Multivariate Data Analysis refers to any statistical technique used to analyze data. This essentially models reality for each situation, product, or decision. Our multivariate capabilities give our clients the ability to obtain a clear picture of what is going on and make intelligent decisions.

Below are examples of the most common functions .Zone of Tolerance Analysis asks respondents their “Desired Service Level” and “Adequate Service Level” for each attribute. A range—between what is ideal and adequate—is calculated for each attribute. It is commonly used in pharmaceutical research.

We are in the following multivariate analysis such as:

• TURF Analysis is an acronym for “Total Unduplicated Reach and Frequency”, is a type of statistical analysis used for providing estimates of media or market potential and devising optimal ways how to use it given the limited resources.
• Structural Equation Modeling (SEM) is a statistical technique for testing and estimating relationships using a combination of statistical data and qualitative causal assumptions. Among its strengths is the ability to model constructs as latent variables.
• Regression Analysis is used for prediction for the affect of individual attributes on a depend variable, such as ‘Purchase Intent’ or ‘Overall Satisfaction’.
• Quadrant Analysis is particularly useful to marketers when trying to determine how product categories, segments and individual SKUs are performing.
• Maximum Difference Analysis shows a set of possible items and asks respondents to indicate the most and least appealing.
• Derived vs. Stated Importance Analysis: A derived quadrant that shows the different between what customers say is important, and what they actions show is important.
• Fair Share Analysis measures the magnitude of brand or product attributes.
• Factor Analysis, a data reduction technique, groups arrays of attributes into families.
• Discriminant Analysis is used in situations where you want to build a predictive model of group membership based on demographic or other data.
• Canonical Analysis is used to asses the relationship between two sets of variables.

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One reason is to group variables or people. In an image study of an electronics company, for example, firms might be rated in reference to inno-vativeness, number of plants, financial strength, capacity, size of research and development, production efficiency, service capability, and so on. Several of these variables might be measuring the same firm attribute. For example, number of plants, financial strength, and capacity might all reflect a size dimension. In this chapter we have contrasted groups of people defined by a single question. Cluster analysis allows the researcher to define groups of people using many questions.

Another reason for multivariate analysis is to improve the ability ¦ predict variables such as usage or to understand relationships between variables such as advertising and usage. A start toward both prediction and understanding is provided by the analysis of questions in pairs, which can identify associations that, in turn, provide evidence of causation. The problem is that relationships between two variables can be confounded by third variable. The third variable can cause the other two. analysis. These techniques are applied to survey analysis and provide management with the opportunity to product and communication planning. We explain things in a simple, clear way so that clients understand what the results mean for their business.

We identify how customers conceptualize products and brands and use the analysis to help clients design and market products in ways that maximize their appeal.

How a Multivariate Analysis Works>

A multivariate analysis enables you to avoid the problem of multiple tests that would arise if you tested the effect of each independent variable on each dependent variable separately. Another way to handle the same problem is to use the Bonferroni method to correct for multiple tests. For some problems the Bonferroni method is much more powerful than a multivariate test, and for other problems the reverse is true. To understand when a multivariate test is likely to outperform the Bonferroni method and when it is not, it helps to understand in more detail how the multivariate approach tests a hypothesis.

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