Research Methods and Analysis 2
| Code | School | Level | Credits | Semesters |
| PSGY2033 | Psychology | 2 | 10 | Spring UK |
- Code
- PSGY2033
- School
- Psychology
- Level
- 2
- Credits
- 10
- Semesters
- Spring UK
Summary
This module will cover the basic concepts and assumptions and issues relating to regression, multivariate analysis, the reliability and validity issues as well as basic qualitative techniques and quantitative.
Participants should be familiar with SPSS and quantitative methods such as ANOVA. This module continues from Research Methods and Analysis 1, including more advanced ANOVA designs. We cover two multivariate tests (factor analysis and multiple regression), methods for non-laboratory investigations (reliability & validity, free-response methods).
The material is taught with reference to the SPSS software program, as a tool for data analysis. SPSS is freely available in the computer laboratories of the School.
Target Students
Second year psychology, psychology and cognitive neuroscience students and Natural Science students (depending on pathway). Only available to those who have studied Research Methods and Analysis 1.
Classes
- One 1-hour lecture each week for 10 weeks
Assessment
- 5% Coursework 1: Coursework
- 95% ExamSys 1 (2-hour): ExamSys in person
Assessed by end of spring semester
Educational Aims
• To introduce important aspects of psychological research methodology • Further understanding of the fundamental theory and assumptions underlying analysis of experimental and data. • To explain the major types of statistical analysis approaches, including: when they are applicable, the assumptions they require, the procedure to be followed, and how their results should be interpreted. • To provide practical instruction on the use of statistical software to compute and report analyses. • Describe and evaluate methods of data collection in qualitative research • Explain and assess important issues in psychology, including: the reliability and validity of research designs causal inference statistical power questionable research practices and ethical issuesLearning Outcomes
- Explain the basic principles underlying analysis of variance (ANOVA).
- Analyse an empirical data set using the appropriate statistical technique, demonstrate the validity of required assumptions, and interpret the outcome in terms of the original research question.
- Understand where research questions require analyses that examine the relationship between multiple measures, using multiple linear regression and exploratory factor analysis.