What If I Have a Standardized Factor Loading Greater Than 1 in SEM Model?

A standardized factor loading greater than 1 is stating that you are explaining more than 100% of the variance in an indicator. In this instance, you will also see a negative number in your error term, which is often called a “Heywood case”.The causes of a Heywood case are often the result of an outlier, multicollinearity between indicators, or a mis-specified model.You will see Heywood cases more often when a construct has only two indicators. Possible solutions are to remove a covariance between indicator error terms, deleting the problematic indicator, drop- ping outliers, adding another indicator to the unobserved variable, or dropping the maximum likelihood estimation in favor of GLS (generalized least squares—this can be done the Analysis Properties window). If you are only concerned with the standardized factor loadings (and not the unstandardized), I have seen Heywood cases addressed by constraining the unobserved vari- able’s variance to “1” and then labelling all the paths from the unobservable construct to all the indicators the same term (Like an “A”). By labeling all of the paths to the indicators the same name, it will constrain all the paths to be equal. So, the unstandardized estimates will all be the same using this technique, but the standardized estimates will reflect the difference in the indica- tors.This is not an ideal method to address a Heywood case, but it is an option if all else fails.

Note: you can have unstandardized loadings greater than 1 and it is perfectly acceptable.

Source: Thakkar, J.J. (2020). “Procedural Steps in Structural Equation Modelling”. In: Structural Equation Modelling. Studies in Systems, Decision and Control, vol 285. Springer, Singapore.