The goal of this exercise is to find out what happens if I intentionally use a level2 variable at level 1 in HLM. I found that the coefficients and standard errors remain about the same. The parameter that differed was just the degree of freedom, which was consistent with my expectation.
Using my old […]
Effects in this context refer to group outcome averages estimed by a regression model. In a regular regression models (OLS, Logistic Regression model), these are often estimated as coefficients of a series of dummy variables representing group units (e.g., school A, school B, .. each coded as 0 or 1). These are […]
Not common: If your data is a census data (everyone is in the dataset, like US Census), you do not need to use HLM. You do not even need statistical testing because every estimate you get is a true estimate. (Note: my friend wrote me and said he disagrees. He said even if he had […]
I encountered a situation where somehow PROC GLIMMIX can't produce results while PROC MIXED can. In this WORD document, I showed you how I edited the PROC GLIMMIX syntax to be a PROC MIXED syntax. Please ignore my macro %w.
While running PROC GLIMMIX with a large dataset with categorical variables explicitly treated as CLASS variables AND with weight, I got the following error message:
WARNING: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed.
The weight and the use of categorical variables were the cause of the problem as […]
I have the copy of the first edition. The second edition is here:
HLM (multilevel models) and econometric analyses (e.g., time series analysis, ARIMA, etc.) are treated as different approaches (the goal of which is to deal with data dependency problem), they can be implemented in the same model via. SAS PROC GLIMMIX. However, I believe doing this is computationally demanding and models may not converge.
When I first learned HLM (Hierarchical linear modeling) at graduate program in 1994/5, I struggled with the following expression:
Errors are correlated.
Up to that point in Stat 101, correlation was about two columns of data (e.g., math test score and science test score). Errors in the context of regression analysis are residuals from the […]