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Qualitative Methods
Quantitative Methods
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Quantitative methods are normally applied when empirical observations of a few selected characteristics are required. This method provides a platform for the testing and statistical validation of a hypothesis. Some examples of when this type of method might be used include:
the statistical evaluation of qualitative results or planned campaigns
examinations where the statistical accuracy is more important than in-depth assessment (such as for customer satisfaction analyses, analyses of the competition etc.)
 
The analysis of quantitative data is carried out by means of univariate, bivariate or multivariate procedures. It is geared to the client’s individual information needs. Amongst others, we offer the following multivariate techniques:
regression analysis
factor analysis
multidimensional scaling
conjoint analysis
cluster analysis
Analysis of structural equation modeling
Correspondence analysis
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Statistical procedures

Regression analysis
Aims at describing and explaining the coherences between one dependent and one or morel independent variables. Alternatively, a regression analysis can be used for the calculation of prognoses.

Example:
Description of the influence of advertising expenses, number of shops and national income on a product’s quantity of sales

Prognosis of the development of sales in case of changes in prices, advertising expenses etc.
  Factor analysis
Is deployed when a question is surveyed through many different variables, which are to be bundled together into so-called factors. In this context, the following question is in the spotlight: Is it possible to reduce the main characteristics to a few factors?

Example:
Compression of the numerous technical features of cars to few dimensions like performance and security
 
  Multidimensional scaling (MDS)
Generally, multidimensional scales are used for positioning analyses so that answers to questions of global similarities between objects are acquired. With the help of this technique, the dimensions of perception underlying these similarities are derived. Multidimensional scaling is utilized particularly when there is no or barely any knowledge about characteristics relevant to the subjective judgment of objects.

Example:
Inquiry about the global similarities of brands
 
  Conjoint analysis
It is used for the measurement of preferences. Its aim is to determine the contribution of individual product features to the total utility of the product. The conjoint analysis is an important field of application for the configuration of new products.

Example:
Analysis of the influence or contribution of alternative product features (materials, forms, colors, prices) to the judgements made with respect to the product’s advantages
 
  Cluster analysis
In this case similar objects are clustered into separate groups rendering the differences between the groups as large as possible.

Example:
Differentiation of competing groups e.g. based on range of products and target groups
 
  Analysis of structural equation modeling
Within the area of market and opinion research, different questions are formulated regarding the complex relationship structures of latent characteristics. The term “structural equation modeling” does not indicate one individual technique, but rather a whole group of models for multivariate, statistical data analyses, which examine these characteristics. They are suitable for displaying explanatory, confirmative or causal dependencies between the characteristics in question.

Example:
Modeling of purchase decision processes for end customers
 
  Correspondence Analysis
Correspondence analysis is an explorative, multivariate method which aims to display the complex facts about different characteristics in clear scatter diagrams. The characteristics are placed within a two dimensional range which has been previously defined so that the positioning and distances between the different points can be interpreted for their content. In this way, the relationships between the characteristics, which could not be seen within the mass of data, can be revealed.

Example:
Image and positioning analyses, market segmentation inthe form of target groups and product identification using selected characteristics (product/target groups).
 
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Practical example for a quantitative analysis

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