The authors update and extend their 1978 review of conjoint analysis. In addition to discussing several new developments, they consider alternative approaches for measuring preference structures in...

https://www.jstor.org/stable/info/1251756

Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice:

Quasi-experimentation - design and analysis issues for field settings - cook,td, campbell,dt

This paper surveys economic choice theory, stressing developments that permit use of data from psychometric and conjoint experiments to produce market demand forecasts. Alternatives to the widely used multinomial logit model are summarized, and a new method for estimating multinomial probits is described. An integration of choice models with attitudinal scaling and perceptual mapping, within a latent variable system, is described. Estimation of such systems under either “random effects” or “fixed effects” descriptions of heterogeneity across individuals is discussed. Issues in the use of choice models to describe responses from conjoint experiments are presented. New regression diagnostic tests for the consistency of multinomial logit representations are discussed.

The Choice Theory Approach to Market Research

Summarising a high dimensional data set with a low dimensional embedding is a standard approach for exploring its structure In this paper we provide an overview of some existing techniques for discovering such embeddings We then introduce a novel probabilistic interpretation of principal component analysis (PCA) that we term dual probabilistic PCA (DPPCA) The DPPCA model has the additional advantage that the linear mappings from the embedded space can easily be non-linearised through Gaussian processes We refer to this model as a Gaussian process latent variable model (GP-LVM) Through analysis of the GP-LVM objective function, we relate the model to popular spectral techniques such as kernel PCA and multidimensional scaling We then review a practical algorithm for GP-LVMs in the context of large data sets and develop it to also handle discrete valued data and missing attributes We demonstrate the model on a range of real-world and artificially generated data sets

/pdf/probabilistic-non-linear-principal-component-analysis-with-3x820u3jaz.pdf

Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

The authors develop a new survey-based method for measuring and understanding a brand's equity in a product category and evaluating the equity of the brand's extension into a different but related ...

A Survey-Based Method for Measuring and Understanding Brand Equity and Its Extendibility

Simple procedures are described for obtaining maximum likelihood estimates of the location and uncertainty parameters of the Hefner model. This model is a probabilistic, multidimensional scaling model, which assigns a multivariate normal distribution to each stimulus point. It is shown that for such a model, standard nonmetric and metric algorithms are not appropriate. A procedure is also described for constructing incomplete data sets, by taking into consideration the degree of familiarity the subject has for each stimulus. Maximum likelihood estimates are developed both for complete and incomplete data sets.

Probabilistic multidimensional scaling: Complete and incomplete data

A probabilistic multidimensional scaling model that estimates both location and variance parameters for proximity and preference data is described and compared to a deterministic scaling model. Simulated and empirical choice data are used to compare models. Variance estimates from the probabilistic model are used to test a hypothesis about the homogeneity of stimulus perception under alternative modes of stimulus presentation.

A Probabilistic Model for the Multidimensional Scaling of Proximity and Preference Data

Computing unsegmented product maps from preference data by means of single ideal point models is commonly thought to be impossible because of indeterminacy problems. The authors show that this math...

https://www.jstor.org/stable/info/3152178

A Single Ideal Point Model for Market Structure Analysis

Two multidimensional scaling algorithms are used to externally measure cognitive maps of supermarket locations for a sample of sixty-one supermarket shoppers. Issues raised in the construction of the cognitive maps are discussed. The relationship between the derived cognitive maps and consumer behavior is compared to the relationship between physical maps and consumer behavior. Behavior is shown to be more closely related to cognitive maps. Differences in cognitive maps are defined and canonically correlated with consumers' socioeconomic and locational attributes. While aggregate cognitive maps are closely associated with physical maps, there is substantial variation between individual maps. This variation is related to shopping behavior and selected socioeconomic and locational characteristics of consumers.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.1975.tb01021.x

Cognitive Maps and Spatial Behavior Of Consumers

The writers raise several conceptual issues concerning direct similarity judgments and provide data on their reliability and validity. The results present a discouraging view of the reliability and...

On the Validity and Reliability of Direct Similarity Judgments

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