last reminder: open to sit in on course lectures: what would it take to change your inference? March 30 & April 1,1pm-2:20pm eastern time

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Ken Frank

Mar 29, 2020, 1:27:16 PM3/29/20
to KonFound-it!

I teach sensitivity analysis as part of my course on regression.  I divide the standard workshop into two pieces.  Join us if you like.


When: March 30 1-2:20 pm and April 1 1-2:20 p.m. eastern time.


What: course lectures: "What Would it take to Change your Inference?"

            Materials: powerpoint for combined frameworks 3 hours

Note that we will reverse the order of frameworks in the ppt. 

March 30:

  Introduction: slides 1-7. 

  Correlation framework: slides 91-120

April 1 (possibly into April 6):

  Case replacement framework: slides 8-80.


What Would it take to Change your Inference?


Statistical inferences are often challenged because of uncontrolled bias.  There may be bias due to uncontrolled confounding variables or non-random selection into a sample.  We will answer the question about what it would take to change an inference by formalizing the sources of bias and quantifying the discourse about causal inferences in terms of those sources.  For example, we will transform challenges such as “But the inference of a treatment effect might not be valid because of pre-existing differences between the treatment groups” to questions such as “How much bias must there have been due to uncontrolled pre-existing differences to make the inference invalid?”


In part I we will use Rubin’s causal model to interpret how much bias there must be to invalidate an inference in terms of replacing observed cases with counterfactual cases or cases from an unsampled population. This generates statements such as “One would have to replace qqq% of the cases with cases with no effect to invalidate the inference.” In part II, we will quantify the robustness of causal inferences in terms of correlations associated with unobserved variables or in unsampled populations. This generates statements such as “An omitted variable would have to be correlated at rrr with the treatment and outcome to invalidate the inference.” Calculations for bivariate and multivariate analysis will be presented using an app: as well as macros in STATA and R and a spreadsheet for calculating indices [KonFound-it!].



The format will be a mixture of presentation, individual exploration, and group work. Participants may include graduate students and professors, although all must be comfortable with basic regression and multiple regression. Participants should bring their own laptop, or be willing to work with another student who has a laptop. Participants may choose to bring to the course an example of an inference from a published study or their own work, as well as data analyses they are currently conducting.



All related materials can be found at:



To participate on zoom you will click on

Phone: One tap mobile
+16468769923,,783760435# US (New York)
+16699006833,,783760435# US (San Jose)

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        +1 646 876 9923 US (New York)
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Meeting ID: 783 760 435

Please do not engage zoom more than a few minutes before our meeting as I may be in another Zoom meeting.  Thanks






Check out the R Shiny app for sensitivity analysis

R Shiny app KonFound-it


Our approach to school governance:



Ken Frank

MSU Foundation Professor of Sociometrics

Measurement and Quantitative Methods

Counseling, Educational Psychology and Special Education

Room 462 Erickson Hall

620 Farm Lane

Michigan State University

East Lansing, MI 48824-1034 phone: 517-355-9567 fax: 517-353-6393


pronouns he/him/his


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