| Unit name | Psychological Statistics and Research Tools |
|---|---|
| Unit code | PSYCM0041 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Stollery |
| Open unit status | Not open |
| Pre-requisites |
n/a |
| Co-requisites |
n/a |
| School/department | School of Psychological Science |
| Faculty | Faculty of Life Sciences |
The central aim of this unit is to extend coverage of psychological research tools psychologists will encounter during their literature search, research and assessment activities, focusing on multivariate methods to structure large sets of variables and their relationships. The unit is designed to update and extend students knowledge and understanding of generic analysis methods routinely used in psychological investigations, covering topics from more complex forms of ANOVA, multiple and logistic regression and factor analysis, to calculation and reporting of effect size and study power, and by extension meta-analysis. Moreover, students will be introduced to state-of-the art computational modelling and programming techniques. The aim is to provide a firm foundation for understanding how the variety of research tools currently being used in psychology can be integrated. Two enrichment periods further reinforce this knowledge by providing continuing hands-on experience with the diverse range of research techniques currently used within the research environment of the School of Experimental Psychology. The unit finishes with a reading week.
To gather an in-depth knowledge of experimental design and data analysis in psychology. Students will learn about the methodologies of data collection, and subsequent analysis, including meta-analysis of existing data sets and computational modelling.
Ten two-hour lectures and ten two-hour practicals. The lectures are given by research active staff members, with a strong interactive component. For the generic analysis sessions, conceptual coverage is delivered in conjunction with the use of statistical analysis packages so that statistical theory and practice are blended.
One piece of coursework involving analysing and reporting complex experimental data (50%).
A 2-hour unseen examination assessing the conceptual and practical knowledge of experimental and statistical methodology (50%).
Other references to on-line publications will be made during the unit.
