In this week’s episode Greg and Patrick revisit a topic they addressed in their 2nd-ever episode: statistical power. Here they continue their discussion by attempting to clarify the power of what, and they explore ways of obtaining meaningful power estimates using the structural equation modeling framework. Along the way they also discuss tearing arms off, German dentists, booby prizes, Dr. Strangelove, making it look like an accident, shrug emojis, the whale petting machine, baseball and war, where’s Waldo, whale holes, the big R-squared, throwing reviewers against the wall, DIY power, in fairness to me, eggplants, and screw you guys, I’m going home.
Lightly Edited Transcript
We provide a lightly-edited and obviously imperfect audio transcript of the episode available here. This is not an exact representation of the audio, but does provide a searchable document with identified speakers and associated time stamps.
Related Episodes
S1E02: (Statistical) Power Struggles
S1E14: Model Fit & The Curse of the Black Pearl
S4E11 The Centrality of Noncentral Distributions
Suggested Readings
Chen, F., Curran, P.J., Bollen, K.A., Kirby, J., and Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods and Research, 36, 462-494.
Feng, Y., & Hancock, G. R. (in press). SEM as a framework for power analysis. In R. H. Hoyle (Ed.), Handbook of structural equation modeling (2nd ed.). The Guilford Press.
Hancock, G. R., & French, B. F. (2013). Power analysis in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 117–159). IAP Information Age Publishing.
Kim, K.H. (2005). The relation among fit indexes, power, and sample size in structural equation modeling. Structural Equation Modeling, 12, 368-390.
Lee. T., Cai, L., & MacCallum, R.C. (2012). Power Analysis for tests of structural equation models. In R. Hoyle, D. Kaplan, g. Marcoulides, & S. West (Eds.), Handbook of Structural Equation Modeling (pp.181-194), New York: Guilford.
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.
MacCallum, R.C., Lee, T., & Browne, M.W. (2010). The issue of isopower in power analysis for tests of structural equation models. Structural Equation Modeling, 17, 23-41.
Muthén, L.K., & Muthén, B.O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620.
Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50, 83-90.
Satorra, A., Saris, W. E., & De Pijper, W. M. (1991). A comparison of several approximations to the power function of the likelihood ratio test in covariance structure analysis. Statistica Neerlandica, 45, 173-185.