Abstract
Software Engineering (SE) researchers are increasingly paying attention to organizational and human factors. Rather than focusing only on variables that can be directly measured, such as lines of code, SE research studies now also consider unobservable variables, such as organizational culture and trust. To measure such latent variables, SE scholars have adopted Partial Least Squares Structural Equation Modeling (PLS-SEM), which is one member of the larger SEM family of statistical analysis techniques. As the SE field is facing the introduction of new methods such as PLS-SEM, a key issue is that not much is known about how to evaluate such studies. To help SE researchers learn about PLS-SEM, we draw on the latest methodological literature on PLS-SEM to synthesize an introduction. Further, we conducted a survey of PLS-SEM studies in the SE literature and evaluated those based on recommended guidelines.
Highlights
IntroductionOf particular interest is SEM’s ability to define latent variables, which cannot be measured directly
Introduction to Latent VariablesUllman [185, p. 35] defined SEM as:“a collection of statistical techniques that allow a set of relations between one or more independent variables (IVs), either continuous or discrete, and one or more dependent variables (DVs), either continuous or discrete, to be examined.”Of particular interest is SEM’s ability to define latent variables, which cannot be measured directly
Partial Least Squares Structural Equation Modeling (PLS-SEM) is more widely used in other fields, including the related field of Information Systems, and those fields exhibit a high level of maturity of reporting [141]
Summary
Of particular interest is SEM’s ability to define latent variables, which cannot be measured directly. Examples include constructs such as trust, organizational culture, and project success. To measure such a construct, a researcher identifies a series of observed variables that represent the construct. A set of variables that together represent a latent variable is referred to as a measurement instrument. One measurement instrument for the construct “trust” contains the following items [33], each of which is an observed variable—typically a question on a survey instrument:
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