Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling

Winter, Sonja D.; Depaoli, Sarah

doi:10.1080/10705511.2022.2164286

Cited by 8 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Close Replication: In the most constrained condition, factor loadings and thresholds were assigned Normal prior distributions with mean hyperparameters set to the corresponding 5 Herein, we demonstrate prior predictive similarity checking in the context of factor analysis, but the same methodology applies to other models. Readers who wish to extend this method to other models should consult our reading list (https://osf.io/q6rvf/) for relevant tutorials on prior specification and prior predictive model checking (e.g., van Zundert et al, 2022;Winter & Depaoli, 2023;Zondervan-Zwijnenburg et al, 2017). 6 We focused on prior specifications that are currently available in accessible software.…”

Section: Empirical Applicationmentioning

confidence: 99%

Good fit is weak evidence of replication: Increasing rigor through prior predictive similarity checking

Bonifay,

Winter,

Skoblow

et al. 2024

Preprint

View full text Add to dashboard Cite

Jerzy Neyman declared, “Models become plausible by repetition.” What he called repetition is now known as model replication and entails applying the same statistical model to data from different studies. Typically, researchers rely on goodness-of-fit – indexing the degree to which the model represents the data – as evidence of successful replication. But we demonstrate that goodness-of-fit testing fails to capture important (dis)similarities between studies. As an alternative, we propose prior predictive similarity checking: a method for rigorous comparison of the data patterns and/or model parameters from different studies. We illustrate this method using psychopathology data from the National Comorbidity Survey (NCS) and its purported replication, the NCS-R. Both data sets yielded excellent fit, but often failed to pass our more demanding similarity checking technique. We conclude with recommendations for applied model replication research.

show abstract

Section: Empirical Applicationmentioning

confidence: 99%

Good fit is weak evidence of replication: Increasing rigor through prior predictive similarity checking

Bonifay,

Winter,

Skoblow

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In prior sensitivity analysis, multiple candidate sets of priors are implemented, and the impact of varying prior specifications on the estimates of interest evaluated (Kruschke, 2021). Prior predictive checking (Winter & Depaoli, 2023) in which model predictions are generated from the prior distributions of the parameters may be useful for parameters that are difficult to directly interpret. Demonstration of these steps was out of scope for this essay, but in practice they are a relevant part of analysis workflow (Gelman et al, 2020).…”

Section: Model Specificationmentioning

confidence: 99%

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

Woodward

2024

Vet Pharm & Therapeutics

View full text Add to dashboard Cite

Sophisticated mathematical and computational tools have become widespread and important in veterinary pharmacology. Although the theoretical basis and practical applications of these have been widely explored in the literature, statistical inference in the context of these models has received less attention. Optimization methods, often with frequentist statistical inference, have been predominant. In contrast, Bayesian statistics have not been widely applied, but offer both practical utility and arguably greater interpretability. Veterinary pharmacology applications are generally well supported by relevant prior information, from either existing substantive knowledge, or an understanding of study and model design. This facilitates practical implementation of Bayesian analyses that can take advantage of this knowledge. This essay will explore the specification of Bayesian models relevant to veterinary pharmacology, including demonstration of prior selection, and illustrate the capability of these models to generate practically useful statistics, including uncertainty statements, that are difficult or impossible to obtain otherwise. Case studies using simulated data will describe applications in clinical trials, pharmacodynamics, and pharmacokinetics, all including multilevel modeling. This content may serve as a suitable starting point for researchers in veterinary pharmacology and related disciplines considering Bayesian estimation for their applied work.

show abstract

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM

Langenberg,

Helm,

Mayer

2024

Multivariate Behavioral Research

View full text Add to dashboard Cite

Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling

Cited by 8 publications

References 45 publications

Good fit is weak evidence of replication: Increasing rigor through prior predictive similarity checking

Good fit is weak evidence of replication: Increasing rigor through prior predictive similarity checking

Bayesian estimation in veterinary pharmacology: A conceptual and practical introduction

Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM

Contact Info

Product

Resources

About