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Factor Analysis

This presentation educates you about Factor Analysis, Types of Factoring, Assumptions, Key concept and terms and Two approaches CFA.<br><br>For more topics stay tuned with Learnbay.

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Factor Analysis

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  1. Factor Analysis Swipe

  2. Factor Analysis Factor analysis is used to uncover the latent structure of a set of variables. It reduces attribute space from a large number of variables to a smaller number of factors and as such is a non dependent procedure.

  3. Types of Factoring: Principal component analysis Common factor analysis Image factoring Maximum likelihood method Factor loading Eigenvalues Factor score Criteria for determining the number of factors Rotation method

  4. Assumptions No outlier: Assume that there are no outliers in data. Adequate sample size: The case must be greater than the factor. No perfect multicollinearity: Factor analysis is an interdependency technique. There should not be perfect multicollinearity between the variables.

  5. Homoscedasticity: Since factor analysis is a linear function of measured variables, it does not require homoscedasticity between the variables. Linearity: Factor analysis is also based on linearity assumption. Non-linear variables can also be used. After transfer, however, it changes into linear variable. Interval Data: Interval data are assumed.

  6. Key concepts and terms Exploratory factor analysis: Assume that any indicator or variable may be associated with any factor. This is the most common factor analysis used by researchers and it is not based on any prior theory. Confirmatory factor analysis (CFA): Used to determine the factor and factor loading of measured variables, and to confirm what is expected on the basic or pre-established theory. CFA assumes that each factor is associated with a specified subset of measured variables.

  7. Two approaches CFA The traditional method: Traditional factor method is based on principal factor analysis method rather than common factor analysis. Traditional method allows the researcher to know more about insight factor loading. The SEM approach: CFA is an alternative approach of factor analysis which can be done in SEM. In SEM, we will remove all straight arrows from the latent variable, and add only that arrow which has to observe the variable representing the covariance between every pair of latents.

  8. Topics for next Post Linear Regression Association Rule hierarchical clustering Stay Tuned with

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