A Bayesian Multi-Level Factor Analytic Model of Consumer Price Sensitivities Across Categories
Abstract
Identifying price sensitive consumers is an important problem in marketing. We develop a Bayesian multi-level factor analytic
model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on
a multivariate probit model of category incidence, this framework also allows the researcher to model overall price sensitivity
(i.e., indicated by higher-order factor scores) as a function of household-level covariates. All model parameters are estimated
simultaneously to circumvent the downward bias resulting from two-stage estimation. The modeling framework is illustrated
using scanner panel data from multiple categories of instant coffee.
model of the covariation among household-level price sensitivities across product categories that are substitutes. Based on
a multivariate probit model of category incidence, this framework also allows the researcher to model overall price sensitivity
(i.e., indicated by higher-order factor scores) as a function of household-level covariates. All model parameters are estimated
simultaneously to circumvent the downward bias resulting from two-stage estimation. The modeling framework is illustrated
using scanner panel data from multiple categories of instant coffee.
- Content Type Journal Article
- DOI 10.1007/s11336-010-9167-3
- Authors
- Sri Devi Duvvuri, SUNY at Buffalo 215F Jacobs Management Center Buffalo NY 14260 USA
- Thomas S. Gruca, University of Iowa Iowa City IA USA
- Journal Psychometrika
- Online ISSN 1860-0980
- Print ISSN 0033-3123
P. Sprent & N.C. Smeeton (2007). Applied Nonparametric Statistical Methods (4th ed.).
P. Sprent & N.C. Smeeton (2007). Applied Nonparametric Statistical Methods (4th ed.).
- Content Type Journal Article
- Category BOOK REVIEW
- DOI 10.1007/s11336-010-9166-4
- Authors
- Laura M. Schultz, Rowan University Department of Mathematics 201 Mullica Hill Road Glassboro NJ 08028 USA
- Journal Psychometrika
- Online ISSN 1860-0980
- Print ISSN 0033-3123
Bayesian Analysis of Multivariate Probit Models with Surrogate Outcome Data
Abstract
A new class of parametric models that generalize the multivariate probit model and the errors-in-variables model is developed
to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via
surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To obtain a rapidly converged algorithm,
the parameter expansion technique is applied to the correlation structure of the multivariate probit models. The proposed
model and method of analysis are demonstrated with real data examples and simulation studies.
to model and analyze ordinal data. A general model structure is assumed to accommodate the information that is obtained via
surrogate variables. A hybrid Gibbs sampler is developed to estimate the model parameters. To obtain a rapidly converged algorithm,
the parameter expansion technique is applied to the correlation structure of the multivariate probit models. The proposed
model and method of analysis are demonstrated with real data examples and simulation studies.
- Content Type Journal Article
- DOI 10.1007/s11336-010-9164-6
- Authors
- Wai-Yin Poon, The Chinese University of Hong Kong Department of Statistics Shatin Hong Kong China
- Hai-Bin Wang, Xiamen University School of Mathematical Sciences Xiamen 361005 China
- Journal Psychometrika
- Online ISSN 1860-0980
- Print ISSN 0033-3123
Nested Logit Models for Multiple-Choice Item Response Data
Abstract
Nested logit item response models for multiple-choice data are presented. Relative to previous models, the new models are
suggested to provide a better approximation to multiple-choice items where the application of a solution strategy precedes
consideration of response options. In practice, the models also accommodate collapsibility across all distractor categories,
making it easier to allow decisions about including distractor information to occur on an item-by-item or application-by-application
basis without altering the statistical form of the correct response curves. Marginal maximum likelihood estimation algorithms
for the models are presented along with simulation and real data analyses.
suggested to provide a better approximation to multiple-choice items where the application of a solution strategy precedes
consideration of response options. In practice, the models also accommodate collapsibility across all distractor categories,
making it easier to allow decisions about including distractor information to occur on an item-by-item or application-by-application
basis without altering the statistical form of the correct response curves. Marginal maximum likelihood estimation algorithms
for the models are presented along with simulation and real data analyses.
- Content Type Journal Article
- DOI 10.1007/s11336-010-9163-7
- Authors
- Youngsuk Suh, University of Texas at Austin Department of Educational Psychology 1 University Station D5800 Austin TX 78712 USA
- Daniel M. Bolt, University of Wisconsin-Madison Madison WI USA
- Journal Psychometrika
- Online ISSN 1860-0980
- Print ISSN 0033-3123
