Advice to PhD students

A consistent scheme for naming your variables is very helpful. It makes coming back to a project after it’s been under review for 3 months much easier and is especially valuable when collaborating with someone else. This is one of those points where there are bad practices and good practices, but no “right” practice. More important is consistent project within (ideally across) projects. So, as a starting point for your consideration, here is what I have developed over time, through lots of trial and error. I think this approach make it easy to find variables and understand their provenance.

Technological progress continues. In an older posting, I mentioned the role of specialized packages that addressed models not available in the general purpose software, such as LISREL for structural equation modeling (SEM). That example is now somewhat moot, as Stata 12 has an extensive SEM capability and new add-ons for R allow modeling of SEMs. I suspect that if I were a power user, I would find limitations in Stata/R relative to the dedicated packages, but at my level, I haven’t found them.

There are many different approaches to writing and documenting the many steps that go into an empirical project. J. Scott Long has a great book, The Workflow of Data Analysis Using Stata, which I strongly recommend. He recommends developing a series of small, highly focused do files, which are run in sequence as needed. I take a different approach, which is keep all of a project’s code in one honking large do file, which is divided into sections.

Excel has caused more trouble for more doctoral students than I care to think about. Doctoral students can hurt themselves with Stata in at least two ways (there may be more). Using it to clean, combine and otherwise manage data Cutting and pasting results into Excel (or worse yet, Word) and then formatting them for presentation Both of these a very inefficient uses of time. The first is a disaster for data integrity, because it is hard to document, almost impossible to revise, and very easy to mess up (sort only have the variables, be one row off when pasting, etc.

This is an amazingly contentious question. My first answer is “If you are comfortable with a package and it is serving your needs, keep using it.” That can be complicated, of course, if you have a co-author dedicated to a given statistics package. If your only need to is pass data back and forth with that co-author, I strongly recommend Stat Transfer, which can convert from pretty much any statistical format to any other.

A presentation from the doctoral workshop at the 2016 West Coast Research Symposium on Innovation and Entrepreneurship.

A presentation from the orientation session for incoming PhD students at the University of Illinois. My central point was that students should think of the entire PhD experience holistically, rather than as a series of discrete phases, e.g., classes, dissertating, being on the market.