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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