Browse
Submit Data
Databases
API
Help

Dataset Information

18 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models.

ABSTRACT: Selecting between competing Structural Equation Models (SEMs) is a common problem. Often selection is based on the chi square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with SEMs compared to other fit indices. This article examines several new and old Information Criteria (IC) that approximate Bayes Factors. We compare these IC measures to common fit indices in a simulation that includes the true and false models. In moderate to large samples, the IC measures outperform the fit indices. In a second simulation we only consider the IC measures and do not include the true model. In moderate to large samples the IC measures favor approximate models that only differ from the true model by having extra parameters. Overall, SPBIC, a new IC measure, performs well relative to the other IC measures.

SUBMITTER: Bollen KA

PROVIDER: S-EPMC6663110 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

BIC and Alternative Bayesian Information Criteria in the Selection of Structural Equation Models.

Bollen Kenneth A KA Harden Jeffrey J JJ Ray Surajit S Zavisca Jane J

Structural equation modeling : a multidisciplinary journal 20140131 1

Selecting between competing Structural Equation Models (SEMs) is a common problem. Often selection is based on the chi square test statistic or other fit indices. In other areas of statistical research Bayesian information criteria are commonly used, but they are less frequently used with SEMs compared to other fit indices. This article examines several new and old Information Criteria (IC) that approximate Bayes Factors. We compare these IC measures to common fit indices in a simulation that in ...[more]

PMID: 31360054

Similar Datasets

Bayesian lasso for semiparametric structural equation models.

Project description:There has been great interest in developing nonlinear structural equation models and associated statistical inference procedures, including estimation and model selection methods. In this paper a general semiparametric structural equation model (SSEM) is developed in which the structural equation is composed of nonparametric functions of exogenous latent variables and fixed covariates on a set of latent endogenous variables. A basis representation is used to approximate these nonparametric functions in the structural equation and the Bayesian Lasso method coupled with a Markov Chain Monte Carlo (MCMC) algorithm is used for simultaneous estimation and model selection. The proposed method is illustrated using a simulation study and data from the Affective Dynamics and Individual Differences (ADID) study. Results demonstrate that our method can accurately estimate the unknown parameters and correctly identify the true underlying model.

| S-EPMC3371320 | biostudies-literature

A Practical Guide to Variable Selection in Structural Equation Models with Regularized MIMIC Models.

Project description:Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This poses a difficult modeling scenario that entails large models with a comparably limited number of observations given the number of parameters. We here describe a particular strategy to overcoming this challenge, called regularization. Regularization, a method to penalize model complexity during estimation, has proven a viable option for estimating parameters in this small n, large p setting, but has so far mostly been used in linear regression models. Here we show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts, and how it can be extended to regularized structural equation modeling (Jacobucci, Grimm, & McArdle, 2016). Our approach is evaluated through the use of a simulation study, showing that regularized SEM outperforms traditional SEM estimation methods in situations with a large number of predictors and small sample size. We illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short term memory, as well as identifying demographic correlates of stress, anxiety and depression. We illustrate the performance of the method and discuss practical aspects of modeling empirical data, and provide a step-by-step online tutorial.

| S-EPMC6713564 | biostudies-literature

INCORPORATING BIOLOGICAL INFORMATION INTO LINEAR MODELS: A BAYESIAN APPROACH TO THE SELECTION OF PATHWAYS AND GENES.

Project description:The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.

| S-EPMC3650864 | biostudies-literature

Model Fit Estimation for Multilevel Structural Equation Models.

Project description:Structural equation modeling (SEM) provides an extensive toolbox to analyze the multivariate interrelations of directly observed variables and latent constructs. Multilevel SEM integrates mixed effects to examine the covariances between observed and latent variables across many levels of analysis. However, while it is necessary to consider model fit, traditional indices are largely insufficient to analyze model fit at each level of analysis. The present paper reviews i) the partially-saturated model fit approach first suggested by Ryu and West (2009) and ii) an alternative model parameterization that removes the multilevel data structure. We next describe the implementation of an algorithm to compute partially-saturated model fit for 2-level structural equation models in the open source SEM package, OpenMx, including verification in a simulation study. Finally, an example empirical application evaluates leading theories on the structure of affect from ecological momentary assessment data collected thrice daily for two weeks from 345 participants.

| S-EPMC7410097 | biostudies-literature

Testing students' e-learning via Facebook through Bayesian structural equation modeling.

Project description:Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

| S-EPMC5590745 | biostudies-literature

Integrating occupancy models and structural equation models to understand species occurrence.

Project description:Understanding the drivers of species occrrece s a fundamenal goal in basic and applied ecology. Occupancy models have emerged as a popular approach for inferring species occurrence because they account for problems associated with imperfect detection in field surveys. Current models, however, are limited because they assume covariates are independent (i.e., indirect effects do not occur). Here, we combined structural equation and occupancy models to investigate complex influences on species occurrence while accounting for imperfect detection. These two methods are inherently compatible because they both provide means to make inference on latent or unobserved quantities based on observed data. Our models evaluated the direct and indirect roles of cattle grazing, water chemistry, vegetation, nonnative fishes, and pond permanence on the occurrence of six pond-breeding amphibians, two of which are threatened: the California tiger salamander (Ambysloma californiense) and the California red-legged frog (Rana draytonil). While cattle had strong effects on pond vegetation and water chemistry, their overall effects on amphibian occurrence were small compared to the consistently negative effects of nonnative fish. Fish strongly reduced occurrence probabilities for four of five native amphibians, including both species of conservation concern. These results could help to identify drivers of amphibian declines and to prioritize strategies for amphibian conservation. More generally, this approach facilitates a more mechanistic representation of ideas about the causes of species distributions in space and time. As shown here, occupancy modeling and structural equation modeling are readily combined, and bring rich sets of techniques that may provide unique theoretical and applied insights into basic ecological questions.

| S-EPMC4877056 | biostudies-literature

Bayesian variable selection for latent class models.

Project description:In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

| S-EPMC3035762 | biostudies-literature

Bootstrap-adjusted quasi-likelihood information criteria for mixed model selection.

Project description:We propose two model selection criteria relying on the bootstrap approach, denoted by QAICb1 and QAICb2, in the framework of linear mixed models. Similar to the justification of Akaike Information Criterion (AIC), the proposed QAICb1 and QAICb2 are proved as asymptotically unbiased estimators of the Kullback-Leibler discrepancy between a candidate model and the true model. However, they are defined on the quasi-likelihood function instead of the likelihood and are proven to be asymptotically equivalent. The proposed selection criteria are constructed by the quasi-likelihood of a candidate model and a bias estimation term in which the bootstrap method is adopted to improve the estimation for the bias caused by using the candidate model to estimate the true model. The simulations across a variety of mixed model settings are conducted to demonstrate that the proposed selection criteria outperform some other existing model selection criteria in selecting the true model. Generalized estimating equations (GEE) are utilized to calculate QAICb1 and QAICb2 in the simulations. The effectiveness of the proposed selection criteria is also demonstrated in an application of Parkinson's Progression Markers Initiative (PPMI) data.

| S-EPMC10896141 | biostudies-literature

Two-Level Dynamic Structural Equation Models with Small Samples.

Project description:Advances in data collection have made intensive longitudinal data easier to collect, unlocking potential for methodological innovations to model such data. Dynamic structural equation modeling (DSEM) is one such methodology but recent studies have suggested that its small N performance is poor. This is problematic because small N data are omnipresent in empirical applications due to logistical and financial concerns associated with gathering many measurements on many people. In this paper, we discuss how previous studies considering small samples have focused on Bayesian methods with diffuse priors. The small sample literature has shown that diffuse priors may cause problems because they become unintentionally informative. Instead, we outline how researchers can create weakly informative admissible-range-restricted priors, even in the absence of previous studies. A simulation study shows that metrics like relative bias and non-null detection rates with these admissible-range-restricted priors improve small N properties of DSEM compared to diffuse priors.

| S-EPMC7451754 | biostudies-literature

FSEM: Functional Structural Equation Models for Twin Functional Data.

Project description:The aim of this paper is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white-matter tracts obtained from the UNC early brain development study.

| S-EPMC6497081 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data