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Using Machine Learning Techniques to
Examine the Relationship between
Money, Personality, and Well-being
Rosa Lavelle-Hill
Introduction
Aim: To illustrate how machine learning tools can aid
psychological investigation
Collaborators: James Goulding2, David D. Clarke1, Anya Skatova3, and Peter A. Bibby1
1 Department of Psychology, University of Nottingham
2 N-LAB, Business School, University of Nottingham
3 School of Experimental Psychology, University of Bristol
Why a Machine Learning Approach?
•The goal of understanding human behaviour involves being able to explain behaviour,
and then to predict it.
•Prediction and explanation are not synonymous (Shmueli, 2010)
•Increased false positive results/ inflated effect sizes (Yarkoni & Westfall, 2017).
•‘Overfitting’
•Successful replications in Psychology is relatively low (Open Science Collaboration, 2015; Yarkoni &
Westfall, 2017).
•Machine learning models can be optimised to the point in which they generalise best
Our Data
•Loyalty card transaction data
•Large health and beauty retailer
•80,000 customers
•12,968 fully completed and matched to participants’ purchasing data
•2,474,011 individual transactions
•91% were female
✔Participants gave informed consent for their questionnaire to be matched with
pseudo-anonymized loyalty card data prior to full anonymization
Case study 1: Predicting Plastic Bag Purchases
Q: What profile of person continues to consistently buy plastic bags after the 5p
Levy was brought in?
Bottom-up approach:
•What can the best predictive model tell us about the motivations for the
behaviour?
Findings
•76% accuracy on a binary classification task
•Most important predictors were not the questions relating to environmental
concerns
People who bought more plastic bags were:
•Younger
•Less frugal
(questions on saving and disciplined spending)
•More impulsive
•And had lower self-control
Implications: Future interventions or plastic reduction campaigns need to appeal
to/target these personality profiles
Methodological Highlights
•Not restricted by theory as model makes prior assumptions
•Duration was predictive – hypothesis generating
•Confident we have the best estimate of variable importance
•Confident our findings generalise
Case study 2: Predicting Well-being
Demographics
Personality Well-being
Spending
Understanding the Variables Predicting Well-being
In psychology, we not only want to build strong predictive models, but we want to be
able to know why they work.
Interpretability
Decision Tree Analysis Predicting Well-being
✔Visualise
✔Non-linear
✔Any data, no
transformations
✔Missing data
✔60% less input data
✔Additional insights…
Insight 1: ‘Protective’ effect of Extroversion
The positive effect of social
support.
Insight 2: Age and Well-being
There is only one part of
the age variable which is
highly predictive of
well-being.
Insight 3: Money and Happiness
The data supports a ‘basic needs’
hypothesis, where after a
threshold amount, money doesn’t
strongly relate to happiness.
Decision Trees
Summary
•More (not less) interpretable
•Subgroup analysis, can inform where to target interventions
•Inform a future reduction in data collection
(i.e. sensitive questions)
Conclusion
Psychology
Prediction
Science
Thank-You.
References
Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A Very Brief Measure of the
Big Five Personality Domains. Journal of Research in Personality, 37, 504-528.
Open Science Collaboration. (2015). Estimating the reproducibility of psychological
science. Science, 349(6251), aac4716.
Shmueli, G. (2010). To explain or to predict?. Statistical science, 25(3), 289-310.
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology:
Lessons from machine learning. Perspectives on Psychological Science, 12(6),
1100-1122.
