Negative Covariate on Negative Slope PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
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 anonymous posted on Monday, August 04, 2008 - 12:21 pm
Hello,
I'm attempting to interpret the effect of family distress (high scores mean more family distress) on a decreasing linear slope in agressive symptoms (the linear slope mean is negative). The standardized regression coefficient of family distress on the linear slope factor is also negative. Would this indicate a slower decline in aggressive symptoms if there is high family distress? A faster decline? Or would this change the direction of the slope (i.e.,high family distress increases aggression over time?)
 Bengt O. Muthen posted on Monday, August 04, 2008 - 5:44 pm
A negative influence on a slope factor implies that it is lower when the influence is higher, no matter the mean of the slope. So here it implies that the slope has a larger negative value, that is, aggression goes down faster for higher family distress.
 anonymous posted on Monday, August 04, 2008 - 6:36 pm
This seems a little odd. Could it possibly be a regression to the mean effect? That is, the higher in family distress you are (and if family distress is positively correlated with aggression at baseline), the more you can decline over time in aggression?
 Bengt O. Muthen posted on Monday, August 04, 2008 - 6:44 pm
Perhaps.
 anonymous posted on Tuesday, August 05, 2008 - 1:42 pm
Thanks very much for your help! So, given this, does a positive beta coefficient to a slope (with a mean negative value) indicate that the effect of family distress increases the rate of aggression over time? That is, aggression increases faster for higher family distress?
 Bengt O. Muthen posted on Tuesday, August 05, 2008 - 6:44 pm
The value of the aggression slope may still be negative - so increasing family distress makes it less negative.
 Gabriela Roman posted on Friday, February 11, 2011 - 4:37 am
Hi, I think this might be incorrect. If the slope is negative, it means:
- if the correlation with item *a* (distress) is positive, then the larger the *a*, the larger/more abrupt the slope is. So the for a positive correlation, the more distress, the bigger the slope of the aggression scores (i.e., aggression goes down faster)
- if the correlation with item *a* is negative, then the higher the value if *a*, the less abrupt the slope is. In this case, the higher the distress, the smaller the slope of aggression scores (i.e. aggression goes down slower)

Am I right?

Thank you,
Gabriela
 Bengt O. Muthen posted on Friday, February 11, 2011 - 10:30 am
I would say the opposite in both cases. A simple picture to have in mind is a clock face where the clock hand (say the minute hand) represents the slope. The clock hand can move clock-wise or counter clock-wise. For an increasing covariate value:

- a positive slope for the covariate moves the hand counter clock-wise

- a negative slope moves the hand clock-wise

The initial position of the clock hand is for covariate value of zero, so that it's position is determined by the slope intercept. So a large negative intercept for the slope (slope value at x=0) means that the clock hand points to say 5 o'clock. With a positive slope, increasing x increases the value of the slope, and therefore will move the clock-hand up to say 4 o'clock, so still a negative slope. A bigger change in x can give a positive slope, say at 2 o'clock.
 Gabriela R posted on Saturday, February 12, 2011 - 3:31 am
Dear Dr Muthen,

Thank you for the very fast reply. This will be very helpful, since I am investigating predictors of a negative slope.

Best wishes,
Gabriela
 Jaci posted on Friday, May 06, 2011 - 6:35 am
I have a similar question, but instead I use my slope as a predictor. I have a negative slope, and this negative slope has a positive effect on another variable. So that means that a more negative slope leads to a lower score on the outcome, and a less negative slope leads to a higher score on the outcome, am I correct?
 Bengt O. Muthen posted on Friday, May 06, 2011 - 9:33 am
Yes. Just think in terms of the predictor value increasing or decreasing, which with a positive slope gives an increase or a decrease in the DV.
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