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Sarah Lowe posted on Tuesday, June 27, 2017 - 6:25 pm
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Hello! I am working with an integrated dataset and am trying to run a LCGA with t-scores. Unfortunately, I am getting the following error: The number of fixed time scores is not sufficient for model identification in the following growth process: I S Q For your reference, here is my model: ****** USEVARIABLES ARE v1 v2 v3 v4 TIME1 TIME2 TIME3 TIME4; TSCORES = TIME1 TIME2 TIME3 TIME4; CLASSES = C(1); ANALYSIS: TYPE = RANDOM MIXTURE; ESTIMATOR IS MLR; STARTS = 500 20; STITERATIONS = 20; MODEL: %OVERALL% i s q | v1 v2 v3 v4 @ TIME1 TIME2 TIME3 TIME4; i s q WITH i s q; i; s; q; v1; v2; v3; v4; %C#1% [i s q]; i s q WITH i s q; i; s; q; v1; v2; v3; v4; **** I am wondering if this has to do with the large variability in timing within each t-score, as well as the extent of missing data? Here are the ranges for the t-scores: Time 1: 0 to 61 days (Median = 18) Time 2: 62 to 183 days (Median = 110) Time 3: 184 to 301 days (Median = 207) Time 4: 305 to 487 days (Median = 376) The between-time covariance coverage ranges from 0.126 to 0.557 Any insight you have on this matter would be greatly appreciated! Thanks so much, Sarah |
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Try the following: i s q | v1 - v4 at TIME1 - TIME4; |
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Sarah Lowe posted on Wednesday, June 28, 2017 - 5:27 pm
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Thanks for the help - changing the @ to AT worked! |
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Dear Mplus team, I am interested in identifying trajectories of adaptation to spinal cord injury during the rehabilitation time using LGMM. I have 240 participants and 3 measurement time points which are assessed as follows: T1: approximately 1 month after injury T2: approximately 3 months after injury T3: at rehabilitation discharge When exploring the raw data, I realized that there is great variability in the time when individuals where assessed at T3 (some individuals were discharged earlier and therefore assessed soon after 3 months, while others were discharged 6 or more months later). I was then wondering what would be the best approach to handle such variability in assessment time. Would the use of individually varying times of observation (TSCORES) be a suitable option? Is there any other approach that could be used to account for the inconsistency of assessment time? I appreciate a lot your insights regarding these issues. |
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TSCORES is the way to go. You can also do 2-level modeling in long format with time as a within-level covariate - but that's the same thing. |
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