Latent Class Analysis in R
Zsuzsa Bakk teaches latent class analysis and latent transition analysis in this on-demand course.
Discounted pre-registration open now
In this self-paced online course, Dr. Zsuzsa Bakk gives in-depth tutorials on analyzing and interpreting mixture models in R. With crystal clear hands-on lessons, she’ll take you through each step of the analysis, along with interpreting and reporting results. Use the dropdown menu below to see what’s included in this course:
Welcome and introduction to the course
Handouts & references for download
Introduction to R
Day 1 Theory: Intro to LCA with dichotomous items
FREE PREVIEWDay 1 Practice: LCA with dichotomous items in R
Day 2 Theory: Dichotomous items: Model selection
Day 2 Practice: Dichotomous items: Model selection in R
Day 3 Theory: Dichotomous items: Detailed Example application
Day 3 Practice: Dichotomous items: Model assumptions in R
Day 4 Theory: LCA with nominal items
Day 4 Practice: LCA with nominal items in R
Day 5 Theory: Nominal items: Coding & ML estimation
Day 5 Practice: Nominal items: Coding & ML estimation in R
Day 6 Theory: Nominal items: Model selection
Day 6 Practice: Nominal items: Model selection in R
Day 1 Theory: Covariates: Model selection & interpretation
Day 1 Practice: Covariates: Model selection & interpretation in R
Day 2 Theory: Categorical covariates
Day 2 Practice: Categorical covariates in R
Day 3 Theory: Continuous covariates
Day 3 Practice: Continuous covariates in R
Day 4 Theory: Multinomial logit models
Day 4 Practice: Multinomial logit models in R
Day 5: Bonus session: Stepwise estimators of LC models
Day 1 Theory: Introduction to multilevel LCA
Day 2 Theory: Multilevel measurement models
Day 2 Practice: Multilevel measurement models in R
FREE PREVIEWDay 3 Theory: Model selection
Day 3 Practice: Model selection in R
Day 4 Theory: Model assumptions
Day 4 Practice: Model selection in R cont.
Day 5 Theory: Structural models (Part 1)
Day 5 Practice: Structural models in R (Part 1)
Day 6 Theory: Structural models (Part 2)
Day 1 Theory: The Markov chain
Day 1 Practice: The Markov chain in R, the LMest package
Day 2 Theory: The latent Markov chain & simple latent Markov models
Day 2 Practice: The latent Markov chain & simple latent Markov models in R
Day 3 Theory: LMMs with multiple items
Day 3 Practice: LMMs with multiple items in R
Day 4 Theory: Time-heterogeneous LMMs
Day 4 Practice: Time-heterogeneous LMMs in R
Day 5 Theory: Covariates of initial time point
Day 5 Practice: Covariates of initial time point in R
Day 6 Theory: Covariates of transition probabilities
Day 6 Practice: Covariates of transition probabilities in R
Day 7 Theory: Summary & extensions of LMMs
Day 7 Practice: Thank you and final tips
Dr. Zsuzsa Bakk
Zsuzsa Bakk is an Assistant Professor in the Department of Methodology & Statistics of the Institute of Psychology at Leiden University. Her research focuses on latent variable modeling and survey methodology.
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This course is for learners with a working knowledge of categorical data modeling. Prior experience with R is not required.
Yes.! 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.
The instructors can 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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