstrate an understanding of methodological issues concerned with experimental design and procedure and selection of participants

• Demonstrate an understanding of 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

• Develop an understanding of the nature of scientific research

• Develop an understanding of the use of qualitative methods and carry out basic qualitative analysis

• Express both written and spoken research findings with confidence

• Discuss and implement a range of strategies to support your learning

CONTENT

1. RESEARCH METHODOLOGY

Quantitative methods

• Recap selecting statistical tests based on experimental design and level of measurement

• Describing the circumstances under which specific tests are appropriate in an applied context with a consideration of the strengths and limitations of each

• 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 (e.g. situation sampling, time sampling, event sampling) with a particular discussion of the challenges of these in an applied context

• External and internal validity of observational methods with a consideration of the particular challenges of the method in an applied context

• Qualitative analysis techniques (grounded theory, thematic analysis, discourse analysis Interpretative Phenomenological Analysis - IPA)

• Converting verbal data to numeric codes (content analysis)

• Ensuring rigor in observational methods (e.g. 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

• Understanding one-way ANOVA

• Making multiple comparisons, family-wise error rates (Corrected t-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 & 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 with a particular consideration of working with children and young people and other vulnerable groups

• The work of ethics committees

• Designing a consent form

• Considering physical and psychological harm when briefing and debriefing participants

2. SPSS

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

3. MATHS AND STUDY SKILLS

Advanced maths and study skills for Research Methods

• 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