Logo Doctoral Certificate Program in Agricultural Economics Deutsch

High quality research data - Sources, collection and processing


Dr. Lena Kuhn, Kuhn@iamo.de
Dr. Ihtiyor Bobojonov, Bobojonov@iamo.de
Prof. Dr. Dr. h. c. Thomas Glauben
Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale)

Course description

The quality of research data has strong influence on the success of any scientific work. The selection, collection and processing of data influences the quality and time required for research projects. This course offers guidelines for PhD students to plan their data strategy and thus build the fundament for a high-quality thesis. The aim of this lecture is to improve practical, basic methodological and analytical skills of participants in preparation and is thus complimentary to existent courses on statistics and econometrics. While we touch upon the topic of qualitative data collection and procession, the examples and exercises are mainly focusing on quantitative data.

The course helps to find answers to following question:
Which type of data is appropriate for my research aim?
How to properly design and implement my own survey?
How to process and employ primary and secondary data to guarantee scientifically sound results? How to avoid methodological mistakes that will bias my data?
How to check the trustworthiness of my data source?
How can experimental data and Big Data help to solve more complex research problems?

Course requirements

The course aims at junior researchers from the field of economics, sociology and geography. Participants are required to fill a questionnaire on their research interests as well as prepare a short presentation of a research topic of their choice prior to the course. Demonstrations and exercises will be conducted with R Studio. Participants without any prior experience in R and quantitative methods are recommended to get accustomed with the basic functions of the software prior to the course. Advanced econometric and statistical methods are not the focus of this course.

Course credits

You will receive course credits (3 credits) for completing exercises during the course and submitting your seminar assignment within four weeks after the conclusion of the course.

Course outline

Day 1: Basics of research data collection and management

Day 2: Survey data

Day 3: Working with secondary data

Day 4: Mastering causality

Day 5: Big Data