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

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

Methodology ◽

10.1027/1614-2241/a000026 ◽

2011 ◽

Vol 7 (3) ◽

pp. 88-95 ◽

Cited By ~ 2

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.

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

PsycEXTRA Dataset ◽

10.1037/e413792005-866 ◽

2000 ◽

Author(s):

Curtis P. Haugtvedt ◽

Sharon Shavitt ◽

Bonnie Sherman-William

Keyword(s):

Graduate Programs ◽

Marketing Research ◽

Consumer Psychology

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

Marketing ZFP ◽

10.15358/0344-1369-2019-4-21 ◽

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.

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