Propensity Score Analysis

Propensity Score Analysis using R

The Quantitative Research Methods Training Unit (QRM-TU) at University is the only institute in the UK to offer training in Propensity Score Analysis using R. Propensity score analysis is a relatively new and innovative class of statistical methods that has proven useful for evaluating the effects of treatments or interventions when using non-experimental or observational data.

In the statistical analysis of observational data, Propensity Score Analysis is a statistical technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. It attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among participants that received the treatment versus to those that did not. Results produced by propensity score methods are typically easier to communicate to lay audiences. Furthermore, propensity score estimates are often more robust to differences in the distributions of the confounding variables across the groups being compared.

The seminar uses R to demonstrate the implementation of propensity score analysis. The R software can be downloaded for free. The course is run by Dr Daniel Boduszek who has used propensity score matching in his numerous psychological, social science, and medical research publications.

Course outline

  1. Why and when propensity score analysis is needed
  2. Theoretical Introduction to Propensity Score Analysis and Matching Techniques
  3. Introduction to the R environment 
  4. R packages (“MatchIt”, “Zelig”, “QuantPsyc”) 
  5. Logistic Regression
  6. Multiple Regression
  7. Propensity Score Analysis in R
  8. Greedy matching
  9. Optimal matching
  10. Post-matching multivariate analysis
  11. Interpretation and reporting of results for publication purpose
  12. Practical session

Who should attend?

The course is designed for researchers and postgraduate students who are engaged in intervention research, program evaluation, or more generally causal inference, when their data were not generated by a randomised clinical trial. The prerequisite for taking this seminar is basic knowledge of regression analysis. Researchers from economics, public health, epidemiology, psychology, sociology, social work, medical research, education, and similar disciplines are welcome.

Why Quantitative Research Methods Training Unit?

  • We know that learning statistical analysis can be a daunting and unpleasant experience at times. This is why we present complicated procedures in a simple way, avoiding jargon and confusing mathematical formulas at all costs! We teach researchers everything they need to know in order to finish their projects in a relaxed and friendly atmosphere where they are helped and encouraged every step of the way.
  • The uniqueness of Quantitative Research Methods Training Unit is the focus on the practical application of Propensity Score Analysis, not mathematical procedures.
  • Dr Daniel Boduszek has an extensive experience in the application of propensity score matching techniques which is documented in his numerous publications.
  • The course is delivered by means of lectures and practical sessions. All analyses are conducted under supervision of Dr Daniel Boduszek and three tutors (Dr Susie Kola, Dr Katie Dhingra, and Ms Kathryn Sharratt) to maximise learning outcomes (we offer a 1:5 instructor student ratio). Each course concludes with a question and answer session, and there is always the opportunity for participants to discuss their own studies with the tutors.
  • As already mentioned, The Quantitative Research Methods Training Unit (QRM-TU) at University of Huddersfield is the only institute in the UK to offer training in Propensity Score Analysis using R.

Location

The Quantitative Research Methods Training Unit (QRM-TU), Ramsden Building, University of Huddersfield.

Book now

To find out when the next training session will be held, please visit our online store where you can also book your place.