Milica Miočević
Biographic Data
| ID | 6629995 |
|---|---|
| NAME | Milica Miočević |
| GIVEN NAMES | Milica |
| FAMILY NAME | Miočević |
| SIGNATURE | MIOČEVIĆ M |
| AFFILIATIONS | McGill University |
| ORCID | 0000-0001-8487-3666 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 14 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models
In Markov chain Monte Carlo estimation of Bayesian latent variable models, sign reflection can cause multiple chains to settle onto equivalent but numerically different solutions, resulting in poorly mixed chains and nonconvergence. Sign reflection can be handled using various methods, such as adopting unit loading identification (ULI), assigning range restricted prior distributions, or using a relabeling algorithm. Some statistical software auto…
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items followed by estimation of structural relationships, more recently, approaches that estimate latent parameters without prior knowledge of anchor items have been developed. Specifically, regularization and alignme…
Effectiveness of Mental Health Promotion in Primary Schools
BACKGROUND: This study evaluated the effectiveness of the mental health promotion program MindMatters in primary schools. METHODS: A cluster randomized controlled trial (2021-2023) included 37 German primary schools (18 intervention, 19 control). Pupils in grades 1-3 (ages 6-9) and their teachers were surveyed before and after implementation. Data from 2896 pupils were analyzed, covering mental health (SDQ), social-emotional and academic skills, …
Controlling for Large Sets of Measured Confounders in Mediation Analysis
Latent Variable Interactions with Categorical Indicators
Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recent…
Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon’s correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. W…
Tackling Challenges in Data Pooling
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle …
Bayesian mediation analysis in trauma research
We provide guidelines for reporting and interpreting results obtained in the Bayesian framework, and two extensions to the mediation model are discussed: adding covariates to the model and selecting informative priors. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Prior Predictive Checks for the Method of Covariances in Bayesian Mediation Analysis
Bayesian mediation analysis using the method of covariances requires specifying a prior for the covariance matrix of the independent variable, mediator, and outcome. Using a conjugate inverse-Wishart prior has been the norm, even though this choice assumes equal levels of informativeness for all elements in the covariance matrix. This paper describes separation strategy priors for the single mediator model, develops a Prior Predictive Check (PrPC…
Bayesian Mediation Analysis with Power Prior Distributions
Bayesian methods are often suggested as a solution for issues encountered in small sample research, however, Bayesian methods often require informative priors to outperform classical methods in these settings. Specifying accurate priors with respect to the true value of the parameter of interest is challenging and inaccurate informative priors can have detrimental effects on conclusions from the statistical analysis. This paper proposes an object…
Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables
50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated eff…
Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts
A Note on Testing Mediated Effects in Structural Equation Models
Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method to detect the significance of the mediated effect is the bias-corrected bootstrap method. Both these …
The Distribution of the Product Explains Normal Theory Mediation Confidence Interval Estimation
The distribution of the product has several useful applications. One of these applications is its use to form confidence intervals for the indirect effect as the product of 2 regression coefficients. The purpose of this article is to investigate how the moments of the distribution of the product explain normal theory mediation confidence interval coverage and imbalance. Values of the critical ratio for each random variable are used to demonstrate…
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The Distribution of the Product Explains Normal Theory Mediation Confidence Interval Estimation
The distribution of the product has several useful applications. One of these applications is its use to form confidence intervals for the indirect effect as the product of 2 regression coefficients. The purpose of this article is to investigate how the moments of the distribution of the product explain normal theory mediation confidence interval coverage and imbalance. Values of the critical ratio for each random variable are used to demonstrate…
A Note on Testing Mediated Effects in Structural Equation Models
Methods to assess the significance of mediated effects in education and the social sciences are well studied and fall into two categories: single sample methods and computer-intensive methods. A popular single sample method to detect the significance of the mediated effect is the test of joint significance, and a popular computer-intensive method to detect the significance of the mediated effect is the bias-corrected bootstrap method. Both these …
Bayesian Versus Frequentist Estimation for Structural Equation Models in Small Sample Contexts
Different Roles of Prior Distributions in the Single Mediator Model with Latent Variables
50 and 100. Consequences of a small amount of inaccuracy in priors for loadings can be alleviated by making the prior less informative, whereas the same is not always true of inaccuracy in priors for structural paths. Finally, the consequences of using informative priors depend on the inferential goals of the analysis: inaccurate priors are more detrimental for accurately estimating the mediated effect than for evaluating whether the mediated eff…
Prior Predictive Checks for the Method of Covariances in Bayesian Mediation Analysis
Bayesian mediation analysis using the method of covariances requires specifying a prior for the covariance matrix of the independent variable, mediator, and outcome. Using a conjugate inverse-Wishart prior has been the norm, even though this choice assumes equal levels of informativeness for all elements in the covariance matrix. This paper describes separation strategy priors for the single mediator model, develops a Prior Predictive Check (PrPC…
Bayesian Mediation Analysis with Power Prior Distributions
Bayesian methods are often suggested as a solution for issues encountered in small sample research, however, Bayesian methods often require informative priors to outperform classical methods in these settings. Specifying accurate priors with respect to the true value of the parameter of interest is challenging and inaccurate informative priors can have detrimental effects on conclusions from the statistical analysis. This paper proposes an object…
Bayesian mediation analysis in trauma research
We provide guidelines for reporting and interpreting results obtained in the Bayesian framework, and two extensions to the mediation model are discussed: adding covariates to the model and selecting informative priors. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples
Factor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon’s correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. W…
Tackling Challenges in Data Pooling
Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle …
Controlling for Large Sets of Measured Confounders in Mediation Analysis
Latent Variable Interactions with Categorical Indicators
Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recent…
Evaluating Approaches for the Handling of Sign Reflection in Bayesian Latent Variable Models
In Markov chain Monte Carlo estimation of Bayesian latent variable models, sign reflection can cause multiple chains to settle onto equivalent but numerically different solutions, resulting in poorly mixed chains and nonconvergence. Sign reflection can be handled using various methods, such as adopting unit loading identification (ULI), assigning range restricted prior distributions, or using a relabeling algorithm. Some statistical software auto…
A Comparison of Regularization, Alignment, and a Traditional Method for Estimating Structural Relationships Across Two Groups
Establishing the correct partial measurement invariance model is crucial for ensuring unbiased comparisons of relationships between latent variables across multiple groups. While traditional approaches rely on detecting noninvariant items followed by estimation of structural relationships, more recently, approaches that estimate latent parameters without prior knowledge of anchor items have been developed. Specifically, regularization and alignme…
Effectiveness of Mental Health Promotion in Primary Schools
BACKGROUND: This study evaluated the effectiveness of the mental health promotion program MindMatters in primary schools. METHODS: A cluster randomized controlled trial (2021-2023) included 37 German primary schools (18 intervention, 19 control). Pupils in grades 1-3 (ages 6-9) and their teachers were surveyed before and after implementation. Data from 2896 pupils were analyzed, covering mental health (SDQ), social-emotional and academic skills, …
Econometrics (8 works) · Mathematics (8 works) · Statistics (8 works) · Computer Science (7 works) · Bayesian probability (5 works) · Latent variable (5 works) · Psychometric Methodologies and Testing (5 works) · Statistical Methods and Bayesian Inference (5 works) · Advanced Statistical Modeling Techniques (4 works) · Artificial Intelligence (4 works)