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

MANUAL Published: 2009

MNL63-EB

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

Editor(s): Lori Rothman, Merry Jo Parker

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.

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

Structure and Use of Just-About-Right Scales

Lori Rothman, Merry Jo Parker

Appendix A: Graphical Data Display

Colleen Conley

Appendix B: Graphical Scaling

Anne Goldman, Jagoda Mazur

Appendix C: Percent Difference from Norm and Percent Difference from Just Right

Gloria A. Gaskin, Joni L. Keith

Appendix D: The Mean

Lori Rothman

Appendix E: Mean Direction and Mean Absolute Deviation

Veronika Jones

Appendix F: Mean versus Scale Mid-Point

Sandra Pitts

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

Carl Fritz

Appendix H: A Proportional Odds/Hazards Approach to JAR Data

Rui Xiong, Jean-Francois Meullenet

Appendix I: Student's -Test—Analysis of Variance of Two Samples

Merry Jo Parker

Appendix J: Analysis of Variance (ANOVA)

Merry Jo Parker

Appendix K: Thurstonian Ideal Point Modeling

Jeannine Delwiche

Appendix L: Penalty Analysis or Mean Drop Analysis

Mary Schraidt

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

Dave Plaehn, Gregory Stucky, David Lundahl, John Horne

Appendix N: A Regression-Based Approach for Testing Significance of JAR Variable Penalties

Dave Plaehn, John Horne

Appendix O: Bootstrapping Penalty Analysis

Rui Xiong, Jean-Francois Meullenet

Appendix P: Opportunity Analysis

Tony Gualtieri

Appendix Q: PRIMO Analysis

Efim Shvartsburg

Appendix R: Chi-Square

Lynn Templeton

Appendix S: Biplots, Correspondence Analysis, and Principal Components Analysis

Horn Elizabeth, Ford Cindy

Appendix T: Correlation

Amy Takkunen

Appendix U: Regression

Joseph E. Herskovic

Appendix V: Preference Mapping from JAR Data Using Tree-Based Regressions

Jean-Francois Meullenet, Rui Xiong

Appendix W: Application of JAR Data to Preference Mapping Using Dummy Variables

Rui Xiong, Jean-Francois Meullenet

Appendix X: Collecting Intensity and Hedonic Information Separately

Gloria A. Gaskin, Joni L. Keith

Appendix Y: Designed Experiments

Merry Jo Parker, B. Thomas Carr

Appendix Z: Ideal Scaling

Anne Goldman, Jagoda Mazur

Bibliography Free

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Details

Developed by Committee: E18

Pages: 120

DOI: 10.1520/MNL63-EB

ISBN-EB: 978-0-8031-6739-1

ISBN-13: 978-0-8031-7010-0