NIFK17003U Managing and Analysing Cross Sectional and Spatial Data in Social Science

The amount of data publically available from online sources is increasing dramatically. It is crucial that aspiring researchers and natural resource managers know how to handle these large quantities of data and how to extract information from them. Recognizing these challenges and the inadequacy of spreadsheet approaches to data analysis this course aspires to equip students to the challenge ahead.

The course aims to provide students insight into procedures for appropriate data management and critical analysis of empirically derived quantitative socioeconomic and spatial data as would be required to conduct a MSc thesis or do research.

Students will be introduced to concepts, terminology and methods relevant to handling cross-sectional data (and time series and panel data if time permits) and geo-referenced spatial information originating from individual, household and institutional level quantitative socioeconomic surveys. This includes using scripts for writing and debugging code for data management procedures in relation to merging datasets, overlaying GIS map layers, assessing data quality, data cleaning and coding of different types of variables (outlining the coding principles), identifying and handling outliers, selecting appropriate transformation methods and generating new variables intended for testing specific hypotheses.

Students will also be introduced to basic statistical data analysis procedures. This includes developing testable hypothesis based on research questions, developing a data analysis strategy, and selecting appropriate statistical methods to test specific hypothesis such as about intergroup and spatial patterns in the data. Examples that will be used are tabulating basic statistical measures, specification of linear regression models including interactions and interpreting and visualizing model results. Throughout the course, 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 a series of lectures, hands-on group exercises and student presentations of assignments based on provided empirical research datasets. The last week of the course will be independent (supervised) group project work.

The course uses the free statistical software package R and the geographical information software Q-GIS.