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Mplus Users Guide v6.. - Muthén & Muthén

Mplus Users Guide v6.. - Muthén & Muthén

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CHAPTER 14GENERAL CONVERGENCE PROBLEMSIt is useful to distinguish between two types of non-convergence. Thetype of non-convergence can be determined by examining theoptimization history of the analysis which is obtained by using theTECH5 and/or TECH8 options of the OUTPUT command. In the firsttype of non-convergence, the program stops before convergence becausethe maximum number of iterations has been reached. In the second typeof non-convergence, the program stops before the maximum number ofiterations has been reached because of difficulties in optimizing thefitting function.For both types of convergence problems, the first thing to check is thatthe variables are measured on similar scales. Convergence problemsmay occur when the range of sample variance values greatly exceeds 1to 10. This is particularly important with combinations of categoricaland continuous outcomes.In the first type of problem, as long as no large negativevariances/residual variances are found in the preliminary parameterestimates, and each iteration has not had a large number of trys,convergence may be reached by increasing the number of iterations orusing the preliminary parameter estimates as starting values. If there arelarge negative variances/residual variances, new starting values shouldbe tried. In the second type of problem, the starting values are notappropriate for the model and the data. New starting values should betried. Starting values for variance/residual variance parameters are themost important to change. If new starting values do not help, the modelshould be modified.A useful way to avoid convergence problems due to poor starting valuesis to build up a model by estimating the model parts separately to obtainappropriate starting values for the full model.CONVERGENCE PROBLEMS SPECIFIC TO MODELINGWITH RANDOM EFFECTSRandom effect models can have convergence problems when the randomeffect variables have small variances. Problems can arise in models inwhich random effect variables are defined using the ON or AT options416

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