
Message/Author 

Sophia posted on Friday, April 10, 2015  1:41 pm



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? 

Sophia posted on Friday, April 10, 2015  1:52 pm



3)Would you generally recommend to use the ML or MLR estimator for this analysis? 


Q1. Yes. Q2. You should have at least 3050 clusters. See the Hox multilevel book for recommendations. Q3. MLR has some robustness built into it. 

Back to top 

