Re: Interpretation of interaction with time-varying predictor (Growth model)

Posted by Rich Ulrich on Mar 15, 2021; 7:11pm
URL: http://spssx-discussion.165.s1.nabble.com/Interpretation-of-interaction-with-time-varying-predictor-Growth-model-tp5740294p5740295.html

I have problems with the statistical design, particularly because

I've been involved with studies of Depression.  And it seems that

your Pain may have this same feature:  It is HIGH at start, and

decreases rapidly to Time 2, after which it stays the same or

decreases a bit more.

For my analyses, I looked at Time 1 vs. 2, then 2-5, in order to

separate the huge changes from the lesser ones.  If you have that

sort of time profile, I recommend doing the same.  It reduces the

complications of interpretation.

I don't know how your Depression is measured, but I know that

many Dep scales do have a somatic component -- like, severe pain

will interfere with ability to sleep, and bad sleep can count towards

Depression.  (Somatic "depression" items were horrible, but not

the "sadness" ones, on a tuberculosis ward in the 1960s.)

--

Rich Ulrich

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From: SPSSX(r) Discussion <[hidden email]> on behalf of Oliver <[hidden email]>
Sent: Monday, March 15, 2021 2:40 PM
To: [hidden email] <[hidden email]>
Subject: Interpretation of interaction with time-varying predictor (Growth model)

 

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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