21 August 2020
Managing and Analyzing Data in Social Science
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.
Basic statistics course recommended and some experience with R and insight in simple data management and analysis expected.
- 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
DKK 6375: EU/EEA citizens
DKK 11400: Non-EU citizens