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