When size matters: advantages of weighted effect coding in observational studies

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.

• Publication year

  2016

• Venue

  International Journal of Public Health

• Publication date

  2016-10-28

• Fields of study

  Mathematics, Medicine

• Identifiers
  DOI 10.1007/s00038-016-0901-1PMID 27796415PMCID 5288425
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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