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Latent difference score and XWITH |
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Ryan Vogel posted on Friday, November 15, 2013 - 11:32 am
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Hello, Following syntax provided by Selig & Preacher (at http://quantpsy.org/ supp.htm.), I have created a latent difference score representing the change in a measured variable from one time to another. Now, using the XWITH function, I'm attempting to create a latent interaction term between the latent difference variable and another variable. Then, this interaction term will be used in an equation to predict a third variable. When I attempt to run the model, I receive the error message: "THE ESTIMATED COVARIANCE MATRIX COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 1. 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." I'm wondering if anybody out there has familiarity with running this type of variable/model. I have seen others do this before (e.g., Toker & Biron, 2012, Journal of Applied Psychology); however, I am having problems obtaining their syntax. |
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Stoppage in iteration 1 is often caused by starting values that give an inadmissible covariance matrix. Others? |
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Zhi Li posted on Thursday, November 08, 2018 - 11:22 am
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Hello Dr. Muthen, I have encountered the exact same problem, do you think this is caused by a non-zero mean for the latent difference score? I am wondering how does XWITH work with creating interaction term for latent variables. If we were to manually create the interaction term, would it be possible? Thanks! |
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Q1: No. Q2: No. Send your output to Support along with your license number. |
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Daniel Lee posted on Wednesday, December 26, 2018 - 10:29 am
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Hi Dr. Muthen, can the mean of latent change factor scores be negative? I ask this simple question because while my descriptive statistics (observed means) show decreases in a certain construct over time, the latent change score factor means are statistically significant and positive. Thank you in advance for your thoughts! |
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Factor means can be different from observed variable means due to the latter also being a function of the measurement intercepts. |
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