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[Resource sharing] Statistical Power Analyses

已有 2137 次阅读 2021-9-2 14:06 |个人分类:结构方程模型|系统分类:科研笔记

[Resource sharing] Statistical Power Analyses

上周有同事被国际期刊的reviewers要求提供统计检定力, 我也不一定能解决。

至少提供资源如下, 请自行参考,不要问我细节,谢谢!

统计检定力常用的三种分析方式。

第1种方式

Post-hoc power analysis in SmartPLS and AMOS

https://www.danielsoper.com/statcalc/calculator.aspx?id=9

Choose: Post-hoc Statistical Power Calculator for Multiple Regression

This calculator will tell you the observed power for your multiple regression study, given the observed probability level, the number of predictors, the observed R2, and the sample size.

第2种方式

Sample size calculation using G*power Analysis

http://www.gpower.hhu.de/

第3种方式

Required sample size and power for SEM

http://timo.gnambs.at/en/scripts/powerforsem

  • MacCallum et al. (1996, 1997, 2006) and Kim (2005) propose methods to calculate the required sample size (given a desired power) or the achieved power (given a sample size) to assess the fit of structural equation models based upon different fit indices (e.g. RMSEA or AGFI). The code on this page implements these routines in R and SPSS.

  • Satorra, A., & Sarris, W. E. (1985). The power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83–90.

  • McQuitty, S. (2004). “Statistical power and structural equation models in business research”, Journal of Business Research, 57. pp. 175-183. 

  • Kim, K. H. (2005). The Relation Among Fit Indexes, Power, and Sample Size in Structural Equation Modeling. Structural Equation Modeling, 12, 368-390. doi: 10.1207/s15328007sem1203_2.

  • MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11, 19-35. doi: 10.1037/1082-989X.11.1.19.

  • MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149. doi: 10.1037/1082-989X.1.2.130.

  • MacCallum, R. C. & Hong, S. (1997). Power Analysis in Covariance Structure Modeling Using GFI and AGFI. Multivariate Behavioral Research, 32, 193




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