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Hello Drs Muthen, I specified a cross-lagged model using MLR with two waves and 5 variables (1 = manifest, continuous + 4 = latent factors assuming strong measurement invariance). This model and moderations by age and gender (via multi-group) worked fine. I was asked to rerun this model including only manifest, binary variables to assess the clinical meaningfulness. I'm wondering if it's adequate to specify such a model? If yes, is this specification done properly using WLSMV: X1 ON SES; X2-4 ON age gender SES; Y1 ON SES; Y2-4 ON age gender SES; X1 WITH X2 X3 X4 X5; X2 WITH X3 X4 X5; X3 WITH X4 X5; X4 WITH X5; Y1 WITH Y2@0 X3@0 X4@0 X5@0; Y2 WITH Y3@0 X4@0 X5@0; Y3 WITH Y4@0 X5@0; Y4 WITH Y5@0; Y2 ON X2 X1; Y1 ON X1 X2; Y3 ON X3 X1; Y1 ON X1 X3; Y4 ON X4 X1; Y1 ON X1 X4; Y5 ON X5 X1; Y1 ON X1 X5; In addition, would you recommend allowing correlations between manifest variables at t2. Thanks for your help in advance and kind regards! |
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Looks ok although I don't understand the argument for dichotomizing to study clinical significance. Regarding correlating variables as t2, I would discuss on SEMNET. |
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Thank you for your quick response. The variables assessed can be categorized into "normal" vs. "abnormal" scores on the basis of recommended cutoff scores for clinical diagnoses. |
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Dear Drs. Muthen, I specified a cross-lagged panel model between three variables (X, Y, Z) and I have 5 time points. The participants come from two datasets and I would like to adjust the model for 2 confounding variables, age and the dataset. I am confused where I add these two confounders in my model. Do I add them to all the paths (Y2 on X1 Z1 age dataset; Y3 on X2 Z2 age dataset; etc)? Or only the association between these confounders and X Y Z at the first measurement (X1 on age dataset; Y1 age dataset; Z1 age dataset; Y2 on X1 Z1; Y3 on X2 Z2; etc.)? Or something completely else? Kind regards, Lisa |
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Use the RI-CLPM and let the age and data set dummy influence the random intercepts. See http://www.statmodel.com/RI-CLPM.shtml |
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Thank you for your suggestion. Two of the three main variables in my model are dichotomous. Is the RI-CLPM also possible with dichotomous variables? And if so, do I have to change something to the syntax (other than stating these variables as categorical). Kind regards, Lisa |
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Which of your variables are dichotomous? RI-CLPM with categorical DVs needs special considerations and is perhaps more of a methods research topic at this point. |
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Out of the three variables that are measured 5 times, XYZ, are X and Y dichotomous. Z is continuous. For the two confounders (age and cohort): age is continuous and cohort is dichotomous. |
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Sounds like 2 of your 3 DVs that are repeatedly measured are dichotomous. You are then in a methods research area where the RI aspect of the RI-CLPM is the issue. |
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Oh, that's a pity. I dichotomized those two repeatedly measured DV's because there were no linear relations (thus the linearity assumption was violated). If I would specify a CLPM without the RI, thus an old-fashioned CLPM, how would you than suggest that I correct my model for these two confounder? Kind regards, Lisa |
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I would as you say add them to all the paths (Y2 on X1 Z1 age dataset; Y3 on X2 Z2 age dataset; etc) |
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Thank you, I have added the confounding variables to all the paths. This is my syntax: VARIABLE: NAMES = id cohort Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 Z1 Z2 Z3 Z4 Z5 age sex ; MISSING IS ALL (-99); IDVARIABLE = id; USEVARIABLES ARE cohort age X1 X2 X3 X4 X5 Z1 Z2 Z3 Z4 Z5 Y1 Y2 Y3 Y4 Y5; CATEGORICAL = Y2 Y3 Y4 Y5 X2 X3 X4 X5; ANALYSIS: type=general; parameterization=THETA; MODEL: ! Estimate the covariance between the observed variables at the first wave Z1 with X1 Y1; Y1 with X1; ! Estimate the covariances between the residuals of the observed variables Z2 with Y2 X2; Y2 with X2; Z3 with Y3 X3; Y3 with X3; Z4 with Y4 X4; Y4 with X4; Z5 with Y5 X5; Y5 with X5; ! Estimate the lagged effects between the observed variables Z2 ON Z1 Y1 X1 cohort age; Z3 ON Z2 Y2 X2 cohort age; Z4 ON Z3 Y3 X3 cohort age; Z5 ON Z4 Y4 X4 cohort age; X2 on Z1 Y1 X1 cohort age; X3 ON Z2 Y2 X2 cohort age; X4 ON X3 Z3 Y3 cohort age; X5 ON X4 Z4 Y4 cohort age; Y2 ON Y1 Z1 X1 cohort age; Y3 ON Y2 Z2 X2 cohort age; Y4 ON Y3 Z3 X3 cohort age; Y5 ON Y4 Z4 X4 cohort age; I checked with MOD INDICES if there would be interesting suggestions to improve the model. These included ON statements between the 3 DV's at the first measurement and the confounders, such as: X1 ON AGE; Z1 ON COHORT; Z1 ON AGE; etc. I find it difficult to determine if this makes sense in a CLPM. What would you suggest? |
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I would regress Y1, X1, Z1 on all the confounding variables because the latter are background variables. |
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Dear Profs Muthén, I am estimating a cross-lagged model with 4 time points. The model fit is poor. The modindices command suggest that I add additional on statements between x4, x3, x2 and x1, and y4, y3, y2 and y1, respectively: x4 on x2 x1; x3 on x1; y4 on y2 y1; y3 on y1; does it make sense to add these paths? Can I still interpret the cross-lagged effects similarly with these paths included in the model? Thank you very much in advance and kind regards. |
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I would recommend that you first try an RI-CLPM. See http://www.statmodel.com/RI-CLPM.shtml |
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