nwickelmaier/lead_lmm
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Nora Wickelmaier 2025-06-24 08:55:15 +02:00
parent 73897bba5e
commit 24ef469e8e
2 changed files with 11 additions and 2 deletions
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slides/lead_lmm.pdf
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slides/lead_lmm.tex
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@ -365,6 +365,15 @@
\section{Hierarchical modeling} \section{Hierarchical modeling}
\begin{frame}{HSB data set} \begin{frame}{HSB data set}
\begin{itemize}
\item Two levels
\begin{itemize}
\item Level 1: Student attributes
\item Level 2: School attributes
\end{itemize}
\end{itemize}
\vfill
\centering \centering
\begin{tabular}{llp{10cm}} \begin{tabular}{llp{10cm}}
\hline \hline
@ -474,8 +483,8 @@ $
\begin{frame}{Results} \begin{frame}{Results}
{Random effects} {Random effects}
\begin{itemize}[<+->] \begin{itemize}[<+->]
\item The estimate $\hat\sigma^2_{\upsilon_0} = 2.32$ of the variance of \item The (ML) estimate $\hat\sigma^2_{\upsilon_0} = 2.32$ of the variance
mean school performance provides room for improving prediction by of mean school performance provides room for improving prediction by
including additional predictors including additional predictors
\item However, there is virtually no variation in the dependence of math \item However, there is virtually no variation in the dependence of math
achievement on \texttt{cses} across schools ($\hat\sigma^2_{\upsilon_1} = achievement on \texttt{cses} across schools ($\hat\sigma^2_{\upsilon_1} =