Winter, Spring/summer are dummy variables coded 1 and 0 with the base season being fall and trend is the time period.
does SALES decrease by 0.923 in the winter and 0.963 in the spring/summer?
does the intercept 2143.869 have any significant meaning?
Thanks in advance
bmuthen posted on Monday, December 06, 2004 - 5:34 pm
The intercept is the SALES value when all the predictors have value 0 (so for Fall with TREND=0). So, yes on your last question.
And yes on your first question, with some elaboration. E.g., -0.923 for Winter says that for any given TREND value, Winter has 0.923 less SALES value than Fall. Analogous for -0.963 for Spring/Summer.
tonkin posted on Tuesday, April 19, 2005 - 5:03 pm
Three questions: First, I ran a two group growth model of continuous outcomes at four time points. I believe Dr. Muthen said I could do a t-test for the intercepts and slopes for the two groups to see if they differed signficantly. Is this so? If so, what is the formula? a Z-test for two samples? What do I put in the denominator?
Second, I have read several of your articles on gc and still do not really understand exactly what the latent intercept and slope are (technically). I know they are defined as the initial status (when timepoint is designated zero) and trend, but is it a factor score, or mean, or something else? When I look at the plots, they say "means" and are in the metric of my measures, but I think I need to know how the latent intercept and slope are calculated? Does this make sense?
Finally, Can I use censored variables in gc models, and particularly in three level models?
Thank You, Peggy Tonkin
BMuthen posted on Wednesday, April 20, 2005 - 9:18 am
Yes, you could do a t-test for two samples. Or, you could do two analyses where the growth factor means are held equal in one analysis and are not held equal in the other. The chi-square difference test can be used to determine whether the means are the same across groups.
The latent intercept and slope are latent variables, that is, scores on factors. We are interested in their means, variances, and covariances. The plots are for the model estimated means.
Yes, growth models can be estimated for censored outcomes.
tonkin posted on Wednesday, April 20, 2005 - 2:10 pm
Thank you for your quick response you have been very helpful. One more question, can I treat my outcomes (continuous) as censored if I am doing a three level gc with covariates in the within model? Thanks so much again, Peggy Tonkin
A two-level growth model(three-level in HLM terms for example) cannot be estimated for a censored outcome. Only a one-level growth model (two-level in HLM terms) can be estimated for a censored outcome.
Amy Tobler posted on Wednesday, September 15, 2010 - 11:29 am
My question is: how do we interpret the beta of treatment regressed on sb? Is it the difference between the control group slope and the treatment group slope? If so how can we recover these slope from the output?
The regression coefficient in difference between the control and treatment group means of sb. The mean for the control group is the intercept. The mean for the treatment group is the intercept plus the slope.
Amy Tobler posted on Thursday, September 16, 2010 - 11:05 am
Thanks for the response. One quick clarification. We get the following output:
SB ON TRT -0.037 0.022 -1.650 0.099 Means IB 0.000 0.000 999.000 999.000 Intercepts SB 0.063 0.137 0.456 0.648
When you say the mean for the control group is the intercept, do you mean the mean of the intercept factor (in out output this would be 0)? or the intercept for the slope factor (in our output this would be 0.063)? Then the mean slope of the treatment group would be either the 0 or the 0.063 + (-0.037)? Thanks for your time.
The mean for the control group is the intercept of sb.
fred posted on Tuesday, October 25, 2016 - 1:22 am
Hi, I am running a LGC with one intercept and two slope of which the first one is negative (estimate= -12.477. SE=1.795). To achieve model fit this slope needs to be regressed on the intercept, which makes theoretical sense. The regression weight is 0.133, SE=0.021. I am somehow unsure if this can imply that the higher the intercept the "slower" or "faster" decrease as indicated by the slope since the regression weight is less than 1. Thanks Fred