Parameter uncertainty in structural equation models: Confidence sets and fungible estimates

• Main Authors: Pek, J. (Author), Wu, H. (Author)
• Format: Article
• Language: English
• Published: American Psychological Association Inc. 2018
• Subjects: caregiver; child; Confidence sets; conversation; Data Interpretation, Statistical; drawing; effect size; Fungible estimates; human; human experiment; Humans; juvenile; Models, Statistical; palliative therapy; procedures; Profile likelihood; psychology; Psychology; religion; rest; sampling; scientist; Sensitivity analysis; simulation; statistical analysis; statistical model; structural equation modeling; Structural equation modeling; theoretical study; uncertainty; Uncertainty; validity
• Online Access: View Fulltext in Publisher

 Current concerns regarding the dependability of psychological findings call for methodological developments to provide additional evidence in support of scientific conclusions. This article highlights the value and importance of two distinct kinds of parameter uncertainty, which are quantified by confidence sets (CSs) and fungible parameter estimates (FPEs; Lee, MacCallum, & Browne, 2017); both provide essential information regarding the defensibility of scientific findings. Using the structural equation model, we introduce a general perturbation framework based on the likelihood function that unifies CSs and FPEs and sheds new light on the conceptual distinctions between them. A targeted illustration is then presented to demonstrate the factors which differentially influence CSs and FPEs, further highlighting their theoretical differences. With 3 empirical examples on initiating a conversation with a stranger (Bagozzi & Warshaw, 1988), posttraumatic growth of caregivers in the context of pediatric palliative care (Cadell et al., 2014), and the direct and indirect effects of spirituality on thriving among youth (Dowling, Gestsdottir, Anderson, von Eye, & Lerner, 2004), we illustrate how CSs and FPEs provide unique information which lead to better informed scientific conclusions. Finally, we discuss the importance of considering information afforded by CSs and FPEs in strengthening the basis of interpreting statistical results in substantive research, conclude with future research directions, and provide example OpenMx code for the computation of CSs and FPEs. © 2018 American Psychological Association.