Differential Effects of Omitting Formative Indicators: A Comparison of Techniques

Research examining the formative specification of constructs has highlighted the need for researchers to capture all relevant causes of a construct of interest. However, the consequences of omitting a formative indicator have not been thoroughly examined. Given that one of the commonly employed techniques for modeling formatively specified constructs, Partial Least Squares, implicitly assumes that all relevant causes of a construct have been modeled, the consequences of omitting one of those are of prime importance. In this research we compare latent variable and PLS techniques on this issue based on theoretical arguments and results from Monte Carlo simulations. In particular, we focus on the presence or absence of estimation bias in the relationships between formative indicators and the formatively specified construct, and between the latter and other constructs in the research model. Our results highlight differences in how these two techniques cope with the omission of formative indicators, and discuss why those differences occur.