Managing and Analysing Cross Sectional and Spatial Data in Social Science

About

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.

Target group

Master

Course aim

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 natural and social sciences and beyond.

Knowledge:

Describe different types of datasets and variables (incl. the nature of maps and geodata) and the implications for 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 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 log-files and developing an analysis strategy in relation to good scientific practice

Present an overview of basic approaches to quantitative data analysis

Explain the concepts of predictions and residuals

Describe the underlying assumptions and conditions for valid application of relevant statistical tests

Skill:

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 and select appropriate approaches to test the hypothesis using basic statistical methods

Implement statistical analysis in R and Q-GIS 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 and Q-GIS.

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

Competencies:

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

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 and model assumptions and limitations in relation to conclusions drawn from the analysis

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