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Am I running the correct syntax to examine 2 variables at 3 time points in a cross-lagged panel analysis? Syntax is below:
VARIABLE:
NAMES are
PCL2 PCL3 PCL4 AC2 AC3 AC4;
MISSING is
PCL2(999) PCL3 (999) PCL4 (999) AC2 (999) AC3 (999) AC4 (999);
ANALYSIS: type=general;
estimator=mlm;
MODEL:
AC3 on AC2 PCL2;
PCL3 on PCL2 AC2;
AC4 on AC3 PCL3;
PCL4 on PCL3 AC3;
PCL2 with AC2;
PCL3 with AC3;
PCL4 with AC4;
OUTPUT:
stdyx |
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| That looks right. Also note the RI-CLPM approach shown on our website. |
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Thank you very much. I ran the RI-CLPM and the model did not converge.
With the syntax in my original post, my model fit was poor (see below). Any suggestions as to how to improve my RMSEA and my TLI? I expected the chi-square to be significant because this is a fairly large N, but I'm wondering about the rest. Thanks again.
MODEL FIT INFORMATION
Number of Free Parameters 23
Chi-Square Test of Model Fit
Value 150.883
Degrees of Freedom 4
P-Value 0.0000
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.158
90 Percent C.I. 0.137 0.180
Probability RMSEA <= .05 0.000
CFI/TLI
CFI 0.958
TLI 0.855
Chi-Square Test of Model Fit for the Baseline Model
Value 3550.314
Degrees of Freedom 14
P-Value 0.0000
SRMR (Standardized Root Mean Square Residual)
Value 0.036 |
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| I think your 4 df come from zero lag-2 paths. That is, the time 2 outcomes may predict the time 4 outcomes (directly). |
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So would I run it like this then?:
MODEL:
PCL3 on PCL2 AC2; PCL4 on PCL2 AC2;
AC3 on PCL2 AC2; AC4 ON AC2 PCL2;
PCL2 with AC2;
PCL4 with AC4;
When I run it this way, I get no fit indices. Should I have time 2 outcomes predict both time 3 and time 4 outcomes? |
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Correct. Perfect fit because the model doesn't have any zero paths. You can see which lag-2 effects are significant.
But you should try to get the RI-CLPM going because it may alleviate the need for lag-2 effects. You can send output to Support along with your license number. |
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| I’m running a cross-lagged model with only two time points (interval 4 years). Several variables (a, b, c, d, and e), were measured at T1 and T2 with a sample size of 120. Given the two waves, I'm not able to use the RI-CLPM approach. Is the 'standard' cross-lagged panel analysis the best approach in this case, and if so, is there a tutorial available for helping to conduct this analysis? |
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| Similar to the previous question, how can I modify my syntax to check for mediation in a cross lagged panel? |
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Regular CLPM can be done simply either by fixing the random intercept variances at zero. Or, just say
y1 with z1;
y2 on y1 z1;
z2 on z1 y1;
etc
Regarding mediation, it is not clear if you have "an exposure variable", that is, a treatment/control type of cause in the model. |
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| I have two (manifest) variables measured at two time points (t-1, t0) and an independent variable measured at t0 that the four variables should predict. I also would like to add co-variates (control variables) to the model which are stable across time points. I am not sure how to do this. Is there an example available? |
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CORRECTION:
I have two (manifest) variables measured at two time points (t-1, t0) and a DEPENDENT variable measured at t0 that the four variables should predict. I also would like to add co-variates (control variables) to the model which are stable across time points. I am not sure how to do this. Is there an example available? |
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You can simply say:
y on x11 x12 x21 x22 c;
where y is your DV; x11, x12 are measured at t-1; x21, x22 are measured at t0; and c represents control variables.
This means that you are not putting any structure on the relationships among the x's. |
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| Thank you! I probably just thought too complicated... |
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