Hi,

Exploratory factor analysis is Used to reduce a set of data so that it may be described and used easily. It is applied to both the development of scales and to the development of theory. Also, one can say that it is  a statistical tool for analyzing large numbers of variables (items) to see if there are identifiable groups of variables (referred to as factors) that

It leads mathematically to construct factors {subscales}, the scale is a linear combination of variables.

Thanks

Hi,

Exploratory factor analysis is Used to reduce a set of data so that it may be described and used easily. It is applied to both the development of scales and to the development of theory. Also, one can say that it is  a statistical tool for analyzing large numbers of variables (items) to see if there are identifiable groups of variables (referred to as factors) that

It leads mathematically to construct factors {subscales}, the scale is a linear combination of variables.

Thanks

Exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables

Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables. CFA allows the researcher to test the hypothesis that a relationship between observed variables and their underlying latent constructs exists.

Confirmatory factor analysis (CFA)  is used to test how well the measured variables represent the number of constructs. Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are similar techniques, but in exploratory factor analysis (EFA), data is simply explored and provides information about the numbers of factors required to represent the data. In exploratory factor analysis, all measured variables are related to every latent variable. But in confirmatory factor analysis (CFA), researchers can specify the number of factors required in the data and which measured variable is related to which latent variable. Confirmatory factor analysis (CFA) is a tool that is used to confirm or reject the measurement theory.

http://www.statisticssolutions.com/confirmatory-factor-analysis/

How does Factor Analysis help us in statistical analysis? Why do researchers use Factor Analysis?

Factor analysis is to reduce a large number of questionnaire items into smaller group of factors by loading / grouping questionnaire items to the correct factor / variable so that each factor / variable is only representative by the specific questionnaire items.  Only with the correct representation / relationship between a factor / variable with its questionnaire items, then further statistical testing will be more meaningful e.g. multiple regression, path analysis, structural modeling, hypotheses testing etc.

Factor analysis is divided into Exploratory Factor Analysis (EFA) & Confirmatory Factor Analysis (CFA).  Key difference is the relationship between the questionnaire items & factor.  When conducting an EFA, we don't know what questionnaire items are related to what factor - we just let EFA to freely load / group those questionnaire items into the different factors statistically.  Whereas in CFA, the researcher has a prior knowledge on how those specific questionnaire items are related to which factor / variable based on literature review on survey questionnaire adopted / adapted etc.  The researcher just need to confirm again with CFA whereby its requirement is more stringent i.e. questionnaire items with factor loading < 0.7 will be dropped.