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  • Reply to: Making a short presentation based on your research: 11 tips   1 day 16 hours ago

    This article is something that I have been looking for It was a great read!

  • Reply to: Some advice from survey implementers: Part 1   4 days 21 hours ago

    Fantastic advice, says an SO! The IRB point is of particular importance. I'd say it is important to learn from the SO's experience and guidance here if they have a good track record with the local IRB application process.
    I look forward to Part 2!

  • Reply to: Finally, a way to do easy randomization inference in Stata!   6 days 10 hours ago

    Hi! I wondered if you ever solved this? I have a similar problem where I have aggregate state-level data from 2000-2012 & a policy that was implemented in some states in 2007 and others in 2009. I have a similar dichotomous variable - treatment - which is 1 during treatment years in treated states, and 0 otherwise). The code for my regression is:

    xtreg outcome treatment i.year i.state, vce(robust)

    For the ritest, I understand I need an interaction variable so that I'm evaluating _b[c.treatment#c.postperiod]. But my problem is, given the postperiod differs between states (sometimes it is 2007+ and sometimes 2009+) & we don't know what it would have been for the control countries, how do I model this? I think I need to program a two-stage randomization process (randomized to control/treatment & randomized to 2007/2009) - but I am struggling with writing this program. Specifically, I can't tie the second randomization (randomization of years in the postperiod variable) into the program properly so that this part of randomization is implemented.

    Thanks for any advice!

  • Reply to: Finally, a way to do easy randomization inference in Stata!   1 week 7 hours ago

    In the past months I received several emails asking about how to implement the pairwise tests for experiments with more than two treatment groups (Point 7 above). I thus added a new option to -ritest- that makes this easy.

    Using the option fixlevels(list of levels), one can restrict the re-randomization to hold all observations fixed that have the specified values for the treatment variable. In terms of the example David gave in this post, this could be simply done by specifying:

    ritest treatment (_b[0.treatment]-_b[1.treatment]), cluster(clustervar) strata(stratavar) fixlevels(2): reg y i.treatment

    The way the option is implemented internally is fairly straightforward, by creating a temporary stratum for each of the specified levels during the re-randomization (Jason Kerwins's idea). The new version of -ritest- can be obtained from my GitHub. Via Stata:

    net describe ritest, from(https://raw.githubusercontent.com/simonheb/ritest/master/)

    Please don't hesitate to contact me for bug reports or if you have questions.

  • Reply to: Make Your Research Known – 10 Tools to Increase Consumption of Your Research   1 week 1 day ago

    Very useful the information