Analysis of Psychometric Data 4H PSYCH4080
- Academic Session: 2022-23
- School: School of Psychology and Neuroscience
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
Advanced statistical techniques for the analysis of psychometric data, focusing on reliability analysis, principal component analysis, cluster analysis, and various regression techniques. The course should enable students to evaluate psychometric scales and use them for prediction and measurement.
10 hours of lectures over a 5 week block
Requirements of Entry
Successful completion of level 3H psychology single honours or equivalent with 'B3' or higher in L3 Statistics (PSYCH4037)
At the end of the semester, there will be a 90 minute exam with a mixture of multiple choice (2 thirds of the exam) and short essay questions (1 third) assessing students' understanding of key concepts and interpretation of statistical output.
Main Assessment In: December
Are reassessment opportunities available for all summative assessments? No
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
This course aims to teach students various techniques for the evaluation and use of psychometric scales, enabling them to examine and improve the internal consistency of psychometric measurements, identify the dimensional structure of psychometric (sub-) scales, identify potential clusters of observations and use psychometric scales for prediction and measurement of psychological constructs.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ evaluate and, if necessary, improve the internal consistency of a multi-item psychometric measurement;
■ identify the dimensional structure of a psychometric measurement;
■ perform reliability and principal component analyses in R;
■ identify clusters of observations (e.g., participant subgroups) in a given set of data;
■ perform k-means cluster analysis in R;
■ use psychometric measurements as predictor or criterion variables in a regression analysis;
■ perform various regression techniques in R.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.