Common Statistical Tools

Listed below are the most commonly used statistical tools for consumer and industrial marketing research. Each is described in an "outline" form to help you quickly understand why, and under what circumstances, the tool is used. Note:

• We deliberately avoided more exotic techniques, because virtually all of the so-called "new" techniques link back to these fundamental tools.
• Design wisely: the usefulness of any statistical tool hinges on the quality of the underlying data, interpretation of scales, and construct assumptions.
• Consider discussing the details of your project with us before you zero in on a specific technique. This will maximize your overall research investment.

As always, we welcome your comments on this section, and any suggestions for new or expanded statistical tools descriptions.

Analysis of Covariance
To adjust data for the effects of other variables and thus permit analysis of variance to be used effectively. More Info »

Analysis of Variance
To decompose the total variance of an experiment into variance attributable to the effects of specific variables. More Info »

Cluster Analysis
To form homogeneous groups of objects of some kind (e.g., people, brands, markets). More Info »

Canonical Correlations
To measure in precise quantitative terms the relationship between two sets of variables. More Info »

Conjoint Analysis
To measure the relative importance of benefits and/or product attributes, overall and for individual respondents. More Info »

Discriminant Analysis
To classify people or objects into pre-defined groups. More Info »

Factor Analysis
To reduce a set of inter-correlated items to a smaller set of independent items; to obtain insight into the underlying structure. More Info »

Perceptual Mapping
To obtain a simplified picture of customers' perceptions of brands and brand images. More Info »

Regression Analysis
To measure in precise quantitative terms the relationship between one dependent variable and one or more independent variables. More Info »

Robert Walker of Surveys & Forecasts, LLC wishes to gratefully acknowledge Professor Russell Haley, formerly of the Whittemore School of Business & Economics, for the conceptual framework for this section.

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