Quentin André
Assistant Professor of Marketing

Leeds School of Business, University of Colorado Boulder

My research focuses on judgment and decision making in general, with a special interest in consumer financial decision-making, numerical cognition, and probabilistic reasoning. I also care about open-science, replicability, and reproducibility.

On this website, I share a summary of my current projects and publications, tools and software that I have programmed, and occasional thoughts and tutorials on various research-related topics.

Interests

Financial Decision-Making
Probabilistic Reasoning
Numerical Cognition
Open Science

Education

PhD in Marketing, 2018
INSEAD, France
MSc in Managerial and Financial Economics, 2012
HEC Paris, France
Bachelor in History of Arts, 2009
Sorbonne University, France

Papers

Outlier Exclusion Procedures Must be Blind to the Researcher's Hypothesis

How should researchers exclude outliers: Across the data, or within conditions? In this paper, I show that when outlier exclusions are performed in a way that is not ‘blind’ to the researchers’ hypothesis (e.g., within conditions), they increase Type I error to unacceptable levels.

PDF Code Dataset DOI Notebook

Outlier Exclusion Procedures Must be Blind to the Researcher's Hypothesis

No Evidence for Loss Aversion Disappearance and Reversal in Walasek and Stewart (2015)

Can loss aversion disappear and reverse? In this article, we show that the shifts in loss aversion reported in Walasek and Stewart (2015) are driven by an invalid experimental paradigm. After correcting this error, we do not see evidence that loss aversion is shaped by the distribution of gains and losses that people have encountered.

PDF Code Dataset DOI Notebook

No Evidence for Loss Aversion Disappearance and Reversal in Walasek and Stewart (2015)

Can Consumers Learn Price Dispersion? Evidence for Dispersion Spillover across Categories

Should I search for a better price? Should I accept this job offer? To answer those questions, consumers form beliefs about the dispersion of prices or salaries… but how accurate are those beliefs? We provide evidence for a phenomenon we call dispersion spillover: Seeing more (vs. less) dispersed prices in one category leads people to perceive more price dispersion in another category. This dispersion spillover influences consumers’ judgments of price attractiveness and changes their likelihood to search for (and find) better options.

Can Consumers Learn Price Dispersion? Evidence for Dispersion Spillover across Categories

Healthy through Presence or Absence, Nature or Science? A Framework for Understanding Front-of-Package Food Claims

Consumers intuitively categorize front-of-packaging claims displayed on food into 4 broad categories: (1) Claims about “removing negatives,” (2) claims about “adding positives,” (3) claims about “not adding negatives,” and (4) claims about “not removing positives.” Each type of claim is associated with different beliefs about the healthiness, tastiness, and dieting properties of the food.

Healthy through Presence or Absence, Nature or Science? A Framework for Understanding Front-of-Package Food Claims

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

In this summary of a Choice Symposium session, we explore the opportunities and challenges that the development of automation and artificial intelligence pose to consumers. They can, on the one hand, contribute to consumer well-being by making choices easier, more practical, and more efficient. On the other hand, they can also undermine consumers’ sense of autonomy, the absence of which is detrimental to well-being. Drawing from diverse perspective, we explore the relevance and importance of autonomy to consumers, and identify open research questions in this domain.

Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

Softwares

affectiveSamplerJS

A Javascript library to record people’s real-time affective reactions to audio/video sequences.

See on GitHub See on CodePen

distBuilder

Are you studying people’s perceptions of probabilities or other numerical quantities? distBuilder allows you to add distribution builders to your experiments with minimal programming knowledge.

See on GitHub Documentation Download

distBuilder

pypcurve

This Python library implements Simonsohn, Simmons and Nelson’s ‘p-curve’ analysis.

pypcurve

pyprocessmacro

This Python library facilitates the estimation of moderation, mediation, and conditional processes. It replicates the features and syntax of Andrew F. Hayes’ Process Macro, ensuring a seamless transition from one library to the other.

See on GitHub Documentation Example Notebook Example Data

pyprocessmacro

pysprite

pysprite is a Python implementation of the SPRITE procedure. The original code on which this library is based was developed by James Heathers, Jordan Anaya, Tim van der Zee and Nicholas JL Brown.

See on GitHub Read Article

pysprite

wordSearchJS

A Javascript library to add Word Search Tasks to online and offline experiments.

See on GitHub See on CodePen

wordSearchJS

Recent Posts

Quentin André

Mar 2, 2021 9 min read

Correctly Dealing with Outliers (Part Two)

A two-part blog post on outlier exclusion procedures, and their impact on false-positive rates.

Quentin André

Feb 25, 2021 13 min read

Correctly Dealing with Outliers (Part One)

A two-part blog post on outlier exclusion procedures, and their impact on false-positive rates.

Quentin André

Aug 13, 2020 7 min read

How Not To Deal with Outliers

A short case study showing how not to deal with your outliers, featuring a recent paper published in psychology.

Quentin André

Apr 14, 2018 3 min read

Recording how Participants Use distBuilder

Do you wonder how respondents are using distBuilder to construct their final distribution? This blog post shows how you can record the history of allocations that participants have constructed in distBuilder.

Quentin André

Apr 8, 2018 8 min read

Cleaning and Analyzing distBuilder Data

You have used the distBuilder library to collect data, now what? This post walks you through the basics of cleaning and analyzing distribution builder data.