Analysis Factor Analysis Cluster Analysis

7/28/2019 Analysis Factor Analysis Cluster Analysis

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Discriminant Analysis,Factor Analysis, Cluster

Analysis, Multidimensional

scaling and Conjoint

analysis


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Discriminant Analysis

A technique for analyzing

marketing research data when

the criterion/dependent variable

is categorical and the

predictor/independent variable

are interval in nature


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Objectives of discriminant

analysis

1. Development of discriminant functions(linear combinations of the predictorwhich will best discriminate between thecategories of the criterion)

2. Examination whether significantdifferences exist among the group

3. Determination of which predictorvariables contribute to most of theintergroup differences

4. Classification of cases to one of thegroups based on the values of thepredictor variables

5. Evaluation of the accuracy ofclassification


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Similarities and Differences

among ANOVA, Regression and

Discriminant Analysis

Similarities:

Number of dependent variables -

1

Number of independent variables -

Multiple

Differences

Nature of dependent variable:Metric, Metric, Categorical

Nature of independent variable:

categorical, metric, metric


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Steps involved in Discriminant

Analysis

1. Formulate the problem

2. Estimate the discriminate

function coefficient

3. Determine the significance of

the discrimination function

4. Interpret the results5. Assess the validity of

discriminant analysis


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Marketing Research Applications

In term of demographic characteristics, howdo customers who exhibit store loyalty differfrom those who do not

Do heavy, medium and light users of softdrink differ in terms of their consumption of

frozen food What psychographic characteristics help

differentiate between price-sensitive andnon-price sensitive buyers of groceries

Does the various market segment differ in

their media consumption habits In terms of lifestyles, what are the differences

between heavy patrons of general stores andMall stores


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Factor analysis

A class of procedures primarily used for datareduction and summarization

It is use in the following circumstances

1. To identify underlying dimensions/factors

that explain correlations among a set ofvariables

2. To identify a new, smaller set ofuncorrelated variables to replace theoriginal set of correlated variables insubsequent multivariate analysis

3. To identify a smaller set of salientvariables from a larger set for use insubsequent multivariate analysis


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Steps involved in factor

analysis

1. Formulate the problem

2. Construct the correlation matrix

3. Determine the method of factor

analysis

4. Determine the number of factors

5. Rotate the factors

6. Interpret the factors1. Calculate the factor scores

2. Select the surrogate variables

7.

Determine the model fit


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Cluster Analysis

A class of techniques used to classifyobjects/cases into relatively homogeneousgroups called clusters.

Objects in each cluster tend to be similar to

each other and dissimilar to other cluster A.k.a. classification analysis/numerical

taxonomy

Both cluster analysis and discriminantanalysis is used for classification but incluster analysis there is no a prioriinformation about the group/clustermembership


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Steps involved in cluster

analysis

Formulate the problem

Select a distance measure

Select a clustering procedure Decide on the number of cluster

Interpret and profile cluster

Assess the validity of clustering


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Marketing Research

Application

Segmenting the market

Understanding buyer behaviour

Identifying new productopportunities

Selecting test markets

Reducing data


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Multidimensional scaling

A class of procedures for representingperceptions and preferences ofrespondents spatially by means of avisual display.

MDS has been used in marketing toidentify:

1. The number and nature of dimensionsconsumer use to perceive differentbrands in the market place

2. The positioning of current brands onthese dimensions

3. The positioning of consumers idealbrand on these dimensions


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Steps in MDS


Obtain input data

Select an MDS procedure Decide on the number of

dimensions

Label the dimensions andinterpret the configurations

Assess reliability and validity


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Marketing Research

Application

1. Image measurement

2. Market segmentation

3. New product development

4. Assessing advertisingeffectiveness

5. Pricing analysis

6. Channel decisions

7. Attitude scale construction


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Conjoint Analysis

A technique that attempts todetermine the relative importanceconsumers attach to salientattributes and the utilities they attachto the levels of attributes

It seeks to develop the part-worth/utility functions describing theutility consumers attach to the levelsof each attribute.

It is complementary to MDS


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Steps involved in Conjoint

Analysis


Construct the stimuli

Decide on the form of input data Select a conjoint analysis

procedure

Interpret the resultsAssess reliability and validity


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Market Research Application

Determining the relative importanceof attributes in the consumer choiceprocess

Estimate market share of brands thatdiffer in attribute levels

Determining the composition of mostpreferred brand

Segmenting the market based onsimilarity of preferences for attributelevels

Analysis Factor Analysis Cluster Analysis

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