A custom R function to create ggplot2 visualizations of power curves generated by the simr package's powerCurve function for mixed-effects models.
Research has suggested that conceptual processing depends on both language-based and vision-based information. We tested this interplay at three levels of the experimental structure: individuals, words and tasks. To this end, we drew on three …
Multilevel analyses investigating the interplay between language-based and vision-based information in conceptual processing across semantic priming, semantic decision and lexical decision paradigms, with power analyses revealing sample size requirements for examining perceptual simulation and individual differences.
The powercurve function from the R package ‘simr’ (Green & MacLeod, 2016) can incur very long running times when the method used for the calculation of p values is Kenward-Roger or Satterthwaite (see Luke, 2017). Here I suggest three ways for cutting down this time.
Where possible, use a high-performance (or high-end) computing cluster. This removes the need to use personal computers for these long jobs.
In case you’re using the fixed() parameter of the powercurve function, and calculating the power for different effects, run these at the same time (‘in parallel’) on different machines, rather than one after another.
The first study (Bernabeu et al., 2021) will merge existing datasets (Lynott et al., 2020; Pexman et al., 2017; Pexman & Yap, 2018; Wingfield & Connell, 2019). The second study will collect novel data to investigate questions such as the unique roles of vocabulary size, sensorimotor experience and attentional control.