Latent Class & Profile Analysis: The Danger of Local Maxima

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QuantFish instructor Dr. Christian Geiser explains the problem of local likelihood maxima in LCA and LPA and how you can avoid them.

#Mplus #statistics #SPSS #geiser #statisticstutorials #mixture #lca #lpa #quantfish #LTA #mplusforbeginners

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ABOUT THIS INSTRUCTOR:
Dr. Christian Geiser is a quantitative psychologist, author of two books on Mplus, and a leader in the development of latent variable techniques for complex data. With his accessible books and sought-after workshops, he has helped thousands of researchers and students around the world to achieve their analytic goals.

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The story of the mountain really helped me in understanding what local maxima are. Thank you!

michelluuuh
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Excelent video! however, is it possible to further comment on other types of errors, even if the loglikelihood value has been replicated properly? such as de non-positive definitive first order derivative product matrix. For example, running a 3 profile LPA with 3 indicators with n=500:
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.405D-16. PROBLEM INVOLVING THE FOLLOWING PARAMETER:Parameter 2, %P#1%: [ RESP ]. How to interpret this using tech1 output in mplus? in order to confirm if its too many classes for example.

alambaker