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Small sample size approaches in relational and intervention designs



This course provides an overview of potential approaches when you (need to) work with small sample sizes. We distinguish at least two origins of small sample sizes. First, you may need to work with an unpredictably small sample due to e.g. large scale dropout or data collection being unexpectedly cut off. We discuss which (statistical) approaches might aid in obtaining scientifically grounded and robust results.

Second, you may know a priori that you have to deal with small samples, either by design or because the target population is small due to low prevalence, or just very hard to reach. In clinical settings this is often the case. In order to be able to test causal relationships in small sample sizes research, Single Case Experimental Studies (SCED) can be used. This approach will be discussed in detail, with focus on the design, and several visual and statistical analysis techniques. Several tools will be used to gain hands-on experience with these techniques. We will discuss implications and limitations of small sample analyses, as well as ethical considerations.

Learning Goals

After this course, the student is able to…

  • gain advanced, up to date knowledge of quantitative and qualitative research methodology in the context of small samples;

  • critically evaluate philosophical and ethical matters concerning scientific research with small samples;

  • critically select, study and analyze literature relevant to the issues and problems encountered in the context of small samples;

  • independently formulate, perform and assess scientific research at a level suitable to preparing scientific publications.

  • connect scientific knowledge and insights to small sample issues in developmental psychopathology and educational science.

  • write scientific reports in English;

  • engage in the international academic debate.

  • independently acquire new knowledge and skills relevant in a professional context or PhD-programme;


For the timetable of this course please refer to MyTimetable

Mode of instruction

  • Combined lecture and workgroups, through interactive meetings every week

  • Practical exercises

  • Discussion of relevant literature

Assessment method

Performance will be evaluated through
a) presentations in each session discussing the relevant literature for that particular week,
b) a reflective paper describing a suggested approach for a given research/design/data collection scenario,
c) a written exam consisting of multiple choice and open questions.

Part (a) will be graded with a “pass” or “fail”, based on presentations of and participation in the discussion of the weekly literature, to the combined judgment of the instructors.

The two parts (b) and (c) are equally weighted into a single final grade. This grade will be released under the condition that (active) participation in (a) was graded with a “pass”.

The final grade should be higher than 5.5 to pass the course. Any of the parts can only be retaken if that individual part grade is unsatisfactory (lower than 5.5).

Reading list

Recent advances through research papers, to be announced around the start of the course.


Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register via My Studymap up to 5 days prior to the start of the course.

It is mandatory for all students to register for each exam and to confirm registration for each exam in My Studymap. This is possible up to and including 10 calendar days prior to the examination. You cannot take an exam without a valid pre-registration and confirmation in My Studymap.

Carefully read all information about the procedures and deadlines for registering for courses and exams.


Co-ordinator of this course is dr. Ralph Rippe