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.
On successful completion of the module, students will be able to:
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.
| 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 | |
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.
| 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 |
| 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% | |
It IS NOT a requirement that any major assessment category is passed separately in order to achieve an overall pass for the module
Field, A. P. (2009). Discovering statistics using SPSS. (3rd ed.). London: Sage.
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