Statistical power and structural equation models in business research

It has long been recognized that statistical power is important for structural equation models, but only recently has it become possible to estimate the power associated with the test of an entire model. This article discusses the relevance of power for structural equation models and measurement validation, then examines the question of the degree of power associated with models published in business journals. Addressing this matter is essential, because statistical power directly affects the confidence with which test results can be interpreted. The issue is particularly appropriate in light of the increased use of structural equation models in business research. Using articles from some leading business journals as examples, a survey finds that power tends to be either very low, implying that too many false models will not be rejected (Type II error), or extremely high, causing overrejection of tenable models (Type I error). The implications of this discovery are explored, and recommendations that should improve the validity and application of structural equation modeling in business research are offered.