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Sophia posted on Friday, April 10, 2015 - 1:41 pm
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I would like to run a HGLM. Observations are nested within persons (P). I have explanatory variables on Level 1 (X1, X2, X3, X4) and my outcome variable (Y) is binary. I would be grateful if you could help me with the following questions: 1) Is the difference between a multilevel model with a continuous outcome variable (HLM) and a multilevel model with a binary outcome variable (HGLM) in Mplus language that I add categorical = “my outcome variable” to the variable command? variable: names = X1 X2 X3 X4 X5 Y P; usevar = X1 X2 X3 X4 Y; categorical = Y; cluster = P; within = X1 X2 X3 X4; centering = grandmean (X1) grandmean (X2) grandmean (X3) grandmean (X4); analysis: type = twolevel random; model: %within% Beta1j | Y on X1; Beta2j | Y on X2; Beta3j | Y on X3; Beta4j | Y on X4; %between% Y with Beta1j; Y with Beta2j; Y with Beta3j; Y with Beta4j; output: sampstat; 2)Is there a minimum cluster size (minimum of mandatory observations per person) or a minimum number of clusters in order to run the analysis (a HGLM)? Or could you recommend me a reference dealing with the necessary sample size? |
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Sophia posted on Friday, April 10, 2015 - 1:52 pm
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3)Would you generally recommend to use the ML or MLR estimator for this analysis? |
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Q1. Yes. Q2. You should have at least 30-50 clusters. See the Hox multilevel book for recommendations. Q3. MLR has some robustness built into it. |
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