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Yi-Fen Tseng posted on Wednesday, September 29, 2010 - 3:03 pm
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A proper fit for a measurement portion with four correlated errors is first obtained: LAS BY few s2cat twopw4; PI BY adult fedu fwork private; MD4 BY md1w4 md2w4 md3w4 md4w4; PD4 BY pd1w4 pd2w4 pd3w4 pd4w4; ND4 BY nd1w4 nd2w4 nd3w4 nd4w4; CD BY ap wd ad as dq; few WITH s2cat; ND4w4 WITH ND2w4; AS WITH WD; DQ WITH AD; Then the path portion was followed: MD4 ON LAS PI; PD4 ON LAS PI; ND4 ON LAS PI; CD ON LAS PI MD4 PD4 ND4; The message indicates "no convergence, number of iterations exceeded." Is the problem in the default number of iterations? Something else? Could you please advice? Thank you very much. |
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Yi-Fen Tseng posted on Wednesday, September 29, 2010 - 11:23 pm
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In the modification, number of iterations is increased to 10000 and it then can converge. However, a warning message appears and the S.E. cannot be computed. Some variables are found to be ill-scaled because the differences in variances between variables are huge. Will you recommend to rescale the variables and try again? Do you have other reminder? Thank you very much. |
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Yes, you should rescale the variables using the DEFINE command. Divide each variable by a constant that brings the variances between one and ten. |
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Yi-Fen Tseng posted on Thursday, September 30, 2010 - 10:09 pm
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Tech4 shows that most of the 20 estimated covariance for abovementioned latent variables are within the absolute value of 1, except for 3 of them. The covariance between PD4 and PD4=32.583; PD4 and ND4=28.602; ND4 and ND4=48.483. Is this a sign of ill-scaled problem or linear dependency among latent variables or related to both? Based on the tech4 information, should I build up the path model in small pieces first in order to see what latent variable brings in the problem or rescale the variables first? Thank you for your advice. |
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Yet to rescale the variables, SEM models for one latent variable a time with that of the main endogenous variable were run for each path. Problems mainly appear to be two types: (1) residual covariance is not positive definite involving one specific indicator; (2) one indicator measuring one factor is found to measure another factor. Should I solve these issues before rescaling the variables? Thank you so much. |
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Please send an output that shows what you are looking at and your license number to support@statmodel.com. |
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Hello, I have run SEM model on longitudinal data (Time 1, 2, and 3). I worked only on Time 1 and 2. I waited 4 hours to get the results, and I got the following message "NO CONVERGENCE. NUMBER OF ITERATIONS EXCEEDED.". My sample size is 233, could it be the problem? because it is not a large sample size. Also, could parceling the indicators overcome the issue? The model input is posted in the next post. Thank you, Owis |
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Hi, Here is the model: categorical are ODI1-ODI10 HADS_A1-HADS_A3 HADS_D6 HADS_A7 HADS_A9 HADS_D10 HADS_A13 PHQ_1A PHQ_1B PHQ_1D PHQ_1E PHQ_1F PHQ_1I SF2A SF2B SF3A SF3B SF4A SF4B SF5 SF_6A SF_6B SF_6C SF_7 ODI13 HADS3_A1-HADS_A33 HADS_A53-HADS_A73 HADS_A93 HAD3_D10 HAD3_A13 PHQ_1A3 PHQ_1B3 PHQ_1E3 PHQ_1F3 PHQ_1I3; Model: pain by ODI1 BPI_3 BPI_4 BPI_5 BPI_6 SF5; Psycho by SF4A SF4B SF_6A SF_6C HADS_A1-HADS_A3 HADS_D6 HADS_A7 HADS_A9 HADS_D10 HADS_A13 PHQ_1A PHQ_1B PHQ_1E PHQ_1F PHQ_1I; Function by ODI1-ODI10 SF2A SF2B SF3A SF3B SF_7; Efficacy by SE10* SE20 SE30 SE40 SE50 SE60 SE70 SE80; Efficacy @1; Fatigue by SF_6B PHQ_1D; Efficacy3 By SE13 SE23 SE33 SE43 SE53 SE63 SE73 SE83; pain3 by ODI13 BPI_33 BPI_43 BPI_53 BPI_63; Psycho3 by HAD3_D10 HADS3_A1-HADS_A33 HADS_A53-HADS_A73 HADS_A93 HAD3_A13 PHQ_1A3 PHQ_1B3 PHQ_1E3 PHQ_1F3 PHQ_1I3; Pain on Efficacy; Psycho on pain Efficacy; Fatigue on Pain Psycho Efficacy; Function on Fatigue Pain Psycho Efficacy; Pain3 on pain Psycho Efficacy; Psycho3 on pain Psycho Efficacy Pain3 Efficacy3; Efficacy3 on pain Psycho Efficacy; |
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Please send the output to Support along with your license number. We request that questions only use 1 post. |
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