Managing and Analyzing Data in Social Science

Course content

Are you feeling the constraints of excel spreadsheets? The amount of data available is increasing dramatically. In 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 and QGIS. At course end, you will have a toolbox of scripts enabling you to optimise 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 testing hypothesis. This includes tabulating basic statistical measures, the specification of regression models and interpreting and visualising results. Throughout the course, the focus will be on making the data handling process transparent and reflecting on the implications of data management choices and choice of statistical approach in relation to validity and reliability of the results of the analysis and good scientific practice.

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 (supervised) group project work with empirical datasets.

The course uses the free statistical software package R and 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

Learning outcome

The aim of this course is to provide participants with the tools and experience in managing and analysing data, with a focus on socioeconomic and spatial data, that would be required to conduct a MSc thesis project or do research based on quantitative data in social sciences and beyond.


Describe 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

Explain principles of good conduct in relation to data storage, documentation and anonymization of person sensitive data

Show an overview of principles and procedures for importing, merging, coding, transforming and otherwise preparing data for statistical analysis in R and Q-GIS

Describe the arguments for using scripts

Present an overview of basic approaches to quantitative data analysis


Apply procedures for managing different types of data in R and Q-GIS in preparation for statistical analysis 

Combine different data sets and produce composite maps from multiple sets of digital spatial data

Develop research questions and hypothesis

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

Interpret, visualize and present statistical results in a clear and concise manner


Formulate relevant research questions and hypothesis to address analytical research problems in relation to empirical datasets in the context of social science

Program a script to answer specific research questions

Argue convincingly for appropriate choice of data management procedure and statistical methods suitable to answer basic research questions and test hypothesis based on available data and specific empirical problems

Discuss the results of empirical data analysis in terms of relevance, reliability, validity and interpretation

Reflect critically on the implications of data quality, data handling procedures, statistical methods and tests in relation to conclusions drawn from the analysis 


Practical and theoretical considerations in relation to procedures and methods are presented in lectures supported by relevant examples. Learning outcomes are achieved through illustrative exercises for individual work and group work presented and discussed in plenum. Lecture examples and exercises will be based on small data sets from case studies as well as larger surveys focusing on natural resource management problems examined from a natural and social science perspective. Students will obtain experience and practice in evaluating empirical evidence and putting results into perspective and discussing their implications in relation to published interpretations and conclusions from these surveys. During the exercises the students will accumulate a command library for the relevant tasks applicable to a similar data management and analysis project.

Examples of relevant literature:

Paradis, E.: 2005, R for beginners.

Ricci V.: 2005 - R Functions For Regression Analysis.

Thiede R., Sutton T., Düster H., Sutton M.: 2014,  Quantum GIS Training Manual Release 1.0.

Abedin, J., & Das, K. K. (2015). Data Manipulation with R. Packt Publishing Ltd.

Osborne, J. W. (2012). Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data. Sage.


The precise literature list will be present on the course homepage in 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 is recommended.

Continuous feedback during the course of the semester
Feedback by final exam (In addition to the grade)
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Oral examination, 15 minutes
Students will be assessed individually based on a short oral presentation in plenum of test of own developed research hypothesis, script with data management procedures, and output of analysis such as tables, figures and models based on the written assignment.
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
One or more internal examiners
Criteria for exam assessment

To pass the course the student must convincingly fullfil the Learning Outcome described above.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 30
  • Practical exercises
  • 40
  • Project work
  • 96
  • Preparation
  • 40
  • English
  • 206