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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

  • 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

 

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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

  • 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

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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

  • 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

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Conjoint (Trade-Off) Analysis

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

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Discriminant Analysis

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

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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

  • 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

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