
SEMinR Lecture Series
This is step 1 of the evaluation of the reflective measurement model. The focus is on the introduction to the reflective measurement model and how to assess indicator reliability.
This is step 1 of the evaluation of the reflective measurement model. The focus is on the introduction to the reflective measurement model and how to assess indicator reliability.
The first step in reflective measurement model assessment involves examining how much of each indicator’s variance is explained by its construct, which is indicative of indicator reliability.
To compute an indicator’s explained variance, we need to square the indicator loading, which is the bivariate correlation between indicator and construct.
As such, the indicator reliability indicates the communality of an indicator.
Indicator loadings above 0.708 are recommended, since they indicate that the construct explains more than 50 percent of the indicator’s variance, thus providing acceptable indicator reliability.
Researchers frequently obtain weaker indicator loadings (< 0.708) for their measurement models in social science studies, especially when newly developed scales are used.
Rather than automatically eliminating indicators when their loading is below 0.70, researchers should carefully examine the effects of indicator removal on other reliability and validity measures.
Generally, indicators with loadings between 0.40 and 0.708 should be considered for removal only when deleting the indicator leads to an increase in the internal consistency reliability or convergent validity (discussed in the next sections) above the suggested threshold value.
Another consideration in the decision of whether to delete an indicator is the extent to which its removal affects content validity, which refers to the extent to which a measure represents all facets of a given construct.
As a consequence, indicators with weaker loadings are sometimes retained. Indicators with very low loadings (below 0.40) should, however, always be eliminated from the measurement model .
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook.
The tutorials on SEMinR are based on the mentioned book. The book is open source and available for download under this link.