Comparison of Correspondence Analysis based on Hellinger and chi-square distances to obtain sensory spaces from check-all-that-apply (CATA) questions

Sample configurations from check-all-that-apply (CATA) questions are obtained using Correspondence Analysis (CA). Classical CA is based on chi-square distance, which has been reported to be strongly affected by infrequently selected terms. The Hellinger distance has been proposed as an alternative distance metric, and the aim of the present work was to compare product spaces from CATA questions obtained using CA based on chi-square and Hellinger distances. Data sets from 71 studies (5121 consumers), differing by product category, number of consumers, number of samples and number of terms included in the CATA question, as well as frequency of infrequently used terms, were analyzed. For each of the studies, frequency tables were input to CA based on chi-square and Hellinger distances. Sample and term configurations in the first two dimensions were compared using the RV coefficient. Furthermore, the stability of sample and term configurations for each type of distance was evaluated by simulating repeated experiments using a bootstraping resampling approach. Sample and term configurations obtained using Hellinger and chi-square distances were similar (average RV coefficients for sample configurations = 0.99; average RV coefficients between term configurations = 0.89). The stability of sample and term configurations were not largely affected by the type of distance used to analyze frequency tables. Results from the present work suggest that CA based on chi-square and Hellinger distances provide similar results. Contributing to guidelines for practitioners, this research therefore supports classical CA analysis as an acceptable approach to the analysis of sensory-specific CATA data.

[1]  Gastón Ares,et al.  Check-all-that-apply (CATA) responses elicited by consumers: Within-assessor reproducibility and stability of sensory product characterizations , 2013 .

[2]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[3]  Jérôme Pagès,et al.  Testing the significance of the RV coefficient , 2008, Comput. Stat. Data Anal..

[4]  John C. Castura,et al.  Check-All-That-Apply Questions , 2014 .

[5]  Sébastien Lê,et al.  FactoMineR: An R Package for Multivariate Analysis , 2008 .

[6]  Jason Parcon,et al.  A method to investigate the stability of a sorting map , 2012 .

[7]  C. R. Rao,et al.  An Alternative to Correspondence Analysis Using Hellinger Distance. , 1997 .

[8]  P. Robert,et al.  A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient , 1976 .

[9]  M. Greenacre.  Correspondence analysis in practice , 1993 .

[10]  S. Jaeger,et al.  Examination of sensory product characterization bias when check-all-that-apply (CATA) questions are used concurrently with hedonic assessments , 2015 .

[11]  Carles M. Cuadras,et al.  A parametric approach to correspondence analysis , 2006 .

[12]  Gastón Ares,et al.  Further investigations into the reproducibility of check-all-that-apply (CATA) questions for sensory product characterization elicited by consumers , 2014 .

[13]  John C. Castura,et al.  Existing and new approaches for the analysis of CATA data , 2013 .

[14]  Hervé Abdi,et al.  An ExPosition of multivariate analysis with the singular value decomposition in R , 2014, Comput. Stat. Data Anal..

[15]  S. Jaeger,et al.  Investigation of bias of hedonic scores when co-eliciting product attribute information using CATA questions , 2013 .

[16]  Gastón Ares,et al.  Comparison of check-all-that-apply and forced-choice Yes/No question formats for sensory characterisation , 2014 .

[17]  P. Legendre,et al.  Ecologically meaningful transformations for ordination of species data , 2001, Oecologia.

[18]  Gastón Ares,et al.  Investigation of the number of consumers necessary to obtain stable sample and descriptor configurations from check-all-that-apply (CATA) questions , 2014 .

[19]  Sébastien Lê,et al.  Introduction to Multivariate Statistical Techniques for Sensory Characterization , 2014 .

[20]  Paula Varela,et al.  Introduction to Multivariate Statistical Techniques for Sensory Characterization , 2014 .