Presentations: US

Past Presentations

Annual Statistical Modeling Week

Annual conference featuring applications-oriented seminars focusing on the latest trends in statistical analysis.   2007 Sawtooth Software Conference   October 6-8, 2010, Newport Beach Marriott Hotel & Spa, Newport Beach, California. Workshop, Tuesday, October 5: SEGMENTATION AND PREDICTION IN PRACTICE: Applications of Latent GOLD®, LG Choice, and CORExpress™ Jay Magidson, Statistical Innovations Inc. & Tony Babinec, AB Analytics In this tutorial we will use popular software to account for heterogeneity in data by identifying actionable segments and developing predictive models that allow us to better understand the segments. Output will be interpreted from a practical perspective. For each analysis we will begin with basic models understandable by non-statisticians and then introduce some advanced features. Latent GOLD -- We will uncover segments with ratings-based conjoint data and show how to use the output to customize a separate product for each segment. An advanced feature includes the use of a C-factor (random effects) as an alternative to centering. LG Choice -- We will identify respondents that differ in their brand preference and price sensitivity. We will interpret the output and show how it may be used to develop an Excel-based simulator. Advanced features include the incorporation of scale factors into the model. We will also analyze max-diff data and show how it can be integrated with ratings to improve the reliability of the estimates. CORExpress – We will show how segments can be described/predicted using exogenous variables. Recent advances in high dimensional data analysis allow inferences to be made for large numbers of attributes, respondent characteristics and interactions. We show how these advances can be used to simplify a model to include only the most important attributes/predictors/interactions.   2007 Sawtooth Software Conference   October 15-19, 2007, Hyatt Vineyard Creek Hotel and Spa, Santa Rosa, CA. SI Presentation, Wednesday, October 17: Removing the Scale Factor Confound in Multinomial Logit Choice Models to Obtain Better Estimates of Preference Jay Magidson, Jeroen Vermunt A theoretical weakness of CBC as currently practiced is that individual utility estimates are confounded by differential measures of uncertainty (error variances). By separating the scale factor from the utilities we obtain clearer estimates of preference. Results from extended latent class models indicate that quick respondents have the highest variances.

Joint Annual Meeting of the Interface and the Classification Society of North America

June 8-12, 2005, Washington University School of Medicine, St. Louis, MO. SI Short Course, Wednesday, June 8, 1-4pm: Latent Class Models for Clustering and Classification, Jay Magidson and Anthony Babinec Download the Abstract

16th Annual Advanced Research Techniques FORUM

June 12-15, 2005, Coeur d'Alene Resort, Idaho. SI Short Course, Monday, June 13, 3-3:30pm: Using Parsimonious Conjoint and Choice Models to Improve the Accuracy of Out-of-Sample Share Predictions Jay Magidson, Jeroen Vermunt and Thomas Eagle A primary benefit of conjoint and choice models is the availability of a simulator to generate predictions of ratings and/or market share for new products based on estimated part-worth utilities. To the extent to which the estimated utilities are based on an over-fitted, over-parameterized model, such predictions may not validate outside the sample. In this session we introduce new models that posit continuous factors (C-Factors) underlying the part worth utility parameters to account for respondent heterogeneity and show that they are much simpler to estimate and easier to interpret than HB models. We use data from a rating-based conjoint and from a choice/ranking study to compare various HB, C-Factor, latent class and hybrid models with respect to over-fitting. The results suggest that new hybrid models containing both latent classes and C-Factors can be used to obtain segments and improve the accuracy of out-of-sample predictions.   2004 Sawtooth Software Conference   October 6-8, 2004, Shelter Pointe Hotel and Marina, San Diego, CA. SI Presentation, Tuesday, October 5: APPLICATIONS IN SEGMENTATION MODELING USING LATENT GOLD, GOLDMineR, SI-CHAID, AND LATENT GOLD CHOICE Segmentation modeling is a statistical approach for identifying and describing market segments. Depending upon the type of application, different approaches are available. In this tutorial, emphasizing methodological and practical issues of model building, we use commercially available software packages to illustrate four approaches: Latent class (LC) models. LC/finite mixture models provide powerful ways to obtain segments based on multiple criteria such as 1) ratings/rankings/choices obtained from conjoint studies, or 2) in general clustering applications. Compared to Hierarchical Bayes (HB) models, individual level predictions are of comparable validity, but LC models are much quicker and easier to estimate, and segments are obtained directly as part of the model. (Latent GOLD and LG Choice will be used). CHAID models. CHAID (Chi-Squared Automatic Interaction Detection) is a tree-based segmentation technique effective in obtaining meaningful segments that differ with respect to a single categorical criterion variable such as response to a mailing. (SI-CHAID, the successor to our original SPSS CHAID program, will be used). Regression models. In cases where a single criterion such as profitability is available, a regression approach is often a good way to identify the best (most profitable) segments. (The ordinal logit program GOLDMineR will be used here and the resulting segments will be compared to those obtained from CHAID). Hybrid models. We describe an LC hybrid alternative that maintains the simple structure of a CHAID or regression model, but may be used to obtain segments that are predictive across multiple criteria. We illustrate this in a discrete choice study.   2004 Meeting of the International Federation of Classification Societies   July 15-18, 2004, Illinois Institute of Technology, Chicago, IL. Short Course: Latent Class Models for Clustering and Classification; Jay Magidson & Tony Babinec Presentation Abstract: Latent Class Models for Clustering and Classification The various uses of latent class and finite mixture models for clustering and classification are growing rapidly because of: the lack of restrictive assumptions underlying the general model, major developments in maximum likelihood estimation of these models, and availability of model parameters to use for classifying new cases. This short course introduces the latent class (LC) and finite mixture approach to clustering and focuses on three important LC models -- cluster, factor, and regression -- for combinations of nominal, ordinal, and/or continuous variables. Applications are taken from the fields of marketing research and the biomedical sciences. Topics include (1) relationship to and improvements over K-means clustering, (2) use of simultaneous cluster and regression/discriminant/choice analyses as an improvement over the traditional tandem cluster-regression analyses, and (3) use of covariates and an extended CHAID algorithm to describe the resulting latent class segments. We will provide extensive references and a description of the major latent class modeling software available. The Latent GOLD program and new Latent GOLD Choice module will be used for illustration.   Our Presentations outside the United States E-mail Contact: will@statisticalinnovations.com Address: Statistical Innovations, 375 Concord Avenue, Belmont, MA 02478-3084 Phone: +1.617.489.4490 Fax: +1.617.489.4499 Copyright © 2003 by Statistical Innovations, Inc., Belmont, MA All rights reserved.