Propensity Score Matching (PSM) is a statistical technique used to reduce selection bias by matching participants in treatment and control groups based on their probability (propensity score) of receiving the treatment, given observed covariates. It's commonly used in observational studies where randomization is not possible.

Discussion Points:

1. Why Use Propensity Score Matching?

   • To control for confounding variables

   • To simulate some of the characteristics of randomized controlled trials (RCTs)

   • To improve causal inference in observational data

2. Steps to Perform PSM in SPSS:

   • Step 1: Install SPSS Plugins

     • You may need to install the SPSS Python Essentials or use an extension like the PS Matching plugin (available via SPSS Amos or the IBM extension hub).

     • Alternatively, use R plugins if needed.

   • Step 2: Estimate Propensity Scores

     • Go to: Analyze → Regression → Binary Logistic

     • Set the treatment (binary) as the dependent variable

     • Enter covariates (potential confounders) as independent variables

     • Save the predicted probabilities (Propensity Scores)

   • Step 3: Perform Matching

     • Matching is not directly available in basic SPSS—options include:

       • Use SPSS Custom Dialog: "PS Matching"

       • Use syntax with Python plugins

       • Export data and use R with MatchIt package

   • Step 4: Check Balance

     • Compare covariates between matched groups

     • Standardized mean differences (SMD), t-tests, chi-square tests

   • Step 5: Outcome Analysis

     • Analyze matched pairs with appropriate tests (paired t-tests, conditional logistic regression, etc.)

3. Resources and Tutorials:

   • IBM Extension Hub - PS Matching Dialog

   • SPSS PSM Syntax examples

   • YouTube video tutorials

   • R packages (MatchIt, PSAgraphics) for more advanced matching options

4. Common Pitfalls to Avoid:

   • Poor overlap of propensity scores between groups

   • Unbalanced covariates after matching

   • Small matched sample sizes