Analysis Techniques

• Statistical significance testing (one-way/factorial ANOVA, MANOVA)
• Response surface modeling for optimizing quantitative product design features
• Conjoint and discrete choice: design, model building, segmentation, and simulations
• Multiple paired comparison (MPC) modeling and preference testing
• Data reduction (exploratory, confirmatory, second-order factor analysis, canonical correlation analysis)
• Correspondence and discriminant-based techniques (perceptual mapping and market structure applications)
• Multidimensional scaling (ALSCAL, KYST, MDPREF)
• Quadrant maps of importance and performance/satisfaction attributes
• Segmentation analysis (K-means clustering)
• Correspondence clustering of both qualitative and quantitative data for comprehensive attitudinal, behavioral, and demographic segmentation
• Sequential interaction search: CART/CHAID for numerical outcomes/categorical responses
• Multivariate modeling applications: regression analysis, including linear, non-linear, and ridge regression, Logistic regression (dichotomous or probabilistic responses), Structural analysis of consumer behavior/customer satisfaction (Structural Equation Modeling)
• Discriminant/multinomial logit-based classification modeling
• Neural networks development and forecasting