Jiangang Xia posted on Thursday, December 01, 2016 - 9:31 pm
I have been trying to model a cross-level relationship's effect on level-1 or level-2 outcomes. For example, the relationship is between school principal's influence on decision-making and teacher's influence on decision-making. Here the relationship could be estimated through a 2-level model. My question is, how to if I want to model the relationship's effect on teacher job satisfaction? The relationship is already a slope. I am considering a random slope model that the relationship varies by a higher level such as school district. Could we use this random slope to predict other outcomes such as teacher job satisfaction? If so, how? Should I save the random slopes as a level-3 variable and then run a new regression, or I could directly model this slope's effect? Would appreciate for any thoughts.
The random slope defined on level 2 varies across the units of level 3. This means that this random slope can predict level 3 variables, for instance the level-3 part of the variation in variables measured on level 1 or level 2. That's how multilevel modeling works.
Jiangang Xia posted on Saturday, December 03, 2016 - 4:04 pm
I just want to confirm whether a random slope defined on level 2 varies across the units of level 3 could predict level-1 or level-2 variables. You know the focused outcomes are usually measured at lower levels.
Kirill Fayn posted on Tuesday, February 20, 2018 - 4:56 am
I am trying to use a slope as a predictor in a three-level model. My model is set up as follows:
USEVARIABLES ARE happy TIME swl level2 level3; CLUSTER = level2 level3; WITHIN = TIME; BETWEEN =(level2) swl ; MISSING ARE all (-9999); ANALYSIS: TYPE = THREELEVEL RANDOM; MODEL: %WITHIN% s1 | happy ON TIME; %BETWEEN level2% s1 ON swl; %BETWEEN level3% OUTPUT: TECH1 TECH8;
I get the following error:
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ILL-CONDITIONED FISHER INFORMATION MATRIX....
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO A NON-POSITIVE DEFINITE FISHER INFORMATION MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.650D-16.
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES COULD NOT BE COMPUTED. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 10, %BETWEEN LEVEL2%: HAPPY2
I have tried to play around with some random starting values, but the error persists.