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Prior to the modeling process, we recoded the Race variable into Black (20.3%), Hispanic (47.8%), White (27.9%) and Other or Unknown (3.7%).
Given that the sample contains no Hispanics and whites are the omitted category, the share other race variable refers to the share that is non-Hispanic, non-black, and non-white.
If one or two covariates have an absolute standardized difference above 10% after matching (such as the Hispanic and White race variable at 14.94%), the two groups are still considered to be sufficiently balanced overall, and analysis can proceed so long as the average standardized difference is below 10% (Rosenbaum and Rubin 1983).
To be extra scientific, I decided to add in the race variable, too.
The first model contained only the race variable (model 1).
Our data, however, was provided without a race variable.
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Other information on English proficiency, ethnicity, and race variables were lacking.
We explored interactions between the level 1 race variables and socioeconomic (Medicaid and uninsured) and geographic (metropolitan and state) variables.
The R gradually improved after adding age or race variables, or both, although the degree of improvement was not large.
For local SAHS models, the Akaike Information Criterion AICC) for models with one and two race variables were 588.6 and 590.6, respectively.
For example, black drivers were more than twice as likely to be searched during vehicle stops than white drivers, even after investigators considered non-race variables.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com