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Hi. I am planning to do some growth mixture modeling with adolescent longitudinal data in Korea. Is there any way to identify a certain subgroup which considers growth in two parallel dependent variables such as depression and delinquency? I am hoping to find a subgroup of adolescents who are both high in depression and deliquency over 2 years of assessment with 4 timepoints. Since I am just beginning to study mplus software, would you provide any tips to write an appropriate syntax for this kind of modeling? Thank you. |
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You would need to start with a parallel process growth model like the one shown in Example 6.13 and add TYPE=MIXTURE; and one latent class variable using the CLASSES option of the VARIABLE command. For that see Example 8.1. |
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Thank you very much for your kind help. I have one more question about my study. My study follows a cohort-sequential design. I have transformed my data set and it looks like the one shown in page 405 of User's guide. For this data set, I would like to apply FIML for missing data. In order to do this, is it correct to simply write "TYPE=MIXTURE missing;"? Following your guidance, is it right to include below commands for my study? ========== VARIABLE : CLASSES = c (1) ; ANALYSIS : TYPE = MIXTURE missing ; MODEL : %OVERALL% i1 s1 | y11@0 y12@1 y13@2 y14@3 ; i2 s2 | y21@0 y22@1 y23@2 y24@3 ; s1 ON i2 ; s2 ON i1 ; i1 s1 ON X ; i2 s2 ON X ; c ON X ; ========== I really appreciate your guidance and help for this matter. |
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This looks correct. I would however start with a model without covariates. |
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Again, thank you for your tips! |
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Dear Linda, I actually tried to run a growth mixture model with above syntax. And I started with a model without covariates. However, it seems like that I have a problem with convergence. I keep getting the same warning message as following: THE ESTIMATED COVARIANCE MATRIX FOR THE Y VARIABLES IN CLASS 1 COULD NOT BE INVERTED. PROBLEM INVOLVING VARIABLE B7. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 2. CHANGE YOUR MODEL AND/OR STARTING VALUES. THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES. Could you please provide any tips to solve this kind of problem? I am VERY VERY new to Mplus, and I am having a tough time studying this on my own. If I want to specify starting values, what kind of procedure is needed? Thank you very much. |
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You can read about assigning starting values in the user's guide. I suggest you send your input, data, output, and license number to support@statmodel.com. |
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Dear Linda, Is there any way to exclude the default setting of correlation between slope growth factors in Example 6.13 of parallel processes? And can I see the correlation between intercept and slope in each process? (What is the correct syntax for this?) In a longitudinal data, one can usually assume that residuals of indicators are high correlated. In Example 6.13, how can I specify that residuals from y11 to y14 are correlated or same? Thank you very much for your help. |
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You can fixed the covariance between the slope growth factors at zero as follows: s1 WITH s2@0; You can specify the covariance between the intercept and slope growth factors as follows: i1 WITH s1; Residual covariances are specified using the WITH option, for example, y11 WITH y12; Equalities of the residual covariances are specified as follows: y11 WITH y12-y14 (1); y12 WITH y13-y14 (1); y13-y14 (1); Please see Chapter 16 of the user's guide for more information. |
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Thank you very much for your prompt reply! |
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Daniel Lee posted on Sunday, April 09, 2017 - 6:30 am
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Hello, I am modeling a parallel growth model...when I run growth models without regressing intercepts and slopes between two growth constructs (e.g., s2 on i1 s1), the growth processes have significant slopes and intercepts. However, when I include "on" statements between intercepts and slopes (e.g., s2 on i1 s1), the slopes are no longer significant. I was wondering if you can help me understand why this might happen? Thank you. |
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If you don't have ON statements, you are estimating means and variances for the growth factors. With ON statements, you estimate intercepts and residual variances. Check the output. I think this is what you will see. |
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