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Kira McCabe posted on Monday, February 02, 2015 - 8:43 pm
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Hello, I am running a parallel process model with extraversion and drinking behavior over 4 time points. When I first run the separate, unconditional models, extraversion decreases over time and drinking behavior increases over time. However, once I run the parallel process, the trajectory for drinking appears to change. In the intercepts part of the output, the slope for drinking behavior is negative. When I look at the tech4 output, the mean slope for drinking behavior is still positive. My questions: 1) When interpreting the effects of the intercept on the drinking slope (or any other time-invariant covariates), should I interpret it with the estimate in the intercepts or with the mean in the tech4 output? In this case, the intercept of extraversion had a negative relationship with the drinking slope, so extraversion predicted slower decreases in drinking over time? 2) A follow-up question: Can a growth model change from significant to non-significant when a parallel process is added? Applying the same example, let's say the unconditioned model showed a significant increase in drinking over time. However, when the growth model of extraversion is added, the intercepts section shows that the drinking slope estimate is no longer significant. When reporting a parallel process, should this drinking slope remain significant in the parallel process model? Thank you for your help! |
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1) You should go with the mean in TECH4. See also the plot that you get. 2) The significance should be judged by the TECH4 results. |
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Kira McCabe posted on Tuesday, February 03, 2015 - 5:11 pm
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Hello Bengt, Thank you for your reply. I am using an older version of Mplus (Version 7), and I noticed that the tech4 significance results are in the 7.2 update. Is there a way to calculate this information with the variance-covariance matrix? Or is this too difficult all the latent variables in the model? Thank you for your help! |
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You can express the tech4 quantities in Model Constraint and thereby get the signif tests, but it may be cumbersome if the model is complex. |
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lamjas posted on Thursday, April 09, 2015 - 8:58 pm
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Hello, I am running a parallel process LGM. Both IV and DV have six time-points. The model with quadratic terms of IV and DV fit well. I am wondering whether I should add the following effects in the model: (1) IV_i --> DV_q (2) IV_s --> DV_q (3) IV_q --> DV_q Are the above effects interpretable in some ways? Or, would you suggest that a model with the following effects is good enough? IV_i --> DV_i IV_i --> DV_s IV_s --> DV_s Thanks for you help! |
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Jon Heron posted on Friday, April 10, 2015 - 6:00 am
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Interpreting quadratic terms is usually difficult but the decision to add these terms will depend on your particular application. As an alternative you might consider: (1) A two-part linear spline model (2) Relaxing some of your loadings so the non-linearity is absorbed by a distortion of the time axis. |
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xybi2006 posted on Wednesday, August 05, 2015 - 10:02 am
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Dear Dr. Muthen, I have 5 time points data, with baseline, 1 years, 4 years, 10 years, and 23 years. I ran the LGM with unspecified time points (0, 1 for the first 2 time points, and then estimate the other threes): for one variable, the estimated time points are: 0, 1, 3.646, 4.603, 8.523); and for another variable, the estimated time points are: 0, 1, 1.791, 2.321, and 1.969. Given the estimated time points are so different for the two variables, can I proceed to run the parallel process LGM? Thanks much, |
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If these are two separate processes, like height and weight, for example, it seems unlikely that the estimated time scores would be the same. |
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