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
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