The procedure of attaining the variability between the two of the correlated variables is with the number of variables that are having variables which are lower in number and which are unobserved. This is a method prevalent in statistics.
It is used in interdependency for example if a scientist is having votes, personality characteristics and questionnaire so interdependency refers to the merging of all these sources.
It helps in reducing data for example the data provided to an economist results in 500 entries. For data reduction the whole data is analysed on the basis of various characteristics such as size, location, manner etc.
It helps to analyse the structure of the given statisticsdata.
It helps to provide the components on which the data can be rated.
It tends to categorise the data into different domains such as social domains, behavioural domains and many more.
TYPES OF FACTOR ANALYSIS
Exploratory factor analysis–Exploratory factor analysis depicts the factors that affect the structure in the given data without setting any predefined output. It explores the basic structure of the given data. It creates the process of principal components analysis which means that the there is no error in individual measures and items. Consequently, sometimes the result of principal components analysis and factor analysis give the same output.
Confirmatory factor analysis – It is a process in which confirmation is given for the current existing factors. It also tests that the data fulfil the expectations of the structure.
APPLICATIONS OF FACTOR ANALYSIS
It identifies the underlying factors by breaking the clusters variables into homogenous sets. After that it creates new factors and tends a person to deal in new categories.
It analyses the groups and does a deeper study for representing one variable for many sets.
It summarises the information by describing many variables using few factors.
It has the tendency to form small group of variables from a larger set of variables.
It helps in forming clusters by putting categories depending upon the scores and data of the factors.
METHODS FOR CALCULATION OF FACTOR ANALYSIS
Kaiser – Guttam rule – This process defines that the output of factors should be equal to the variance which is greater than 1.0.
Scree plot – In this process a scree plot is created which depicts the change of rate in the variance of factors.
Interpretability of factors – The main goal of interpretability is evaluation of “theoretical meaningfulness”.
Prior hypotheses – The analysis of factor extraction can be easier if the prior information is provided for appropriate extraction.
ADVANTAGES OF FACTOR ANALYSIS
It acts as a tool to reduce the complex data in a concise form.
When the variables are correlated, then it helps in avoiding duplicacy in the data.
It helps in reduction of data.
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