Logo Doctoral Certificate Program in Agricultural Economics
Course of Study

High quality research data - Sources, collection and processing


Dr. Lena Kuhn
Dr. Ihtiyor Bobojonov, Bobojonov@iamo.de
Prof. Dr. 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 the scientific work. Survey data, experimental data, remote sensing data (e.g. satellite, drones) as well as secondary statistics are common sources of data in agricultural and development economics. Meanwhile, the selection, collection and processing of data influences the quality and time required for the research. This course offers guidelines for PhD students to plan their data strategy and thus build the fundament for a high-quality thesis. At the same time, the course is also suitable for researchers at later stages of their PhD projects, who would like to supplement with additional data sources or plan to develop research projects for further career development. 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.

The aim of this course is to provide students with the necessary methodological skills to obtain and handle the desired data for their scientific work. 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 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?

Course requirements

Course credits

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

Course outline

Day 1: Basics of research data collection and management

Day 2: Quantitative questionnaire design

Day 3: Qualitative data and remote sensing data

Day 4: Survey sampling and implementation

Day 5: Processing data