Popular repositories
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3-Step-ML-auto
3-Step-ML-auto PublicThis R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more ab…
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intro_to_rstudio
intro_to_rstudio PublicForked from dinanajiarch/intro_to_rstudio
This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R.
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quick-lca-mplusauto
quick-lca-mplusauto PublicDemonstrate the speed of running an LCA analysis using MplusAutomation
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BCH-MplusAuto
BCH-MplusAuto PublicThis `R` tutorial automates the BCH two-step axiliary variable procedure (Bolk, Croon, Hagenaars, 2004) using the `MplusAutomation` package (Hallquist & Wiley, 2018) to estimate models and extract …
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immerse-ucsb.github.io
immerse-ucsb.github.io Public templateGH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
Repositories
- immerse-ucsb.github.io Public template
GH pages repository to host all tutorial scripts as websites for sharing (PDF/HTML formats).
- intro-to-mplusautomation Public
This repo will go through how to use Mplus and MplusAutomation in R. For Part 1, we will first walk through how to run basic descriptive statistics using only Mplus. In Part 2, we will use an R package called MplusAutomation to run the same analysis as Part 1, only this time using only RStudio. Part 3 will go over data cleaning in R.
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- continued_training Public
- lca_enum Public
We utilize six focal indicators which constitute the latent class model in our example; three variables which report on harassment/bullying in schools based on disability, race, or sex, and three variables on full-time equivalent school staff hires (counselor, psychologist, law enforcement).
- intro_to_rstudio Public Forked from dinanajiarch/intro_to_rstudio
This walkthrough is presented by the IMMERSE team and will go through some common tasks carried out in R.
- interpret-aux-vars Public
Interpret & Summarize Auxiliary Variables. Two examples provided including a distal outcome model with control covariate & a moderation model.
- 3-Step-ML-auto Public
This R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more about auxiliary variable integration methods and why multi-step methods are necessary for producing un-biased estimates see Asparouhov & Muthén (2014).
- BCH-MplusAuto Public
This `R` tutorial automates the BCH two-step axiliary variable procedure (Bolk, Croon, Hagenaars, 2004) using the `MplusAutomation` package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters.
