PRDA-package: PRDA: Prospective and Retrospective Design Analysis.

In PRDA: Conduct a Prospective or Retrospective Design Analysis

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 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").

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").

Author(s)

Maintainer: Claudio Zandonella Callegher [e-mail] (ORCID)

Authors:

• Massimiliano Pastore [e-mail] (ORCID)

• Angela Andreella [e-mail] (ORCID)

• Anna Vesely [e-mail] (ORCID)

• Enrico Toffalini [e-mail] (ORCID)

• Giulia Bertoldo [e-mail] (ORCID)

• Gianmarco Altoè [e-mail] (ORCID)

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("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://osf.io/preprints/psyarxiv/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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1745691614551642")}

See Also

Useful links:

• https://claudiozandonella.github.io/PRDA/

• https://github.com/ClaudioZandonella/PRDA

• Report bugs at https://github.com/ClaudioZandonella/PRDA/issues

PRDA documentation built on Nov. 6, 2025, 1:17 a.m.