| PRDA {PRDA} | R Documentation |
PRDA: Prospective and Retrospective Design Analysis.
Description
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.
Details
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.
Functions
In PRDA there are two main functions:
retrospective(). Given the hypothetical population effect size and the study sample size, the functionretrospective()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?retrospectiveandvignette("retrospective").prospective(). Given the hypothetical population effect size and the required power level, the functionprospective()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?prospectiveandvignette("prospective").
Hypothetical Effect Size
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").
References
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