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The Interviewer Fallacy: Evidence from 10 years of MBA interviews

The Interviewer Fallacy: Evidence from 10 years of MBA interviews. Photo not necessary. Francesca Gino HBS. Uri Simonsohn. Motivation. How is a journal editor like a venture capitalist? Continuous flow of judgments  “random” “daily” subsets. Research question: Impact of subsetting ?

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The Interviewer Fallacy: Evidence from 10 years of MBA interviews

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  1. The Interviewer Fallacy:Evidence from 10 years of MBA interviews Photo not necessary Francesca Gino HBS Uri Simonsohn

  2. Motivation • How is a journal editor like a venture capitalist? • Continuous flow of judgments “random” “daily” subsets. • Research question: Impact of subsetting? Narrow bracketing +Belief in law of small numbers interviewer fallacy Definition. Reluctance to create subsets of judgments that differ too much from expected distribution.

  3. Paper in one slide • Data: 1-5 Rating of MBA interviewees • Handful per day. • corr[avg(so far), this interview]<0 • Ruled out alternatives: • Contrast effects • Non-random sequence

  4. Data Description • A business school gave us data • 10 years: N=9,323, k=31 ***INTERRUPT THIS TALK TO COMMENT ON ANOTHER PROJECT*** False-Positive (PsychScience2011): “list all your variables” Naysayers: “love to, have too many” Authors of False-Positive: “really?” Uri: “watch me.”

  5. Note: another 22 variables are listed in this page

  6. Other side of that single sheet of paper

  7. Note: The .pdf weighs 13Kb. The Wharton logo from slide 1: 11kb A hardliner may say: Only reason to choose not to post is to hide information from readers.

  8. Back to this talkData Description • A business school gave us data • 10 years: N=9,323, k=31* • Interviews per day M=4.5, SD=1.9 • Cluster SE [repeated measures] • Info on: • Applicant (e.g, GMAT scores, experience, race, gender) • Interviewer identity • Interview: time, date • Ratings (1-5 likert) • 5 subscores: communication, leader, etc. • Overall score (M=2.9, SD=0.9)

  9. Would like to analyze like gambler fallacy • HHHHpr(T)↑ • Problem • Non-binary data • Covariates • Different interviewers

  10. Instead: Scorek,i = OLS(average score so fari, covariates) k: Interviewee, 1 to N that day. i : Interviewer Prediction: <0

  11. Effect Size • Average interview 1 point higher, • Equivalent to losing: • 40 GMAT points, or • 30 months of experience.

  12. Alternative Explanations • Contrast effects • Non-random sequencing of interviews

  13. Contrast vs. Interviewer Fallacy Two divergent predictions: • Same effect on the interview subscores? Explanation Prediction Contrast: yes, and stronger Int.Fallacy: no, or at least weaker. Data: • Every one of five subscores:n.s. • Average a-la Robyn Dawes:n.s. • Biggest point estimate, ¼ as big • one is >0

  14. Contrast vs. Interviewer Fallacy Two divergent predictions: 2) Effect as end of day approaches. Explanation Prediction Contrast: weaker (arguably) Int.Fallacy: stronger (absolutely) Data: Estimate same regressions for: • last interview of day • 1 interview left • 2 interviews left

  15. Effect of previous interviews as day’s end approaches

  16. Alternative Explanations • Contrast effects • Non-random sequencing of interviews

  17. If better candidates follow bad ones or vice-versa  spurious finding. • Can we predict objective quality with average-interview-score-so-far? • Test: GMAT=OLS(avg.score) Job Experience = OLS(avg.score)

  18. Same table + 2 new columns

  19. Possible Mechanisms • Gambler fallacy + confirmation bias • Mental Accounting • Accountability

  20. A note on the internal validity of non-lab data • In the lab: hard to study interviewer fallacy • Participants could be learning about • Scale use • Distribution of underlying stimuli quality • Some psychological questions are better studied outside the lab. • This seems likes one of them.

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