1. Comparison of discriminant vs. logistic regression analyses for predicting consumer acceptance and purchase decision Darryl Holliday, Ashley Bond, Witoon Prinyawiwatkul Department of Food Science, Louisiana State University, Baton Rouge, LA 70803 INTRODUCTION Consumer sensory evaluation is defined as the use of consumers to establish food preferences. Consumer research is quintessential for food companies in determining consumer acceptance and purchase decision prior to launching a new product or when mimicking a competitor’s product. Categorical scales are often used for assessing consumer acceptance and purchase decision. Sensory analysts prefer the 9-point hedonic scale (1=extremely dislike; 5=neither like nor dislike; 9=extremely like) while business and marketing analysts prefer the binomial scale (yes or no). The 9-point hedonic scale evaluates acceptability on consumer sensory attributes while the binomial scale measures overall product acceptance and purchase intent. The correlation of these two scales gives insight into what the most discriminating attributes for a product are and is used to determine product acceptance and purchase decision and is useful for further product refinement allowing for a more accurate identification of consumers’ hook therefore increasing the chance for product success. Regression analysis is often used to predict hedonic acceptability. However, it requires the assumption of normality and variance homogeneity which is not always met due to skewness of sensory data, type of scale used, and heterogeneity of consumer responses The two analysis methods used were logistic regression analysis (LRA) and predictive discriminant analysis (PDA). Logistic regression attempts to fit the categorical variables which in most cases the dependant variable is dichotomous (yes/no) and the independent variables are quantitative or categorical. Meanwhile, PDA is used for classification prediction and to predict consumer acceptance and purchase decision. Whether or not both tests are to be used is up to the researcher since PDA and LRA are likely to provide similar results. However, PDA is more robust if multivariate normality of consumer data is met. MATERIALS AND METHODS Materials: The data tested came from a three-component constrained simplex lattice mixture design butter cake taste test using 300 consumers following the balanced incomplete block design and was only used as a model. Methods: Two sets of attributes for each cake sample were measured. The first set of attributes were evaluated using the 9-point hedonic scale and included visual puffiness, appearance, color, taste, texture, moistness, and overall liking. However, the second set of attributes was evaluated using the binomial scale and measured the panelists’ overall acceptance of the sample and the purchase intent based both the hedonic scores for the first set of attributes and the overall acceptance. ercent h Predictive Discriminant Analysis (PDA) and Logistic Regression Analysis (LRA) were used to determine which sensory attributes were most critical to the products’ acceptance and panelists’ purchase decision. Also, the overall percent hit rate, sensitivity (event), and specificity (non-event) were analyzed using PDA and LRA. Statistical Analysis: All data was analyzed at  = 0.05 using RESULTS AND DISCUSSIONS Table 3. Results for full model percent hit rate through LRA and PDA based on consumer acceptance and purchase decision/intent Table 5. Skewness towards sensitivity and specificity for LRA and PDA based on consumer acceptance and purchase decision/intent CONCLUSIONS 1. This study demonstrated LRA and PDA give similar but not identical results. 2. Caution should be taken when interpreting results obtained from these two techniques. 3. Subtle difference may result when PDA is used in lieu of LRA (or vise versa). 4. Both methods should probably be used to gain the most information which will limit any potential new product entrance catastrophes. ABSTRACT This study was to compare PDA and LRA for predicting consumer acceptance and purchase decisions. Consumers (n=300) evaluated butter cake formulations (used as a model) for liking of visual puffiness, appearance, color, taste, texture, moistness, and overall liking on a 9-point hedonic scale, and for overall acceptance and purchase decision on a binomial (yes/no) scale. Hit rate (overall), sensitivity (event), and specificity (nonevent) were analyzed using PDA and LRA (α=0.05). Liking data were not multivariate-normally distributed and skewed toward positive liking. Hit rate (% correct classification) for acceptance and purchase decision was, respectively, 86.9 and 84.5 from LRA, and 85.5 and 80.9 from PDA. This implied that LRA was more effective in prediction/classification; however, sensitivity and specificity results indicated that LRA was biased toward a larger sample size. Based on % hit rate derived from both full- and single-variable models, both PDA and LRA identified overall liking, texture, and moistness as attributes dictating acceptance and purchase decisions. Based on the Wald's chi-squared test statistics from the LRA full model, moistness was, however, excluded from the model (prob. > 0.1). This implied that the hit rate should not be used to indicate the model fitting in LRA. This study demonstrated that PDA and LRA give similar but not identical results. Caution should be taken when interpreting results obtained from these two techniques. Some subtle differences may result when PDA is used in lieu of LRA. Perhaps both should be used to gain more useful information. AcceptancePurchase Decision LRA86.984.6 PDA85.580.9 AcceptancePurchase Intent LRASensitivitySpecificity PDASensitivity sensitivity = event specificity = non-event cceptance having a higher percent for sensitivity from both LRA and PDA. Meanwhile, purchase intent has a higher value for sensitivity from PDA and for specificity from LRA. The results imply that LRA was more effective in prediction and classification. LRA, however, is known to have bias lean towards sensitivity or specificity depending on which has the larger number of observations. But, based on hit rates, both PDA and LRA identified the same attributes dictating acceptance and purchase decision. The attributes that verall liking, texture, moistness. Table 3 and 5, above, illustrate acceptance having a higher percent for sensitivity from both LRA and PDA. Meanwhile, purchase intent has a higher value for sensitivity from PDA and for specificity from LRA. The results imply that LRA was more effective in prediction and classification. LRA, however, is known to have bias lean towards sensitivity or specificity depending on which has the larger number of observations. But, based on hit rates, both PDA and LRA identified the same attributes dictating acceptance and purchase decision. The attributes that were most critical to the products’ acceptance and panelists’ purchase decision were overall liking, texture, moistness. Table 1. Comparison of percent hit rate for each attribute using LRA and PDA statistical analysis for consumer acceptance AttributeLRA / %hit ratePDA / %hit rate Visual Puffiness75.565.7 Appearance76.858.2 Color75.864.0 Taste85.580.4 Texture74.178.1 Moistness78.976.4 Overall Liking88.383.3 Table 2. Comparison of percent hit rate for each attribute using LRA and PDA statistical analysis for consumer purchase decision/intent AttributeLRA / %hit ratePDA / %hit rate Visual Puffiness64.264.0 Appearance62.459.2 Color63.0 Taste78.272.2 Texture80.7 Moistness71.572.6 Overall Liking84.3 The single-variable model was mixed when comparing LRA and PDA values for variables affecting acceptability or purchase intent. ConsumerAcceptance PurchaseIntent VariablesPr > ChiSqOdds RatioPr > ChiSqOdds Ratio Visual Puffiness0.99 0.940.99 Appearance0.211.140.880.98 Color0.891.010.370.92 Taste0.0011.360.021.33 Texture0.081.18<0.00011.57 Moistness0.361.080.891.01 Overall Liking<0.00012.50<0.00014.02 Table 4. The acceptance and purchase intent of full model for LRA