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  Welcome to Statistical Innovations Inc.

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

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


       March 26, 2015 -- Breakout Session at Sawtooth Software Conference 2015:

Models for MaxDiff and Related Data and their Performance with Latent Class and HB Methods

Jay Magidson, Statistical Innovations Inc.
Gary Bennett, Logit Research
Jaroen Hardon, SKIM Group

By eliciting the worst (least preferred) as well as best (most preferred) options, MaxDiff data are considered the “gold standard” for estimating preference for a group of stimuli (objects). In this presentation we use simulated and real MaxDiff data to compare various MaxDiff and extended Best-Worst (Multi-attribute CBC and “Dual Response None”) models and examine whether the utility estimates improve over those from the standard best-only design. We also compare Latent Class (LC) and Hierarchical Bayes (HB) methods that allow for individual differences. By focusing on the form and magnitude of bias in model estimates, and when it occurs, our results are somewhat surprising.

The Sawtooth Software Conference 2015 will take place in Orlando, FL from March 23-27 2015

Register now!
Segmentation Special

       May 19-20, 2015 -- Modern Modeling Methods (M3) Conference 2015:

Using a Scale-Adjusted Latent Class Model to Establish Measurement Equivalence in Cross-Cultural Surveys: An Application with the Myers-Briggs Personality Type Indicator (MBTI)

Jay Magidson, Statistical Innovations Inc.

Innovative Developments and Applications in Latent Class Analysis

Margot Sijssens-Bennink, Statistical Innovations Inc.
Geert van Kollenburg, Tilburg University
Mattis van der Berg, Tilburg University
Erwin Nagelkerke, Tilburg University

The Modern Modeling Methods (M3) Conference 2015 will take place in Storrs, CT from May 19-20

Register now!

       June 15, 2015 -- 2015 Advanced Research Techniques (ART) Forum:

A New Modeling Tool for Identifying Meaningful Segments and their Willingness to Pay: Improving Validity by Reducing the Confound between Scale and Preference Heterogeneity

Jay Magidson, Founder, Statistical Innovations Inc.
Jeffrey Dumont, Senior Consultant, RSG
Jeroen K. Vermunt, Professor, Tilburg University

With discrete choice data, it is common practice to use HB or latent class modeling to capture heterogeneity across decision-makers. However, a significant part of the heterogeneity retrieved relates to differences in the amount of error variance, a phenomenon referred to as scale heterogeneity. As a result of the preference vs. scale heterogeneity confound, traditional approaches to segmentation may yield spurious segments that differ only in their scale heterogeneity, and may not differ at all in their willingness to pay. In this presentation we introduce a new scale adjusted latent class (SALC) choice model that accounts for both observed and unobserved scale heterogeneity, resulting in homogeneous segments that differ only in their preferences and willingness to pay, increasing the usefulness of segmentation analyses to marketers. We compare results from the SALC approach with other standard and nonstandard approaches.

The 2015 Advanced Research Techniques (ART) Forum will take place in San Diego, CA from June 14-17

Register now!


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