Regularized Structural Equation Modeling

Jacobucci, Ross; Grimm, Kevin J.; McArdle, John J.

doi:10.1080/10705511.2016.1154793

Cited by 169 publications

(179 citation statements)

References 55 publications

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“…One potential solution for this could lie in the method by which the tuning parameter is selected. We used the BIC applied to the entire sample, as this has been shown to perform well (Jacobucci et al, 2016). However, given that the models are specified in the SEM framework, other fit indices are available.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the lasso has been incorporated into SEM, creating a class of techniques called regularized structural equation modeling (RegSEM; Jacobucci, Grimm, & McArdle, 2016). RegSEM uses the same logic as that of Equation 7, only instead of adding a penalty term to the residual sum of squares as is done in regression, the penalty is added to the maximum likelihood fit function.…”

Section: Exploratory Mediation Analysismentioning

confidence: 99%

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Exploratory Mediation Analysis via Regularization

Serang

Jacobucci

Brimhall

et al. 2017

Structural Equation Modeling: A Multidisciplinary Journal

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Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment.

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Section: Discussionmentioning

confidence: 99%

Section: Exploratory Mediation Analysismentioning

confidence: 99%

Exploratory Mediation Analysis via Regularization

Serang

Jacobucci

Brimhall

et al. 2017

Structural Equation Modeling: A Multidisciplinary Journal

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“…We pruned the path analysis using goodness of fit measures (Bayesian Information criterion (BIC), chi-square test of model fit, root mean square error of approximation (RMSEA), Tucker-Lewis Index (TLI) and Comparative Fit Index (CFI)) and individual p-values until all paths in the final model were significant, and the goodness of fit measures indicated that the model fit the observed data well. We checked the pruning using lasso in the R package RegSEM 26 and found that the pruned models were consistent. We report regression coefficients with their associated standard errors and p-values.…”

Section: Methodsmentioning

confidence: 99%

Age, vascular health, and Alzheimer disease biomarkers in an elderly sample

Vemuri

Lesnick

Przybelski

et al. 2017

Annals of Neurology

141

117

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OBJECTIVE To investigate the associations between age, vascular health, and Alzheimer’s disease (AD) imaging biomarkers in an elderly sample. METHODS We identified 430 individuals along the cognitive continuum aged>60 years with amyloid-PET, Tau-PET, and MRI scans from the population-based Mayo Clinic Study of Aging. A subset of 329 individuals had FDG-PET. We ascertained presently existing cardiovascular and metabolic conditions (CMC) from healthcare records and used the summation of presence/absence of hypertension, hyperlipidemia, cardiac-arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke as a surrogate for vascular health. We used global amyloid from PiB-PET, entorhinal cortex tau uptake (ERC-tau) from Tau-PET, and neurodegeneration in AD signature regions from MRI and FDG-PET as surrogates for AD pathophysiology. We dichotomized participants into CMC=0 (CMC−) vs. CMC>0 (CMC+) and tested for age-adjusted group differences in AD biomarkers. Using structural equation models (SEM), we assessed the impact of vascular health on AD biomarker cascade (amyloid leads to tau leads to neurodegeneration) after considering the direct and indirect age, sex, and APOE effects. RESULTS CMC+ participants had significantly greater neurodegeneration than CMC− participants but did not differ by amyloid or ERC-tau. The SEM models showed that i) vascular health had a significant direct and indirect impact on neurodegeneration but not on amyloid; and ii) vascular health specifically the presence of hyperlipidemia had a significant direct impact on ERC-tau. INTERPRETATION Vascular health had quantifiably greater impact on neurodegeneration in AD regions than on amyloid deposition. Longitudinal studies are warranted to clarify the relationship between tau deposition and vascular health.

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“…This rationale (i.e., increased stability at the cost of some bias) is the same used for regularized regression methods such as ridge regression or lasso regression (Tibshirani, 1996), which are used in a frequentist framework but also have a Bayesian interpretation (Park & Casella, 2008). Regularization procedures have been extended to structural equation models with the goal of creating simpler models and minimizing over fitting (Jacobucci, Grimm, & McArdle, 2016; Yuan, Wu, & Bentler, 2011). The stabilizing effect of reasonable priors should also be beneficial for computational problems arising from sparse categorical data because the priors can be used to avoid improper solutions and extreme estimates.…”

Section: Estimation Approaches For Ifa Modelsmentioning

confidence: 99%

“…Regularization procedures have recently been extended to latent variable models with the goals of creating simpler models and minimizing over fitting (Jacobucci, Grimm, & McArdle, 2016; Yuan, Wu, & Bentler, 2011). In this article, I demonstrate that Bayesian estimation with reasonable prior information improves parameter estimate stability, overall variability in estimates, and power for IFA models with sparse, categorical indicators.…”

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confidence: 99%

Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

Bainter

2017

Multivariate Behavioral Research

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Psychometric models for item level data are broadly useful in psychology. A recurring issue for estimating IFA models is low item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to maximum likelihood estimation for IFA models with item sparseness. In this article I use a simulation study to demonstrate that Bayesian estimation incorporating general prior information improves parameter estimate stability, overall variability in estimates, and power for IFA models with sparse, categorical indicators. Importantly, the priors proposed here can be generally applied to many research contexts in psychology, and they do not impact results compared to ML when indicators are not sparse. I then apply this method to examine the relationship between suicide ideation and insomnia in a sample of first year college students. This provides an important alternative for researchers who may need to model items with sparse endorsement.

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Regularized Structural Equation Modeling

Cited by 169 publications

References 55 publications

Exploratory Mediation Analysis via Regularization

Exploratory Mediation Analysis via Regularization

Age, vascular health, and Alzheimer disease biomarkers in an elderly sample

Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

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