Model evaluation for factor analysis consists of computing the standard error of estimate for each variable based on working backwards and re-estimating their values using the scored factors. Estimated values of the original data are made using the factor scoring equation 
= XCT where is the estimated raw data, X is the scored data, and C is the factor pattern matrix or rotated factor pattern matrix if rotation was included in the model. The standard error of estimate for each variable y in the original data Y is then given by:
where each 
is the estimated value of each variable y, n is the number of observations and p is the number of factors.
= XCT where is the estimated raw data, X is the scored data, and C is the factor pattern matrix or rotated factor pattern matrix if rotation was included in the model. The standard error of estimate for each variable y in the original data Y is then given by: is the estimated value of each variable y, n is the number of observations and p is the number of factors.