What Principal components analysis of qualitative data, is
Principal components analysis of qualitative data is a statistical technique used to reduce the number of variables in a dataset by combining similar variables into a single variable. This technique is used to simplify a large dataset by reducing the number of variables and maintaining the important relationships between the variables.
Steps for Principal components analysis of qualitative data:
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Identify the variables that are related to each other.
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Calculate the correlation matrix among the variables.
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Determine the principal components using an eigenvalue-eigenvector decomposition.
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Extract the principal components by rotating the axes of the principal components.
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Interpret the principal components by looking at the loadings of each variable on the principal components.
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Use the principal components in further analyses.
Examples
- Analyzing customer survey data to identify the most important elements that influence customer satisfaction.
- Examining the relationship between student performance and a variety of demographic and socioeconomic factors.
- Identifying key factors in determining the success of a product launch.
- Investigating the factors that influence a person’s decision to purchase a particular item.
- Understanding the underlying factors that drive employee engagement and satisfaction.