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MNL63
Just-About-Right (JAR) Scales: Design, Usage, Benefits, and Risks

Rothman Lori, Parker Merry
Pages: 120
Published: 2009
Format: PDF (11M)
Price: $55  [Download Now]

This new ASTM manual is a comprehensive guide on the use of JAR scales in consumer testing, including their application, construction, analysis, and interpretation. It also identifies the risks associated with the use of JAR scales and ways to reduce those risks.

JAR scales measure levels of a product's attribute relative to a respondent's theoretical ideal level. These scales have an anchored midpoint of "just-about-right" or "just right", and endpoints anchored to represent intensity levels of the attributes that are higher and lower than ideal.

Manual 63 also includes numerous case studies for the analysis of JAR scale data and alternatives to JAR scales.

This is a "must have" reference for professionals who use JAR scales when conducting consumer research.



Table of Contents

Structure and Use of Just-About-Right Scales
Rothman L., Parker M.

Appendix A: Graphical Data Display
Conley C.

Appendix B: Graphical Scaling
Goldman A., Mazur J.

Appendix C: Percent Difference from Norm and Percent Difference from Just Right
Gaskin G., Keith J.

Appendix D: The Mean
Rothman L.

Appendix E: Mean Direction and Mean Absolute Deviation
Jones V.

Appendix F: Mean versus Scale Mid-Point
Pitts S.

Appendix G: Methods for Determining Whether JAR Distributions are Similar Among Products (Chi-Square, Cochran-Mantel-Haenszel (CMH), Stuart-Maxwell, McNemar)
Fritz C.

Appendix H: A Proportional Odds/Hazards Approach to JAR Data
Xiong R., Meullenet J.

Appendix I: Student's t-Test—Analysis of Variance of Two Samples
Parker M.

Appendix J: Analysis of Variance (ANOVA)
Parker M.

Appendix K: Thurstonian Ideal Point Modeling
Delwiche J.

Appendix L: Penalty Analysis or Mean Drop Analysis
Schraidt M.

Appendix M: Using Grand Mean versus Mean of the Proportion of Respondents Who Scored the Product JAR
Plaehn D.

Appendix N: A Regression-Based Approach for Testing Significance of JAR Variable Penalties
Plaehn D., Horne J.

Appendix O: Bootstrapping Penalty Analysis
Xiong R., Meullenet J.

Appendix P: Opportunity Analysis
Gualtieri T.

Appendix Q: PRIMO Analysis
Shvartsburg E.

Appendix R: Chi-Square
Templeton L.

Appendix S: Biplots, Correspondence Analysis, and Principal Components Analysis
Elizabeth H., Cindy F.

Appendix T: Correlation
Takkunen A.

Appendix U: Regression
Herskovic J.

Appendix V: Preference Mapping from JAR Data Using Tree-Based Regressions
Meullenet J., Xiong R.

Appendix W: Application of JAR Data to Preference Mapping Using Dummy Variables
Xiong R., Meullenet J.

Appendix X: Collecting Intensity and Hedonic Information Separately
Gaskin G., Keith J.

Appendix Y: Designed Experiments
Parker M., Carr B.

Appendix Z: Ideal Scaling
Goldman A., Mazur J.

Bibliography


Committee: E18
Paper ID: MNL63-EB
DOI: 10.1520/MNL63-EB
ISBN-EB: 978-0-8031-6739-1

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