Katerina M Marcoulides
Dados Biográficos
| ID | 4420323 |
|---|---|
| NOME | Katerina M Marcoulides |
| PRENOMES | Katerina M |
| SOBRENOME | Marcoulides |
| ASSINATURA | MARCOULIDES K M |
| AFILIAÇÕES | University of Minnesota |
| ORCID | 0000-0001-8829-870X |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 21 |
| TOTAL DE CITAÇÕES | 12 |
| TOTAL COMO AUTOR | 21 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2017 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 2 |
A Bayesian Synthesis Approach to Structural Equation Modeling Data Fusion for the Analysis of Massive Data
Analyzing structural equation models with massive datasets poses substantial computational and parameter estimation challenges. This study proposes integrating a modified divide-and-conquer strategy with Bayesian Synthesis data-fusion methodology to address these issues. Large datasets are randomly partitioned into subsets that are analyzed sequentially, with the posterior from each subset used as the prior for the next. This process yields a fin…
Sensitivity Analyses for Omitted Confounders in Structural Equation Models with Tabu Search Optimization
Structural equation modeling (SEM) is a popular methodological approach for representing and testing hypothesized relationships among observed and unobserved, or latent, variables. Researchers in the social and behavioral sciences commonly encounter three general situations for testing such structural equation models—strictly confirmatory, testing alternative or competing hypothesized models, and model generation. However, none of these involve t…
A Note on Comparing Equivalence Testing with Conventional Criteria in Evaluating the Overall Fit of Structural Equation Models
Correlations between coaching quality and teacher change in social-emotional teaching practices
Minimal-Effect Testing, Equivalence Testing, and the Conventional Null Hypothesis Testing for the Analysis of Bi-Factor Models
A necessary step in applying bi-factor models is to evaluate the need for domain factors with a general factor in place. The conventional null hypothesis testing (NHT) was commonly used for such a purpose. However, the conventional NHT meets challenges when the domain loadings are weak or the sample size is insufficient. This article proposes using minimal-effect testing (MET) and equivalence testing (ET) to analyze bi-factor models. A key elemen…
Testing structural equation model fit in psychological studies
Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification
Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published st…
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
Smoothed Quantiles for χ2 Type Test Statistics with Applications
Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic. However, the sample quantile may be a poor estimate of …
Latina/o/x Immigrant Youth’s Motivations for Disclosing their Family-Undocumented Experiences to a Teacher(s)
Using the revelation risk model (RRM), we examined factors that might motivate family-undocumented youth (i.e., youth who are undocumented or who have an immediate family member who is undocumented) to confide in a teacher(s). Latent transition analysis with 414 Latina/o/x 9th-12th grade students uncovered three profiles: concerned indirect revealers (i.e., moderate teacher-student relational closeness, highest perceived risk of disclosing, lowes…
A Latent Profile Analysis of Undocumented College Students’ Protection-Oriented Family Communication and Strengths-Based Psychological Coping
Drawing from resilience theory, this study explored subgroups of undocumented college students (UCS) based on their patterns of protection-oriented family communication and strengths-based psychological coping. Using survey data from 237 UCS, latent profile analyses revealed three subgroups. Safe optimistic copers reported occasional documentation-seeking and know-your-rights communication, but higher means in prevention and right path communicat…
A Faster Procedure for Estimating CFA Models Applying Minimum Distance Estimators with a Fixed Weight Matrix
This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization t…
Latent growth curve model selection with Tabu search
The purpose of this research note is to introduce a latent growth curve reconstruction approach based on the Tabu search algorithm. The approach algorithmically enables researchers to optimally determine at both the individual and the group levels the order of the polynomial needed to represent the latent growth curve model. The procedure is illustrated using empirical data along with an easy to use computerized implementation
Identifying Correlates of Peer and Faculty/Staff Sexual Harassment in US Students
Sexual harassment and its negative consequences continue to affect a large percentage of higher education students in the US. Previous research has identified a limited number of harassment risk factors, and has generally not examined them in combination. In this study, an expanded set of individual, relationship, and community-level risk factors were examined using hurdle models and classification and regression tree (CART) analyses to identify …
A Two-Level Alternating Direction Model for Polytomous Items With Local Dependence
The chiropractic clinical competency examination uses groups of items that are integrated by a common case vignette. The nature of the vignette items violates the assumption of local independence for items nested within a vignette. This study examines via simulation a new algorithmic approach for addressing the local independence violation problem using a two-level alternating directions testlet model. Parameter values for item difficulty, discri…
A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development
This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and rela…
Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods
A procedure that can be used to evaluate the variance inflation factors and tolerance indices in linear regression models is discussed. The method permits both point and interval estimation of these factors and indices associated with explanatory variables considered for inclusion in a regression model. The approach makes use of popular latent variable modeling software to obtain these point and interval estimates. The procedure allows more infor…
Data Integration Approaches to Longitudinal Growth Modeling
Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies. To date, these methods are not commonly utilized in …
A Bayesian Synthesis Approach to Data Fusion Using Data-Dependent Priors
"A Bayesian Synthesis Approach to Data Fusion Using Data-Dependent Priors." Multivariate Behavioral Research, 52(1), pp. 111–112
Using an Acculturation‐Stress‐Resilience Framework to Explore Latent Profiles of Latina/o Language Brokers
With survey data from 243 Latina/o early adolescent language brokers, latent profile analyses were conducted to identify different types (i.e., profiles) of brokers. Profiles were based on how often Latina/o early adolescents brokered for family members, as well as their levels of family-based acculturation stress, negative brokering beliefs, parentification, and positive brokering beliefs. Three brokering profiles emerged: (1) infrequent-ambival…
Using an Acculturation‐Stress‐Resilience Framework to Explore Latent Profiles of Latina/o Language Brokers
With survey data from 243 Latina/o early adolescent language brokers, latent profile analyses were conducted to identify different types (i.e., profiles) of brokers. Profiles were based on how often Latina/o early adolescents brokered for family members, as well as their levels of family-based acculturation stress, negative brokering beliefs, parentification, and positive brokering beliefs. Three brokering profiles emerged: (1) infrequent-ambival…
Identifying Correlates of Peer and Faculty/Staff Sexual Harassment in US Students
Sexual harassment and its negative consequences continue to affect a large percentage of higher education students in the US. Previous research has identified a limited number of harassment risk factors, and has generally not examined them in combination. In this study, an expanded set of individual, relationship, and community-level risk factors were examined using hurdle models and classification and regression tree (CART) analyses to identify …
Data Integration Approaches to Longitudinal Growth Modeling
Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies. To date, these methods are not commonly utilized in …
A Bayesian Synthesis Approach to Data Fusion Using Data-Dependent Priors
"A Bayesian Synthesis Approach to Data Fusion Using Data-Dependent Priors." Multivariate Behavioral Research, 52(1), pp. 111–112
Using an Acculturation‐Stress‐Resilience Framework to Explore Latent Profiles of Latina/o Language Brokers
With survey data from 243 Latina/o early adolescent language brokers, latent profile analyses were conducted to identify different types (i.e., profiles) of brokers. Profiles were based on how often Latina/o early adolescents brokered for family members, as well as their levels of family-based acculturation stress, negative brokering beliefs, parentification, and positive brokering beliefs. Three brokering profiles emerged: (1) infrequent-ambival…
Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods
A procedure that can be used to evaluate the variance inflation factors and tolerance indices in linear regression models is discussed. The method permits both point and interval estimation of these factors and indices associated with explanatory variables considered for inclusion in a regression model. The approach makes use of popular latent variable modeling software to obtain these point and interval estimates. The procedure allows more infor…
A Two-Level Alternating Direction Model for Polytomous Items With Local Dependence
The chiropractic clinical competency examination uses groups of items that are integrated by a common case vignette. The nature of the vignette items violates the assumption of local independence for items nested within a vignette. This study examines via simulation a new algorithmic approach for addressing the local independence violation problem using a two-level alternating directions testlet model. Parameter values for item difficulty, discri…
A Comparison of Metaheuristic Optimization Algorithms for Scale Short-Form Development
This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while optimizing on several validity criteria, such as adequate model fit, composite reliability, and rela…
Latina/o/x Immigrant Youth’s Motivations for Disclosing their Family-Undocumented Experiences to a Teacher(s)
Using the revelation risk model (RRM), we examined factors that might motivate family-undocumented youth (i.e., youth who are undocumented or who have an immediate family member who is undocumented) to confide in a teacher(s). Latent transition analysis with 414 Latina/o/x 9th-12th grade students uncovered three profiles: concerned indirect revealers (i.e., moderate teacher-student relational closeness, highest perceived risk of disclosing, lowes…
A Latent Profile Analysis of Undocumented College Students’ Protection-Oriented Family Communication and Strengths-Based Psychological Coping
Drawing from resilience theory, this study explored subgroups of undocumented college students (UCS) based on their patterns of protection-oriented family communication and strengths-based psychological coping. Using survey data from 237 UCS, latent profile analyses revealed three subgroups. Safe optimistic copers reported occasional documentation-seeking and know-your-rights communication, but higher means in prevention and right path communicat…
A Faster Procedure for Estimating CFA Models Applying Minimum Distance Estimators with a Fixed Weight Matrix
This paper presents a numerically more efficient implementation of the quadratic form minimum distance (MD) estimator with a fixed weight matrix for confirmatory factor analysis (CFA) models. In structural equation modeling (SEM) computer software, such as EQS, lavaan, LISREL and Mplus, various MD estimators are available to the user. Standard procedures for implementing MD estimators involve a one-step approach applying non-linear optimization t…
Latent growth curve model selection with Tabu search
The purpose of this research note is to introduce a latent growth curve reconstruction approach based on the Tabu search algorithm. The approach algorithmically enables researchers to optimally determine at both the individual and the group levels the order of the polynomial needed to represent the latent growth curve model. The procedure is illustrated using empirical data along with an easy to use computerized implementation
Identifying Correlates of Peer and Faculty/Staff Sexual Harassment in US Students
Sexual harassment and its negative consequences continue to affect a large percentage of higher education students in the US. Previous research has identified a limited number of harassment risk factors, and has generally not examined them in combination. In this study, an expanded set of individual, relationship, and community-level risk factors were examined using hurdle models and classification and regression tree (CART) analyses to identify …
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm
Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to…
Using Ant Colony Optimization for Sensitivity Analysis in Structural Equation Modeling
Studies using structural equation modeling (SEM) to evaluate theories against observed data rely on multiple sources of evidence to support a proposed model, such as fit indices, variance explained, and comparison of alternative models. Additional evidence can be obtained by evaluating the model results’ sensitivity to an omitted confounder. The phantom variable approach to SEM sensitivity analysis requires manual specification of sensitivity par…
Smoothed Quantiles for χ2 Type Test Statistics with Applications
Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic. However, the sample quantile may be a poor estimate of …
Using Simulated Annealing to Investigate Sensitivity of SEM to External Model Misspecification
Sensitivity analyses encompass a broad set of post-analytic techniques that are characterized as measuring the potential impact of any factor that has an effect on some output variables of a model. This research focuses on the utility of the simulated annealing algorithm to automatically identify path configurations and parameter values of omitted confounders in structural equation modeling (SEM). An empirical example based on a past published st…
Minimal-Effect Testing, Equivalence Testing, and the Conventional Null Hypothesis Testing for the Analysis of Bi-Factor Models
A necessary step in applying bi-factor models is to evaluate the need for domain factors with a general factor in place. The conventional null hypothesis testing (NHT) was commonly used for such a purpose. However, the conventional NHT meets challenges when the domain loadings are weak or the sample size is insufficient. This article proposes using minimal-effect testing (MET) and equivalence testing (ET) to analyze bi-factor models. A key elemen…
Testing structural equation model fit in psychological studies
A Note on Comparing Equivalence Testing with Conventional Criteria in Evaluating the Overall Fit of Structural Equation Models
Correlations between coaching quality and teacher change in social-emotional teaching practices
A Bayesian Synthesis Approach to Structural Equation Modeling Data Fusion for the Analysis of Massive Data
Analyzing structural equation models with massive datasets poses substantial computational and parameter estimation challenges. This study proposes integrating a modified divide-and-conquer strategy with Bayesian Synthesis data-fusion methodology to address these issues. Large datasets are randomly partitioned into subsets that are analyzed sequentially, with the posterior from each subset used as the prior for the next. This process yields a fin…
Sensitivity Analyses for Omitted Confounders in Structural Equation Models with Tabu Search Optimization
Structural equation modeling (SEM) is a popular methodological approach for representing and testing hypothesized relationships among observed and unobserved, or latent, variables. Researchers in the social and behavioral sciences commonly encounter three general situations for testing such structural equation models—strictly confirmatory, testing alternative or competing hypothesized models, and model generation. However, none of these involve t…
Computer Science (13 obras) · Mathematics (13 obras) · Statistics (10 obras) · Structural equation modeling (10 obras) · Econometrics (9 obras) · Psychometric Methodologies and Testing (9 obras) · Psychology (7 obras) · Artificial Intelligence (6 obras) · Advanced Statistical Modeling Techniques (5 obras) · Algorithm (5 obras)