Sarah Depaoli
Datos Biográficos
| ID | 1561019 |
|---|---|
| NOMBRE | Sarah Depaoli |
| NOMBRES | Sarah |
| APELLIDO | Depaoli |
| FIRMA | DEPAOLI S |
| AFILIACIONES | University of California |
| ORCID | 0000-0002-1277-0462 |
| VERIFICADO | Sí |
| TOTAL DE OBRAS | 30 |
| TOTAL DE CITAS | 3 |
| TOTAL COMO AUTOR | 30 |
| TOTAL COMO EDITOR | 0 |
| PRIMER AÑO DE PUBLICACIÓN | 2015 |
| AÑO MÁS RECIENTE DE PUBLICACIÓN | 2026 |
| ÍNDICE H | 1 |
A Comprehensive Evaluation of Model Selection Indices for Class Enumeration in Bayesian Latent Growth Mixture Models
Class enumeration remains one of the most critical and error-prone steps in latent growth mixture modeling (LGMM), particularly within the Bayesian framework. This study provides a comprehensive simulation-based evaluation of Bayesian model selection indices, focusing on the impact of likelihood formulation (marginal used in this case) and Dirichlet prior specification for class proportions. Although Bayesian methods offer flexibility and robustn…
Bayesian Variable Selection via Shrinkage Priors in Growth Mixture Models
Growth mixture models (GMMs; Muthén & Shedden, 1999) capture unobserved heterogeneity in longitudinal processes by modeling latent classes that represent qualitatively distinct patterns of change
Class Selection in Growth Mixture Models
Using Small-Variance Priors to Detect Covariate Misspecifications in Latent Class Analysis Models
Model Assumption Violations in Bayesian Latent Mediation Analysis
Parameter Recovery for Misspecified Latent Mediation Models in the Bayesian Framework
Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data
The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers commonly encounter. To address these issues, one could use model fit and selection indices to detect miss…
Under-Fitting and Over-Fitting
We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to determine whether commonly implemented Bayesian indices can detect specification errors. Specifically, we wanted to uncover any differences in detecting under-fitting or over-fitting a model. We examined a conventional Bayesian fit index (the posterior pr…
Bayesian approach to piecewise growth mixture modeling
Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.e., class separat…
Addressing Missing Data in Latent Class Analysis When Using a Three-Step Estimation Approach
Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling
A unique feature of Bayesian estimation is the inclusion of prior knowledge through prior distributions. These prior distributions can benefit or impair many components of the ensuing analysis. Priors are especially important to assess in the context of structural equation models (SEMs), which often carry data and modeling complexities where priors can be particularly influential. In this article, we illustrate a statistical approach to assess th…
Detecting Model Misspecification in Bayesian Piecewise Growth Models
Bayesian estimation has become increasingly more popular with piecewise growth models because it can aid in accurately modeling nonlinear change over time. Recently, new Bayesian approximate fit indices (BRMSEA, BCFI, and BTLI) have been introduced as tools for detecting model (mis)fit. We compare these indices to the posterior predictive p-value (PPP), and also examine the Bayesian information criterion (BIC) and the deviance information criteri…
Sensitivity of Bayesian Model Fit Indices to the Prior Specification of Latent Growth Models
Longitudinal research often involves relatively small samples and missing values. Under these conditions, Bayesian estimation can still result in accurate parameter estimates for latent growth models (LGMs). However, researchers were limited in their options for assessing model fit. Several new (approximate) model fit indices have been introduced into the Bayesian structural equation modeling framework. Through a simulation study, we examined the…
The Specification and Impact of Prior Distributions for Categorical Latent Variable Models
Latent class models can exhibit poor parameter recovery and low convergence rates under the traditional frequentist estimation approach. Bayesian estimation may be a viable alternative for estimating latent class models–especially when categorical items are present and priors can be placed directly on the categorical item-thresholds. We present a simulation study involving Bayesian latent class analysis (LCA) with categorical items. We demonstrat…
Detecting Prior-Data Disagreement in Bayesian Structural Equation Modeling
The choice of prior specification plays a vital role in any Bayesian analysis. Prior-data disagreement occurs when the researcher’s prior knowledge is not in agreement with the evidence provided by the data. We examined the ability of the Data Agreement Criterion (DAC) and Bayes Factor (BF) to detect prior-data disagreement in SEM through a simulation study. The design included four sample size levels and 49 prior specifications. Findings suggest…
Understanding the Deviance Information Criterion for SEM
The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the covariance matrix Φξ as “a” parameter, and the latter places marginal priors on factor variances and co…
A Guide to Detecting and Modeling Local Dependence in Latent Class Analysis Models
Latent class analysis (LCA) assigns individuals to mutually exclusive classes based on response patterns to a set of indicators. A primary assumption made is local independence, which suggests class indicators are uncorrelated within each class. When the indicators are correlated and unmodeled, parameter estimates can be severely biased. We provide a comprehensive resource for applied researchers to statistically detect local independence violati…
Performance of Model Fit and Selection Indices for Bayesian Structural Equation Modeling with Missing Data
New model fit and evaluation tools have been introduced into the Bayesian structural equation modeling framework, including Bayesian versions of classic approximate fit measures (RMSEA, CFI, and TLI), as well as a new adjustment of the posterior predictive p-value to properly account for missing data. We examine the performance of these indices for model fit and selection through a simulation study. This study was designed to assess the performan…
Parameter Specification in Bayesian CFA
The impact of parameter and prior specifications on Bayesian SEM estimates is examined through two simulation studies. The model of focus was a CFA. Simulation conditions for Study 1 included varying sample size, the strength of the factor loadings (also tied to issues of reliability), factor correlation strength, and estimation conditions tied to different parameter specifications. Study 2 extended these factors and included non-zero cross-loadi…
Social–emotional development of students with social–emotional and behavioral difficulties in inclusive regular and exclusive special education
The present study investigated (1) how social relationships with teachers and peers and self-esteem of students with social–emotional and behavioral difficulties (SEBD) in inclusive regular education (regular schools) and students with SEBD in exclusive special education (special schools) develop over time in comparison with each other and in comparison with their typically developing peers and (2) whether factors—present before students with SEB…
Bayesian Model Averaging as an Alternative to Model Selection for Multilevel Models
We investigated the Bayesian model averaging (BMA) technique as an alternative method to the traditional model selection approaches for multilevel models (MLMs). BMA synthesizes the information derived from all possible models and comes up with a weighted estimate. A simulation study compared BMA with additional modeling techniques, including the single "best" model approach, Bayesian MLM using informative, diffuse, and inaccurate priors, and res…
Gender and racial/ethnic differences in adolescent intentions and willingness to smoke cigarettes
Dual-process theories may be effective at predicting adolescent smoking; however, little is known about the effectiveness of these models across race/ethnicity and gender. Adolescents ( N = 4035) completed biopsychosocial and tobacco-related perception measures in Grade 7 and reported on smoking initiation in Grade 10. Using structural equation modeling and comparing models by gender and race/ethnicity showed differences, where both intentions an…
Implementing continuous non-normal skewed distributions in latent growth mixture modeling
Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulat…
Locating U.S. Solicitors General in the Supreme Court's Policy Space
The U.S. Solicitor General (SG) is the most direct link between the executive branch and the Supreme Court. Spatial models of the SG's involvement at the Court necessitate locating the SG in the same policy space as the justices. We treat the SG's positions advocated in amicus curiae briefs as equivalent to votes in these cases and employ an item response model that yields facially valid estimates of the locations of the SGs and justices serving …
Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation
There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has…
Parameter Recovery for Misspecified Latent Mediation Models in the Bayesian Framework
Addressing Missing Data in Latent Class Analysis When Using a Three-Step Estimation Approach
Gender and racial/ethnic differences in adolescent intentions and willingness to smoke cigarettes
Dual-process theories may be effective at predicting adolescent smoking; however, little is known about the effectiveness of these models across race/ethnicity and gender. Adolescents ( N = 4035) completed biopsychosocial and tobacco-related perception measures in Grade 7 and reported on smoking initiation in Grade 10. Using structural equation modeling and comparing models by gender and race/ethnicity showed differences, where both intentions an…
Iteration of Partially Specified Target Matrices
We describe and evaluate a factor rotation algorithm, iterated target rotation (ITR). Whereas target rotation (Browne, 2001) requires a user to specify a target matrix a priori based on theory or prior research, ITR begins with a standard analytic factor rotation (i.e., an empirically informed target) followed by an iterative search procedure to update the target matrix. In Study 1, Monte Carlo simulations were conducted to evaluate the performan…
Gender role orientation is associated with health-related quality of life differently among African-American, Hispanic, and White youth
A systematic review of Bayesian articles in psychology
Improving transparency and replication in Bayesian statistics
Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at least 3 main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian result…
The GRoLTS-Checklist
Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported. To overcome this issue, using a 4-round Delphi study, we developed Guidelines for Reporting on Latent Trajectory Studies (GRoLTS). The purpose of GRoLTS i…
Bayesian PTSD-Trajectory Analysis with Informed Priors Based on a Systematic Literature Search and Expert Elicitation
There is a recent increase in interest of Bayesian analysis. However, little effort has been made thus far to directly incorporate background knowledge via the prior distribution into the analyses. This process might be especially useful in the context of latent growth mixture modeling when one or more of the latent groups are expected to be relatively small due to what we refer to as limited data. We argue that the use of Bayesian statistics has…
Implementing continuous non-normal skewed distributions in latent growth mixture modeling
Recent advances have allowed for modeling mixture components within latent growth modeling using robust, skewed mixture distributions rather than normal distributions. This feature adds flexibility in handling non-normality in longitudinal data, through manifest or latent variables, by directly modeling skewed or heavy-tailed latent classes rather than assuming a mixture of normal distributions. The aim of this study was to assess through simulat…
Locating U.S. Solicitors General in the Supreme Court's Policy Space
The U.S. Solicitor General (SG) is the most direct link between the executive branch and the Supreme Court. Spatial models of the SG's involvement at the Court necessitate locating the SG in the same policy space as the justices. We treat the SG's positions advocated in amicus curiae briefs as equivalent to votes in these cases and employ an item response model that yields facially valid estimates of the locations of the SGs and justices serving …
Parameter Specification in Bayesian CFA
The impact of parameter and prior specifications on Bayesian SEM estimates is examined through two simulation studies. The model of focus was a CFA. Simulation conditions for Study 1 included varying sample size, the strength of the factor loadings (also tied to issues of reliability), factor correlation strength, and estimation conditions tied to different parameter specifications. Study 2 extended these factors and included non-zero cross-loadi…
Social–emotional development of students with social–emotional and behavioral difficulties in inclusive regular and exclusive special education
The present study investigated (1) how social relationships with teachers and peers and self-esteem of students with social–emotional and behavioral difficulties (SEBD) in inclusive regular education (regular schools) and students with SEBD in exclusive special education (special schools) develop over time in comparison with each other and in comparison with their typically developing peers and (2) whether factors—present before students with SEB…
Bayesian Model Averaging as an Alternative to Model Selection for Multilevel Models
We investigated the Bayesian model averaging (BMA) technique as an alternative method to the traditional model selection approaches for multilevel models (MLMs). BMA synthesizes the information derived from all possible models and comes up with a weighted estimate. A simulation study compared BMA with additional modeling techniques, including the single "best" model approach, Bayesian MLM using informative, diffuse, and inaccurate priors, and res…
Gender and racial/ethnic differences in adolescent intentions and willingness to smoke cigarettes
Dual-process theories may be effective at predicting adolescent smoking; however, little is known about the effectiveness of these models across race/ethnicity and gender. Adolescents ( N = 4035) completed biopsychosocial and tobacco-related perception measures in Grade 7 and reported on smoking initiation in Grade 10. Using structural equation modeling and comparing models by gender and race/ethnicity showed differences, where both intentions an…
Sensitivity of Bayesian Model Fit Indices to the Prior Specification of Latent Growth Models
Longitudinal research often involves relatively small samples and missing values. Under these conditions, Bayesian estimation can still result in accurate parameter estimates for latent growth models (LGMs). However, researchers were limited in their options for assessing model fit. Several new (approximate) model fit indices have been introduced into the Bayesian structural equation modeling framework. Through a simulation study, we examined the…
The Specification and Impact of Prior Distributions for Categorical Latent Variable Models
Latent class models can exhibit poor parameter recovery and low convergence rates under the traditional frequentist estimation approach. Bayesian estimation may be a viable alternative for estimating latent class models–especially when categorical items are present and priors can be placed directly on the categorical item-thresholds. We present a simulation study involving Bayesian latent class analysis (LCA) with categorical items. We demonstrat…
Detecting Prior-Data Disagreement in Bayesian Structural Equation Modeling
The choice of prior specification plays a vital role in any Bayesian analysis. Prior-data disagreement occurs when the researcher’s prior knowledge is not in agreement with the evidence provided by the data. We examined the ability of the Data Agreement Criterion (DAC) and Bayes Factor (BF) to detect prior-data disagreement in SEM through a simulation study. The design included four sample size levels and 49 prior specifications. Findings suggest…
Understanding the Deviance Information Criterion for SEM
The deviance information criterion (DIC) is widely used to select the parsimonious, well-fitting model. We examined how priors impact model complexity (pD) and the DIC for Bayesian CFA. Study 1 compared the empirical distributions of pD and DIC under multivariate (i.e., inverse Wishart) and separation strategy (SS) priors. The former treats the covariance matrix Φξ as “a” parameter, and the latter places marginal priors on factor variances and co…
A Guide to Detecting and Modeling Local Dependence in Latent Class Analysis Models
Latent class analysis (LCA) assigns individuals to mutually exclusive classes based on response patterns to a set of indicators. A primary assumption made is local independence, which suggests class indicators are uncorrelated within each class. When the indicators are correlated and unmodeled, parameter estimates can be severely biased. We provide a comprehensive resource for applied researchers to statistically detect local independence violati…
Performance of Model Fit and Selection Indices for Bayesian Structural Equation Modeling with Missing Data
New model fit and evaluation tools have been introduced into the Bayesian structural equation modeling framework, including Bayesian versions of classic approximate fit measures (RMSEA, CFI, and TLI), as well as a new adjustment of the posterior predictive p-value to properly account for missing data. We examine the performance of these indices for model fit and selection through a simulation study. This study was designed to assess the performan…
Illustrating the Value of Prior Predictive Checking for Bayesian Structural Equation Modeling
A unique feature of Bayesian estimation is the inclusion of prior knowledge through prior distributions. These prior distributions can benefit or impair many components of the ensuing analysis. Priors are especially important to assess in the context of structural equation models (SEMs), which often carry data and modeling complexities where priors can be particularly influential. In this article, we illustrate a statistical approach to assess th…
Detecting Model Misspecification in Bayesian Piecewise Growth Models
Bayesian estimation has become increasingly more popular with piecewise growth models because it can aid in accurately modeling nonlinear change over time. Recently, new Bayesian approximate fit indices (BRMSEA, BCFI, and BTLI) have been introduced as tools for detecting model (mis)fit. We compare these indices to the posterior predictive p-value (PPP), and also examine the Bayesian information criterion (BIC) and the deviance information criteri…
Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data
The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is important to accurately capture growth trajectories and carefully consider knot placements. The presence of missing data is another challenge researchers commonly encounter. To address these issues, one could use model fit and selection indices to detect miss…
Under-Fitting and Over-Fitting
We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to determine whether commonly implemented Bayesian indices can detect specification errors. Specifically, we wanted to uncover any differences in detecting under-fitting or over-fitting a model. We examined a conventional Bayesian fit index (the posterior pr…
Bayesian approach to piecewise growth mixture modeling
Bayesian piecewise growth mixture models (PGMMs) are a powerful statistical tool based on the Bayesian framework for modeling nonlinear, phasic developmental trajectories of heterogeneous subpopulations over time. Although Bayesian PGMMs can benefit school psychology research, their empirical applications within the field remain limited. This article introduces Bayesian PGMMs, addresses three key methodological considerations (i.e., class separat…
Addressing Missing Data in Latent Class Analysis When Using a Three-Step Estimation Approach
Using Small-Variance Priors to Detect Covariate Misspecifications in Latent Class Analysis Models
Computer Science (23 obras) · Bayesian probability (21 obras) · Mathematics (21 obras) · Statistics (18 obras) · Econometrics (17 obras) · Statistical Methods and Bayesian Inference (17 obras) · Artificial Intelligence (15 obras) · Prior probability (13 obras) · Bayesian inference (10 obras) · Machine learning (9 obras)