What is exploratory factor analysis in SPSS?
Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It is used to identify the structure of the relationship between the variable and the respondent.
What is exploratory factor analysis in research?
Exploratory factor analysis (EFA) is one of a family of multivariate statistical methods that attempts to identify the smallest number of hypothetical constructs (also known as factors, dimensions, latent variables, synthetic variables, or internal attributes) that can parsimoniously explain the covariation observed …
How do you do exploratory factor analysis in SPSS?
Steps of running PCA and EFA in SPSS
- From the menu, click on Analyze -> Dimension Reduction -> Factor…
- In the appearance window, move all variables to Variables… -> Continue.
How do you do factor analysis in SPSS?
Factor Analysis in SPSS To conduct a Factor Analysis, start from the “Analyze” menu. This procedure is intended to reduce the complexity in a set of data, so we choose “Data Reduction” from the menu. And the choice in this category is “Factor,” for factor analysis.
What is the difference between PCA and factor analysis?
The difference between factor analysis and principal component analysis. Factor analysis explicitly assumes the existence of latent factors underlying the observed data. PCA instead seeks to identify variables that are composites of the observed variables.
What is Promax rotation?
Promax Rotation . An oblique rotation, which allows factors to be correlated. This rotation can be calculated more quickly than a direct oblimin rotation, so it is useful for large datasets.
What is factor analysis how SPSS is used to apply factor analysis?
Factor analysis is a method of data reduction. It does this by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables). Simple structure is pattern of results such that each variable loads highly onto one and only one factor.