Field Experiments in Organizations

Dov Eden’s review of field experiments in organizations is out at Annual Review of Organizational Psychology and Organizational Behavior (link).

Abstract

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.

What happens when you crowdsource peer review?

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: R and Acquiring Data from the Web

Roberto Palloni has a nice series of posts on using R to acquire data from the web

New blog series from Win-Vector LLC on R, Spark, and big data

The Win-Vector LLC data science blog is starting a new series on using Spark and R to handle big data.

Our goal

What we want to do with the “R and big data” series is:

  • Give a taste of some of the power of the R/Spark combination.

  • Share a “capabilities and readiness” checklist you should apply when evaluating infrastructure.

  • Start to publicly document R/Spark best practices.

  • Describe some of the warts and how to work around them.

  • Share fun tricks and techniques that make working with R/Spark much easier and more effective.