Dov Eden’s review of field experiments in organizations is out at Annual Review of Organizational Psychology and Organizational Behavior (link).
Field experimentation, although rare, is the sterling-gold standard of orga-
nizational research methods. It yields the best internally valid and general-
izable ﬁndings compared to more fallible methods. Reviewers in many psy-
chology specialties, including organizational psychology, synthesize largely
nonexperimental research, warn of causal ambiguity, and call for experi-
mental replication. These calls go mostly unheeded. Practical application
is a raison d’ˆetre for much organizational research. With the emergence of
evidence-based management, ﬁeld experiments enable us to deliver the most
actionable tools to practitioners. This review explicates the role of experi-
mental control and randomization and enumerates some of the factors that
mitigate ﬁeld experimentation. It describes, instantiates, and evaluates true
ﬁeld experiments, quasi-experiments, quasi-ﬁelds, combo designs, and tri-
angulation. It also provides practical tips for overcoming deterrents to ﬁeld
experimentation. The review ends describing the merging of new technolo-
gies with classical experimental design and prophesying the bright future of
organizational ﬁeld experimentation.
Chris Lee at arstecnica covers some academics who are trying it
When this approach was tested (with consent) on papers submitted to Synlett, it was discovered that review times went way down—from weeks to days. And authors reported getting more useful comments from their reviewers.
The forum is open, so as a reviewer, you can see comments accumulating, and you know the editor is going to close comments at some point soon. You are either going to do the job now or not do it at all—you can’t put the editor off for three weeks before deciding that you don’t have time.
Roberto Palloni has a nice series of posts on using R to acquire data from the web
I created a short .do file comparing several ways of estimating a two-period difference-in-differences model in Stata.
Code can be found on my Github page.
The Win-Vector LLC data science blog is starting a new series on using Spark and R to handle big data.
What we want to do with the “
R and big data” series is:
Give a taste of some of the power of the
Share a “capabilities and readiness” checklist you should apply when evaluating infrastructure.
Start to publicly document
Spark best practices.
Describe some of the warts and how to work around them.
Share fun tricks and techniques that make working with
Spark much easier and more effective.
Modeling Game of Thrones family network ties in R. Discusses important network measures like centrality, degree, closeness, betweenness centrality, diameter, transitivity, and others.
The University of North Carolina Population Center has a nice overview of ways to export Stata results to Word and other text processors.