Research and Data Analysis

Latent Class Analysis (LCA) and Latent Profile Analysis (LPA) Training Course

Introduction

Latent Class Analysis (LCA) and Latent Profile Analysis (LPA) Training Course offers in-depth, hands-on instruction on advanced statistical techniques used in social science, behavioral research, public health, education, and market research. By mastering these person-centered approaches, learners will uncover hidden subgroups within data, segment populations effectively, and apply models to inform decision-making, policy, and intervention strategies. With the exponential growth of big data and demand for sophisticated analytics, LCA and LPA have become crucial tools for researchers and professionals seeking actionable insights.

This course is tailored to equip participants with the practical knowledge and theoretical grounding to conduct LCA and LPA using cutting-edge tools such as Mplus, R, and Latent GOLD. Whether you're a data scientist, academic researcher, healthcare analyst, or marketing specialist, this training bridges the gap between theoretical modeling and real-world application. Through expert instruction, case studies, and guided exercises, participants will emerge with the skills necessary to design, estimate, interpret, and validate latent class and profile models with confidence.

Programme Curriculum

Latent Class Analysis (LCA) and Latent Profile Analysis (LPA) Training Course

Introduction

Course Objectives

Understand the fundamentals of Latent Class Analysis and Latent Profile Analysis
Differentiate between person-centered and variable-centered approaches
Apply model-based clustering techniques to real-world datasets
Interpret model fit indices (AIC, BIC, entropy) in LCA and LPA
Conduct class enumeration and determine optimal number of classes/profiles
Use R, Mplus, and Latent GOLD software for latent modeling
Identify and handle covariates and distal outcomes in latent models
Evaluate measurement invariance across groups
Incorporate longitudinal data in latent transition analysis (LTA)
Apply latent class regression to explore predictors of class membership
Design customized interventions using latent profile results
Integrate LCA and LPA with big data analytics
Enhance data-driven decision-making using advanced segmentation techniques

Target Audience

Social science researchers and academic professionals
Public health analysts and epidemiologists
Psychologists and behavioral scientists
Data scientists and machine learning practitioners
Educational researchers and institutional analysts
Market researchers and business analysts
Policy makers and program evaluators
Graduate students in quantitative research fields

Course Duration: 5 days

Course Modules

Module 1: Introduction to Latent Class and Profile Analysis

Overview of LCA and LPA principles
Historical development and theoretical foundations
Person-centered vs. variable-centered methods
Key terminology: latent variables, indicators, classes, profiles
Intro to relevant software: Mplus, R (tidyLPA), Latent GOLD
Case Study: Identifying student learning profiles in education data

Module 2: Data Preparation and Assumptions

Preparing categorical and continuous variables
Assessing missing data and assumptions
Normality, skewness, and outliers in latent modeling
Importance of sample size and power considerations
Coding strategies for multiple software platforms
Case Study: Preprocessing behavioral survey data for LCA

Module 3: Model Estimation and Selection

Estimation methods: ML, EM algorithm
Interpreting AIC, BIC, adjusted BIC, and entropy
Selecting optimal number of classes or profiles
Model convergence and identification issues
Addressing local maxima and boundary solutions
Case Study: Finding optimal latent segments in consumer data

Module 4: Covariates and Predictors in LCA/LPA

Including covariates in latent models
Multinomial logistic regression for class membership
Incorporating external variables: 1-step vs. 3-step approach
Moderator and mediator analysis
Interpreting coefficients and class assignment
Case Study: Predicting health-risk behaviors using LCA

Module 5: Longitudinal Extensions: Latent Transition Analysis (LTA)

Basics of longitudinal modeling
Transition probabilities and model specification
Interpreting stability and movement between classes
Adding covariates in LTA
Model visualization and interpretation
Case Study: Monitoring attitude changes in public policy campaigns

Module 6: Measurement Invariance and Multi-Group Models

Understanding measurement invariance
Testing for configural, metric, and scalar invariance
Comparing models across demographic groups
Model fit and statistical comparison
Applications in cross-cultural research
Case Study: Cross-national comparison of mental health profiles

Module 7: Latent Class Regression and Advanced Topics

Combining LCA with regression analysis
Interactions and non-linear effects
Multilevel LCA and mixture modeling
Bayesian estimation in latent models
Challenges in complex survey data
Case Study: Modeling customer churn using latent regression

Module 8: Real-World Applications and Reporting

Writing clear and accurate reports
Visualizing latent class solutions
Ethical considerations and misinterpretation
Stakeholder communication and policy implications
Publishing in peer-reviewed journals
Case Study: Using LPA to inform mental health interventions in schools

Training Methodology

Instructor-led online or in-person lectures
Step-by-step guided software demonstrations
Hands-on lab sessions using sample datasets
Real-world case studies and problem-solving workshops
Peer-reviewed project presentation and feedback
Supplementary readings and resource toolkit

Bottom of Form

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

LCA/LPA classification latent variable modeling

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000