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Generalized power calculations for generalized linear models and more

Author

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  • Roger Newson

    (King's College London)

Abstract

The powercal command can compute any one of the five quantities involved in power calculations from the other four. These quantities are power, significance level, detectable difference, sample number, and the standard deviation (SD) of the influence function, which is equal to the standard error multiplied by the square root of the sample number. powercal can take arbitrary expressions (involving constants, scalars, or variables) as input and calculate the output as a new variable. The user can therefore plot input variables against output variables, and this often communicates the tradeoffs involved better than a point calculation as output by the sampsi command. General formulas are given for calculating the SD of the influence function when the detectable difference is a linear combination of link functions of subpopulation means for an outcome variable distributed according to a generalized linear model (GLM). This general case includes a very broad range of special cases, where the parameters to be estimated are differences between subpopulation proportions, arithmetic means and algebraic means, or ratios between subpopulation proportions, arithmetic means, geometric means, and odds. However, powercal is not limited to GLMsand can even be used with rank methods. Copyright 2004 by StataCorp LP.

Suggested Citation

  • Roger Newson, 2004. "Generalized power calculations for generalized linear models and more," Stata Journal, StataCorp LP, vol. 4(4), pages 379-401, December.
    • Handle: RePEc:tsj:stataj:v:4:y:2004:i:4:p:379-401
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    References listed on IDEAS

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    1. Roger Newson, 2001. "somersd-Confidence intervals for nonparametric statistics and their differences," Stata Technical Bulletin, StataCorp LP, vol. 10(55).
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    Cited by:

    1. Dick M. Carpenter & Sarah J. Kaka & Jennifer A. Tygret & Katy Cathcart, 2018. "Testing the Efficacy of a Scholarship Program for Single Parent, Post-Freshmen, Full Time Undergraduates," Research in Higher Education, Springer;Association for Institutional Research, vol. 59(1), pages 108-131, February.
    2. Jones, Mark & Dobson, Annette & Onslow, Mark & Carey, Brenda, 2009. "Negative binomial mixed models for analysis of stuttering rates," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4590-4600, October.
    3. Efstathios Magerakis & Konstantinos Gkillas & Athanasios Tsagkanos & Costas Siriopoulos, 2020. "Firm Size Does Matter: New Evidence on the Determinants of Cash Holdings," JRFM, MDPI, vol. 13(8), pages 1-35, July.
    4. Mourelatos, Evangelos & Zervas, Panagiotis & Lagios, Dimitris & Tzimas, Giannis, 2024. "Can AI Bridge the Gender Gap in Competitiveness?," GLO Discussion Paper Series 1404, Global Labor Organization (GLO).

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