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Luc Watrin posted on Tuesday, August 23, 2016 - 6:30 am
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I analysed a multidimensional forced-choice questionnaire with a Thurstonian IRT Model. Everything worked well and now I'm trying to get a better understanding of how exactly the MAP factor scores are computed. More specifically, I'd like to know if I can extract the relevant information from the estimated model to be able to compute them manually from the raw scores. Is that possible at all? As I understand it, the computation is an iterative process. In that case, I'd probably use Mplus or R to compute them separately. Thanks in advance! |
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With ML, Mplus uses EAP. And, yes, this is an iterative process which would have to be programmed. |
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Luc Watrin posted on Wednesday, November 16, 2016 - 9:12 am
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The estimated covariance matrix for the latent variables I get in my model is almost singular. While I understand that I'll have to figure what exactly causes this, I am wondering why Mplus isn't running into problems computing the factor scores. Does Mplus alter the matrix in any way before proceeding with further computations? |
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No alteration is done. If it isn't deemed singular by Mplus it goes through. |
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Dirk Pelt posted on Wednesday, August 02, 2017 - 6:16 am
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I am dealing with a similar challenge as Luc. To help with my algorithm, may I ask which optimization procedure is used by MPLUS in MAP (BFGS or L-BFGS-B or the Newton-Raphson method for example)? Best, Dirk |
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It's a Fletcher-Powell type optimizer. |
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Dirk Pelt posted on Thursday, August 03, 2017 - 2:05 am
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Thank you! Is the prior weighed by any means? I am getting kind of similar results but I get much more "shrinkage" (i.e. smaller variances of the estimated traits). Best, Dirk |
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No weighting. |
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Dirk Pelt posted on Friday, September 01, 2017 - 5:19 am
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Thank you for your answers. Is there any way to let Mplus output the standard errors (SEMs) of the factor scores? Best, Dirk |
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We give SEs in most cases. And with Bayes we give the whole distribution for a person's factor score. Be sure to use Version 8. |
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