Introduction to Quantitative Research Methods (QNRM)

Graduate Program (& Advanced Certificate) Status

Course Level: 
Master’s
Campus: 
Vienna
Course Open to: 
Students on-site
Remote students
Academic Year: 
2021-2022
Term: 
Fall
US Credits: 
1
ECTS Credits: 
2
Course Description: 

“Show me the numbers!” or “Do we really need more numbers here?”

Quantitative empirical data and statistical analyses can seem like holy grail of ‘real science’ or like a very complicated way of presenting obvious truths. Through hands-on work with quantitative environmental accounting data, accompanied by short lectures and in-class discussions, students will be introduced to the opportunities and challenges of this line of research and the insights it yields. The overarching aim is to qualify students both as critical consumers of quantitative research (in academic publications and popular media, especially) and as competent producers of their own basic analyses. This course aims to cater to different skill levels, allowing students familiar with quantitative methods to work on semi-independent data projects and assisting those with less experience as well as anyone who has been shying away from ‘anything with numbers’ altogether.

Learning Outcomes: 

At the end of the course, students will be able to

-        identify and describe key terms and concepts in quantitative research,

-        explain the role of measurement and units in data generation,

-        conduct basic statistical analyses of central tendency and variation and interpret the results

-        organize data and run basic functions in graphical user interface (GUI) spreadsheet software (e.g. Calc) but will also recognize command line interface (CLI) programs (e.g. R) and will be able to discuss the differences between and respective advantages of these two groups of interfaces,

-        examine the role of quantitative empirical work for environmental research and research communication,

-        identify different forms of visual representation of quantitative data and critically reflect and discuss how this impacts the message communicated,

-        organize and analyze data and create visual representations for their own mini-projects.

Assessment: 

-        Active participation in class and in-class and at-home exercises: 50%

-        Final mini-project: 50%

Prerequisites: 

None