Postgraduate Course: Quantitative Longitudinal Data Analysis (SHSS11012)
Course Outline
| School | School of Health in Social Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course is part of an integrated suite of PGR training courses in the school of Health in Social Science and is aimed at all MScR and PhD students in the school.
Longitudinal and Time-Series Data Analysis Methods is an advanced statistical analysis course that introduces students to quantitative methods of time-series analysis that are commonly encountered in health and social science research. Students will be given the opportunity to apply these methods using R and interpret and present their results in the style of a research report.
R programming ability is not required, but it is strongly recommended that students have an understanding of basic statistical analysis methods, from descriptive data through multiple linear regression. It is recommended that students with limited statistical knowledge undertake SHSS11004 Introduction to Data Analysis in R, before enrolling on this course.
Students outwith the School of Health in Social Science may apply to take this course, depending on course enrolment. Preference will be given to students taking the course for credit, rather than auditing. |
| Course description |
This course provides an overview of repeated measures methodologies and statistical analysis techniques for data derived from methodologies using high sampling frequencies, i.e. data that are collected across multiple timepoints. The course will address the unique challenges involved in working with such data and introduce techniques to analyse these data (such as mixed-effects modelling). Students will also learn data management, data presentation, and data visualisation techniques. Methods will be viewed through the lens of health and social sciences research. The course will be delivered through a mix of live lectures, coding sessions, and labs during which students will have the opportunity to apply the concepts covered in the lectures to real-world examples in health science. While analyses will primarily be conducted using R, students may be exposed to a range of software options, such as Mplus, Stata, and SPSS.
Basic understanding of R programming would be helpful but is not required.
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Information for Visiting Students
| Pre-requisites | It is strongly recommended that students have taken one of the following:
- CLPS11056 Psychological Research Methods: Data Management and Analysis
- SHSS11004 Introduction to Data Analysis in R
- CLPS11073 Inferential Statistics in Applied Psychology
- NUST11091 Conducting Research in Nursing, Health and Social Care |
Course Delivery Information
|
| Academic year 2025/26, Available to all students (SV1)
|
Quota: 25 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 12,
Supervised Practical/Workshop/Studio Hours 12,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
172 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
3000-word Written Report (100% )
All Learning Outcomes (LO) can be assessed through the written report; students will be provided with data and a research aim and expected to justify their use of a particular model (LO1, LO2), perform the longitudinal analysis (LO3), interpret their results in the context of the research aim (LO4), and produce a written research report that summarises the results and discusses their implications (LO4, LO5). |
| Feedback |
Formative feedback will be ongoing throughout the course during lectures and labs. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Develop critical understanding of theory behind longitudinal modelling
- Identify, select, and apply appropriate models for longitudinal data
- Use R or another software to perform longitudinal data management and analysis
- Critically evaluate and interpret results of longitudinal data analysis
- Communicate results of longitudinal analysis in both written and graphical format
|
Reading List
This course does not follow a single text, but may use information from several resources, including:
Fitzmaurice, G.M., Laird N.M., & Ware, J.H. (2011). Applied longitudinal analysis. Wiley.
Mirman, D. (2016). Growth curve analysis and visualization using R. CRC press.
Wu, L. (2009). Mixed effects models for complex data. Routledge. |
Additional Information
| Graduate Attributes and Skills |
Problem Solving: Students will be required to evaluate longitudinal research aims and select the appropriate analysis to answer research questions linked to these aims
Analytical Thinking: Students must think critically about data and identify appropriate research methodology, given its characteristics
Independent research: Students will learn key research skills specifically related to the analysis and reporting of longitudinal data
Digital literacy: Students will be exposed to several statistical software programs through this course.
Numeracy: Students will be learning the mathematical theories that form the basis of the statistical tests covered in the course
Decision making: Students will be required to select the appropriate analysis method, given a dataset's characteristics
Team working: Students may work in groups to complete each week's labs
Written Communication: Students must produce a written report that summarises their analysis plan, results, and final interpretation |
| Keywords | data analysis,r,longitudinal analysis,time-series analysis,statistical analysis |
Contacts
| Course organiser | Dr Monica Truelove-Hill
Tel:
Email: m.truelovehill@ed.ac.uk |
Course secretary | Ms Yuke Duan
Tel:
Email: yduan@ed.ac.uk |
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