Hi all,
I am running a simple multilevel model to determine whether a group of people had a statistically significant change in an outcome variable over time. Each person was assessed at 5 time points. The outcome variable is continuous.
My idea is that I would run a multilevel model with a random intercept for the person and a random slope for time, using the following code:
In this case, given the fixed variable for time was not statistically significant (p=0.89), I can conclude there was no (statistically) significant change over time in the outcome variable.
Does this sound correct?
Any comments much appreciated.
Thanks!
MJ
I am running a simple multilevel model to determine whether a group of people had a statistically significant change in an outcome variable over time. Each person was assessed at 5 time points. The outcome variable is continuous.
My idea is that I would run a multilevel model with a random intercept for the person and a random slope for time, using the following code:
Code:
. mixed dep time || person: time
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -191.64297
Iteration 1: log likelihood = -191.49105
Iteration 2: log likelihood = -191.48975
Iteration 3: log likelihood = -191.48974
Computing standard errors:
Mixed-effects ML regression Number of obs = 75
Group variable: person Number of groups = 15
Obs per group:
min = 5
avg = 5.0
max = 5
Wald chi2(1) = 0.02
Log likelihood = -191.48974 Prob > chi2 = 0.8878
------------------------------------------------------------------------------
dep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
time | .0095594 .0677819 0.14 0.888 -.1232906 .1424094
_cons | 78.29368 .7173816 109.14 0.000 76.88763 79.69972
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
person: Independent |
var(time) | 4.50e-12 5.45e-11 2.21e-22 .0917611
var(_cons) | 5.107832 2.402147 2.032018 12.83943
-----------------------------+------------------------------------------------
var(Residual) | 7.128148 1.301701 4.983509 10.19573
------------------------------------------------------------------------------
LR test vs. linear model: chi2(2) = 17.59 Prob > chi2 = 0.0002
Note: LR test is conservative and provided only for reference.
Does this sound correct?
Any comments much appreciated.
Thanks!
MJ
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