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New Online Course

       October, 2014 -- Online Course: Latent Class Discrete Choice Modeling with Scale Factors

Latent Class Discrete Choice Modeling with Scale Factors

Dates: October 31 - November 21, 2014

Taught by: Jay Magidson, Statistical Innovations Inc.

Course Overview: Discrete choice models are widely used to forecast market share, design optimal products and services and more. Latent Class (LC) models are natural tools to analyze traditional first choice only, MaxDiff and other rankings data to identify segments with differing preferences. However, due to the difficulty in isolating scale effects, current segmentation models often obtain segments that differ primarily in scale (response error) rather than real preference patterns. Version 5.0 of LG Choice now contains features to easily estimate Scale Adjusted Latent Class (SALC) models, adjusting for the effects of scale to assure that the resulting segments are more meaningful, differing only in preference. This course begins by introducing the theory and practical applications of SALC models in traditional choice, ranking, and MaxDiff experiments in conjunction with the latest version of the Latent GOLD Choice program. We then show how to estimate these models using LG Choice 5.0.

There are no scheduled log-in times. The flexible course structure allows you to participate on your own schedule!

Register Today!

       Latent Class Modeling
Latent GOLD®
LG Choice
LG-Syntax
       Correlated Component Regression
CORExpress®
XLSTAT-CCR (CORExpress "Light")
       Tree Based Analysis
SI-CHAID®
       Ordinal Logistic Regression
GOLDMineR®
       Complete Excel Statistics Package
XLSTAT

       Services
A partial list of companies for which we developed successful models:
  • AC Nielsen
  • Blue Cross Blue Shield Assn.
  • Pfizer
  • TD Bank

Short Term / Advisory
       Review Current Models
       Methodological Advice
       Interpreting Output
       Study Design
       Techniques Used
Latent Class Analysis
Maximum Difference Scaling
CHAID Analysis
Discrete Choice
Correlated Component Regression


Events


       October, 2014 -- Online Course: Latent Class Discrete Choice Modeling with Scale Factors

Latent Class Discrete Choice Modeling with Scale Factors

Online Course, October 31 - November 21, 2014

Taught by: Jay Magidson, Statistical Innovations Inc.

Course Overview: Discrete choice models are widely used to forecast market share, design optimal products and services and more. Latent Class (LC) models are natural tools to analyze traditional first choice only, MaxDiff and other rankings data to identify segments with differing preferences. However, due to the difficulty in isolating scale effects, current segmentation models often obtain segments that differ primarily in scale (response error) rather than real preference patterns. Version 5.0 of LG Choice now contains features to easily estimate Scale Adjusted Latent Class (SALC) models, adjusting for the effects of scale to assure that the resulting segments are more meaningful, differing only in preference. This course begins by introducing the theory and practical applications of SALC models in traditional choice, ranking, and MaxDiff experiments in conjunction with the latest version of the Latent GOLD Choice program. We then show how to estimate these models using LG Choice 5.0.

There are no scheduled log-in times. The flexible course structure allows you to participate on your own schedule!

Register Today!

       October, 2014 -- Online Course: Introduction to Latent Class Modeling

Introduction to Latent Class Modeling

Online Course, October 10 - November 27, 2014

Taught by: Jay Magidson, Statistical Innovations Inc.

Course Overview: Statistical Innovations is pleased to announce an updated online course on Latent Class Modeling which includes important features from the new release of Latent GOLD (version 5.0) particularly useful in scoring new cases. Via lecture notes, examples, and computer exercises, this course introduces a 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, with hands-on exercises and special problem sets not available anywhere else.

There are no scheduled log-in times. The flexible course structure allows you to participate on your own schedule!

Register Today!

 

 
E-mail Contact: support@statisticalinnovations.com
Address: Statistical Innovations, 375 Concord Avenue, Belmont, MA 02478-3084
Phone: +1.617.489.4490
Fax: +1.617.489.4499