Analysing conjoint data with OLS and PLS regression: a case study with wine

TY - JOUR

T1 - Analysing conjoint data with OLS and PLS regression

T2 - a case study with wine

AU - Jaeger, Sara Roust

AU - Mielby, Line Holler

AU - Heymann, Hildegarde

AU - Jia, Yilin

AU - Frøst, Michael Bom

PY - 2013/12

Y1 - 2013/12

N2 - BACKGROUND: This paper presents a case study with wine where two statistical methods for the analysis of rating-based conjoint analysis data were applied. Traditionally, ordinary least squares (OLS) regression is used to estimate the relative importance of the experimental factors and the part-worth utilities of factor levels. Partial least squares (PLS) regression, which is a popular tool in sensory and consumer science, can also be used for the analysis of interval-level conjoint data. RESULTS: Using conjoint analysis, purchase intentions for Californian red and white wine were obtained from a convenience sample of young US adults (n ≈ 250). OLS and PLS regression uncovered the same systematic patterns in the data: negative utility associated with more expensive wine, and positive utility associated with famous wine regions. While OLS regression provided more accessible top-line results, an advantage of PLS regression was the graphical format of results. This provided easy insight to individual differences in the importance attached to the factors driving purchase intention. CONCLUSION: OLS and PLS regression can complement each other in the analysis of interval-level conjoint data. Dual analysis can help to ensure that the right insights are drawn from the study and communicated to internal/external clients. It may also facilitate communication within project teams.

AB - BACKGROUND: This paper presents a case study with wine where two statistical methods for the analysis of rating-based conjoint analysis data were applied. Traditionally, ordinary least squares (OLS) regression is used to estimate the relative importance of the experimental factors and the part-worth utilities of factor levels. Partial least squares (PLS) regression, which is a popular tool in sensory and consumer science, can also be used for the analysis of interval-level conjoint data. RESULTS: Using conjoint analysis, purchase intentions for Californian red and white wine were obtained from a convenience sample of young US adults (n ≈ 250). OLS and PLS regression uncovered the same systematic patterns in the data: negative utility associated with more expensive wine, and positive utility associated with famous wine regions. While OLS regression provided more accessible top-line results, an advantage of PLS regression was the graphical format of results. This provided easy insight to individual differences in the importance attached to the factors driving purchase intention. CONCLUSION: OLS and PLS regression can complement each other in the analysis of interval-level conjoint data. Dual analysis can help to ensure that the right insights are drawn from the study and communicated to internal/external clients. It may also facilitate communication within project teams.

U2 - 10.1002/jsfa.6194

DO - 10.1002/jsfa.6194

M3 - Journal article

C2 - 23629635

SN - 0022-5142

VL - 93

SP - 3682

EP - 3690

JO - Journal of the Science of Food and Agriculture

JF - Journal of the Science of Food and Agriculture

IS - 15

ER -