[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

From |
Jeffrey Simons <[email protected]> |

To |
<[email protected]> |

Subject |
st: Multilevel analysis and GLLAMM |

Date |
Tue, 21 Sep 2004 06:36:37 -0500 |

I am trying to run a multilevel model with GLLAMM. The data consist of many (i.e., 50 - 100) repeated measures on participants plus baseline demographic and trait measures. From what I have read about these analyses, some advocate to context center and some advocate grand mean centering the variables and then including (or not depending on theory) a level-2 mean of the level-1 centered variables. In my analyses, I am frequently getting a correlation between the random intercept and slope of -1. I have several questions. 1. I am wondering if I am doing something wrong or if the -1 correlation signifies that the intercept and slope terms are redundant. That is, should I just be running a random intercept model. I have run a series of analyses to look at this. I notice that the correlation is always high (e.g., .90). It becomes -1 if either I add additional level 2 predictors or if I use the context centered repeated measures predictor. 2. On a related note, I notice that there is a nocor option to set this correlation to zero and am wondering when it is appropriate to do so. 3. If I have gender as a level 2 predictor do I need to use this "s" option? If so how? s(eqname) specifies that the log of the standard deviation (or coefficient of variation) at level 1 for normally (or gamma) distributed responses should be given by the linear predictor defined by eqname. This is necessary if the level-1 variance is heteroscedastic. For example, if dummy variables for groups are used, different variances are estimated for different groups. 4. If using the gamma family with the canonical link function. Is interpretation of the signs of the slope coefficients opposite to the direction of the relationship. That is, given it is a reciprocal link, does a negative coefficient actually signify a positive relationship between the variables? Is it reasonable to use an identity link instead? 5. Finally, being new to this type of analysis, I was wondering if anyone could comment on the relative strengths and weaknesses of using GLLAMM versus a program such as HLM. Here is an example of my commands, varx and vary are the repeated measures the remaining variables are level 2 predictors: Gen cons=1 Eq cons: cons Eq slope: varx gllamm vary varx varz varw ,i(id) family(gamma) nrf(2) eqs(cons slope) adapt Any assistance will be most appreciated. Jeffrey Simons * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Multilevel analysis and GLLAMM***From:*Stas Kolenikov <[email protected]>

- Prev by Date:
**Re: st: VS: Still gllamm** - Next by Date:
**st: various updates on SSC** - Previous by thread:
**st: dummieslab updated** - Next by thread:
**Re: st: Multilevel analysis and GLLAMM** - Index(es):

© Copyright 1996–2024 StataCorp LLC | Terms of use | Privacy | Contact us | What's new | Site index |