3,321 research outputs found
Erratum to Cousineau (2005): Confidence intervals in within-subject designs: A simpler solution to Loftus and Massons method
No full textThree errors were found in Cousineau (2005), Table 2. Here, we present a corrected version of that table
Contrasting activity profile of two distributed cortical networks as a function of attentional demands
No full textThe original publication is available at http://www.jneurosci.orgThis work was supported by R01 grant MH-073610 from the National Institutes of Health to Denis Paré
Errata to Non-central tdistribution and the power of the t test: A rejoinder
No full textErrors were found in equations 3c and 3d of Cousineau and Laurencelle (2011) Non-central t distribution and the power of the t test: A rejoinder. These typographic errors did not affect the results presented in the articl
Born-Open Data for jsPsych
No full textBorn-Open Data is a framework whereby experiments are conceived from the start as making the data openly accessible. This framework was first described in Rouder, 2016, and an actual implementation for E-Prime 2.10 was provided by Cousineau, 2020. Herein, we provide an implementation for jsPsych. It consist in a plugin for jsPsych which can be inserted anywhere in the experiment's timeline and a function called upon completion of the experiment. The data are then uploaded transparently and automatically to a GitHub repository where they are immediately and openly accessible to the research community
Error bars in within-subject designs: a comment on Baguley (2012)
No full textThe problem of calculating error bars in within-subject designs has proven to be a difficult problem and has received much attention in recent years. Baguley (Behavior Research Methods, 44, 158–175, 2012) recommended what he called the Cousineau–Morey method. This method requires two steps: first, centering the data set in a certain way to remove between-subject differences and, second, integrating a correction factor to debias the standard errors obtained from the normalized data set. However, within some statistical packages, it can be difficult to integrate this correction factor. Baguley (2012) proposed a solution that works well in most statistical packages in which the alpha level is altered to incorporate the correction factor. However, with this solution, it is possible to plot confidence intervals, but not standard errors. Here, we propose a second solution that can return confidence intervals or standard error bars in a mean plot
Confidence intervals for Cohen's d_p in within-subject designs
No full textThere exist many proposed confidence intervals for the Cohen's dp in repeated-measure designs. Herein, we review three past proposals (Morris, 2000; Algina & Keselman, 2003, Goulet-Pelletier & Cousineau, 2018) and examine five new ones, four of which are based on the recently discovered distribution of dp in such design. It is found that the first two are pseudo confidence intervals, begin too liberal under some (fortunately uncommon) circumstances. Additionally, they are not asymptotically exact (as sample sizes get large, the non-rejection rate of the true population parameter does not tend toward the nominal confidence level). Finally, they do not have equivalent rejection rates on the left and on the right, being very conservatives towards null effects. Four of the five new techniques are asymptotically exact but some are liberal for small sample sizes. Finally, it is argued that basing error bars on null hypothesis statistical testing is questionable when an important movement in the psychological sciences is to reduce reliance on such procedures. An alternative is the precision interval whose aim is to estimate the extent of the possible range of results
An extended SPSS extension command for generating random data
No full textThe GRD extension command for SPSS (Harding & Cousineau, 2014) has been used in a variety of applications since its inception. Ranging from a teaching tool to demonstrate statistical analyses, to an inferential tool used to find critical values instead of looking into a z-table, GRD has been very well received. However, some users have requested other data generation components that would make GRD a more complete extension command: the possibility to add contaminants to the generated dataset as well as the ability to generate correlated variables. Another component we added is a graphical user interface (or GUI) that makes GRD accessible through the drop-down menus in the SPSS Data Editor window. This GUI allows users to generate a simple dataset by entering parameters in dedicated fields rather than writing out the full script. Finally, we devised a small series of exercises to help users get acquainted with the new subcommands and GUI
Confidence intervals for Cohen's d_p in within-subject designs
No full textThere exist many proposed confidence intervals for the Cohen's dp in repeated-measure designs. Herein, we review three past proposals (Morris, 2000; Algina & Keselman, 2003, Goulet-Pelletier & Cousineau, 2018) and examine five new ones, four of which are based on the recently discovered distribution of dp in such design. It is found that the first two are pseudo confidence intervals, begin too liberal under some (fortunately uncommon) circumstances. Additionally, they are not asymptotically exact (as sample sizes get large, the non-rejection rate of the true population parameter does not tend toward the nominal confidence level). Finally, they do not have equivalent rejection rates on the left and on the right, being very conservatives towards null effects. Four of the five new techniques are asymptotically exact but some are liberal for small sample sizes. Finally, it is argued that basing error bars on null hypothesis statistical testing is questionable when an important movement in the psychological sciences is to reduce reliance on such procedures. An alternative is the precision interval whose aim is to estimate the extent of the possible range of results
Confidence intervals for Cohen's d_p in within-subject designs
No full textThere exist many proposed confidence intervals for the Cohen's dp in repeated-measure designs. Herein, we review three past proposals (Morris, 2000; Algina & Keselman, 2003, Goulet-Pelletier & Cousineau, 2018) and examine five new ones, four of which are based on the recently discovered distribution of dp in such design. It is found that the first two are pseudo confidence intervals, begin too liberal under some (fortunately uncommon) circumstances. Additionally, they are not asymptotically exact (as sample sizes get large, the non-rejection rate of the true population parameter does not tend toward the nominal confidence level). Finally, they do not have equivalent rejection rates on the left and on the right, being very conservatives towards null effects. Four of the five new techniques are asymptotically exact but some are liberal for small sample sizes. Finally, it is argued that basing error bars on null hypothesis statistical testing is questionable when an important movement in the psychological sciences is to reduce reliance on such procedures. An alternative is the precision interval whose aim is to estimate the extent of the possible range of results
The standard error of the Pearson skew
No full textThe Pearson skew is a measure of asymmetry of a distribution, based on the difference between the mean and the median of a distribution. Here we show how to calculate the Pearson skew, estimate its standard error and the confidence interval. The derivation is based on a population following a normal distribution. Simulations explored the validity of this expression when the normality assumption is met in comparison to when the normality assumption is not met. The standard error of the Pearson skew revealed very robust in case of non-normal populations, compared to the Fisher Skew as presented in Harding, Tremblay and Cousineau (2014)
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