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André Ernst
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Propensity Score Matching: Computation and Balance Estimation for two and more groups in R

About
Location:
Online via Zoom
 
General Topics:
Data Analysis
Course Level:
Advanced
Format:
Workshop
Software used:
R
Duration:
2 days
Language:
English
Fees:
Students: 220 €
Academics: 330 €
Commercial: 660 €
 
Keywords
Additional links
Lecturer(s): Julian Urban

About the lecturer - Julian Urban

Course description

Are you currently engaged in quasi-experimental field research? Or are you concerned about the possible impact of a covariate on the internal validity of your experiment or observational study? Perhaps you want to conduct group comparisons with your observational (survey) data? For example, you may want to evaluate an intervention (e.g., training, medicament, etc.) or compute group differences (e.g., socio-economic groups, neighborhood, political orientation, etc.). However, you might encounter significant group differences in covariates (e.g., age, health, job experience, intelligence) between your experimental or observational groups. If so, you may be interested in learning (more) about Propensity Score Matching, a statistical method that also allows for the estimation of causal effects while controlling for uneven numbers of participants per group!
Propensity scoring helps address the imbalance in covariates between your (quasi-) experimental/observational groups by matching participants from different groups that are otherwise similar in covariates (e.g., background variables). Thus, it enhances the quality of your data and analyses by controlling bias caused by cofounding variables or unequal observational/experimental group sizes. Traditionally. only one grouping variable with two groups could be considered during matching, yet recent developments have expanded this method to accommodate more than two experimental/observational groups (e.g., one intervention group and two control groups) as well as more than one grouping variable (2x2 designs).
This comprehensive two-day online course covers the following topics:
  • An introduction into the statistical and substantive problems of imbalanced group designs for social-science research
  • A review of the rationale, benefits, and drawbacks of propensity score matching in estimating causal effects in various study designs
  • Different techniques and estimation methods for matching participants via propensity scores
  • Utilizing R for the process of computing propensity scores and matching participants
  • A framework for assessing pre- and post-matching balance
  • Guidelines for effectively reporting your matching approach in a manuscript
    • Throughout the workshop, participants will benefit from the instructor's experience, conveyed through lectures and practical exercises. To this end, we will provide a simulated data set in R (though you are encouraged to bring your own data for practicing on it). Moreover, there will be the opportunity to discuss specific issues related to your own research with the instructor during the afternoon session on the second day.
     
    Organizational structure of the course
     
    The workshop is structured into four sessions each day. The session last 90 minutes. The first session of the day commences at 9:00am and involves a lecture. Following a 15-minute break, the second session begins at 10:45am and consists of practical exercises. A one-hour lunch break is scheduled after these two sessions.
    During the afternoon of the first day, the two sessions include a lecture (starting at 1:15pm) and practical exercises (starting at 3:00pm). However, on the second day, the afternoon sessions are specifically designated to individual consultation, allowing participants to reflect on and discuss in depth their own research matters.


    Target group

    Participants will find the course useful if:
    • They conduct quasi-experimental field research.
    • They conduct statistical analyses with groups based on observational (survey) data.
    • They seek to estimate causal effects using propensity score matching.
    • They seek to correct experimental or observational data for imbalanced covariates.
    • They face the challenge of balancing data with more than two relevant groups
    • They face the challenge of balancing data with more than one grouping variable
    • They would like to estimate and report post-matching balance.


    Learning objectives

    By the end of the course participants will:
    • Understand the rationale and current discussion on propensity score matching
    • Be able to conduct propensity score matching in R using R packages as twang, MatchIt, and MAGMA
    • Be able to apply propensity score matching in their own research projects.


    Prerequisites

  • Basic knowledge of R (opening and viewing data, manipulating objects, working with functions and packages, ideally worked with R in earlier projects)
  • Basic knowledge of (quasi-)experimental/observational study designs and understanding the differences between these designs (randomization, possibility to estimate causal effects)
  • Basic knowledge of inference statistics (statistical tests such as t-test, (M)ANOVA, correlations)
  • Basic knowledge of effect sizes (Cohen's d, ideally Hedges' g)
    •  
    Software requirements
    Participants should have the recent R (and R Studio) version installed.


    Schedule

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