Latent Transition Analysis
with Mplus
Christian Geiser teaches longitudinal mixture modeling in this on-demand short course
In this self-paced online workshop, Dr. Christian Geiser provides in-depth tutorials on analyzing and interpreting longitudinal mixture models in Mplus. With crystal clear hands-on lessons, he'll take you through each step of performing latent transition analysis, from writing syntax to interpreting output, all the way to reporting the results. Use the dropdown menu below to see what’s included in this 5-part course:
Welcome!
FREE PREVIEWHandouts, References, & Resources
Review Latent Class Analysis
Introduction to LTA
Formal Details on Model Estimation
Preparing and Exporting Data from SPSS
Data Import and Checking in Mplus
Introduction to the TYPE = MIXTURE Option in Mplus: Classical LCA
Classical LCA Output in Mplus
Basic LTA Specification in Mplus
Basic LTA Output in Mplus
LTA with Continuous Indicators (Latent Profile Transition Analysis)
Plotting LTA Results
Saving Class Membership Information
How to Avoid Local Maxima in LTA
Model Fit Part 1: Introduction
Model Fit Part 2: Residual Statistics
Model Fit Part 3: Model Comparisons
Measurement Equivalence in LTA: Introduction
Measurement Equivalence Testing in Mplus (Categorical Indicators)
Measurement Equivalence Testing in Mplus (Continuous Indicators)
Testing Equivalence of Transition Probabilities
Multigroup LTA: Introduction
Multigroup LTA: Mplus Syntax
Multigroup LTA: Mplus Output
LTA with Covariates: Mplus Syntax
LTA with Covariates: Mplus Output
Dr. Christian Geiser
Dr. Christian Geiser is the Director of Education at Quantfish, former professor of quantitative psychology, and author of two books on Mplus. As a leader in the development of latent variable techniques with complex data, he has helped thousands of researchers and students around the world to achieve their analytic goals. This course is a part of a companion series of workshops to his books and articles.
Tax-exempt institutions in the United States, please start here.
Latent Transition Analysis with Mplus is available for immediate access.
Please contact us if you need an invoice prior to purchase or have a larger group.
Payment is accepted by credit card, Paypal, or bank transfer (euros & GPB). Bank transfer instructions are here.
Group licenses provide the lowest per-person cost.
Further discounts are available for researchers working in LMICs; apply here to get started.
Individual: Student/Post Doc
Individual: Professional
Group | Up to 6 Users
Bank transfers are accepted in euros and GBP. Please contact us if your bank is outside of the EU, SEPA, or UK.
Kindly allow 3 business days for payment to post.
euros (EU & SEPA only) |
GBP (UK only) |
Student/Postdoc: €227 Professional: €318 Group: €907 Account holder: QuantFish LLC BIC/SWIFT: TRWIBEB1XXX IBAN: BE20 9672 6025 0356 Address: Avenue Louise 54, Room S52 Brussels 1050 Belgium Required notes:
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Student/Postdoc: £197 Professional: £276 Group: £789 Account holder: QuantFish LLC Sort code: 23-14-70 Account number: 87960744 IBAN: Address:
56 Shoreditch High Street, London E1 6JJ, United Kingdom Required notes:
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This course is for learners with a foundational knowledge of graduate-level multivariate statistics. Experience with latent class analysis is helpful, but not required. Mplus experience is not required.
We understand completely! The timing is entirely up to you. Once you enroll and the course has launched, you will have unlimited access to the content. You are free to pause and return to your lessons as needed.
Dr. Geiser is happy to answer questions related to course content and Quantfish promptly resolves any technical issues related to the course platform. If you are interested in personal consultation on your own data, please contact us to get started.
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QuantFish is dedicated to providing courses that improve your analytic skills with accessible lessons from the world's leading methodologists. Latent Transition Analysis with Mplus is backed by a complete money-back guarantee for 7 days following the start of the course.