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Examples from Singer s Using SAS Proc Mixed to Fit Multilevel Models

Example 1 in HLM: Unconditional Means Model. Focus on showing how to make .mdm file based on a single Stata fileDecomposition of variance into between and within varianceIntraclass correlationExploring the data graphically: File?Graph Data?box-whisker plots (outcome variable)File?Graph Data?lin

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Examples from Singer s Using SAS Proc Mixed to Fit Multilevel Models

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    1. Examples from Singers Using SAS Proc Mixed to Fit Multilevel Models To use the paper effectively, in particular the reader must understand: The difference between a fixed effect and a random effect The notion of multiple levels with a hierarchy The notion that error variance-covariance matrix can take on different structures That centering can be a helpful way of parameterizing the models so that the results are more easily interpreted

    2. Example 1 in HLM: Unconditional Means Model Focus on showing how to make .mdm file based on a single Stata file Decomposition of variance into between and within variance Intraclass correlation Exploring the data graphically: File?Graph Data?box-whisker plots (outcome variable) File?Graph Data?line plots, scatter plots (outcome variable on a predictor variable)

    3. Example 2 in HLM: Include both level-1 and level-2 predictors Level-1 variable SES is group-mean centered Using level-2 variables to model random intercept and random slope Showing the mixed model version

    4. Continued Estimation method: REML vs. ML Hypothesis testing

    5. Example 3 in HLM: Unconditional Linear Growth Model Use existing .mdm file to build up a model Exploring the data graphically Exploring the model graphically

    7. Continued Stata2mlwin by Michael Mitchell (ATS), creating an ASCII data file and an MLwiN command file (.obe file) to read the ASCII file with variable names into MLwiN stata2mlwin using hsb12, replace

    8. Continued REML vs. ML Decomposition of variance into between and within variance Intraclass correlation

    9. Example 2 in MLwiN: Include both level-1 and level-2 predictors

    10. Example 3 in MLwiN: Unconditional Linear Growth Model

    11. Model-based Graphics

    12. Model-based Graphics

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