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Category: Methods and Measures

Description: Field data is often limited regarding causal inference. This is partly because randomization techniques are often impractical or unethical within certain fields (e.g., randomly assigning individuals to different types of classroom instruction in educational settings). Matching procedures, like propensity score matching (PSM; Rosenbaum & Rubin, 1983), are regularly used to strengthen interpretations of group membership-effects in field research. By matching individuals from different subgroups of a field sample (e.g., participations vs. nonparticipation in a special education program), relevant confounds to group membership-effects (e.g., socio-economic status) can be balanced out and thereby eliminated retrospectively. That way, matching turns field data into quasi-experimental data. Currently, the most prominent approach to matching individuals is nearest neighbor matching (NNM) (see Austin, 2014; Austin & Stuart, 2015; Heinz et al., 2022; Jacovidis, 2017). Available statistical software (e.g., R-packages like MatchIt, Ho et al., 2011), however, does not fully realize the potential of NNM to reduce sample-related bias in field data due to unsystematic procedures for the identification of apt pairs to match. Furthermore, existing matching applications are limited to two-group designs (that being said, weighting applications for more than two groups do exists, e.g., MMW-S, Hong, 2012). In addition, balance estimation, as a matching quality check, is often conducted rudimentarily (e.g., by solely reporting between-group post-matching differences). So far, conventions on balance estimation for more than two groups are absent. To address these shortcomings, we developed a systematic algorithm, designed for matching individuals from two or more groups alongside a set of adequate balance estimates. We call it “MAGMA” (for MAny-Group MAtching). In this work, we demonstrate and evaluate the MAGMA-algorithm, using two empirical examples from extensive field data.