The present script can be used to pre-process data from a frequency list of the Norwegian as Web Corpus (NoWaC).
Before using the script, the frequency list should be downloaded from https://www.hf.uio.no/iln/english/about/organization/text-laboratory/services/nowac-frequency.html. The list is described as ‘frequency list sorted primary alphabetic and secondary by frequency within each character’, and the direct URL is: https://www.tekstlab.uio.no/nowac/download/nowac-1.1.lemma.frek.sort_alf_frek.txt.gz. The download requires signing in to an institutional network. Last, the downloaded file should be unzipped.
The R package ‘simr’ has greatly facilitated power analysis for mixed-effects models using Monte Carlo simulation (which involves running hundreds or thousands of tests under slight variations of the data). The powerCurve function is used to estimate the statistical power for various sample sizes in one go. Since the tests are run serially, they can take a VERY long time; approximately, the time it takes to run the model supplied once (say, a few hours) times the number of simulations (nsim, which should be higher than 200), and times the number of different sample sizes examined.
To assess whether convergence warnings render the results invalid, or on the contrary, the results can be deemed valid in spite of the warnings, Bates et al. (2023) suggest refitting models affected by convergence warnings with a variety of optimizers. The authors argue that, if the different optimizers produce practically-equivalent results, the results are valid. The allFit function from the ‘lme4’ package allows the refitting of models using a number of optimizers.
OpenSesame offers options to counterbalance properties of the stimulus across participants. However, in cases of more involved assignments of session parameters across participants, it becomes necessary to write a bit of Python code in an inline script, which should be placed at the top of the timeline. In such a script, the participant-specific parameters are loaded in from a csv file. Below is a minimal example of the csv file.
In the preparation of projects, files are often downloaded from OSF. It is good to document the URL addresses that were used for the downloads. These URLs can be provided in a code script (see example) or in a README file. Better yet, it’s possible to specify the version of each file in the URL. This specification helps reduce the possibility of inaccuracies later, should any files be modified afterwards.
The need for covariates—or nuisance variables—in statistical analyses is twofold. The first reason is purely statistical and the second reason is academic.
First, the use of covariates is often necessary when the variable(s) of interest in a study may be connected to, and affected by, some satellite variables (Bottini et al., 2022; Elze et al., 2017; Sassenhagen & Alday, 2016). This complex scenario is the most common one due to the multivariate, dynamic, interactive nature of the real world.
Liu et al. (2018) present a study that implements the conceptual modality switch (CMS) paradigm, which has been used to investigate the modality-specific nature of conceptual representations (Pecher et al., 2003). Liu et al.‘s experiment uses event-related potentials (ERPs; similarly, see Bernabeu et al., 2017; Collins et al., 2011; Hald et al., 2011, 2013). In the design of the switch conditions, the experiment implements a corpus analysis to distinguish between purely-embodied modality switches and switches that are more liable to linguistic bootstrapping (also see Bernabeu et al.
This open-source, R-based web application is suitable for educational or research purposes in experimental sciences. It allows the **creation of varied data sets with specified structures, such as between-group or within-participant variables, that …