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Marketing Research Data Use Articles

In Data Use articles market researchers with a background in statistics explain a specific technique or discuss ways to tackle data analysis tasks. View, sort, and refine more than 25 years of Quirk's marketing research Data Use articles.

 

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Data Use: Integrating your marketing research software

Published
February 1987
Author
Stanely Cohen
Abstract
The author walks readers through the process of using computer software to analyze, report and manage research data.

Data Use: Taking the risk out of buying PC software

Published
March 1987
Author
Joseph Curry
Abstract
The author advises MR professionals on how to research and purchase software that will increase productivity, accuracy and cut costs.

Data Use: Kudos for correspondence analysis

Published
April 1987
Author
Gary M. Mullet
Abstract
This article offers a non-technical introduction to correspondence analysis, a multivariate statistical technique.

Data Use: Statistical significance testing may hinder proper decision making

Published
May 1987
Authors
Michael Baumgardner and Ron Tatham

Data Use: Spurious enhancement of statistical significance

Published
June 1987
Author
Robert Zimmermann
Abstract
Researchers must take care to conduct analyses with multiple criteria appropriately when incorporating statistics in a decision-making process.

Budget for living data

Published
July 1987
Author
Harris Goldstein
Abstract
Interactive perceptual mapping, conjoint analysis and "living databases" are helping marketing research practitioners get a competitive edge and gain respect.

Data Use: Q-Factors or K-Means? A market segmentation dilemma

Published
December 1987
Author
Stanely Cohen
Abstract
In a constantly changing marketplace, marketers must focus on subgroups, tailoring their product and message to achieve their goals. Demographics often aren't enough, consumer attitudes and perceptions must be understood to gain a competitive edge.

Multivariate analysis - some vocabulary

Published
March 1988
Author
Gary M. Mullet
Abstract
People new to multivariate analysis can sometimes feel as though coworkers are speaking a foreign language. Gary Mullet, of the Georgia Institute of Technology, explains some of the requisite vocabulary for multivariate statistics and analysis.

Working with a statistical expert and surviving

Published
April 1988
Author
Paul M. Gurwitz
Abstract
The author gives advice, aid and comfort to all of those marketing professionals who have required the services of a statistician. While the prospect of hiring a statistician is not always appealing, the author gives a few tips to help the relationship between statistician and market professional.

Factor analysis: A useful tool but not a panacea

Published
May 1988
Author
Randy Hanson
Abstract
This article describes the purpose of using factor analysis and the four-step process required to complete this type of quantitative analysis. It also describes potential shortfalls of using this method, including possible misapplications and problems related to subjectivity.

Data Use: 'No preference' in paired-preference testing

Published
June 1988
Authors
Michael Baumgardner and Ron Tatham
Abstract
In simple paired comparison tests, the authors argue that the trinomial approach is fundamentally correct.

Correspondence analysis in action

Published
October 1988
Author
Tony Babinec
Abstract
This article describes some uses of correspondence analysis for analyzing survey research and market research data, comparing this approach to the traditional analysis of tabular data.

Correspondence analysis offers easy sophistication

Published
December 1988
Author
Betsy Goodnow
Abstract
This article demonstrates how to apply correspondence analysis, a perceptual mapping technique for describing correlations among categories in table data. The article also evaluates correspondence analysis software called MAPWISE.

Data Use: Weighting survey data

Published
January 1989
Author
Paul V. Schrock
Abstract
Data weighting, or sample balancing, is both under- and misused. This article discusses data weighting, providing descriptions and examples that may foster more effective use of the technique by researchers in a variety of organizations.

Data Use: Getting the most from your paired preference testing

Published
January 1989
Author
Mark J. Moody
Abstract
Blinded product—paired—preference testing is a common marketing research technique. Using an example, this article addresses how to best conduct paired product preference testing to avoid ambiguous results, which includes repeating the pair testing with respondents and conducting a binomial test statistically.

Data Use: Marketplace segmentation by demographic characteristics

Published
February 1989
Author
C. Ying Li
Abstract
Researchers today have an abundance of information and sophisticated tools with which to work. Still, the best trained statisticians are not always clear how best to use these materials and methods. This article addresses geo-demographic clusters, which are marketed by their developers as the definitive answer to market-segmentation problems.

Data Use: A look at item non-response

Published
April 1989
Author
Gary M. Mullet
Abstract
Because respondents do not always provide complete data in their questionnaires, data tabulation and statistical analysis can be tricky. This article addresses item non-response, including tabulation and summary statistics, providing a simple example.

Data Use: Understanding conjoint analysis in 15 minutes

Published
June 1989
Author
Joseph Curry
Abstract
Marketers use conjoint analysis determine what features a new product should have and how it should be priced. This article details the basics of conjoint analysis using a simple example.

Data Use: Multidimensional scaling for market research

Published
July 1989
Author
Tony Babinec
Abstract
Multidimensional scaling is a powerful tool that helps market researchers determine brand or product position, or find market segments. This article discusses MDS, providing multiple examples, including metric and nonmetric.

Data Use: Predicting housing value from income: a simple example of the logit response model

Published
December 1989
Author
C. Ying Li
Abstract
The housing industry has grown tremendously in the 1980s. This article describes a way to analyze the table of households cross-classified by housing value and housing income from the 1980 Census by using the logit response model.

 

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