Postgraduate Course: Introduction to Data Analysis in R (SHSS11004)
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 will provide a comprehensive introduction to quantitative data analysis using 'R' and is designed to equip students with no prior statistical experience, enabling them to possess basic competencies in data management, statistical testing, and regression modelling.
The course will introduce students to the overall process of data analysis, provide insights on data cleaning and manipulation, provide students with experience in creating new variables, and conducting descriptive and inferential tests. The students will be taught these concepts within the R environment and will be given 3 course datasets to engage with during the course.
The course will provide an R-recipe-book to students for easy access of R code.
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| Course description |
This postgraduate course will equip students to have a critical understanding of the core principles of quantitative data analysis within a modern and versatile statistical software, R. The course will enable students to conduct basic descriptive and inferential statistical tests on their data and will instill an extensive understanding of the underlying assumptions for each test. Additionally, students will be introduced to regression modelling at a basic level. Students who wish to use quantitative methods in research or in postgraduate study will have a firm footing in quantitative data analysis.
Students will be introduced to (i) data analysis in research (ii) the importance of having clear research questions (iii) data cleaning and manipulation (iv) descriptive data analysis (v) inferential data analysis (including regression modeling) (vi) recipes in R, and (vii) scientific communication.
The course will be provided over 10 weeks. Each lecture will be followed by a practical on the following week. Students will engage in peer learning during the practical sessions and will be given the opportunity to analyse data from 3 different scenarios and will be taught how to simulate their own data. Some of these will include data from common study designs such as pre-post designs. The incorporation of R programming in the course will be done in a spiral approach: at first, students will copy R code into the online interface to generate output. Gradually, they will be introduced to opportunities where they type in their own code.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 40 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 6,
Supervised Practical/Workshop/Studio Hours 6,
Formative Assessment Hours 3,
Summative Assessment Hours 3,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
178 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
1. Written Assignment - 90% of overall course mark (Learning outcomes 1,2,3,4 and 5)
Students will write a research brief outlining a background, research questions, methods, results and discussion. The research questions and dataset will be provided to the student and they will be required to choose the appropriate descriptive and inferential tests to address the question at hand. The student will be required to conduct all analyses in R. The format for the research brief will be of a standard format.
2. Group presentation in the form of a video - 10% of overall course mark (Learning outcomes 2,3 and 5)
This will be submitted in week 6 and will provide students the opportunity to present basic scientific results to a lay audience. The results will be provided to the students and they will be required to present the results in the form of a power-point video presentation with succinct interpretation.
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| Feedback |
Students will be provided with feedback from academics and peers after formative presentations.
Written feedback will be provided after submission of the written assignment. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Use R to read in spreadsheet data into R, remove rows with missing data, rename columns, derive new variables from the existing data, and reshape data from wide to long and vice versa.
- Demonstrate an in-depth understanding of the underlying assumptions for t-tests, ANOVA, Chi-Square tests, and simple linear regression.
- Develop a critical understanding of the principles of data visualization.
- Use R to generate graphical output (including choropleths) using the key R packages, including ggplot2.
- Use R to conduct cross-tabulations, t-tests, ANOVA, Chi Square tests and simple linear regression, logistic regression, and critically interpret the output.
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Reading List
i. Chambers, Ray L., and Chris J. Skinner, eds. Analysis of survey data. John Wiley & Sons, 2003. Analysing surveys with R
ii. http://r-survey.r-forge.r-project.org/survey/
iii. https://datascienceplus.com/using-r-to-analyze-evaluate-survey-data-part-1/
Epidemiologist handbook
iv. https://epirhandbook.com/en/survey-analysis.html
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Additional Information
| Graduate Attributes and Skills |
1. To demonstrate knowledge of descriptive and inferential statistical approaches.
2. To be able to apply appropriate statistical tests to a defined problem and critically interpret the results.
3. To effectively work in a peer relationship in problem solving activities.
4. To critically apply knowledge and skills to communicate statistical output effectively to a non-specialist audience.
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| Keywords | Data analysis,survey,R,introduction,spreadsheet,regression |
Contacts
| Course organiser | Dr Glenna Nightingale
Tel: (0131 6)50 6651
Email: Glenna.Nightingale@ed.ac.uk |
Course secretary | Mr David Morris
Tel: (0131 6)51 3969
Email: dmorri14@exseed.ed.ac.uk |
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