Rense Nieuwenhuis » dummy coding

Weighted Effect Coding: New publication in the R Journal

Rense Nieuwenhuis — Mon, 03 Jul 2017 08:00:21 +0000

Weighted effect coding is a technique for dummy coding that can have attractive properties, particularly when analysing observational data. In a new publication in the R Journal we explain the rationale of weighted effect coding, introduce the ‘wec’ package, and provide examples that include interactions.

The attractive property of applying weighted effect coding to categorical (‘factor’) variables is that each category represents the deviation of that category from the sample mean. This is unlike the more commonly used treatment coding where each a specific category has to be selected as a reference. Weighted effect coding is a generalized form of effect coding that applies to both balanced and unbalanced data.

A form of weighted effect coding was already formulated in 1972 by Sweeney and Ulveling, but it seems to never have found its place in statistical repertoires. Weighted effect coding was not implemented in mainstream statistical software. In an ongoing project, we have now further developed weighted effect coding to also apply to interactions (with both categorical and continuous variables), and provide procedures for mainstream statistical software. For R, we developed the ‘wec’ package, and procedures for STATA and SPSS are available as well.

A key innovation in our article in the R Journal is the formulation of interactions between a categorical variable with a continuous variable. This is visualised in the Figure above. The benefit of estimating such an interaction with weighted effect coding is that upon entering the interaction terms the estimate for the continous variable (as well as the ‘main effects’ for the categorical variable) does not change. The ‘main’ continous term reflects the average effect in the sample, and the interaction terms represent the deviation of the effect size for each category.

References

Grotenhuis, Te, M, Pelzer, B., Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017b). A novel method for modelling interaction between categorical variables. International Journal of Public Health, 62(3), 427–431. (open access!)

Grotenhuis, Manfred, Ben Pelzer, Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017a). When size matters: advantages of weighted effect coding in observational studies. International Journal of Public Health, (62), 163–167. (open access!)

Nieuwenhuis, R., Grotenhuis, Te, M., & Pelzer, B. (2017). Weighted Effect Coding for Observational Data with wec. R Journal, 9(1), 477–485. (open access!)

Sweeney, R. E., & Ulveling, E. F. (1972). A transformation for simplifying the interpretation of coefficients of binary variables in regression analysis. The American Statistician.

A novel method for modelling interaction between categorical variables

Rense Nieuwenhuis — Tue, 18 Apr 2017 08:00:43 +0000

We have been developing weighted effect coding in an ongoing series of publications (hint: a publication in the R Journal will follow). 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’.

To avoid choosing a reference category, weighted effect coding provides estimates representing deviations from the sample mean. This is particularly useful when the data are unbalanced (i.e., categories holding different numbers of observation). The basics of this technique, with applications in R, were detailed here.

In a new publication, available open access,, we show that weighted effect coding can also be applied to regression models with interaction effects (also commonly referred to as moderation). The weighted effect coded interactions represent the additional effects over and above the main effects obtained from the model without these interactions.

To apply the procedures introduced in these papers, called weighted effect coding, procedures are made available for R, SPSS, and Stata. For R, we created the ‘wec’ package which can be installed by typing:

install.packages(“wec”)

References (Open Access!)

Grotenhuis, M., Ben Pelzer, Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). A novel method for modelling interaction between categorical variables. International Journal of Public Health, 62(3), 427–431. http://link.springer.com/article/10.1007/s00038-016-0902-0

Grotenhuis, M., Ben Pelzer, Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. International Journal of Public Health, 62(1), 163–167. http://doi.org/10.1007/s00038-016-0901-1

Presenting Weighted Effect Coding

Rense Nieuwenhuis — Tue, 08 Nov 2016 07:25:55 +0000

Weighted effect coding is a variant of dummy coding to include categorical variables in regression analyses, in which the estimate for each category represents the deviation of that category from the sample mean. The ‘wec’ package for R provides tools to use weighted effect coding.

Manfred te Grotenhuis is currently visiting the Swedish Institute of Social Research (SOFI), where he presented our joint work on Weighted Effect Coding. The recoding of his presentation is available on Manfred’s YouTube channel, and embedded below:

The relevant papers are available here (open access):

More information, as well as software for SPSS and STATA are available from the project website.

ps. Stay updated for an exciting update to the ‘wec’ R package!