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Terri Scott posted on Tuesday, September 03, 2013 - 6:04 pm
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I'm testing if the 12 continuous variables in my model are moderated by 2 categorial (0, 1) variables and/or the interaction term (racexsex). Classes = c(3); Analysis: Type = Mixture; Parameterization=loglinear; Starts = 40, 2; Processors = 2; Define: racexsex=race*sex; Model: %overall% c on sex race racexsex; Output: samp Stand Tech11; Plot: Type = Plot3; series = PDS_tot (1) PCL_tot (2) YLS_off (3) MCAA_RPT (4) PTSD(5) YSR_tot (6) ARSQ_fea(7) GSES_tot (8) YLS_educ(9) DAST_27(10) YFAD_GF (11) SAI_Free (12); Summary shows only half the observations are used (155 out of 326). Am I misspecifying something thatprevents all cases from being analyzed? SUMMARY OF ANALYSIS Number of groups 1 Number of observations 155 Number of dependent variables 12 Number of independent variables 3 Number of continuous latent variables 0 Number of categorical latent variables 1 Kind regards. |
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Please send the output and your license number to support@statmodel.com. |
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Milan R posted on Saturday, February 24, 2018 - 5:57 am
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Hello, I am analyzing a 3-trajectory GMM with a covariate (M). There is a trajectory with a significant intercept but non-significant slope (despite its magnitude is negative). Although the mean trend of this trajectory is not significantly different from zero, this covariate has a significant and negative effect on the slope. How can I probe the effect of this covariate on individual slopes within this trajectory using Mplus? I'd like to probe simple slopes treating the covariate as a moderator, time as X, my outcomes as Y. Thank you! |
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If the covariate M has a significant effect on s and this regression has a >0 residual variance, I think the model is ok. |
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Milan R posted on Saturday, February 24, 2018 - 3:28 pm
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Thank you, Dr. Muthen! How can I write out syntax in Mplus to probe the effect of this covariate on individual slopes within this trajectory? I'd like to probe simple slopes treating the covariate as a moderator, time as X, outcome as Y. Thank you! |
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You can do that in a twolevel run with time as level 1 and subject as level 2. We have a UG example on doing growth in a twolevel fashion. The latent class variable needs to be a Between= variable. Then add a Time*Moderator interaction. |
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Milan R posted on Sunday, February 25, 2018 - 1:38 pm
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Dr. Muthen, I followed the UG example 10.2 to write up the model. However, two problems showed up that I could not figure out why and how to fix. After reshaping my data into long format, my model is as follows: Define: int=time*moderator; Variable: Names are moderator sex Y time id; Usevariables are moderator sex Y time int; Classes = c(3); Cluster = id; Within=time; Between= sex moderator c; Analysis:type= twolevel mixture random; Model: %within% %overall% s1| y ON time; %between% %overall% c y ON sex moderator int; %c#1% [s1]; %c#2% [s1]; %c#3% [s1]; %c#4% [s1]; Two problems: 1. the proportions of membership in each class are different from when I used type= mixture (not multilevel approach). 2. The effects of int (time*moderator) were fixed in all three class. Thank you! |
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First, note that int defined as int=time*moderator; will be a within-level variable because time is within. Regarding different class percentages, first make sure that you have the same number of parameters in a model where you don't have the moderator. For instance, the single-level model has free residual variances whereas the twolevel model holds them equal - so hold them equal in the single-level model too. |
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Milan R posted on Monday, February 26, 2018 - 11:44 am
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Dr. Muthen, Once I switched "int" to be a within-level variable, I could not use it on the between-level. My goal is to test the effect of the moderator (which is a subject/between level variable) on s1 (individual slopes). But since s1 is between-level, I don't know how to write out the effect of int on s1 and where to place this statement to achieve my goal. Thanks again! |
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You can write s1 on moderator; on the Between level. This way time and moderator form a cross-level interaction. |
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Milan R posted on Wednesday, February 28, 2018 - 6:40 am
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Thank you, Dr. Muthen! You mentioned earlier that "Regarding different class percentages, first make sure that you have the same number of parameters in a model where you don't have the moderator. " So, to solve the issue of different class memberships in the multilevel context than in the single-level one, I freed up several parameters as follows. But the results were still not consistent with those under the single-level model. Can you help and point out if I missed to free up any other parameters? Thank you so much! ----- CLASSES = c(3); CLUSTER = id; WITHIN=time; BETWEEN= sex moderator c; %WITHIN% %OVERALL% s1| y ON time; %BETWEEN% %OVERALL% c ON sex moderator; %c#1% s1 ON moderator; y; [s1*]; s1*; %c#2% s1 ON moderator; y; [s1*]; s1*; %c#3% s1 ON moderator; y; [s1*]; s1*; |
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You should focus on restricting the single-level model to match the twolevel. A key difference is that single-level allows residual variances to differ across time points whereas with two-level they are the same - so hold them equal in the single-level run. |
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