CR2: Compute CR0, CR1, CR2 cluster robust standard errors with empirical-degrees of freedom adjustments. (July 2022).
This is now on CRAN so can be installed using:
install.packages('CR2'). The development version can also be installed using:
The SPSS version is available here.
For more details:
Huang, F. & Li, X. (2022). Using cluster robust standard errors when analyzing group randomized trials with few clusters. Behavior Research Methods, 54, 1181-1199. doi: 10.3758/s13428-021-01627-0
Huang, F., Wiedermann, W., & Zhang, B. (2022). Accounting for heteroskedasticity resulting from between-group differences in multilevel models. Multivariate Behavioral Research. doi: 10.1080⁄00273171.2022.2077290.
Huang, F., Zhang, B., & Li, X. (2022). Using robust standard errors for the analysis of binary outcomes with a small number of clusters. Journal of Research on Educational Effectiveness. https://doi.org/10.1080/19345747.2022.2100301
MLMusingR: Companion package to “Practical Multilevel Modeling Using R” (forthcoming, 2023). (July 2022).
gendata: Generate and modify synthetic datasets. Create synthetic datasets based on a correlation table. Additional functions can be used to rescale, transform, and reverse code variables. DOC
hornpa: A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn’s [1965, Psychometrika] parallel analysis). DOC
Testing out Shiny– a web application framework for R.
Conduct a parallel analysis online to determine the number of components/factors to retain. No need to download any syntax files or packages. NOTE: For some reason, the EFA PA is not working properly (but it does in the package).
Conducting multilevel CFA in R
I wrote a short note a while back (which I’ve kept updating) on conducting a multilevel confirmatory factor analysis using R (with lavaan). The note and the directions on using the function can be found using this link. NOTE: the updated version of lavaan now has a feature that automates the cumbersome multilevel setup. Additional notes can be found here.
Please cite as:
Huang, F. (2017). Conducting multilevel confirmatory factor analysis using R. doi: 10.13140/RG.2.2.12391.34724. Retrieved from https://francish.netlify.app/docs/MCFAinRHUANG.pdf
You can access the function at this link on https://github.com/flh3/mcfa.