How do you incorporate propensity score matching into a larger quantitative research design?

1 Define your research question and variables

Before applying PSM, you need to have a clear and specific research question that can be answered with observational data. For example, you might want to know the effect of a training program on employees' productivity, or the effect of a policy intervention on citizens' behavior. You also need to identify your treatment and outcome variables, as well as any potential confounders that might affect both the treatment and the outcome. For example, if you want to measure the effect of the training program on productivity, your treatment variable might be a binary indicator of whether an employee received the training or not, your outcome variable might be a continuous measure of productivity, and your confounders might include variables such as age, education, experience, and motivation.

2 Choose your matching algorithm

Once you have defined your research question and variables, you need to choose a matching algorithm that suits your data and research objectives. There are many different types of matching algorithms, such as nearest neighbor, caliper, kernel, radius, and stratification. Each algorithm has its own advantages and disadvantages, depending on factors such as the size and distribution of your data, the balance and overlap of your treatment and control groups, and the complexity and robustness of your matching procedure. You should compare and contrast different algorithms and select the one that best matches your criteria and assumptions.

5 Report your results

The final step of incorporating PSM into your research design is to report your results in a clear and transparent way. You should provide a detailed description of your research question, data sources, variables, matching algorithm, matching quality assessment, statistical analysis, and causal effect estimates. You should also discuss the limitations and assumptions of your PSM method, as well as the implications and recommendations of your findings. You should also provide the code and data that you used for your PSM procedure, so that other researchers can replicate and verify your results.