<P> In multivariate statistics, 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 . It is commonly used by researchers when developing a scale (a scale is a collection of questions used to measure a particular research topic) and serves to identify a set of latent constructs underlying a battery of measured variables . It should be used when the researcher has no a priori hypothesis about factors or patterns of measured variables . Measured variables are any one of several attributes of people that may be observed and measured . Examples of a measured variables could be the physical height, weight, and pulse rate of a human being . Usually, researchers would have large number of measured variables, which are assumed to be related to a smaller number of "unobserved" factors . Researchers must carefully consider the number of measured variables to include in the analysis . EFA procedures are more accurate when each factor is represented by multiple measured variables in the analysis . </P> <P> EFA is based on the common factor model . In this model, manifest variables are expressed as a function of common factors, unique factors, and errors of measurement . Each unique factor influences only one manifest variable, and does not explain correlations between manifest variables . Common factors influence more than one manifest variable and "Factor loadings" are measures of the influence of a common factor on a manifest variable . For the EFA procedure, we are more interested in identifying the common factors and the related manifest variables . </P> <P> EFA assumes that any indicator / measured variable may be associated with any factor . When developing a scale, researchers should use EFA first before moving on to confirmatory factor analysis (CFA). EFA is essential to determine underlying factors / constructs for a set of measured variables; while CFA allows the researcher to test the hypothesis that a relationship between the observed variables and their underlying latent factor (s) / construct (s) exists . EFA requires the researcher to make a number of important decisions about how to conduct the analysis because there is no one set method . </P>

An acceptable eigenvalue in factor analysis is set at