effect_nominal

Computes descriptive statistics and effect size for nominal data

Balancing quasi-experimental field research for effects of covariates is fundamental for drawing causal inference. Propensity Score Matching deals with this issue but current
techniques are restricted to binary treatment variables. Moreover, they provide several solutions without providing a comprehensive framework on choosing the best model. The
MAGMA R-package addresses these restrictions by offering nearest neighbor matching for two to four groups. It also includes the option to match data of a 2x2 design. In addition,
MAGMA includes a framework for evaluating the post-matching balance. The package includes functions for the matching process and matching reporting. We provide a tutorial on
MAGMA as vignette. More information on MAGMA can be found in Feuchter, M. D., Urban, J., Scherrer V., Breit, M. L., and Preckel F. (2022) <https://osf.io/p47nc/>.

Julian Urban

MAGMA.R

MAny-Group MAtching

Markus D. Feuchter

Vsevolod Scherrer

Moritz L. Breit

Franzis Preckel

effect_nominal function

<dl><dt>Data</dt>
<dd>A data set</dd>
<dt>group</dt>
<dd>A character specifying the grouping variable</dd>
<dt>variable</dt>
<dd>Variables for which the effect should be estimated</dd></dl>

Arguments

Author

effect_nominal — effect_nominal

<dl>

<dt>Data</dt>
<dd>A data set</dd>


<dt>group</dt>
<dd>A character specifying the grouping variable</dd>


<dt>variable</dt>
<dd>Variables for which the effect should be estimated</dd>

</dl>

effect_nominal: effect_nominal

Description

Usage

Value

Arguments

Author

Details