1. When size matters: advantages of weighted effect coding in observational studies
- Author
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Rob Eisinga, R.P. Konig, Ben Pelzer, Rense Nieuwenhuis, Alexander W. Schmidt-Catran, and Manfred te Grotenhuis
- Subjects
Ordinal data ,Generalized linear model ,Health (social science) ,050109 social psychology ,Health(social science) ,Inequality, cohesion and modernization ,03 medical and health sciences ,0302 clinical medicine ,Dummy variable ,Statistics ,Humans ,0501 psychology and cognitive sciences ,030212 general & internal medicine ,Ongelijkheid, cohesie en modernisering ,Hirschberg test ,Models, Statistical ,Data Collection ,05 social sciences ,Public Health, Environmental and Occupational Health ,Regression analysis ,Communication and Media ,Data Interpretation, Statistical ,Ordinary least squares ,Observational study ,Coding (social sciences) ,Hints & Kinks - Abstract
Contains fulltext : 166462.pdf (Publisher’s version ) (Open Access) To include nominal and ordinal variables as predictors in regression models, their categories first have to be transformed into so-called 'dummy variables'. There are many transformations available, and popular is 'dummy coding' in which the estimates represent deviations from a preselected 'reference category'. A way to avoid choosing a reference category is effect coding, where the resulting estimates are deviations from a grand (unweighted) mean. An alternative for effect coding was given by Sweeney and Ulveling in 1972, which provides estimates representing deviations from the sample mean and is especially useful when the data are unbalanced (i.e., categories holding different numbers of observation). Despite its elegancy, this weighted effect coding has been cited only 35 times in the past 40 years, according to Google Scholar citations (more recent references include Hirschberg and Lye 2001 and Gober and Freeman 2005). Furthermore, it did not become a standard option in statistical packages such as SPSS and R. The aim of this paper is to revive weighted effect coding illustrated by recent research on the body mass index (BMI) and to provide easy-to-use syntax for SPSS, R, and Stata on http://www.ru.nl/sociology/mt/wec/downloads. For didactical reasons we apply OLS regression models, but it will be shown that weighted effect coding can be used in any generalized linear model. 5 p.
- Published
- 2016