Posted by
Oliver on
Mar 15, 2021; 6:40pm
URL: http://spssx-discussion.165.s1.nabble.com/Interpretation-of-interaction-with-time-varying-predictor-Growth-model-tp5740294.html
Hi everyone,
I have a question regarding the interpretation of an interaction effect
involving time-varying (i.e., Level 1) variables in a growth model. In this
study, participants (n = 1000) provided ratings of pain (Outcome: 0-10) and
depressive symptoms (Dep: 0-10) across 5 time points (i.e., baseline, 3m,
6m, 9m, 12m). Results indicated a significant (linear) effect of time (B =
-.49, p = .000), indicating that pain decreased linearly over time. There
was also a significant main effect of depression on pain (B = .23) as well
as a significant (Time * Dep) interaction (*B = .05,* p = .000). The syntax
is copied below.
I am a bit uncertain about the interpretation of the interaction effect. The
variables are not centered (uncentered). By examining the beta coefficient
of the interaction, would results suggest that the effect of time on the
outcome (i.e., linear decrease in pain over time) is more pronounced among
those who have higher depressive (Dep) symptoms ?
MIXED Pain WITH Time Dep
/METHOD = REML
/PRINT = SOLUTION TESTCOV
/FIXED = Time Dep Time*Dep | SSTYP(3)
/RANDOM = INTERCEPT | SUBJECT(ID) COVTYPE(UN)
/REPEATED = Wave | SUBJECT(ID) COVTYPE(AR1).
Thanks in advance for your assistance.
O.
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