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Two group LGM with categorical data |
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Daniel posted on Monday, April 05, 2004 - 7:55 am
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Hi, I am running a two group model and am testing for group difference on a fixed effect (e.g., slope on gender). I am using WLS as my estimator for the chi-square difference test. However, when I constrain paths to equality, I keep on getting a 2 df difference when I should only get a 1 df difference, isn't that correct? Is there something I'm missing? In other words, shouldn't the degrees of freedom only differ by 1 for constraining one path to equality across groups? |
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Daniel posted on Monday, April 05, 2004 - 8:24 am
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Never mind, I found the problem. I was a dope! I didn't realize I used one of the same numbers in parens to constrain my paths to equality as was used for threshold constraining previously. Sorry to bother you. |
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I am trying to run a multiple group latent growth curve analysis with a categorical (in this case binary) outcome. First I used the following sytanx... USEVAR ARE H1SU1 H2SU1 H3TO130; CATEGORICAL ARE H1SU1 H2SU1 H3TO130; WEIGHT IS gswgt1; CLUSTER IS psuscidn; GROUPING IS smy_dc (0 = hetero 1 = SMY); ANALYSIS: TYPE = MEANSTRUCTURE Complex; ESTIMATOR = MLR; MODEL: Isuicide BY H1SU1@1 H2SU1@1 H3TO130@1; Lsuicide BY H1SU1@0 H2SU1@1 H3TO130@6.5; [H1SU1@0 H2SU1@0 H3TO130@0] [Isuicide]; [Lsuicide]; Model Hetero: [Isuicide]; [Lsuicide]; Model SMY: [Isuicide]; [Lsuicide]; OUTPUT: tech4 sampstat; And got the following error: *** ERROR in Analysis command ALGORITHM = INTEGRATION is not available for multiple group analysis. Try using the KNOWNCLASS option for TYPE = MIXTURE. I then switched to a default estimator instead of MLR, and I switched to theta paramaterization, and got the following error: *** ERROR Cluster ID cannot appear in more than one group. Problem with cluster ID: 371 There is not a problem with the cluster variable. Before and after I got this error warning, I used these same data and the cluster command in my MG LGM with continiuos outcomes and it worked fine. Second, I'm not sure what this error means... by "group" I assume it does not mean the two groups I am analyzing since it is fine if individuals from these different groups are in the same cluster ID. Then, just to peel back the onion layers, I got rid of the cluster command and dropped the "complex" from the analysis command, and received the following error: *** ERROR The following MODEL statements are ignored: * Statements in the GENERAL group: [ H1SU1 ] [ H2SU1 ] [ H3TO130 ] Perhaps the strategy I'm using now is a close to correct and just needs a few changes, or perhaps I am way off. Either way, can you suggest a way to conduct multiple group LGM with a continous outcome. Also, ideally I'd rather do it in "meanstructure" opposed to "mixture" because I am more familiar with analyses using meanstructure, and I don't think it is even possible for me to carry-out my planned subsequent analyses when using mixture. Thanks! |
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If you use the weighted least squares estimator (WLSMV), you can use the GROUPING option with categorical outcomes. |
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As suggested I used WLSMV as the estimator, and the model "ran" without an error. However, the output lists no estimates or standard errors since I have no free parameters. I should have 2 free parameters since, according to the tech 1 output, I am estimating less parameters (16) then my limit (18 since I have a 3 observed variables for each of my 2 groups). Here is my syntax: USEVAR ARE H1SU1 H2SU1 H3SU1; CATEGORICAL ARE H1SU1 H2SU1 H3SU1; WEIGHT IS gswgt1; GROUPING IS smy_dc (0 = hetero 1 = SMY); ANALYSIS: TYPE = MEANSTRUCTURE; ESTIMATOR = WLSMV; PARAMETERIZATION = THETA; MODEL: Isuicide BY H1SU1@1 H2SU1@1 H3SU1@1; Lsuicide BY H1SU1@0 H2SU1@1 H3SU1@6.5; [H1SU1$1@0 H2SU1$1@0 H3SU1$1@0] [Isuicide]; [Lsuicide]; Model Hetero: [Isuicide]; [Lsuicide]; H1SU1; H2SU1; H3SU1; Model SMY: [Isuicide]; [Lsuicide]; OUTPUT: tech4 sampstat; Here is my tech 1 output: TECHNICAL 1 OUTPUT PARAMETER SPECIFICATION FOR HETERO TAU H1SU1$1 H2SU1$1 H3SU1$1 ________ ________ ________ 1 0 0 0 NU H1SU1 H2SU1 H3SU1 ________ ________ ________ 1 0 0 0 LAMBDA ISUICIDE LSUICIDE ________ ________ H1SU1 0 0 H2SU1 0 0 H3SU1 0 0 THETA H1SU1 H2SU1 H3SU1 ________ ________ ________ H1SU1 1 H2SU1 0 2 H3SU1 0 0 3 ALPHA ISUICIDE LSUICIDE ________ ________ 1 4 5 BETA ISUICIDE LSUICIDE ________ ________ ISUICIDE 0 0 LSUICIDE 0 0 PSI ISUICIDE LSUICIDE ________ ________ ISUICIDE 6 LSUICIDE 7 8 PARAMETER SPECIFICATION FOR SMY TAU H1SU1$1 H2SU1$1 H3SU1$1 ________ ________ ________ 1 0 0 0 NU H1SU1 H2SU1 H3SU1 ________ ________ ________ 1 0 0 0 LAMBDA ISUICIDE LSUICIDE ________ ________ H1SU1 0 0 H2SU1 0 0 H3SU1 0 0 THETA H1SU1 H2SU1 H3SU1 ________ ________ ________ H1SU1 9 H2SU1 0 10 H3SU1 0 0 11 ALPHA ISUICIDE LSUICIDE ________ ________ 1 12 13 BETA ISUICIDE LSUICIDE ________ ________ ISUICIDE 0 0 LSUICIDE 0 0 PSI ISUICIDE LSUICIDE ________ ________ ISUICIDE 14 LSUICIDE 15 16 What I am I missing? I assume that whatever it is, my problem is related to either the estimator I'm using (WLSMV) or the fact I'm using categorical outcomes. Thanks |
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This is an Mplus support question. Please send your input, data, output, and license number to support@statmodel.com. |
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gibbon lab posted on Wednesday, October 12, 2011 - 7:57 am
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Hi Professor Muthen, For longitudinal binary outcomes (1/0), if the proportion of 1's increases along study waves and assume 1 does not change back to 0, (e.g., suppose the outcome is yes/no smoking and no one quit smoking during the study), is latent growth model still applicable in this case? Thanks. |
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Answered under another thread. |
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