Yves Rosseel
Dados Biográficos
| ID | 4262833 |
|---|---|
| NOME | Yves Rosseel |
| PRENOMES | Yves |
| SOBRENOME | Rosseel |
| ASSINATURA | ROSSEEL Y |
| AFILIAÇÕES | Ghent University |
| ORCID | 0000-0002-4129-4477 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 34 |
| TOTAL DE CITAÇÕES | 43 |
| TOTAL COMO AUTOR | 34 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2007 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 4 |
Mixture multigroup structural equation modelling for ordinal data
Social scientists often compare groups in terms of relations between latent variables (LV) (i.e. structural relations) using Structural Equation Modelling (SEM). LVs are measured indirectly by questionnaires; thus, measurement invariance must be evaluated before comparisons can be made. To efficiently compare many groups, the recently proposed Mixture Multigroup SEM (MMG‐SEM) clusters groups based on their structural relations while accounting fo…
Bias-Reduced Estimation of Structural Equation Models
Finite-sample bias is a pervasive challenge in the estimation of structural equation models (SEMs), especially when sample sizes are small or measurement reliability is low. A range of methods have been proposed to improve finite-sample bias in the SEM literature, ranging from analytic bias corrections to resampling-based techniques, with each carrying tradeoffs in scope, computational burden, and statistical performance. We introduce the reduced…
Using Mixture Multigroup Structural Equation Modeling to Compare Structural Relations Across Many Groups
The growing availability of large-scale survey data has increased interest in comparing relations among latent variables (often called structural relations) across many groups (e.g., countries or schools) using Structural Equation Modeling (SEM). Traditional multigroup or multilevel SEM become impractical in these settings, as they require pairwise comparisons to identify specific differences between groups. These pairwise comparisons become daun…
Consistent Factor Score Regression
Researchers in the behavioral, educational, and social sciences often aim to analyze relationships among latent variables. Structural equation modeling (SEM) is widely regarded as the gold standard for this purpose. A straightforward alternative for estimating the structural model parameters is uncorrected factor score regression (UFSR), where factor scores are first computed and then employed in regression or path analysis. Unfortunately, the mo…
An Evaluation of Non-Iterative Estimators in Confirmatory Factor Analysis
In confirmatory factor analysis (CFA), model parameters are usually estimated by iteratively minimizing the Maximum Likelihood (ML) fit function. In optimal circumstances, the ML estimator yields the desirable statistical properties of asymptotic unbiasedness, efficiency, normality, and consistency. In practice, however, real-life data tend to be far from optimal, making the algorithm prone to convergence failure, inadmissible solutions, and bias…
The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling
Researchers seeking valid statistical inference in the presence of measurement error often apply approaches that ignore measurement error. This may result in biased estimates, inflated type I error rates, diminished power, and therefore, increases the risk of drawing erroneous conclusions. However, current advice on accounting for random measurement error is limited to large samples and traditional linear models. This article aims to address this…
A Model-Based Shrinkage Target to Avoid Non-convergence in Small Sample SEM
Structural equation modeling is prone to a variety of problems when the sample size is small. One solution that attempts to solve the (non-convergence) problem of small sample SEM is found in shrinkage estimation, where a weighted average between the sample variance-covariance matrix (S) and a highly structured shrinkage target (T) is calculated to construct an adjusted sample variance-covariance matrix (Sa), which is then used as input for the a…
An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM)
In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of poss…
Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling
In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response…
The Conceptual, Cunning, and Conclusive Experiment in Psychology
The ideal experiment in physics must be conceptual, cunning, and conclusive. Adoption of these same standards in psychology has led to experiments that are uninformative and frivolous. We explain why we believe that psychology is fundamentally incompatible with hypothesis-driven theoretical science and conclude that this erodes the logic behind recent proposals to improve psychological research, such as stricter statistical standards, preregistra…
Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling
The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small (N<100) or very small (N<50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study t…
Distributionally-Weighted Least Squares in Growth Curve Modeling
Growth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free (ADF) estimator does not need to rely on any distr…
Computational Options for Standard Errors and Test Statistics with Incomplete Normal and Nonnormal Data in SEM
This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or expected, whether the observed information matrix is estimated numerically or using an analytic asym…
Exploratory Graph Analysis for Factor Retention
Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and b…
Teacher’s Corner
This Teacher’s Corner paper introduces Bayesian evaluation of informative hypotheses for structural equation models, using the free open-source R packages bain, for Bayesian informative hypothesis testing, and lavaan, a widely used SEM package. The introduction provides a brief non-technical explanation of informative hypotheses, the statistical underpinnings of Bayesian hypothesis evaluation, and the bain algorithm. Three tutorial examples demon…
Assessing Fit in Ordinal Factor Analysis Models
This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the SRMR with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almo…
Multilevel Modeling in the ‘Wide Format’ Approach with Discrete Data
In multilevel data, units at level 1 are nested in clusters at level 2, which in turn may be nested in even larger clusters at level 3, and so on. For continuous data, several authors have shown how to model multilevel data in a ‘wide’ or ‘multivariate’ format approach. We provide a general framework to analyze random intercept multilevel SEM in the ‘wide format’ (WF) and extend this approach for discrete data. In a simulation study, we vary resp…
Multilevel Factor Score Regression
Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method f…
New Developments in Factor Score Regression
Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We als…
Assessing Fit in Structural Equation Models
We compare the accuracy of confidence intervals (CIs) and tests of close fit based on the root mean square error of approximation (RMSEA) with those based on the standardized root mean square residual (SRMR). Investigations used normal and nonnormal data with models ranging from p = 10 to 60 observed variables. CIs and tests of close fit based on the SRMR are generally accurate across all conditions (even at p = 60 with nonnormal data). In contra…
Blavaan
This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS. The methodology and software are intended to provide users with a general means of estimating Bayesian SEMs, both classical and n…
Cognitive and Motivational Challenges in Writing
In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement‐level differences in el…
Hypothesis Testing Using Factor Score Regression
In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation…
The EffectLiteR Approach for Analyzing Average and Conditional Effects
We present a framework for estimating average and conditional effects of a discrete treatment variable on a continuous outcome variable, conditioning on categorical and continuous covariates. Using the new approach, termed the EffectLiteR approach, researchers can consider conditional treatment effects given values of all covariates in the analysis and various aggregates of these conditional treatment effects such as average effects, effects on t…
Self-Report of Empathy
For more than 30 years, the Interpersonal Reactivity Index (IRI) has been used to measure the multidimensional aspects of empathy. But the 28-item, 4-factor model of Davis (1980 ) is currently contested because of methodological issues and for theoretical reasons. Confirmatory (CFA) and exploratory factor analyses (EFA) were applied in two French-speaking Belgian student samples (1,244 participants in the first and 729 in the second study) to tes…
The influence of student characteristics and interpersonal teacher behaviour in the classroom on student's wellbeing
Student Perception As Moderator For Student Wellbeing
Poverty and Decision Making in Child Welfare and Protection
The influence of socio-economic background factors, such as poverty, on the risk of children to be disproportionately represented and placed in residential care has increasingly been the subject of international research. This article reports on the findings of a research project that focused on the relationship between poverty and child welfare and protection (CWP) interventions in Flanders (the Dutch-speaking part of Belgium). Using logistic re…
Finding Our Way in the Social World
People readily use social categories in their daily interactions with others. Although many scholars have focused on social categorization, they have largely neglected the cognitive representation of stimuli as a basis of this process. The present work aims to determine what dimensions are commonly used to organize the social world. The main dimensions of the social mental map are extracted from sorting data pertaining to a wide variety of social…
Social Classification Occurs at the Subgroup Level
Although the categorization of novel social stimuli according to general qualities of gender, age, and race is known to be automatic and primordial, categorizing stimuli into more specific social subgroups (e.g., hippies or businesswomen) is much more informative and cognitively efficient. In this paper, we show that social stimuli are more likely to be grouped into subgroups with an intermediate degree of specificity than into broad, general cat…
The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling
Researchers seeking valid statistical inference in the presence of measurement error often apply approaches that ignore measurement error. This may result in biased estimates, inflated type I error rates, diminished power, and therefore, increases the risk of drawing erroneous conclusions. However, current advice on accounting for random measurement error is limited to large samples and traditional linear models. This article aims to address this…
Cognitive and Motivational Challenges in Writing
In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement‐level differences in el…
The influence of student characteristics and interpersonal teacher behaviour in the classroom on student's wellbeing
Student Perception As Moderator For Student Wellbeing
The relationship between the perception of distributed leadership in secondary schools and teachers' and teacher leaders' job satisfaction and organizational commitment
This study investigates the relation between distributed leadership, the cohesion of the leadership team, participative decision-making, context variables, and the organizational commitment and job satisfaction of teachers and teacher leaders. A questionnaire was administered to teachers and teacher leaders (n = 1770) from 46 large secondary schools. Multiple regression analyses and path analyses revealed that the study variables explained signif…
Development and Validation of Scores on the Distributed Leadership Inventory
Systematic quantitative research on measuring distributed leadership is scarce. In this study, the Distributed Leadership Inventory (DLI) was developed and evaluated to investigate leadership team characteristics and distribution of leadership functions between formally designed leadership positions in large secondary schools. The DLI was presented to a sample of 2,198 respondents in 46 secondary schools. The input from a first subsample was used…
Personality and Psychopathology in Flemish Referred Children
The relation between elementary students' recreational and academic reading motivation, reading frequency, engagement, and comprehension
In a judgment of 14 December 2010, in the case of Madam Ternovszky v. Hungary, the European Court of Human Rights has considered that a State should provide an adequate regulatory scheme concerning the right to choose in matters of child delivery (at home or in a hospital). In the context of homebirth, regarded as a matter of personal choice of the mother, this implies that the mother is entitled to a legal and institutional environment that enab…
Finding Our Way in the Social World
People readily use social categories in their daily interactions with others. Although many scholars have focused on social categorization, they have largely neglected the cognitive representation of stimuli as a basis of this process. The present work aims to determine what dimensions are commonly used to organize the social world. The main dimensions of the social mental map are extracted from sorting data pertaining to a wide variety of social…
Self-Report of Empathy
For more than 30 years, the Interpersonal Reactivity Index (IRI) has been used to measure the multidimensional aspects of empathy. But the 28-item, 4-factor model of Davis (1980 ) is currently contested because of methodological issues and for theoretical reasons. Confirmatory (CFA) and exploratory factor analyses (EFA) were applied in two French-speaking Belgian student samples (1,244 participants in the first and 729 in the second study) to tes…
Poverty and Decision Making in Child Welfare and Protection
The influence of socio-economic background factors, such as poverty, on the risk of children to be disproportionately represented and placed in residential care has increasingly been the subject of international research. This article reports on the findings of a research project that focused on the relationship between poverty and child welfare and protection (CWP) interventions in Flanders (the Dutch-speaking part of Belgium). Using logistic re…
Social Classification Occurs at the Subgroup Level
Although the categorization of novel social stimuli according to general qualities of gender, age, and race is known to be automatic and primordial, categorizing stimuli into more specific social subgroups (e.g., hippies or businesswomen) is much more informative and cognitively efficient. In this paper, we show that social stimuli are more likely to be grouped into subgroups with an intermediate degree of specificity than into broad, general cat…
Hypothesis Testing Using Factor Score Regression
In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation…
The EffectLiteR Approach for Analyzing Average and Conditional Effects
We present a framework for estimating average and conditional effects of a discrete treatment variable on a continuous outcome variable, conditioning on categorical and continuous covariates. Using the new approach, termed the EffectLiteR approach, researchers can consider conditional treatment effects given values of all covariates in the analysis and various aggregates of these conditional treatment effects such as average effects, effects on t…
Assessing Fit in Structural Equation Models
We compare the accuracy of confidence intervals (CIs) and tests of close fit based on the root mean square error of approximation (RMSEA) with those based on the standardized root mean square residual (SRMR). Investigations used normal and nonnormal data with models ranging from p = 10 to 60 observed variables. CIs and tests of close fit based on the SRMR are generally accurate across all conditions (even at p = 60 with nonnormal data). In contra…
Blavaan
This article describes blavaan, an R package for estimating Bayesian structural equation models (SEMs) via JAGS and for summarizing the results. It also describes a novel parameter expansion approach for estimating specific types of models with residual covariances, which facilitates estimation of these models in JAGS. The methodology and software are intended to provide users with a general means of estimating Bayesian SEMs, both classical and n…
Cognitive and Motivational Challenges in Writing
In the past, several assessment reports on writing repeatedly showed that elementary school students do not develop the essential writing skills to be successful in school. In this respect, prior research has pointed to the fact that cognitive and motivational challenges are at the root of the rather basic level of elementary students' writing performance. Additionally, previous research has revealed gender and achievement‐level differences in el…
New Developments in Factor Score Regression
Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We als…
Assessing Fit in Ordinal Factor Analysis Models
This study introduces the statistical theory of using the Standardized Root Mean Squared Error (SRMR) to test close fit in ordinal factor analysis. We also compare the accuracy of confidence intervals (CIs) and tests of close fit based on the SRMR with those obtained based on the Root Mean Squared Error of Approximation (RMSEA). The current (biased) implementation for the RMSEA never rejects that a model fits closely when data are binary and almo…
Multilevel Modeling in the ‘Wide Format’ Approach with Discrete Data
In multilevel data, units at level 1 are nested in clusters at level 2, which in turn may be nested in even larger clusters at level 3, and so on. For continuous data, several authors have shown how to model multilevel data in a ‘wide’ or ‘multivariate’ format approach. We provide a general framework to analyze random intercept multilevel SEM in the ‘wide format’ (WF) and extend this approach for discrete data. In a simulation study, we vary resp…
Multilevel Factor Score Regression
Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method f…
Teacher’s Corner
This Teacher’s Corner paper introduces Bayesian evaluation of informative hypotheses for structural equation models, using the free open-source R packages bain, for Bayesian informative hypothesis testing, and lavaan, a widely used SEM package. The introduction provides a brief non-technical explanation of informative hypotheses, the statistical underpinnings of Bayesian hypothesis evaluation, and the bain algorithm. Three tutorial examples demon…
The Conceptual, Cunning, and Conclusive Experiment in Psychology
The ideal experiment in physics must be conceptual, cunning, and conclusive. Adoption of these same standards in psychology has led to experiments that are uninformative and frivolous. We explain why we believe that psychology is fundamentally incompatible with hypothesis-driven theoretical science and conclude that this erodes the logic behind recent proposals to improve psychological research, such as stricter statistical standards, preregistra…
Using Bounded Estimation to Avoid Nonconvergence in Small Sample Structural Equation Modeling
The most frustrating outcome of an SEM analysis is nonconvergence. Nonconvergence typically happens when the sample size is small (N<100) or very small (N<50). To minimize the frequency of nonconvergence, this paper proposes a solution called bounded estimation. The idea is to use data-driven lower and upper bounds for a subset of the model parameters during estimation. In this paper, we provide a rationale to compute these bounds, and we study t…
Distributionally-Weighted Least Squares in Growth Curve Modeling
Growth curve modeling is commonly used in psychological, educational, and social science research. The mainstream estimators for growth curve modeling are based on normal theory, but real data are unlikely to be exactly normally distributed. To improve estimation and inference with non-normal data, various estimators have been proposed. Among these estimators, the asymptotically distribution free (ADF) estimator does not need to rely on any distr…
Computational Options for Standard Errors and Test Statistics with Incomplete Normal and Nonnormal Data in SEM
This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or expected, whether the observed information matrix is estimated numerically or using an analytic asym…
Exploratory Graph Analysis for Factor Retention
Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and b…
Mathematics (19 obras) · Statistics (18 obras) · Psychometric Methodologies and Testing (16 obras) · Structural equation modeling (16 obras) · Computer Science (14 obras) · Psychology (14 obras) · Advanced Statistical Modeling Techniques (11 obras) · Econometrics (10 obras) · Social Psychology (9 obras) · Statistical Methods and Bayesian Inference (9 obras)