Shortly after I posted Tools of the trade 1—Spelling and grammar checkers, version 3.0 of the Hemingway Editor became available. The changes are evolutionary, rather than revolutionary, but provide some useful new features. The update is free to owners of version 2, which is generous for an update of this magnitude.
This is the first of a series of posts on “Tools of the trade.” It will focus mostly on software, although you can see my thought on using a Mac rather than a Windows computer here
Mindmapping has been around for a long time in various forms and there are many software packages available. In this post, I discuss how I use iThoughtsX to plan my classes. Nothing I’ll discuss is specific to iThoughts since many other packages offer similar features. Having tried most of them, I’ve found that iThoughts best meets my working style. I especially appreciate its amazing export options and the fact that files move seamlessly from its Mac to iPad versions.
I’ve been a Mac fan since 1984 because, well, I think the Mac OS is more intuitive, stable and enjoyable to use. But, totally aside from that, I think there are three reasons that a Mac may be a better choice for many PhD students.
One of the features of Stata 13 and later is “Projects”, which are meant to provide easier access to multiple files related to a, well, project you are working on. The files can be do files, data, logs, graphs, etc. In fact, they don’t even need to be Stata files. One advantage I have found is that they make it possible to maintain a strict organization of certain types of files going in certain directories, while still having access to all of those files from one pane within Stata.
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.