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The advantages of factor analysis are as follows: Identification of groups of inter-related variables, to see how they are related to each other. Factor analysis can be used to identify the hidden dimensions or constructs which may or may not be apparent from direct analysis. Both objective and subjective attributes can be used. Reduction of number of variables, by combining two or more variables into a single factor. There is flexibility in naming using dimensions. It is not extremely difficult to do, inexpensive, and accurate.
Disadvantages
The disadvantages of factor analysis are as follows: Naming of the factors can be difficult – multiple attributes can be highly correlated with no apparent reason. If observed variables are completely*…show more content…*

In using factor analysis, the researcher examines the co-variation among a set of observed variables in order to gather information on their underlying latent constructs (i.e. factors). There are two basic types of factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The factor analytic model (EFA or CFA) focuses solely on how, and the extent to which, the observed variables are linked to their underlying latent factors. Specifically speaking, it is concerned with the extent to which observed variables are generated by the underlying latent constructs and thus strength of the regression paths from the factors to the observed variables (the factor loadings) are of primary interest. Exploratory Factor Analysis is designed for situations where links between the observed and latent variables are unknown or uncertain. Hence after the formulation of questionnaire items, an EFA will be conducted to determine the extent to which the item measurements are related to the latent*…show more content…*

A just identified model is one in which there is a one to- one correspondence between the data and the structural parameters. That is, the number of data variances and co variances equals the number of parameters to be estimated. An under-identified model is one in which the number of parameters to be estimated exceeds the number of variances and co-variances. As such the model would contain insufficient information for attaining a solution. An over-identified model is one which the number of estimable parameters is less than the number of data points (i.e. variances and co variances of the observed

In using factor analysis, the researcher examines the co-variation among a set of observed variables in order to gather information on their underlying latent constructs (i.e. factors). There are two basic types of factor analysis: Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The factor analytic model (EFA or CFA) focuses solely on how, and the extent to which, the observed variables are linked to their underlying latent factors. Specifically speaking, it is concerned with the extent to which observed variables are generated by the underlying latent constructs and thus strength of the regression paths from the factors to the observed variables (the factor loadings) are of primary interest. Exploratory Factor Analysis is designed for situations where links between the observed and latent variables are unknown or uncertain. Hence after the formulation of questionnaire items, an EFA will be conducted to determine the extent to which the item measurements are related to the latent

A just identified model is one in which there is a one to- one correspondence between the data and the structural parameters. That is, the number of data variances and co variances equals the number of parameters to be estimated. An under-identified model is one in which the number of parameters to be estimated exceeds the number of variances and co-variances. As such the model would contain insufficient information for attaining a solution. An over-identified model is one which the number of estimable parameters is less than the number of data points (i.e. variances and co variances of the observed

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