Mind & Behaviour Research Group is housed at the University of Oxford
Research methods at the intersection of behavioural economics & psychology

In conjunction with the Centre for the Study of African Economies (CSAE), we'll be sharing  measures and analysis code employed by our team that cut across economics and psychology disciplines. See below for repository on how-to guides on measurements, as well as analysis code tips from CSAE's Coder's Corner series. 

Welcome to our Measures Matter series!

How do you accurately measure a person's beliefs? What's the best approach to measure risk attitudes? What methods minimise reporting bias? Answers to such questions can be difficult, but are crucial to researchers studying psychology and behaviour in research evaluations. In our Measures Matter series, our goal is to provide information on measurement tools which might be particularly useful for researchers in behavioural economics, psychology and psychiatry.

Aimed at fellow researchers, content published monthly in this series will include step-by-step instructions on how to replicate certain techniques in the spirit of knowledge-sharing and as part of an effort to make methods more widely available. 

Behavioural researcher in need of coding tips?

The Centre for the Study of African Economies runs a popular Coder's Corner series for advice on handling your data or writing code for particular analysis techniques. Below are links to a few of their posts which might be particularly useful for researchers in behavioural economics, psychology and psychiatry.

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Generic Machine Learning for Heterogeneous Treatment Effects

The interests of researchers and policymakers often extend beyond a simple average treatment effect when evaluating interventions in randomised experiments. Exploring heterogeneous treatment effects, or average treatment effects by subgroups and covariates, can provide useful answers to a variety of important questions. This post explores using machine learning methods for inference on heterogeneous treatment effects. 

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Endogenous Stratification

When we analyse the results of an experiment, we are often interested in understanding what the treatment effects are on sub-groups. This type of sub-group analysis is usually estimated using in-sample information on the relationship between the outcome of interest and the covariates in the control group to predict outcome for all groups without treatment. However, this procedure generates substantial bias due to overfitting. This post discusses a method for overmining this bias.

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Regularisation in Stata

Including control variables in regressions can substantially increase the statistical power of your analysis. However, deciding which control variables to select is arbitrary. Pre-analysis plans allow researchers to credibly commit to a set of controls, yet these controls might turn out to be suboptimal ex-post. Regularisation techniques deal with this problem and ensure that you make the most of your existing data.

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Specification Plots

Extensive robustness checks have become a requirement for empirical research. This often leads to Online Appendices with hundreds of result tables that are very hard to digest for readers and referees. Stata16’s speccurve command written by Martin Eckhoff Andresen is an easy to use command that facilitates the generation of specification curves. A specification curve plots a large number of regression coefficients and confidence intervals sorted by estimated impact from different specifications that allow the assessment of robustness in a single figure.

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Mediation Analysis

How can mediation analysis be useful in an experiment that has a behavioural component? With multiple follow-ups on behavioural characteristics and socioeconomic variables, researchers can use mediation to test whether socioeconomic outcomes in later rounds can plausibly be explained by changes in the psychological variables at intermediate follow-up rounds after the behavioural intervention.

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Factor Analysis

Measuring psychological outcomes can be difficult when the constructs we are interested in are unobservable (e.g., the Big Five personality traits) or very costly and time consuming to measure (e.g., clinical depression). Factor analysis is a statistical technique used widely by psychologists and social scientists. It enables us to test if a given set of measures captures an underlying, unobservable construct (factor). This helps us to select and verify our measurement instruments.

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Creating Summary Tables in Stata

Field experiments in (behavioural) development economics have become increasingly complex. Many trials test whether cost-effective behavioural additions to more traditional interventions and rigorous analysis of heterogeneous treatment effects across sub-groups has become the norm. This post shows how you can create publication style balance and summary tables taking into account these complexities.