TY - JOUR
T1 - PLS-SEM for software engineering research
T2 - An introduction and survey
AU - Russo, Daniel
AU - Stol, Klaas Jan
N1 - Funding Information:
This work was supported by Science Foundation Ireland grant 13/RC/2094_P2 and 15/SIRG/3293. Authors’ addresses: D. Russo, Department of Computer Science, Aalborg University, Selma Lagerlöfs Vej 300, 9220, Aalborg, Denmark; email: [email protected]; K.-J. Stol, Lero—The Irish Software Research Centre and University College Cork, School of Computer Science and Information Technology, Cork, Ireland; email: [email protected].
Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Critical review
KW - Partial least squares
KW - Research methodology
KW - Structural equation modeling
UR - http://www.scopus.com/inward/record.url?scp=85109211585&partnerID=8YFLogxK
U2 - 10.1145/3447580
DO - 10.1145/3447580
M3 - Review article
AN - SCOPUS:85109211585
SN - 0360-0300
VL - 54
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 4
M1 - 78
ER -