The new command gsem allows us to fit a wide variety of models; among the many possibilities, we can account for endogeneity on different models. As an example, I will fit an ordinal model with endogenous covariates. Read more…
Update 07 June 2018: See Export tabulation results to Excel—Update for new features that have been added since this original blog.
There is a new command in Stata 13, putexcel, that allows you to easily export matrices, expressions, and stored results to an Excel file. Combining putexcel with a Stata command’s stored results allows you to create the table displayed in your Stata Results window in an Excel file. Read more…
Today I want to talk about effect sizes such as Cohen’s d, Hedges’s g, Glass’s Δ, η2, and ω2. Effects sizes concern rescaling parameter estimates to make them easier to interpret, especially in terms of practical significance.
Many researchers in psychology and education advocate reporting of effect sizes, professional organizations such as the American Psychological Association (APA) and the American Educational Research Association (AERA) strongly recommend their reporting, and professional journals such as the Journal of Experimental Psychology: Applied and Educational and Psychological Measurement require that they be reported. Read more…
There’s a new release of Stata. You can order it now, it starts shipping on June 24, and you can find out about it at www.stata.com/stata13/.
Well, we sure haven’t made that sound exciting when, in fact, Stata 13 is a big — we mean really BIG — release, and we really do want to tell you about it.
Rather than summarizing, however, we’ll send you to the website, which in addition to the standard marketing materials, has technical sheets, demonstrations, and even videos of the new features.
And all 11,000 pages of the manuals are now online.
What is it about round numbers that compels us to pause and reflect? We celebrate 20-year school reunions, 25-year wedding anniversaries, 50th birthdays and other similar milestones. I don’t know the answer but the Stata YouTube Channel recently passed several milestones – more than 1500 subscribers, over 50,000 video views and it was launched six months ago. We felt the need for a small celebration to mark the occasion, and I thought that I would give you a brief update.
I could tell you about re-recording the original 24 videos with a larger font to make them easier to read. I could tell you about the hardware and software that we use to record them including our experiments with various condenser and dynamic microphones. I could share quotes from some of the nice messages we’ve received. But I think it would be more fun to talk about….you!
YouTube collects data about the number of views each video receives as well as summary data about who, what, when, where, and how you are watching them. There is no need to be concerned about your privacy; there are no personal identifiers of any kind associated with these data. But the summary data are interesting, and I thought it might be fun to share some of the data with you. Read more…
In my last posting, I introduced you to the concepts of hierarchical or “multilevel” data. In today’s post, I’d like to show you how to use multilevel modeling techniques to analyse longitudinal data with Stata’s xtmixed command. Read more…
In the last 15-20 years multilevel modeling has evolved from a specialty area of statistical research into a standard analytical tool used by many applied researchers.
Stata has a lot of multilevel modeling capababilities.
I want to show you how easy it is to fit multilevel models in Stata. Along the way, we’ll unavoidably introduce some of the jargon of multilevel modeling.
I’m going to focus on concepts and ignore many of the details that would be part of a formal data analysis. I’ll give you some suggestions for learning more at the end of the post. Read more…