Research Design and Statistics

Course manual 2019/2020

Course content

Flaws in the design of experiments hamper the reliability of outcomes and slow down scientific progress. Unnecessary replication studies may not only be costly in terms of time, but also have ethical consequences (e.g. use of experimental subjects). Developing the capacity for apppropriate experimental design is therefore important.

This course will increase your awareness of the big and small decisions that need to be taken in order to carefully design experiments and improve the quality of experimental studies. To increase this awareness students will practice careful, imaginative, and develop critical reflection on features of existing experimental studies. Students will also design a study themselves yourself during this course.and critically reflect on the design of others.

Critical reflection will be done in weekly written assignments on which you will give and receive feedback from your peers. As part of the feedback process, questions and issues will be discussed in plenary meetings and one-to-one contact during practicals.

The written weekly assignments (also graded), peer feedback, lectures and class discussions will prepare you for the final assignment of this course: the design of an experimental study that you will defend and discuss in an oral exam.

Alongside the experimental design meetings there will be lectures and practical trainings to refresh and expand your knowledge of state-of-the art statistical analyses.

Students will learn to assign the appropriate statistical analyses for specific hypotheses and how to apply those to data sets.  

Objectives

At the end of this course, students:

• Have a better understanding of the connection between research question, research design and the appropriate statistics
• Be more aware of unwanted effects such as bias and confounding variables, and how to control them
• Have improved their skills in reading, understanding, and criticizing a study’s design
• Have improved skills in designing an experiment and justifying its design
• Know how to estimate the statistical power of statistical analyses
• Have refreshed and expanded their knowledge of statistical techniques to summarize quantative data and to test multivariate hypotheses with ANOVA, ANCOVA and multiple linear regression
• Know how to use R with common statistical analyses

Teaching methods

• Lecture
• Seminar
• Computer lab session/practical training
• Presentation/symposium
• Self-study
• Working independently on e.g. a project or thesis
• Supervision/feedback meeting

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. In the case of practicals, the student must attend at least 80%. Should the student attend less than 80%, he/she must redo the practical, or the Examinations Board may have one or more supplementary assignments issued.
2. In the case of study-group sessions with assignments, the student must attend at least 80% of the study-group sessions. Should the student attend less than 80%, he/she must redo the study group, or the Examinations Board may have one or more supplementary assignments issued.
3. The student must attend 80% of the teaching per study unit of the mandatory courses, entry courses and specialisation courses.

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Last year's course evaluation

In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.

Contact information

Coordinator

• drs. E. Lobach