This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2016-2017 (archived)
Module PSYS3111: ADVANCED RESEARCH METHODS FOR APPLIED PSYCHOLOGY
Department: Psychology (Applied Psychology) [Queen's Campus, Stockton]
PSYS3111: ADVANCED RESEARCH METHODS FOR APPLIED PSYCHOLOGY
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 3 | 20 | Not available in 2016/2017 | Queen's Campus Stockton |
Tied To
Pre-requisites
- 100 credits from C817 Psychology (Applied) Level 2 modules; or PSYC2101 Statistics for Psychology
Corequisites
- None
Excluded Combinations
- None
Aims
- This module has two aims:
- - first, students will learn how to apply a variety of advanced statistical techniques to undertake data analysis on a variety of data sets
- - second, they will learn to combine their various research skills
Content
- Introduction to advanced research methods
- Major types of quantitative method and their advantages and disadvantages
- A variety of techniques taken form:
- Structural Equation Modeling
- (Path Analysis/Confirmatory Factor Analysis)
- Non-Linear regression
- Logistic regression
- Model Building for GLM
- Hierarchical Anova
- Anova via Regression
- Analysis of Co-variance /2
- Cluster analysis / Multidimensional scaling
- Meta Analysis
- Manova
- Correspondence Analysis
Learning Outcomes
Subject-specific Knowledge
- Detailed knowledge of advanced research methods in psychology including current theory, evidence, and practice
- In-depth knowledge of advanced research methods in applied psychology
Subject-specific Skills
- Ability to review critically and consolidate understanding of a coherent body of psychological knowledge and apply it appropriately:
- - competent data analysis skills with the base programmes of SPSS
- - competent data analysis skills with AMOS
Key Skills
- Good written communication skills
- Good IT skills in word processing, data manipulation, and data presentation
- Ability to work independently in scholarship and research within broad guidelines
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Knowledge and understanding of advanced research methods and statistics, and the ability to select appropriate methods in addressing a specific question are developed through the weekly lectures. Skills in applying this knowledge using advance computing packages is acquired during the weekly computing practicals
- This knowledge will be assessed in the summative assessments in which students must demonstrate practical skills in reporting and undertaking open ended data analysis using appropriate techniques
- These abilities are also assessed via formative assessment throughout the practical classes
- Good IT and data handling skills are required for the formative and summative work in this module. Feedback is provided regarding the adequacy of these skills where necessary
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 22 | 1 per week | 1 hour | 22 | |
| Practicals | 22 | 1 per week | 1 hour | 22 | Yes |
| Preparation and Reading | 156 | ||||
| Total | 200 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Reports | 100% | 2000 word assignment taken from a topic(s) covered in Term 1 | 50% | ||
| 2000 word assignment taken from a topic(s) covered in Term 2 | 50% |
Formative Assessment
Continuous throughout the course
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.