ICPSR course July 28-Aug 1 (virtual). Sensitivity Analysis: Quantifying the Robustness of Inferences to Alternative Factors or Data
Ken Frank
Hope your summer is off to a good start!
You are receiving this because you have taken one of my workshops in the past or have indicated interest in our work on sensitivity analysis. Please circulate widely to anyone you think might be interested. Apologies for any cross postings.
Ken
ICPSR course July 28-Aug 1, virtual format: Sensitivity Analysis: Quantifying the Robustness of Inferences to Alternative Factors or Data
See below for short description
See here for detailed syllabus
Most social scientists need to consider the unobserved conditions that could change an inference. This workshop is about the statistical tools (R, Stata or the konfound-it app) to do just that, quantifying the strength of evidence to inform policy, practice, or general social science.
The course is 20 instruction hours -- half a semester's worth of instruction in just 5 days. That gives us time to tailor to specific examples or approaches in your field.
Register here. Group discounts available as well.
Check out the R Shiny app for sensitivity analysis or the web site: https://konfound-it.org/ or Podcast
Short Description (Register here)
The phrase “But have you controlled for …” is fundamental to social science but can also create a quandary. Even after controlling for the most likely alternative explanations for an inferred effect there may be some alternative explanation that cannot be ruled out with observed data. The first response is to develop the best models that maximally leverage the available data. After that, sensitivity analyses can inform discourse about an inference by quantifying the unobserved conditions necessary to change the inference.
Specifically, in this course participants will learn how to generate statements such as “An omitted variable would have to be correlated at ___ with the predictor of interest and with the outcome to change the inference.” Or “To invalidate the inference, __% of the data would have to be replaced with counterfactual cases for which the treatment had no effect.” Because these statements express sensitivity in terms of correlations or cases they have wide accessibility. Rooted in the foundations of the general linear model and potential outcomes, the techniques can be adapted to a range of analyses, including logistic regression, propensity-based approaches, and multilevel models. As a result, they can broadly facilitate discourse about inferences among researchers who seek to make an inference, challengers of that inference, policymakers and clinicians.
Participants in this sensitivity course will learn statistical conceptualization, application, and software including the pkonfound commands in R (Stata also available) as well as the app http://konfound-it.com. Participants should be familiar with statistical control as in the general linear model (i.e., regression). Participants should have their preferred statistical software installed on their computer, and optimally to have identified one or more statistical inferences (e.g., published, straight from recent output) for which they would like to quantify the robustness.
Ken Frank (zoom https://msu.zoom.us/j/783760435)
MSU Foundation Professor of Sociometrics
Measurement and Quantitative Methods
Counseling, Educational Psychology and Special Education
Michigan State University
kenf...@msu.edu
https://sites.google.com/msu.edu/kenfrank
pronouns he/him/his