UTServicesHRCTDCourse finderResearch Methodology and Descriptive Statistics Course

# Research Methodology and Descriptive Statistics Course

### Aims

This blended learning course is aimed at PhD candidates who have little or no experience with empirical research and data analysis

After completion of this course students will be able to:

1. formulate a clear empirical research questions, with clear units of analysis, variables and with a well-defined descriptive and/or explanatory aim;
2. formulate of a well-phrased and testable causal hypothesis;
3. identify and comprehend the implications of a causal statement (correlation, time order and the absence of a third variable);
4. select an appropriate research design, and have knowledge about the factors that may undermine validity associated with the various designs;
5. develop measurement instruments and to assess their reliability and validity;
6. sample data from a larger population, are aware of possible biases introduced in the selection process and are aware of the idea of statistical inference based on sampled data;
7. describe data, using an appropriate statistical program, in frequency tables, bar charts, histograms and box plots;
8. describe the relationship between variables, using bivariate tables and scatterplots;
9. draw conclusions and report about the results of a basic data analysis

### CONTENT

This course introduces the basic principles of empirical research in the social sciences. The role of research in the context of the empirical cycle (i.e. testing theories) and research in the context of problem solving and design will be discussed. Students will learn to formulate clear and answerable empirical research questions. They will also learn to select from various correlational and experimental research designs and different data collection methods to answer these research questions. During the course, students will develop a first understanding of the concepts of validity and reliability, and will comprehend factors that may undermine (measurement/internal/external) validity of research. Finally, they will get a basic understanding of descriptive and inferential data analysis.

You follow this course together with pre-master students of the faculty BMS. We expect that PhD students who register for this course participate actively. Although lectures and tutorials will not be organized for PhD students separately, the teachers will create a separate ‘niche’ where you can meet fellow PhD students. Although participation in lectures, tutorials and discussion boards is not formally required, it is strongly recommended.

For this course there are two partial exams and an assignment.
The assignment is graded ‘pass’ or ‘fail’, and must be passed to complete the course.
Both exams count for 50% of the final mark. The minimum mark for the partial exams must be at least 5.0 and the final mark must be at least 5.5 (so a 5.0 for one of the partial exams can be compensated via a 6.0 or higher for the other partial exam).
For both partial tests and the assignment there is a retake offered.
Results for a partial test/assignment are only valid until the following semester.

The lecturers can impose additional restrictions on the participations in the tests: only students showing sufficient participation in the course will be allowed to take part in the tests. This cannot be repaired after the course is finished.

• Trainers

Dr. Henk van der Kolk

Dr. Lyset Rekes-Mombarg

• ECTS

5

• Location

Blended Learning

• Requirements

You follow this course together with pre-master students of the faculty BMS. We expect that PhD students who register for this course participate actively. Although lectures and tutorials will not be organized for PhD students separately, the teachers will create a separate ‘niche’ where you can meet fellow PhD students. Although participation in lectures, tutorials and discussion boards is not formally required, it is strongly recommended.

• Schedule Q1