Structural equation modeling and regression: guidelines for research practice

The growing interest in Structured Equation Modeling (SEM) techniques
and recognition of their importance in IS research suggests the need to compare
and contrast different types of SEM techniques so that research designs can be
appropriately selected. After assessing the extent to which these techniques are
currently being used in IS research, the article presents a running example which
analyzes the same dataset via three very different statistical techniques. It then
compares two classes of SEM: covariance-based SEM and partial-least-squaresbased
SEM. Finally, the article discusses linear regression models and offers
guidelines as to when SEM techniques and when regression techniques should
be used. The article concludes with heuristics and rule of thumb thresholds to
guide practice, and a discussion of the extent to which practice is in accord with
these guidelines.