In this e-learning course (with content for one-day which can be spread out over four days), participants will be introduced to informative hypotheses, which are alternatives for the traditional null and alternative hypotheses, and to the AIC-type criterion GORIC(A) and the Bayes factor, which are alternatives for the p-value.
This course is applied, hands-on, and build around concepts and not formulas.
Since Cohen’s (1994) paper “the earth is round, p< .05” in Psychological Bulletin, there is increasing awareness that the null hypothesis – e.g., H0: mu1=mu2=mu3, where the mu’s denote the means in three groups – only rarely represents the expectations that researchers have. Informative hypotheses use equality and inequality constraints to formally represent researcher’s expectation. Two (hypothetical) examples of such hypotheses are: H1: mu1 > mu2 > mu3 and H2: mu1 - mu2 > mu2 - mu3.
Additionally, in the last years, there is increasing interest in alternatives for null-hypothesis significance testing. Two such alternative, (informative) hypothesis evaluation techniques will be introduced: model selection using the AIC-type information criterion GORIC(A) (more specifically, using GORIC(A) weights) and Bayesian model selection (using the Bayes factor). A ratio of GORIC(A) weights and the Bayes factor quantify the support in the data for a pair of hypotheses based on the fit and the complexity of the hypotheses. If, for example, the ratio of GORIC(A) weights or the Bayes factor for comparing H1 with H2 is 5, this means that the support in the data for H1 is 5 times larger than the support for H2. This would imply that H1 is the best available description of the population of interest.
In this e-learning course, it will be elaborated what the GORIC(A) weights and Bayes factor are, how they can be applied, and how they should be interpreted.
This course is tailored to PhD students, (junior) lecturers, and researchers who want to understand this approach and/or apply it for the analysis of their own data. The course is built around concepts and examples, not formulas.
The e-learning course has content for one day and will be available for four days. The first day, we will briefly meet online to get started. On the fourth day, there will be an online Q&A session and/or lab with a possibility to meet in person (depending on your wishes).
Before starting the course, make sure to either install the latest version of JASP (https://jasp-stats.org/) and/or the latest version of Rstudio and R (in that case, also install the restriktor and bain packages).
Please note that there are no graded activities included in this course. Therefore, we are not able to provide participants with a transcript of grades, only a pass.
Dr. Rebecca Kuiper
PhD students, (junior) lecturers, and junior and senior researchers.
For an overview of all our Winter school courses offered by the Department of Methodology and Statistics please click here.
Introduction to (Bayesian) informative hypothesis evaluation using JASP and/or R.
Learning goals of this course
After following this course,
you are able to:
- think outside of the null-hypothesis box;
- evaluate informative hypothesis using JASP and/or R;
- interpret the JASP and/or R output.
The total workload, that is, watching the video clips and completing the suggested lab materials, is estimated to be around 8 hours. This can be spread out over the course of four days.
You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore, we cannot provide a transcript of grades.
The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.
PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.
Utrecht Summer School does not offer scholarships for this course.