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THREEFOLD WP8 Girona, 26 27 March 2007 Consumer Trends and Marketing Part I: Market Segmentation, Targeting and Positioning Dr. Renata Januszewska Ir. Bianka K HNE Prof. Xavier Gelling Prof. Jacques

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Component analysis, also known as factor analysis, is a statistical technique used to identify underlying factors or components within a dataset. It helps to uncover relationships between variables and reduce data dimensions.

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Any individual or organization conducting a research study or analyzing data may choose to perform component analysis if it is relevant to their objectives.

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To perform component analysis, you need to follow certain steps. These include selecting variables, collecting data, determining the appropriate factor extraction method, conducting the analysis using statistical software, interpreting the results, and reporting your findings.

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The purpose of component analysis is to reveal the underlying structure or patterns in a dataset, identify the most important variables contributing to the observed variability, and simplify complex data by reducing dimensions.

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The information reported in component analysis typically includes the extracted factors or components, their corresponding eigenvalues, factor loadings, communalities, and variance explained by each component. Additionally, researchers may report the factor structure and interpret the meaning of each factor.

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