Please prepare the following for your first meeting with your advisor.
(Re)share your thesis proposal
- Please email/post your research proposal to your coach, even if you haven’t updated it after handing it in (PDF format). Do so at least three days before the meeting.
- If you’re supervised in a team, send the research proposal to your thesis partner, too.
Make an overview of the data that you have or plan to use
- e.g., inform yourself about the dataset, make some descriptive statistics if possible; see also the section on describing your data on this site
Make an overview about the software stack you’re planning to use
- Which software to accomplish which (pipeline) task? (e.g., R for data preparation, Python for k-means clustering, R for fixed-effects model, etc.)
Prepare a rough sketch about your project pipeline
Know this website, and the various requirements that are already relevant at this stage of your thesis process
Make an evaluation of your individual strengths and weaknesses and share those with me! (this way, we can anticipate problems)
- What’s your ambition with this thesis?
- Where do you want to be in five years from now?
- What are you really good at?
- Which areas excite you, and wish to learn more about?
- What is it that you really dislike?
Preliminary meeting agenda
We will most likely discuss the contribution of your study, your data, and your project pipeline.
- Getting to know each other (e.g., talk about ambitions and strengths/weaknesses; role of first advisor/second advisor)
- Setting expectations
- Discuss project ideas and contribution
- Study these sections well and be prepared to pitch your research!!!
- Have a list of potential contributions of your research
- Have an initial conceptual model
- Data availability and research fit
- Clearly inform your advisor about data availability, or the steps that you still need to undertake to receive your data.
- Discuss sketch of your project pipeline
- Skill development plan
- Which skills (e.g., data analysis, data preparation) are needed for the successful completion of your thesis?
- Which courses should you still follow (e.g., data preparation in R, online data collections)
- Time planning and next meetings
- Revised introduction
- Prepare literature review
- Organize your project (directory structure, workflows)
- Complete gathering data, start inspecting it