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Examples Of Multivariate Analysis

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As we have discussed in earlier chapter, there are a variety of multivariate techniques available to the researcher or analyst. Also, there are multitude of issues involved in each of their application. Therefore, it becomes evident that successful completion of a multivariate analysis involves not only the selection of correct method but also several other issues. There are other issues to be addressed such as problem definition, empirical issues and critical diagnosis of results.
A six-step approach to multivariate analysis is presented in subsequent paragraphs. This process helps the researcher, analyst or user in applying the appropriate multivariate method or technique to analyse the data. The different stages in the structured approach
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The researcher or analyst needs to define the research problem and analyse the objectives in conceptual terms, before specifying the variables or measures. The role of conceptual model development or theory cannot be overstated. Whether in academic or applied research, the researcher must view the problem in conceptual terms by defining the concepts. Then he has to identify the essential relationships that are to be investigated.

A conceptual model need not be detailed and complex. It can be just a simple representation of relationships to be studied. For example, if a dependence relationship is proposed as research objective, then the researcher has to specify the dependent and independent concepts. For an application of an interdependence technique, the dimensions of structure or similarity should be specified. Sometimes a concept or an idea, rather than specific variables, is defined in both dependence and interdependence situations.

Once the objective and conceptual model are specified, then the researcher has to select the suitable multivariate technique based on the measurement characteristics of dependent and independent variables. Variables for each concept are generally specified prior to the study. But, it may be necessary to respecify or even state in a different form, for example, transformations or creating dummy variables, after the data has been collected.

Stage 2: Develop the analysis
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