 APPLIED SURVEY RESEARCH TOOLS

Analysis of Covariance

Objective

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

Examples/Applications

• Sample matching

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

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Analysis of Variance

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

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Cluster Analysis

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

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Canonical Correlations

Objective

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

Examples/Applications

• 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

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Regression Analysis

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

• 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

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Objective

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

Examples/Applications

• Product/service design

• 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

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Discriminant Analysis

Objective

To classify people or objects into pre-defined groups.

Examples/Applications

• "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

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Factor Analysis

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

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Perceptual Mapping

Objective

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

Examples/Applications

• Hypothesis development

• 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

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