Chapter Seventeen Copyright © 2006 McGraw-Hill/Irwin Data Analysis: Multivariate Techniques for the...

Chapter Seventeen

Chapter Seventeen

Copyright © 2006McGraw-Hill/Irwin

Data Analysis:

Multivariate Techniques for the Research Process

McGraw-Hill/Irwin 2

1. Define multivariate analysis.

2. Understand how to use multivariate analysis in marketing research.

3. Distinguish between dependence and interdependence methods.

4. Define and understand factor analysis and cluster analysis.

5. Define and use discriminant analysis.

Learning Objectives

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• Multivariate analysis--statistical techniques used when there are two or more measurements of each element and the variables are analyzed simultaneously. – Multivariate techniques are concerned with the

simultaneous relationships among two or more phenomena.

– Important in marketing research because most business problems are multidimensional

Define multivariate analysis

Value of Multivariate Techniques in Data

Analysis

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Exhibit 17.1 Define multivariate analysis

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Exhibit 17.2 Define multivariate analysis

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• Dependence Method –multivariate technique appropriate when one or more of the variables can be identified as dependent variables and the remaining as independent variables

– Dependence techniques–multiple regression analysis, discriminant analysis, and MANOVA

– Multiple discriminant analysis–dependence technique which predicts customer usage based on several independent variables

• Age, income, peer group, education, lifestyle.

Dependence MethodClassification

Multivariate Techniques

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• Interdependence techniques – multivariate statistical techniques in which a whole set of interdependent relationships is examined

• No single variable is defined as dependent or independent

– Multivariate procedure–analysis of all variables in the data set simultaneously

– Goal of this method–to group things together– Simplify data

– No one variable is predicted or explained by the others

– Interdependence techniques– factor analysis, cluster analysis, Perceptual Mapping and multidimensional scaling

Interdependence techniquesClassification


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• Nature of the Measurement Scales

– Determine which multivariate technique is appropriate to analyze the data

• Dependence vs. Interdependence

• Dependent variable

– Measured nonmetrically(Nominal)–Discriminant analysis, Conjoint

– Measured metrically (ratio or interval) –multiple regression, ANOVA, and MANOVA

First StepClassification


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• Independent variable

– Require metric independent variable–multiple regression and discriminant analysis–can use nonmetric dummy variables

– Nonmetric independent variables–ANOVA and MANOVA

– Metrically measured variables and nonmetric adaptions–factor analysis and cluster analysis

Classification Multivariate Techniques

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• Factor Analysis–used to summarize information contained in a large number of variables into a smaller number of subsets or factors

• Purpose of Factor Analysis–to simplify the data

– No distinction between dependent and independent variables

– all variables under investigation are analyzed together–to identify underlying factors

Factor Analysis Interdependence

Techniques


Factor Analysis Exhibit 17.3


• Factor Loading–simple correlation between the variables

• Starting Point–interpreting factor analysis is factor loadings

• Factor loading–measure of the importance of the variable in measuring each factor

– Like correlations–vary from +1.0 to –1.0

– Statistical analysis associated with factor analysis–produces factor loadings between each factor and each of the original variables


Techniques


• Next Step in Factor Analysis– name the resulting factors– Factor 1 Service Quality– Factor 2 Food Quality

• Final Aspect of Factor Analysis– the number of factors to retain


Techniques


• Factor Analysis Applications in Marketing Research

– Advertising• to better understand media habits of various customers

– Pricing• to identify the characteristics of price-sensitive and prestige-

sensitive customers

– Product• to identify brand attributes that influence consumer choice

– Distribution• to better understand channel selection criteria among

distribution channel members

Define and understand factor analysis and cluster analysis

Interdependence Techniques


Define and understand factor analysis and cluster analysisExhibit 17.6


• Cluster analysis–multivariate interdependence technique whose primary objective is to classify objects into relatively homogeneous groups based on the set of variables considered

• Basic Purpose

– To classify or segment objects into groups so that objects within each group are similar to one another on a variety of variables

– To classify segments or objects such that there will be as much similarity within segments and as much difference between segments as possible

– To identify natural groupings or segments among many variables, without designating any of the variables as a dependent variable

Cluster analysisInterdependence

Techniques


Cluster analysisExhibit 17.11


• Statistical Procedures for Cluster Analysis

– Degree of similarity between objects–determined through a distance measure

– Distance between any pair of points is positively related to how similar the corresponding individuals are when the two variables are considered together

Cluster analysisInterdependence Techniques


• Clusters–developed from scatter plots

– This is a very complex, trial and error process

– Requires the use of computer algorithms

Cluster analysis Scatter PlotsInterdependence Techniques


• Applications in Marketing Research

• New product research–to examine product offerings relative to competition

• Test marketing–to group test cities into homogeneous clusters for test marketing purposes

• Buyer behavior–to identify similar groups of buyers who have similar choice criteria

• Market segmentation–to develop distinct market segments on the basis of geographic, demographic, psychographic, and behavioral variables

Cluster analysisInterdependence

Techniques


SPSS exercise

• Use the Santa Fe database

• Find different subgroups of customers with different levels of commitment

• Use Variables 22, 23,24

• Anaylse-classify-hierarchical cluster

• Select wards method

• Save box select 2

• This procedure takes time


End Here


• Discriminant Analysis–multivariate procedure used for predicting group membership on the basis of two or more independent variables

• Purpose–to classify objects or groups by a set of independent variables

• Dependent variable–nonmetric or categorical

• Independent variables–metric

Define and use discriminant analysis

Analysis of Dependence


• Purpose of discriminant analysis–prediction of a categorical variable by studying the direction of group differences based on finding a linear combination of independent variables

– Discriminant function–linear combination of independent variables developed by discriminant analysis which will best discriminate between the categories of the dependent variable

• Discriminate analysis–statistical tool for determining linear combinations of those independent variables and using this to predict group membership




• Discriminant score (Z-score)–basis for predicting to which group the particular individual belongs and is determined by a linear function

Zi = b1X1i + b2X2i + b⋅ ⋅ ⋅ nXni

• Zi = ith individual’s discriminant score

• bn = Discriminant coefficient for the nth variable

• Xni = Individual’s value on the nth independent variable

– Discriminant score–the score of each respondent on the discriminant function




• Discriminant function coefficients–– estimates of the discriminatory power of a

particular independent variable

– multipliers of variables in the discriminant function when variables are in the original units of measurement

• Coefficients–computed by means of the discriminant analysis software




• Important goal of discriminant analysis–classification of objects or individuals into groups

• Classification (Prediction) Matrix–to determine whether the estimated discriminant function is a good predictor

– Classification (or prediction) matrix–classification matrix in discriminant analysis contains the number of correctly classified and misclassified cases




• Applications for Marketing Research

• Product research–to distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles

• Image research–to discriminate between customers who exhibit favorable perceptions of a store or company and those who do not

• Advertising research–In distinguishing how market segments differ in media consumption habits

• Direct marketing–in distinguishing characteristics of consumers who respond to direct marketing solicitations and those who don’t




• Value of Multivariate Techniques in Data Analysis

• Classification Multivariate Techniques

• Interdependence Techniques

• Analysis of Dependence

Summary

Chapter Seventeen Copyright © 2006 McGraw-Hill/Irwin Data Analysis: Multivariate Techniques for the...

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