Mark de Rooij
Biographic Data
| ID | 5545660 |
|---|---|
| NAME | Mark de Rooij |
| GIVEN NAMES | Mark |
| FAMILY NAME | de Rooij |
| SIGNATURE | DE ROOIJ M |
| AFFILIATIONS | Leiden University |
| ORCID | 0000-0001-7308-6210 |
| VERIFIED | Yes |
| TOTAL WORKS | 9 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 9 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2003 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Regularized reduced rank regression for mixed predictor and response variables
In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR 4 ), an extension of the GMR 3 model designed to improve performance in high‐dimensional settings. GMR 3 is a regression method for a mix of numeric, binary and ordinal response variables, while also allowing for mixed‐type predictors through optimal scaling. GMR 4 extends this approach by incorporating regularization techniques, such as Ridge, Lasso,…
A Two-Step Estimator for Growth Mixture Models with Covariates in the Presence of Direct Effects
Growth mixture models (GMMs) are popular approaches for modeling unobserved population heterogeneity over time. GMMs can be extended with covariates, predicting latent class (LC) membership, the within-class growth trajectories, or both. However, current estimators are sensitive to misspecifications in complex models. We propose extending the two-step estimator for LC models to GMMs, which provides robust estimation against model misspecification…
Students' proficiency evaluation
In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of studen…
SEM-Based Out-of-Sample Predictions
Predictive modeling is becoming more popular in psychological science. Machine learning techniques have been used to develop prediction rules based on items of psychological tests. However, this approach does not take into account that these items are noisy indicators of the constructs they intend to measure. Structural equation modeling does take this into account. Several authors have concluded that it is impossible to make out-of-sample predic…
A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data
Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this artic…
Estimating Subgroup Effects Using the Propensity Score Method
OBJECTIVE: Our aim was to demonstrate the feasibility of the univariate and generalized propensity score (PS) method in subgroup analysis of outcomes research. METHODS: First, to estimate subgroup effects, we tested the performance of 2 different PS methods, using Monte Carlo simulations: (1) the univariate PS with additional adjustment on the subgroup; and (2) the generalized PS, estimated by crossing the treatment options with a subgroup variab…
A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means
In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) criterion of irrelevance, which is a graphical, exploratory method for evaluating the "relevance" of dichotomous…
The Mixed Effects Trend Vector Model
Maximum likelihood estimation of mixed effect baseline category logit models for multinomial longitudinal data can be prohibitive due to the integral dimension of the random effects distribution. We propose to use multidimensional unfolding methodology to reduce the dimensionality of the problem. As a by-product, readily interpretable graphical displays representing change are obtained. The methodology can be applied to both nominal and ordinal r…
Multivariate Multinomial Logit Models for Dyadic Sequential Interaction Data
The analysis of discrete dyadic sequential behavior and, in particular, the problem of forecasting future behavior from current and past behavior in such data is the main theme of the present article. We propose to use multivariate multinomial logit models and the potential of which will be demonstrated with data on Imagery play therapy. In such a therapy, the therapist tries to draw a child into Imagery play, so that it can act out its emotions …
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Multivariate Multinomial Logit Models for Dyadic Sequential Interaction Data
The analysis of discrete dyadic sequential behavior and, in particular, the problem of forecasting future behavior from current and past behavior in such data is the main theme of the present article. We propose to use multivariate multinomial logit models and the potential of which will be demonstrated with data on Imagery play therapy. In such a therapy, the therapist tries to draw a child into Imagery play, so that it can act out its emotions …
A Model-Free Diagnostic for Single-Peakedness of Item Responses Using Ordered Conditional Means
In this article we propose a model-free diagnostic for single-peakedness (unimodality) of item responses. Presuming a unidimensional unfolding scale and a given item ordering, we approximate item response functions of all items based on ordered conditional means (OCM). The proposed OCM methodology is based on Thurstone & Chave's (1929) criterion of irrelevance, which is a graphical, exploratory method for evaluating the "relevance" of dichotomous…
The Mixed Effects Trend Vector Model
Maximum likelihood estimation of mixed effect baseline category logit models for multinomial longitudinal data can be prohibitive due to the integral dimension of the random effects distribution. We propose to use multidimensional unfolding methodology to reduce the dimensionality of the problem. As a by-product, readily interpretable graphical displays representing change are obtained. The methodology can be applied to both nominal and ordinal r…
Estimating Subgroup Effects Using the Propensity Score Method
OBJECTIVE: Our aim was to demonstrate the feasibility of the univariate and generalized propensity score (PS) method in subgroup analysis of outcomes research. METHODS: First, to estimate subgroup effects, we tested the performance of 2 different PS methods, using Monte Carlo simulations: (1) the univariate PS with additional adjustment on the subgroup; and (2) the generalized PS, estimated by crossing the treatment options with a subgroup variab…
A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data
Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this artic…
SEM-Based Out-of-Sample Predictions
Predictive modeling is becoming more popular in psychological science. Machine learning techniques have been used to develop prediction rules based on items of psychological tests. However, this approach does not take into account that these items are noisy indicators of the constructs they intend to measure. Structural equation modeling does take this into account. Several authors have concluded that it is impossible to make out-of-sample predic…
Students' proficiency evaluation
In higher education, students' assessment has a two-fold aim: (i) evaluate students' proficiency level concerning the topics of a specific course; (ii) identify students' weaknesses throughout the whole learning activity and, if any, relate them to a set of socio-demographic and psychological covariates/predictors. In this vein, this manuscript proposes a multilevel latent class model as an analytic strategy to detect homogeneous groups of studen…
Regularized reduced rank regression for mixed predictor and response variables
In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR 4 ), an extension of the GMR 3 model designed to improve performance in high‐dimensional settings. GMR 3 is a regression method for a mix of numeric, binary and ordinal response variables, while also allowing for mixed‐type predictors through optimal scaling. GMR 4 extends this approach by incorporating regularization techniques, such as Ridge, Lasso,…
A Two-Step Estimator for Growth Mixture Models with Covariates in the Presence of Direct Effects
Growth mixture models (GMMs) are popular approaches for modeling unobserved population heterogeneity over time. GMMs can be extended with covariates, predicting latent class (LC) membership, the within-class growth trajectories, or both. However, current estimators are sensitive to misspecifications in complex models. We propose extending the two-step estimator for LC models to GMMs, which provides robust estimation against model misspecification…
Mathematics (7 works) · Econometrics (6 works) · Statistics (6 works) · Statistical Methods and Bayesian Inference (5 works) · Artificial Intelligence (4 works) · Computer Science (4 works) · Artificial Intelligence (3 works) · Latent variable (3 works) · Psychology (3 works) · Psychometric Methodologies and Testing (3 works)