Search our site
Search our site

Research Design and Analysis

  • Module code: PS7002
  • Year: 2018/9
  • Level: 7
  • Credits: 30
  • Pre-requisites: Research Methods 2 (PS5001) or equivalent
  • Co-requisites: None

Summary

The module provides an advanced coverage of the design and analysis of psychological research. Building on a revision of intermediate inferential statistics (e.g. ANOVA, factorial ANOVA, regression and multiple regression), the course moves quickly towards a consideration of more advanced and specialised quantitative methods (e.g., multivariate statistics, co-variance, structural equation modelling, factor analysis, meta-analysis and advanced regression techniques) and their applications. The course introduces principles of questionnaire design, evaluation and data analysis, along with advanced qualitative research methods. The laboratory workshops combine formal teaching with hands-on activities. The material provides an important foundation for the development and execution of the master's level research dissertation.

Aims

  • To provide students with advanced skills in the design and analysis of psychological research.
  • To provide advanced knowledge of some of the major quantitative and qualitative methods used in psychological enquiry.
  • To develop advanced data analysis skills using computer-based packages.

Learning outcomes

On successful completion of the module, students will be able to:

  • Demonstrate practical experience of a range of research procedures;
  • Identify the underlying principles of various analytical methods commonly applied in psychological research;
  • Select appropriate analytical techniques for different types of data;
  • Compute advanced descriptive and inferential statistics using SPSS;
  • Evaluate qualitative research data.

Curriculum content

  • The nature of the experimental method
  • Revision of intermediate descriptive and inferential statistics
  • Advanced quantitative statistical procedures
  • Qualitative methodologies and analyses
  • Questionnaire design, construction and evaluation
  • Principles underlying different methods of data collection
  • Experience of psychological experiments

Teaching and learning strategy

This module will be delivered through 22 one-hour weekly keynote lectures (which will review research design topics and present underlying assumptions and techniques for carrying out advanced quantitative and qualitative data analyses). The lectures will be followed by 22 two-hour small group laboratory workshops where students will gain practical experience in the construction of questionnaires as well as hands-on experience with computer-based statistical and research tools.

Breakdown of Teaching and Learning Hours

Definitive UNISTATS Category Indicative Description Hours
Scheduled learning and teaching Lectures 22
Scheduled learning and teaching Workshops 44
Guided independent study Independent study 234
Total (number of credits x 10) 300

Assessment strategy

Learning of statistical methods, SPSS skills, capacity to design and evaluate research projects as well as the ability to select appropriate analytical techniques and apply them to specific data sets will be assessed via a design and analysis portfolio (3,000 words) comprised of two assignments, one at the end of each teaching block, and worth 35% of the final mark. Understanding and interpretation of a range of intermediate and advanced inferential statistics as well as theoretical aspects of quantitative and qualitative research design and analysis will be assessed via a portfolio of two 90-minute in-class tests, one at the end of each teaching block, and each worth 30% of the final mark. Students' practical experience of a wide range of research designs will be assessed via 2 hours of research participation (240 research participation credits) worth 5% of the final mark.

Mapping of Learning Outcomes to Assessment Strategy (Indicative)

Learning Outcome Assessment Strategy
Identify the underlying principles of various analytical methods commonly applied in psychological research; Formatively through workshops and summatively through the in-class tests
Select appropriate analytical techniques for quantitative and qualitative data Formatively through workshops and summatively through the design and analysis portfolio
Compute advanced descriptive and inferential statistics using SPSS Formatively through workshops and summatively through the design and analysis portfolio
Demonstrate practical experience of a range of research procedures Research participation credits

Elements of Assessment

Description of Assessment Definitive UNISTATS Categories Percentage
Coursework Portfolio of assignments 35
Coursework Portfolio of in class tests 60
Coursework SONA Credits 5
Total (to equal 100%) 100%

Achieving a pass

It IS NOT a requirement that any major assessment category is passed separately in order to achieve an overall pass for the module

Bibliography core texts

Field, A. P. (2009). Discovering statistics using SPSS. (3rd ed.). London: Sage.

Bibliography recommended reading

Aiken, L. S., & West, S. G. (1991). Multiple Regression: Testing and interpreting interactions. Newbury Park, CA: Sage.

Blunch, N. (2008). Introduction to Structural Equation Modelling Using SPSS and Amos. London: Sage.

Byrne, B. M. (2009). Structural equation modeling with AMOS: Basic concepts, applications and programming. London: Routledge Academic.

Cohen, J. (2003). Applied multiple regression/correlation analysis for the behavioral sciences. London: Laurence Erlbaum.

Dancey, C.P. & Reidy, J.G. (2007). Statistics without maths for psychology (4th ed.): Prentice Hall.

Smith, J.A., & Osborn, M. (2008). Interpretative phenomenological analysis. In JA Smith (Ed.), Qualitative Psychology: A Practical Guide to Methods. London: Sage.

Smth, J.A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis.Theory, method and research. London: Sage

Find a course

Course finder

>
Postgraduate study
Site menu