Sample size estimation for simple mixed model

I'm looking for ways to estimate the sample size for a within-subject repeated measures design with continuous moderators. There are two conditions with 5 stimuli per condition, and each participant sees all of them. Analytic strategy will be a linear mixed model (using the lme4 package in R).

First I need a simple method, based on an effect size estimate. I've tried the smpsize_lmm function from the sjstats package, but I'm unsure whether it can be applied to my case. https://strengejacke.github.io/sjstats/reference/samplesize_mixed.html

samplesize_mixed(eff.size = .5, df.n = 5, k = 2, power = .8)


Is this appropriate for a fully within-design? k is for the number of level-2 clusters, but since all participants are completing all conditions there are no level-2 clusters in my case (or am I getting this wrong?) ... I still entered 2 here for the number of conditions, as the formula doesn't work with df.n = 10 and k = 1. Could someone tell me if I can use this for my case, or point me towards a more appropriate but ideally equally straightforward method for my design?

model <-lmer(dv~ 1 + (1|subject) +  m1*cond + m2*cond, data, na.action = na.omit)

• We need much more information. What do you mean by continuous moderators? What is the eventual lmer or lme model you plan to run? There are no simple ways to calculate power for this design. You will need to use simulation. Check out simglm and simr. cran.r-project.org/web/packages/simglm/vignettes/… humburg.github.io/Power-Analysis/simr_power_analysis.html Commented Feb 11 at 20:44