Managing and Analyzing Data in Social Science

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.

Knowledge:

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

Skill:

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

Competencies:

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