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Exploratory Factor Analysis (EFA).

Key points. 01 Introduction to Exploratory Factor Analysis (EFA).

Introduction Factor analysis is a statistical method of data reduction. It is used to study the dimensionality of a set of variables. It reduces a large number of overlapping variables to a smaller set of factors that reflect construct(s) or different dimensions of construct(s). Take many variables and explain them with a few “factors” or “components”.

Introduction Correlated variables are grouped together and separated from other variables with low or no correlation These variables with high intercorrelations could well measure one underlying variable, which is called a “factor”..

Objectives Specifying the unit of analysis Achieving data summarization versus data reduction Variable selection Using factor analysis with other multivariate techniques.

Assumptions in Factor Analysis Conceptual issues → underlying factor analysis relate to the set of variable selected (some underlying structure does exist) Statistical issues → Substantial no. of correlations > 0.30 in correlation matrix → Bartlett test of sphericity → Kaiser-Meyer- Olkin (KMO) test Sample size.

Methods of Factors extraction Principal component analysis considers the total variance and derives factors that contain small proportions of unique variance and, in some instances, error variance. Common factor analysis considers only the common or shared variance, assuming that both the unique and error variance are not of interest in defining the structure of the variables..

Interpreting the Factors Examine the factor matrix of loading/rotated factor matrix Identifying the significant loading Re-specify the factor model if needed Naming the factors.