Factor Analysis: Stages Of Factor Analysis Decision Process

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3.2.1 Stages of Factor Analysis Decision process
Factor analysis is a statistical method, which is used in multivariate to describe variability between correlated variables they are known as factors. These factors are assumed to represent the dimensions within the data. The information which is gathered by performing factor analysis can later reduce the set of variables in a dataset. The basic concept of factor analysis is to represent a set of variables by a smaller set of variables.
The primary purpose of factor analysis is to define the underlying structure between the variables in the analysis. Factor analysis condenses the information into smaller sets of variates (factors), with minimum loss of information.
There are five stages,
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· Q factor analysis: Correlation matrix of individual variables based on their characteristics. Condenses large number of people into distinctly different groups.
Achieving data outline vs. data reduction
Data outlining: If it is the definition of a structure, then the set of variables, which are in a generalised format can be obtained from different ranges. The linear composite of the variables from the generalised sets is known as variate or factor.
Data reduction: Replacing the previous original values completely with a new set of variables with empirical value (factor score).
Variable selection: It is the process of picking a subset of variables to be used in model construction. The analyst must consider the conceptual ideas of the variables and use judgment to the fit of the variables to conduct factor analysis.
Utilising factor analysis with other multivariate techniques: Is the numerical value which indicates the relative standing on a latent factor.

Stage 2: Designing a factor
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