[Propensity score matching in SPSS]

Area of Science:

• Biostatistics
• Statistical Software Applications
• Data Analysis

Background:

• Propensity score matching (PSM) is a valuable statistical technique for estimating causal effects in observational studies.
• Implementing PSM often requires specialized software or complex coding, posing a barrier for some researchers.

Purpose of the Study:

• To demonstrate the implementation and utility of a new Propensity Score Matching (PSM) module within SPSS software.
• To provide researchers with a user-friendly tool for conducting PSM analyses.

Main Methods:

• The study involved installing R software and a specific plug-in compatible with SPSS.
• A dedicated PS Matching module was integrated into the SPSS interface.
• The module's functionality was demonstrated using test datasets.

Main Results:

• The PS Matching module successfully performed score estimation and nearest neighbor matching.
• Results were visualized using graph matching, offering both qualitative and quantitative statistical descriptions.
• The analysis confirmed the feasibility of conducting PSM within the SPSS environment.

Conclusions:

• Propensity score matching (PSM) can be conveniently performed using SPSS software.
• The integrated module simplifies the process of PSM for researchers.
• SPSS now offers a more accessible platform for causal inference studies.