 |  Marketing Research: Within a Changing Information Environment, 2/e Joseph Hair,
Louisiana State University
Robert Bush,
University of Memphis
David Ortinau,
University of South Florida
Data Analysis: Multivariate Techniques for the Research Process
Chapter SummaryDefine multivariate analysis.
Multivariate analysis refers to a group of statistical procedures used to simultaneously analyze three or more variables. Factor analysis, cluster analysis, multidimensional scaling, discriminant analysis, and conjoint analysis are commonly used multivariate statistical techniques.
Understand why you should use multivariate analysis in marketing research.
Multivariate analysis is extremely important in marketing research because most business problems are multidimensional. Marketing managers are often concerned with various aspects of the consumer (e.g., demographics, lifestyles); consumers' purchasing process (e.g., motives, perceptions); and competition. Thus, techniques such as factor analysis, cluster analysis, and discriminant analysis assist marketing managers in simultaneously assessing a set or sets of important variables.
Distinguish between dependence and interdependence methods.
Multivariate data analysis techniques can be classified into dependence and interdependence methods. A dependence method is one in which a variable of set of variables is identifies as the dependent variable to be predicted or explained by other, independent variables. Dependence techniques include multiple regression analysis, discriminant analysis, and conjoint analysis. An interdependence method is one in which no single variable or group of variables is defined as being independent or dependent. The goal of interdependence methods is data reduction, or grouping things together. Cluster analysis, factor analysis, and multidimensional scaling are the most commonly used interdependence methods.
Define and understand factor analysis and cluster analysis.
Factor analysis and cluster analysis are both interdependence methods. Factor analysis is used to summarize the information contained in a large number of variables into a smaller number of factors. Cluster analysis classifies observations into a small number of mutually exclusive and exhaustive groups. In cluster analysis, these groups should have as much similarity within each group and as much difference between groups as possible.
Understand perceptual mapping.
Perceptual mapping is used to develop maps that show perceptions of respondents visually. These maps are graphic representations that can be produced from the results of several multivariate techniques. The maps provide a visual representation of how companies, products, brands, or other objects are perceived relative to each other on key attributes such as quality of service, food taste, and food preparation.
Define and use discriminant analysis and conjoint analysis.
Multiple discriminant analysis and conjoint analysis are dependence methods. The purpose of techniques such as discriminant and conjoint analysis is to predict a variable from a set of independent variables. Discriminant analysis uses independent variables to classify observations into mutually exclusive categories. Discriminant analysis can also be used to exist between the average discriminant score profiles to two or more groups. Conjoint analysis is a technique that uses consumer ranking or preference ratings of a group of product profile descriptions to estimate attribute importance coefficients through the use of part-worth estimates. Each level of each attribute in the product description is given a weight and the weights are added together to form a product utility. Conjoint can be used to compare consumer preferences for different product attribute combinations.
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2003 McGraw-Hill Higher Education