Current concerns regarding the dependability of psychological findings call for methodological developments to provide additional evidence in support of scientific conclusions. This paper highlights the value and importance of two distinct kinds of parameter uncertainty which are quantified by confidence sets (CSs) and fungible parameter estimates (FPEs); 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 three empirical examples on initiating a conversation with a stranger, posttraumatic growth of caregivers in the context of pediatric palliative care, and the direct and indirect effects of spirituality on thriving among youth, 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.
Sharp Hybrid Confidence Bands for Partially Identified Treatment Effects under Tail Uncertainty with an Application to Workforce Gender Diversity and Firm Performance
The Inclusion Initiative, London School of Economics and Political Science（伦敦政治经济学院包容性倡议）；Data Science Institute, London School of Economics and Political Science（伦敦政治经济学院数据科学研究所）
Non-parametric Differentially Private Confidence Intervals for the Median
Institute for Employment Research（就业研究所）；University of Maryland（马里兰大学）；University of Pennsylvania（宾夕法尼亚大学）；Apple（苹果公司）；Harvard John A. Paulson School of Engineering and Applied Sciences（哈佛大学约翰·A·保尔森工程与应用科学学院）；Boston University（波士顿大学）
