Update: This post has been updated to use the dplyr library.
This brief post provides the R syntax to calculate SF-12 v2 scores using Australian population weights. The population weights are derived from:
Hawthorne, G., Osborne, R., Taylor, A., & Sansoni, J. (2007). The SF36 Version 2: critical analyses of population weights, scoring algorithms and population norms. Quality of Life Research, 16(4), 661-673. doi: 10.1007/s11136-006-9154-4
Resampling under the null hypothesis instead of case resampling will preserve the correlations and distributional characteristics of the data and allow you to perform hypothesis testing. For more information, please refer to Westfall and Young (1993).
If you want to test whether the change in R2 is statistically significant for nested linear models with heteroscedasticity-consistent (HC) standard errors (e.g., hierarchical regression), then you can use vcovHC() from the sandwich package and waldtest() from the lmtest package.
This is an update to my previous post and runs the PROCESS Model 1 without needing to use structural equation modelling.
This post was was inspired by Nicholas Michalak’s Novum R-ganum blog posts on reproducing Hayes’ PROCESS Model 1 in R. He has two posts where he presents the R code for examining a continuous × continuous moderation and a dichotomous × continuous moderation.
However, a quick Google search suggests a paucity of information for conducting moderation analyses using a multicategorical moderating variables.
Therefore, this post will outline how to run the PROCESS Model 1 with a multicategorical moderator (M) in R. We will examine how to code the M variable, simulate some data, run the PROCESS analyses in both SPSS and R, and compare the results from both software packages.
You may have guessed from my last post that I am an advocate for transparency, openness, and reproducibility in research. Part of this involves making data and code publically available.
Nosek and colleagues (2015) published an article in Science outlining the Transparency and Openness Promotion (TOP) guidelines and is well worth a read.
It is available from Science or, if you don’t have access to Science, it’s available from ResearchGate.
Nosek, B.A., Alter, G., Banks, G.C., Borsboom, D., Bowman, S.D., Breckler, S.J., . . . Yarkoni, T. (2015). Promoting an open research culture. Science, 348, 1422-1425. doi: 10.1126/science.aab2374
