Message/Author 

YiFen Tseng posted on Wednesday, September 29, 2010  3:03 pm



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. 

YiFen Tseng posted on Wednesday, September 29, 2010  11:23 pm



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 illscaled 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. 


Yes, you should rescale the variables using the DEFINE command. Divide each variable by a constant that brings the variances between one and ten. 

YiFen Tseng posted on Thursday, September 30, 2010  10:09 pm



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 illscaled 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. 


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. 


Please send an output that shows what you are looking at and your license number to support@statmodel.com. 


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 


Hi, Here is the model: categorical are ODI1ODI10 HADS_A1HADS_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_A1HADS_A33 HADS_A53HADS_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_A1HADS_A3 HADS_D6 HADS_A7 HADS_A9 HADS_D10 HADS_A13 PHQ_1A PHQ_1B PHQ_1E PHQ_1F PHQ_1I; Function by ODI1ODI10 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_A1HADS_A33 HADS_A53HADS_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; 


Please send the output to Support along with your license number. We request that questions only use 1 post. 

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