[R-sig-ME] Calculating effect sizes of fixed effects in lmer
jepu@to @end|ng |rom gm@||@com
Thu Sep 24 16:57:39 CEST 2020
I agree very much with Wolfgang's perspective that one would ideally use
outcomes such that unstandardized effects can be interpreted directly. If
one does have to fall back on standardized effect sizes, there's a further
question of what metric to use. Researchers often jump immediately to
standardized mean differences, but there are certainly other possibilities,
such as log response ratios for outcomes that are measured on ratio scales.
All that said, there has been a fair amount of work on standardized mean
difference effect sizes for certain types of research designs that would
usually be analyzed with multi-level models. A sampling (including some of
- Hedges, L. V. (2007). Effect sizes in cluster-randomized designs. *Journal
of Educational and Behavioral Statistics*, *32*(4), 341-370.
- Hedges, L. V. (2011). Effect sizes in three-level cluster-randomized
experiments. *Journal of Educational and Behavioral Statistics*, *36*(3),
- Pustejovsky, J. E., Hedges, L. V., & Shadish, W. R. (2014).
Design-comparable effect sizes in multiple baseline designs: A general
modeling framework. *Journal of Educational and Behavioral Statistics*,
- Stapleton, L. M., Pituch, K. A., & Dion, E. (2015). Standardized
effect size measures for mediation analysis in cluster-randomized
Journal of Experimental Education*, *83*(4), 547-582.
- Feingold, A. (2009). Effect sizes for growth-modeling analysis for
controlled clinical trials in the same metric as for classical
Methods*, *14*(1), 43.
One of my students and I have also developed an R package for estimating
standardized mean differences from multilevel models fitted with nlme::lme()
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