Given an hypothetical value of effect size, PRDA performs a prospective
or retrospective design analysis to evaluate the inferential risks (i.e.,
power, Type M error, and Type S error) related to the study design. See
vignette("PRDA") for a brief introduction to Design
Analysis.
PRDA package can be used for Pearson's correlation between two variables
or mean comparisons (i.e., one-sample, paired, two-sample, and Welch's
t-test) considering an hypothetical value of \(\rho\) or Cohen's d
respectively. See vignette("retrospective") for more details.
In PRDA there are two main functions:
retrospective(). Given the hypothetical population
effect size and the study sample size, the function retrospective()
performs a retrospective design analysis. According to the defined
alternative hypothesis and the significance level, the inferential risks
(i.e., Power level, Type M error, and Type S error) are computed together
with the critical effect value (i.e., the minimum absolute effect size value
that would result significant). To know more about function arguments and
examples see the function documentation
?retrospective and
vignette("retrospective").
prospective(). Given the hypothetical population
effect size and the required power level, the function prospective()
performs a prospective design analysis. According to the defined alternative
hypothesis and the significance level, the required sample size is computed
together with the associated Type M error, Type S error, and the critical
effect value (i.e., the minimum absolute effect size value that would
result significant). To know more about function arguments and examples see
the function documentation ?prospective
and vignette("prospective").
The hypothetical population effect size can be defined as a single value
according to previous results in the literature or experts indications.
Alternatively, PRDA allows users to specify a distribution of plausible
values to account for their uncertainty about the hypothetical population
effect size. To know how to specify the hypothetical effect size according
to a distribution and an example of application see
vignette("retrospective").
Altoè, G., Bertoldo, G., Zandonella Callegher, C., Toffalini, E., Calcagnì, A., Finos, L., & Pastore, M. (2020). Enhancing Statistical Inference in Psychological Research via Prospective and Retrospective Design Analysis. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02893
Bertoldo, G., Altoè, G., & Zandonella Callegher, C. (2020, June 15). Designing Studies and Evaluating Research Results: Type M and Type S Errors for Pearson Correlation Coefficient. Retrieved from https://psyarxiv.com/q9f86/
Gelman, A., & Carlin, J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspectives on Psychological Science, 9(6), 641–651. https://doi.org/10.1177/1745691614551642