LCA in R Workshop

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:

    1. Welcome and introduction to the course

    2. Handouts & references for download

    3. Introduction to R

    4. Intro to LCA with dichotomous items

      FREE PREVIEW

    5. LCA with dichotomous items in R

    6. Dichotomous items: Model selection

    7. Dichotomous items: Model selection in R

    8. Dichotomous items: Detailed Example application

    9. Dichotomous items: Model assumptions in R

    10. LCA with nominal items

    11. LCA with nominal items in R

    12. Nominal items: Coding & ML estimation

    13. Nominal items: Coding & ML estimation in R

    14. Nominal items: Model selection

    15. Nominal items: Model selection in R

    1. Covariates: Model selection & interpretation

    2. Covariates: Model selection & interpretation in R

    3. Categorical covariates

    4. Categorical covariates in R

    5. Continuous covariates

    6. Continuous covariates in R

    7. Multinomial logit models

    8. Multinomial logit models in R

    9. Bonus session: Stepwise estimators of LC models

    1. Introduction to multilevel LCA

    2. Multilevel measurement models

    3. Multilevel measurement models in R

      FREE PREVIEW

    4. Model selection

    5. Model selection in R

    6. Model assumptions

    7. Model selection in R continued

    8. Structural models (Part 1)

    9. Structural models in R (Part 1)

    10. Structural models (Part 2)

    1. The Markov chain

    2. The Markov chain in R, the LMest package

    3. The latent Markov chain & simple latent Markov models

    4. The latent Markov chain & simple latent Markov models in R

    5. LMMs with multiple items

    6. LMMs with multiple items in R

    7. Time-heterogeneous LMMs

    8. Time-heterogeneous LMMs in R

    9. Covariates of initial time point

    10. Covariates of initial time point in R

    11. Covariates of transition probabilities

    12. Covariates of transition probabilities in R

    13. Summary & extensions of LMMs

    14. Thank you and final tips

Meet Your Instructor

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.

Pricing

Tax-exempt institutions in the United States, please start here.

Latent Class Analysis in R 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, WeChat, or bank transfer (euros & GPB). Contact us for WeChat invoicing. 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.

Bank Transfer Information

Bank transfers are accepted in euros and GBP. Please contact us if your bank is outside of the EU, SEPA, or UK.

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euros (EU & SEPA only)
 GBP (UK only)
Student/Postdoc:  €361
Professional: €452
Group: €1077

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Required notes:  
  • Name of person enrolling (only the contact person if enrolling a group)
  • Email address
  • Requested course(s)

 Student/Postdoc:    £304
Professional:  £380
Group:  £907

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Account number: 87960744
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Required notes:  
  • Name of person enrolling (only the contact person if enrolling a group)
  • Email address
  • Requested course(s)


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FAQ

  • What level of statistical training is required for the course?

    This course is for learners with a working knowledge of categorical data modeling. Prior experience with R is not required.

  • I won't be able to complete this course all at once. Is the timing flexible?

    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.

  • Will you give me feedback on my analyses?

    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.

How the Course Works

Follow these steps to get started:

  • 1. Enroll in the Course

    You will receive a payment receipt by email from QuantFish and instructions for accessing your Student Dashboard. LCA in R is available to start immediately.

  • 2. Start Learning

    Begin the course when the time is right for you. Visit your student dashboard, click on the course, and watch the introductory video to get started. Subsequent lessons are shown to you in sequential order, along with the PDF resources. Plan on each day (module) to take about three hours, plus practice time. At the completion of the course, we will provide a certificate of completion that you can use for your own professional needs.

  • 3. Share Your Success!

    The best part of our work here at QuantFish is seeing our trainees implement their newfound analytic skills in their own research. We LOVE hearing about published papers and successful defenses. Find us on LinkedIn or Twitter or shoot us an email so we can celebrate with you!

Let's Keep in Touch

New courses are always coming! Add your email to our list to get the latest updates.

Thank You

Satisfaction Guarantee

QuantFish is dedicated to providing courses that improve your analytic skills with accessible lessons from the world's leading methodologists.  Latent Class Analysis in R  is backed by a complete money-back guarantee for 7 days following the start of the course.