NIFK19006U Managing and Analyzing Data in Social Science
MSc Programme in Agricultural Economics
MSc Programme in Environmental and Natural Resource Economics
Are you feeling the constraints of excel spreadsheets?
The amount of data available is increasing dramatically. Your future career as well as doing your Master thesis will require you to handle and extract information from large quantities of data. We have designed this hands-on course to equip you to meet the data challenges ahead.
You will be introduced to concepts, terminology and methods relevant to handling data and spatial information in R. At course end, you will have a toolbox of scripts enabling you to optimize data management procedures by looping through data and using vector oriented iterative processes. You will work in R studio writing and debugging code for merging datasets, data cleaning and coding of different types of variables as well as overlaying spatial layers.
You will also be introduced to basic procedures for analysis. This includes tabulating basic statistical measures, the specification of regression models and interpreting and visualizing results. Throughout the course, the focus will be on writing, adapting and implementing code in R scripts.
The course aims to develop students’ skills to conduct own data management and analysis through hands-on work is groups. The last week of the course will be independent (unsupervised) group project work with empirical datasets.
The course mainly uses the free statistical software package R and briefly introduces the geographical information software Q-GIS.
Don’t be a slave to the spreadsheet. Join our course and become part of an ever-increasing vibrant community using the object-oriented programing environment R as their playground.
The aim of this course is to provide participants with tools and experience in managing and analyzing data, using cross sectional and spatial data from the social sciences as examples, that would be required to conduct a MSc thesis project or do research based on quantitative data in social sciences and beyond.
Knowing codes required to identify different types of datasets and variables (incl. the nature of maps and geodata) and the implications for the choice of appropriate data management procedure and analysis strategy
Show an overview of principles and procedures for importing, merging, coding, transforming and otherwise preparing data for statistical analysis in R
Know the arguments for using scripts
Possess an overview of basic approaches to quantitative data analysis
Apply procedures for managing different types of data in R in preparation for statistical analysis
Ability to combine different data sets and produce composite maps from multiple sets of digital spatial data
Implement statistical analysis in R to derive basic cross-sectional and spatial metrics and estimate linear regression models
Solve coding problems in data management and basic statistical analysis in R
Generate figures and graphs to interpret, visualize and present statistical results in a clear and concise manner
Formulate and implement a strategy for solving data management and analysis problems by combining tools from different packages in R to address analytical research problems in relation to empirical datasets in the context of social science
Program a script including debugging using internet and other sources to answer specific research questions
No obligatory literature curriculum. Relevant material will be shared through Absalon.
Academic qualifications equivalent to a BSc degree are recommended.
- Practical exercises
- Project work
- 7,5 ECTS
- Type of assessment
- Oral examination, 15 minutes
- Type of assessment details
- The exam involves a plenum presentation of relevant code with the objective of furthering learning, including through failed attempts to solve coding problems. Students will be assessed individually based on a short oral presentation, in plenum, of the course project taking departure in their script with data management procedures, and output of analysis such as tables, figures and models testing their research questions and hypothesis.
- Exam registration requirements
The exam is conditional on students' course participation, which includes handing in a written unsupervised group assignment (the course project) Thursday, the third week of the course. Students will select a dataset and develop a data management and analysis strategy for their course project. Students will receive feedback on their project Friday, the last day of the course.
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
several internal examiners
- Exam period
The exam is scheduled Friday in week 34 - the last day of the course.
As the ordinary exam.
If the student has not handed in a written assignment (i.e. the course project) then it must be handed in three weeks prior to re-exam. It must be approved before the exam.
Criteria for exam assesment
To pass the course the student must convincingly fulfil the learning outcomes described above and display command of the packages and individual commands and procedures covered by the curriculum.
- Course code
- 7,5 ECTS
- Full Degree Master
- The course begins om Monday, 5 August 2024 and ends on Friday, 23 August 2024
- Summer course. Every day from 9 to 16 the first two weeks. The third week is independent work on group assignments.
- Course capacity
- 40 persons
The number of seats may be reduced in the late registration period
- Study Board of Natural Resources, Environment and Animal Science
- Department of Food and Resource Economics
- Faculty of Science
- Martin Reinhardt Nielsen (4-7277736e456e6b777433707a336970)
Toke Emil Panduro and Martin Reinhardt Nielsen