Managing and Analyzing Data in Social Science
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.
MSc Programme in Agricultural Economics
MSc Programme in Environmental and Natural Resource Economics
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
The course involves hands-on writing of R code, focusing on providing students with practical programming skills. Students will implement codes from packages relevant for data management as well as analysis. Hence, learning outcomes are achieved by students individually but supported by peer groups, working on scripts with illustrative exercises. Teachers will assist when students are stuck, but the goal is for the students to become self-reliant and independent. Hence, students are expected to solve problems by, for instance, Googling how others before them have solved similar programming problems. Exercises will be based on data sets from small case studies as well as larger surveys focusing on natural resource management problems examined from a natural and social science perspective. During the exercises, the students will accumulate a command library for the relevant tasks applicable to similar data management and analysis projects.
No obligatory literature curriculum. Relevant material will be shared through Absalon.
Basic statistics course recommended, and some experience with R
and insight in simple data management and analysis expected.
Academic qualifications equivalent to a BSc degree are recommended.
- 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.
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
several internal examiners
Criteria for exam assessment
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.
Single subject courses (day)
- Practical exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- 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.
- 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 (4-7075716c436c697572316e7831676e)
Toke Emil Panduro and Martin Reinhardt Nielsen
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