Since I am almost done with my first year of PhD (apart from a research proposal which is due three weeks from now) and I am exempted from qualifiers, I have started my own research project on household migration decisions in Indonesia.
I have realized that good project management practices are essential to productive researchers. This (continuously updating) post will jot down some useful tips from my view.
I: General Project Management Practices
- Make plans for the implementation of research projects and monitor progress on a regular basis.
- Create separate folders for research projects and research methods. The latter is more of a toolbox and not necessarily research-topic-specific, and separating it out makes it more readily accessible.
- Have a readme file for every folder. Five years from now, you are going to forget what “figure1” means, and you need a master file documenting (at the proper level of detail) what each file is about.
- Keep a folder for notes you take during discussions with your advisors, your colleagues, and people not in the academia. Instead of brutally repeat what they said, try to dig deeper for your own insights.
II. Data Management Using Stata
While reading this great book on data management with Stata, I took down the following notes which I found to be most helpful.
- create a master do file to link all steps in data cleaning and analysis.
- ask a friend to check my do files to make sure others can understand.
- create documentation for each project
- document key information that you may forget over time
- labeling data is not the only way; comments in do files, a journal, flow diagrams, etc can be useful.
- pick something that works for you and stick with it.
- add “version” at the top of each do file to make sure it will be executed accordingly.
- separate intermediate work from final work by
- regularly checking the old/new versions of the documents and pruning the unused ones
- create a separate folder for the intermediate work, or
- use version control software
- develop routines: use the “research methodology” folder; decompose big tasks into smaller ones; be aware of the steps to identify a research topic, find relevant literature and gaps in the literature, and data analysis.
- back up your data: use a hard drive and save it in Dropbox.
- A few notes about macros
- can be used to set “default” options for regressions, e.g. “local regopts noheader beta vce(robust)”
- identify multiple assertions without halting the do file: assert …,rc0
- locals can be combined by defining a new local
- Accessing saved results
- use “quietly summarize” and refer to the results r() to immediately generate new variables.
- for estimation results, use “ereturn list” to list the estimation results.
- use “statsby” to save estimation results in a new dataset.