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LC Online Course 
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SI Online Course: Introduction to Latent Class Modeling
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Introduction to Latent Class Modeling
Dates: July 5, 2013 - August 2, 2013
Course Cost:
- $495 (commercial)
- $295 (academic)
- multiple attendees – after the first attendee registers at the full price, additional attendees from the same organization receive a 50% discount.
(Further discounts are available for 4 or more registrants from the same organization – contact us)
- Includes all course materials and access to the pre-release demo version of Latent GOLD 5.0.
To Enroll: Please go to our Course Registration Form to register for the course.
Course Homepage: The course website will contain links to all the assignments and discussions, and it will contain all the materials for each Session.
Course Overview: Latent class (LC) modeling is a technique for analyzing case level data with the goal of finding and introducing to the model "latent classes," or segments that characterize similar groups of cases
(e.g. customer segments, medical diagnoses, types of survey respondents, etc.). Interest in LC models is increasing rapidly. This is due to the growing evidence that they provide better solutions than the more traditional
approaches to cluster, factor and regression analysis when the population is not homogenous. In particular, LC modeling has now become the gold standard for cluster analysis.
In this course we introduce LC as a probability model and describe various applications using the pre-release demo version of Latent GOLD version 5.0. Initial lessons focus on model fitting strategies and the interpretation of output.
Subsequent lessons consider several advanced topics including identification issues, problems caused by data sparseness, use of bivariate residuals and repeated observations.
Note: Participants need not license a copy of the Latent GOLD program. All participants will have free access to the pre-release demo version of Latent GOLD 5.0, which allows unrestricted analyses of all course datasets.
Updated Material! New material has recently been added, including output of the classification equations for scoring new cases using a pre-release version of Latent GOLD 5.0, and new exercises.
Who should sign up for this course: Marketing researchers, biomedical researchers, survey analysts, and anyone who wants to learn the latest tools to analyze data in more depth than allowed by conventional methods, and to identify latent (hidden) segments that underlie survey data, customer, patient or prospect databases, diagnostic, test or other cross-sectional or longitudinal data.
Prerequisite: Participants should have taken at least two courses in statistics, and be familiar with the use of linear regression.
Course Structure: The course takes place online at statisticalinnovations.com. Course participants will be given a username and password for access to a private bulletin board that serves as a forum for discussion and interaction with the instructor. The course is divided into four weekly sessions. Attendees typically spend about 5-15 hours on each session. At the beginning of each week, participants receive the relevant material, in addition to answers to exercises from the previous session. All course materials are posted to a dedicated course homepage, which can be accessed via the same username and password. During each session, participants review the course materials and work through exercises using the Latent GOLD program. The instructor will provide answers to the exercises and to posted questions, but participants may also engage in discussions with other course participants.
Course Material: No text required -- copies of published articles and other material will be made available. All participants will have free access to the demo version of Latent GOLD 5.0, which allows unrestricted analyses of all course datasets.
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Instructor: Dr. Jay Magidson, founder and president of Statistical Innovations Inc.. Dr. Magidson's clients have included A.C. Nielsen Co., Household Finance Corp., Blue Cross Blue Shield Association, and Pfizer.
He taught statistics at Tufts and Boston University and is widely published on the theory and applications of multivariate statistical methods. Dr. Magidson designed SPSS CHAID, SI-CHAID®, GOLDMineR® and CORExpress®, is the co-developer (with Jeroen K. Vermunt) of the Latent GOLD® and Latent GOLD® Choice programs, and is co-developer (with Thierry Fahmy) of the XLSTAT-CCR module. |
Course Program
Session 1: Introduction to Latent Class Cluster Models
- Basic ideas of latent class analysis
- The general probability model for categorical variables
- Determining the number of classes/clusters
- Fit measures, model specification and selection strategies
- Classifying cases into latent class segments (obtaining scoring equations)
- Interpreting Latent GOLD output
- Example from survey analysis
Session 2: Most important model extensions
- Including covariates in LC models
- Extension to continuous variables and other scale types
- Boundary and local solution issues; Bayes constants
- Including direct effects to relax the assumption of local independence
- Example with Diabetes data (obtaining scoring equations)
Session 3: Simple LC Regression Models
- Guidelines for estimating LC regression models
- LC Regression Models with Predictors
- LC Regression Models with repeated measurements
- LC Growth Models and other examples (obtaining scoring equations)
Session 4: Introduction to some advanced topics
- Using previously saved LC models to score new cases
- Incorporating/accommodating cases with known class membership
- Selecting variables for LC models
- Recruiting LC segments from a reduced number of variables
- Multilevel models
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