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Hi, Sorry if this is a repost, I don't think it went through the first time. In tech10, how are the standardized residuals for bivariate cell counts calculated? It doesn't seem to be (OE)/sqrt(E)? thanks, Jeannie 


The standardized residuals given in tech10 are not the Pearson residuals but are the standardized Pearson residuals. See Agresti's Categorical Data Analysis book, Sections 3.3.1 and 4.5.5. The original article on this topic is The Analysis of Residuals in CrossClassified Tables, Shelby J. Haberman, Biometrics, Vol. 29, No. 1 (Mar., 1973), pp. 205220. The advantage of the standardized Pearson residuals is that they are just like standard normal residuals, while the unstandardized Pearson residuals are less variable. For the bivariate tables these are computed by (OE)/[sqrt(E)*sqrt(1E/n)]. 

QianLi Xue posted on Sunday, December 07, 2008  6:25 pm



Is it true that when sampling weights are used in LCA fitting, the Pearson Chisquare statistics for model fit in the general output and for response pattern frequencies in TECH10 are no longer valid? Is that why some of the Chisquare values in TECH10 become negative? 


The response pattern frequencies in TECH10 do not take weights into account. This could be why some of the chisquare values become negative. In the future, TECH10 will take weights into account. 


Hi, I am running a latent class analysis with 10 binary indicators and sample weights. If the tech10 output (the bivariate residuals) does not take account of the sample weights (as mentioned in the post above) how can I assess model fit? thanks 


The current version of Mplus takes the weights into account for TECH10. 


Thanks Linda. Just to check  by current version do you mean 6.11? I'm currently using version 5.21. 


Yes, I mean 6.11. I don't know what it was in 5.21. You can run it both with and without weights to see if TECH10 differs. 

LAS posted on Monday, August 01, 2011  3:26 pm



Hello. I am running a GMM negative binomial model and I would like to examine the standardized residuals; however, the section of the output where the tech 10 results should appear is blank (except for the heading "technical 10"). Is it possible to obtain standardized residuals with the negative binomial? Thank you. 


Are you using version 6.11? 

LAS posted on Tuesday, August 02, 2011  9:13 am



I was not, but I have since updated my version to 6.11 and it has not resolved the issue. Thank you again. 


Please send the output and your license number to support@statmodel.com. 


Dear Dr. Muthen, I am running a CFA model using MLR for parameter estimation. The data structure includes a booklet design (i.e., data missing at random), and both 72 categorical indicators (binary responses indicating an ability factor) and 72 continuous indicators (logtransformed response times indicating a slowness factor). To obtain model fit information for the categorical dependent variables in the model, I wanted to look at TECH10 for the standardized residuals. However, with a higher number of indicators the TECH10 Output disappeared. When I used 6, 9, or 15 indicators per factor TECH10 was regularly provided. With about 30 Items per factor it wasn’t. How do I get TECH10 in this case? Thank you in advance! With kind regards, Annette 


We don't provide TECH10 for large frequency tables. You would get the following message if this is the case: TECH10 OUTPUT FOR CATEGORICAL VARIABLES IS NOT AVAILABLE BECAUSE THE FREQUENCY TABLE FOR THE LATENT CLASS INDICATOR MODEL PART IS TOO LARGE. 


Thank you for your quick answer! This message didn´t show up in my outputfile, however just „TECHNICAL 10 OUTPUT“ was written in the last output row: "TECHNICAL 10 OUTPUT Beginning Time: 10:25:13 Ending Time: 10:25:30 Elapsed Time: 00:00:17..." How do I compute the model fit information in my case? 


Please send the output and your license number to support@statmodel.com. 


what does output of TECH10 of latent class model refer to? how to interprets? 


Study the short course Topic 5 handout and video on our website. 


Thanks a lot... I will do 


ok. 

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