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100,000 power analyses on WebPower

WebPower has exceeded 100,000 power analyses today. Thank you for using WebPower.

2019/11/06 16:50 · johnny zhang · 0 Comments

QSG presentation by Miao Yang on Statistical power analysis for multilevel models

Statistical power analysis is an important topic in research designs. Power analysis methods for a variety of models, such as multiple regression and ANOVA, have been well developed and implemented in popular statistical software (e.g., SAS PROC POWER). The implementation of power analysis for multilevel models, however, is less common. As part of the Webpower project, we developed online software that allows researchers to conduct power analysis for popular multilevel models.

In this week’s QSG, I am going to (1) introduce widely used multilevel designs, including cluster randomized trials and multisite randomized trials; (2) introduce the procedure of power analysis for these multilevel models; and (3) illustrate the application of Webpower.

2015/04/21 13:42

QSG presentation by Haiyan Liu on Statistical power analysis for logistic regression

The goal of power analysis is to decide (a) how large a sample is needed to enable statistical judgments that are accurate and reliable, and (b) how likely your statistical test will be able to detect the effect of a given size in a particular situation.

In the literature, there is no consensus on the approach to compute the power and sample size with logistic regression. In this talk, I am going to review three test statistics used in the asymptotic way. For the implementation, I follow the procedure advocated by Demidenko(2007). As part of the WebPower project, online software is developed and will be demonstrated at the end of the talk.

For more information about WebPower, please visit http://webpower.psychstat.org/wiki/.

2015/04/21 13:42

New Apps for Power Analysis for Structural Equation Modeling

Two new apps were added to conduct power analysis and sample size planning. One is based on the RMSEA method proposed by MacCallum et al. (1996) and the other is based on Satorra & Saris (1985).

To use the method, follow the links below:

2015/01/10 10:58

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