Objective

To adjust data for the effects of other variables and thus permit analysis of variance to be used effectively.

Examples/Applications

• Sample matching
• Post-experimental adjustments

Assumptions

• Same as analysis of variance
• Regressions for treatment groups have parallel slopes

Mechanics

• Uses regression to statistically match groups by canceling out the effects of initial score differences
• Applies analysis of variance to adjusted scores

Seminal Articles/Texts

• Barrow, Lionel; Journal of Advertising Research, May 1964
• Green, Paul and Tull, Donald; Journal of Advertising Research, June 1964

Objective

To decompose the total variance of an experiment into variance attributable to the effects of specific variables.

Examples/Applications

• In-store experiments
• Market tests
• Product tests/concept tests
• Survey order effects
• Picking discriminators in segmentation studies
• Conjoint measurement

Assumptions

• Samples are drawn from normal population
• All samples have the same variance
• Additivity (all means are a function of marginal means and the general mean)

Mechanics

• Total sum of squares
• Between groups sum of squares
• Within groups sum of squares
• Degrees of freedom
• Mean square
• F ratio

Seminal Articles/Texts

• Winer, B.J., "Statistical Principles in Experimental Design", McGraw-Hill 1962

Objective

To form homogeneous groups of objects of some kind (e.g., people, brands, markets).

Examples/Applications

• Market segmentation
• Clarification of perceptual maps
• Market typologies
• Store typologies

Assumptions

• The dimensions on which clustering is done are the important ones, and usually equally important
• Variables are (relatively) uncorrelated

Mechanics

• Derivation of similarities
  • Correlations
  • Distances
  • Hits
  • Cross-products
• Types
  • Hierarchical build-up (Johnson)
  • Hierarchical build-down (AID, CHAID)
• Bump and push (Singleton-Katz; Howard-Harris)
• Clumping

Seminal Articles/Texts

• P. H. A. Sneath and R. R. Sokal; Numerical Taxonomy; W.H. Freeman, 1973
• John A. Hartigan; Clustering Algorithms; Wiley & Sons, 1975

Objective

To measure in precise quantitative terms the relationship between two sets of variables.

Examples/Applications

• Advertising effectiveness
  • Relating measures of awareness, recall, attitudes, and/or purchase intention to variables such as advertising weight, media mix, or distribution level.
• Sales performance
  • Relating measures such as new accounts opened to variables such as personality characteristics
• Green's relating of two measures of risk-taking to personailty characteristics
• Segmentation solution testing

Assumptions

• Same as regression analysis

Mechanics

• Means and standard deviations
• Correlation matrix
• Canonical correlations
• Coefficients for the first set of variables
• Coefficients for the second set of variables

Seminal Articles/Texts

• Stewart & Love; A General Canonical Correlation Index; Journal of Marketing Research; p.90, February, 1972
• Psychological Bulletin; Vol. 70; pp 160-163, 1968
• Journal of Advetising Research; p.19, June 1969

Objective

To measure in precise quantitative terms the relationship between one dependent variable and one or more independent variables.

Examples/Applications

• Prediction
• Analysis
• Developing performance standards
• Screening variables
• Media/trade program impact
• Matching samples

Assumptions

• Causal, or at least meaningful, relationships
• Linearity
• Independent variables are independent (low collinearity)
• Heteroscedasticity (uncorrelated error terms)
• Relationship can be explained in terms of variance around a line estimate

Mechanics

• Estimating equation
• Total variance
• Explained and unexplained variance
• Coefficient of determination and correlation
• beta (slope) and b (intercept)
• Partial correlations
• Dummy variables
• Recursive regressions (stepwise/best fit)

Seminal Articles/Texts

• A.R. Baagaley, Intermediate Correlational Methods, John Wiley & Sons, 1964

Objective

To measure the relative importance of benefits and/or product attributes, overall and for individual respondents.

Examples/Applications

• Product/service design
• Choice of buying incentives
• Segmentation

Assumptions

• All important variables are included in the list
• All variables are salient for all respondents
• All respondents use a compensatory decision process
• The way in which respondents "play the game" mirrors real world decisions
• People can analyze the importance of emotional or point of sale benefits, and will accurately report it (or, alternatively, that these benefits are unimportant)
• The stimuli accurately and identically to all respondents
• The ranges of intensity are equivalent for each benefit

Mechanics

• Ratings, or alternatively rankings, choice or pairwise trade-offs
• Fractional factorial or orthogonal arrays (Latin squares)
• "Build your own" design (may not be balanced, however)
• Monanova or isotonic regression
• Personal utilities (part-worths)

Seminal Articles/Texts

• Green, Paul; Consumer Research, Vol. 1, No. 2, p. 61; September 1974

Objective

To obtain a simplified picture of customers' perceptions of brands and brand images.

Examples/Applications

• Hypothesis development
• Business opportunity identification
• Defining the competitive environment
• Positioning (brands or categories)
• Segmentation

Assumptions

• Equal covariance matrices for the groups being discriminated, others are the same as for regression analysis

Mechanics

• Obtaining the dissimilarities matrix
  • Direct (via sorting, paired comparisons)
  • Indirect (correlations, cross-products, hits)
• Types of maps
  • Simple space
  • Joint space
  • Vector
  • Discriminant function
  • Correspondence mapping
• Interpreting maps
  • Number of dimensions
  • Clustering (distances)
  • Maximum r / explained variance
  • Ideal points (explicit, implied)

Seminal Articles/Texts

• Green, Paul; Journal of Marketing, p.24, January 1975

Objective

To classify people or objects into pre-defined groups.

Examples/Applications

• "Good" vs. "bad" markets
• "Good" vs. "bad" performers (brands, people)
• Concept acceptors vs. rejecters
• Users vs. non-users (brand, or product category)
• Users vs. prospects
• Responders to an offer vs. non-responders

Assumptions

• Equal covariance matrices for the groups being discriminated, others are the same as for regression analysis

Mechanics

• Means and standard deviations
• Within group matrices
• F ratios
• Discriminant functions
• Classification matrix
• Probabilities of group membership (hit-miss matrix)
• Eigenvalues

Seminal Articles/Texts

• Journal of Marketing Research, p.156, May 1969

Objective

To reduce a set of intercorrelated items to a smaller set of independent items; to obtain insight into the underlying structure.

Examples/Applications

• Identifying underlying structures
  • Brand image
  • Benefits
  • Brand competition
• Data reduction
  • Screening long lists of variables (i.e., benefit screening)
  • Phase II of segmentation studies
  • Simplification for presentation purposes
• Categorizing people into groups

Assumptions

• An underlying structure (i.e., may not always exist)
• Additivity (the original matrix can be reproduced by linear combinations)
• Orthogonality of factors (usually)

Mechanics

• The original (unrotated) matrix
• The extraction process
• Rotation (i.e., varimax, quartimax)
• Analysis (factor naming, meaning)

Seminal Articles/Texts

• Harman, Harry; Modern Factor Analysis; 2nd Ed, University of Chicago Press, 1967
• Fruchter, Benjamin; Introduction to Factor Analysis, D. Van Nostrand Co. 1954