This is the syntax for accounting for missing data/imputing data with large scale assessments (without plausible values). This is Appendix A and accompanies the article:
Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9
This is the syntax for accounting for missing data/imputing data with large scale assessments (with plausible values). This accompanies the article:
Huang, F., & Keller, B. (2025). Working with missing data in large-scale assessments. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00248-9
This is an applied example regarding the use of weights in multilevel models when using large scale assessments. This is using the Germany TIMSS dataset. This accompanies the article:
Atasever, U., Huang, F., & Rutkowski, L. (2025). Reassessing weights in large-scale assessments and multilevel models. Large-scale Assessments in Education. doi: 10.1186/s40536-025-00245-y
A common question with the use of large-scale assessments (LSAs) is related to the use of weights. Another issue is how to specify these weights properly.
Software such as SAS and Mplus, when specifying weights at two levels, require the use of conditional weights at level 1 if the level-2 weight is specified (or you can just use the level-2 weights alone; see Mang et al., 2021, see bottom part of this post).
The following links were helpful: 1. https://cran.r-project.org/web/packages/imager/vignettes/gettingstarted.html 2. https://stats.stackexchange.com/questions/229092/how-to-reverse-pca-and-reconstruct-original-variables-from-several-principal-com
library(tidyverse) #for ggplot, %>%
library(imager) #to read in the jpg
image1 <- load.image("c:/data/snorlax_g2.jpg")
Can download the image from: https://github.com/flh3/pubdata/blob/main/miscdata/snorlax_g2.jpg
This is an update to:
Huang, F. (2024). Using plausible values when fitting multilevel models with large-scale assessment data using R. Large-scale Assessments in Education.
mixPV function where it is now available in the MLMusingR package (no need to load it through Github anymore).library(MLMusingR)
The function has been updated to be able to use parallel processing or multiple cores of your computer (to make computation faster).
mixPV function has been updated. You can read more about it hereSyntax to accompany the article:
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Random notes. Regression based techniques often involve finding a maximum (e.g., the maximum likelihood) or a minimum (e.g., least squares or mean square error) value. Gradient descent is an iterative optimization algorithm used to find the minimum of a function (or gradient ascent to find the maximum).
Logistic regression is a modeling technique that has attracted a lot of attention, especially from folks interested in classification, machine learning, and prediction using binary outcomes. One of the neat things about using R is that users can revisit commonly used procedures and figure out how they work.