A Parametric Multidimensional Unfolding Procedure for Incomplete Nonmetric Preference/Choice Set Data in Marketing Research

1997 ◽  
Vol 34 (4) ◽  
pp. 499 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
Martin R. Young ◽  
Arvind Rangaswamy
Keyword(s):  
Marketing Research ◽  
Multidimensional Unfolding ◽  
Choice Set

Constructing MDS Joint Spaces from Binary Choice Data: A Multidimensional Unfolding Threshold Model for Marketing Research

1987 ◽  
Vol 24 (1) ◽  
pp. 40-54 ◽  
Author(s):  
Wayne S. Desarbo ◽  
Donna L. Hoffman
Keyword(s):  
Choice Model ◽  
Threshold Model ◽  
Marketing Research ◽  
Joint Space ◽  
Space Representation ◽  
Individual Choice ◽  
Choice Data ◽  
Spatial Choice ◽  
Multidimensional Unfolding ◽  
Dichotomous Variables

The authors present a new multidimensional unfolding methodology that can analyze various types of individual choice data. The model represents choice data, defined by dichotomous variables that indicate whether a particular brand was chosen or not, in terms of a joint space of consumers and brands. Explicit treatment of marketing and subject background variables is allowed through optional model reparameterizations of consumers and brands. Together with the joint space representation of both consumers and brands, these optional reparameterizations can provide information on appropriate market segmentation bases and respective product positioning strategies. The authors apply this spatial choice model to data on consumer (intended) choices for 12 residential communications devices and demonstrate how the results can be used for optimal positioning decisions.

A Parametric Multidimensional Unfolding Procedure for Incomplete Nonmetric Preference/Choice Set Data in Marketing Research

1997 ◽  
Vol 34 (4) ◽  
pp. 499-516 ◽  
Author(s):  
Wayne S. Desarbo ◽  
Martin R. Young ◽  
Arvind Rangaswamy
Keyword(s):  
Rank Order ◽  
Marketing Research ◽  
Snack Food ◽  
Data Sets ◽  
Product Positioning ◽  
Full Data ◽  
Multidimensional Unfolding ◽  
Local Optima ◽  
Rank Orders ◽  
Positioning Analysis

Multidimensional unfolding (MDU) is one of the most powerful conceptual and methodological tools used in marketing for product positioning analysis. Unfortunately, the majority of the commercial software programs available for performing such analyses (especially nonmetric analyses) suffer from serious limitations including degenerate solutions, interpretation difficulties, lack of supporting statistical inference and model selection procedures, excessive number of parameters to estimate, requirements of full data sets, and difficulties with local optima. The authors propose a new parametric approach to nonmetric unfolding (PARFOLD) to extend methodological developments in the econometrics and marketing science arenas. The authors develop the technical aspects of the proposed procedure, including options for accommodating incomplete rank orders, constraints, and reparameterizations. Two marketing-related applications are provided: one deals with preferences for snack food items involving complete rank orders, and the second involves incomplete data in which students rank order Master of Business Administration schools in their consideration/application sets. Comparisons are made with existing nonmetric MDU procedures including ALSCAL, PREFMAR and KYST with respect to several newly proposed diagnostic indices of solution degeneracy and positioning implications. Finally, the authors summarize limitations of the proposed model and offer directions for further research.

D-test: A New Test for Analyzing Scale Invariance Using Symbolic Dynamics and Symbolic Entropy

Methodology ◽  
2011 ◽  
Vol 7 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Jose A. Martínez ◽  
Manuel Ruiz Marín
Keyword(s):  
Symbolic Dynamics ◽  
Scale Invariance ◽  
Rating Scales ◽  
Rating Scale ◽  
Marketing Research ◽  
Response Patterns ◽  
Measurement Scales ◽  
Improve Measurement ◽  
Entropy Measures ◽  
The Difference

The aim of this study is to improve measurement in marketing research by constructing a new, simple, nonparametric, consistent, and powerful test to study scale invariance. The test is called D-test. D-test is constructed using symbolic dynamics and symbolic entropy as a measure of the difference between the response patterns which comes from two measurement scales. We also give a standard asymptotic distribution of our statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. We applied D-test to a real marketing research to study if scale invariance holds when measuring service quality in a sports service. We considered a free-scale as a reference scale and then we compared it with three widely used rating scales: Likert-type scale from 1 to 5 and from 1 to 7, and semantic-differential scale from −3 to +3. Scale invariance holds for the two latter scales. This test overcomes the shortcomings of other procedures for analyzing scale invariance; and it provides researchers a tool to decide the appropriate rating scale to study specific marketing problems, and how the results of prior studies can be questioned.

Graduate Programs and Careers in Consumer Psychology, Advertising, and Marketing Research

2000 ◽  
Author(s):  
Curtis P. Haugtvedt ◽  
Sharon Shavitt ◽  
Bonnie Sherman-William
Keyword(s):  
Graduate Programs ◽  
Marketing Research ◽  
Consumer Psychology

MARKETING RESEARCH OF THE MARKET FOR "MARBLE" BEEF IN THE OMSK REGION

2020 ◽  
pp. 35-39
Author(s):  
D.S. Ryabkova ◽  
O.V. Scryabina
Keyword(s):  
Marketing Research

Missing Data - Better "Not to Have Them", but What If You Do? (Part 1)

Marketing ZFP ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 21-32
Author(s):  
Dirk Temme ◽  
Sarah Jensen
Keyword(s):  
Missing Data ◽  
Statistical Power ◽  
Missing Values ◽  
Graphical Representation ◽  
Marketing Research ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Full Information Maximum Likelihood ◽  
Definition Of ◽  
Traditional Approaches

Missing values are ubiquitous in empirical marketing research. If missing data are not dealt with properly, this can lead to a loss of statistical power and distorted parameter estimates. While traditional approaches for handling missing data (e.g., listwise deletion) are still widely used, researchers can nowadays choose among various advanced techniques such as multiple imputation analysis or full-information maximum likelihood estimation. Due to the available software, using these modern missing data methods does not pose a major obstacle. Still, their application requires a sound understanding of the prerequisites and limitations of these methods as well as a deeper understanding of the processes that have led to missing values in an empirical study. This article is Part 1 and first introduces Rubin’s classical definition of missing data mechanisms and an alternative, variable-based taxonomy, which provides a graphical representation. Secondly, a selection of visualization tools available in different R packages for the description and exploration of missing data structures is presented.

MARKETING RESEARCH FOR MANAGERS: REVISITED

2018 ◽  
Vol 2018 ◽  
pp. 1399-1399
Author(s):  
Shivan Sanjay Patel ◽  
◽  
Shivendra Kumar Pandey ◽  
Dheeraj Sharma ◽  
Rama Shankar Yadav
Keyword(s):  
Marketing Research
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