Lourens Waldorp
Biographic Data
| ID | 4469116 |
|---|---|
| NAME | Lourens Waldorp |
| GIVEN NAMES | Lourens |
| FAMILY NAME | Waldorp |
| SIGNATURE | WALDORP L |
| AFFILIATIONS | University of Amsterdam |
| ORCID | 0000-0002-5941-4625 |
| VERIFIED | Yes |
| TOTAL WORKS | 24 |
| TOTAL CITATIONS | 36 |
| AUTHOR COUNT | 24 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2010 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
ReMoDe – Recursive modality detection in distributions of ordinal data
The detection of the number of modes in distributions of ordinal data is relevant for applied researchers across disciplines, from uncovering polarization to detecting incidence groups in clinical symptom scales. Yet, established modality detection methods are either purely descriptive or not developed for ordinal data. In the present paper, we attempt to fill this gap by proposing a recursive modality detection method (ReMoDe) which detects mode…
Nodewise Parameter Aggregation for Psychometric Networks
Psychometric networks can be estimated using nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs generalized linear models with each node as the outcome. Two regression coefficients are obtained for each link, which need to be aggregated to obtain the edge weight (i.e., the conditional association). The nodewise …
Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends
Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i…
Causal inference: Principles unifying experimental and observational accounts
Causal reasoning is closely related to interventions. We believe that if A causes B , then a change in A leads to a change in the probability distribution of B . This interventionist view of causality is natural to many, in particular to social scientists, because it is close to the standard paradigm of experimentation. This has led some to believe that the only meaningful way to obtain any causal claim is by experimentation. Here we suggest that…
Network Inference With the Lasso
Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often used to obtain edges in a network, and the underlying distribution of lasso estimates is discontinuous and has probability one at zero when the estimate is zero, obtaining p-values and confidence intervals is problematic. It is also not always desirable to…
Comparing network structures on three aspects: A permutation test.
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to com…
Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model
The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent article in this journal, Forbes, Wright, Markon, and Krueger (FWMK) voiced the concern that GGMs that are estimated from partial correlations wrongfully remove the variance that is shared by its constituents. If true, this concern has grave consequences for the application of GGMs. Indeed, if partial correlations only captur…
Network analysis of multivariate data in psychological science
In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. This Primer provides an anatomy of these techniques, describes the current state of the art and discu…
Invisible Hands and Fine Calipers: A Call to Use Formal Theory as a Toolkit for Theory Construction
In recent years, a growing chorus of researchers has argued that psychological theory is in a state of crisis: Theories are rarely developed in a way that indicates an accumulation of knowledge. Paul Meehl raised this very concern more than 40 years ago. Yet in the ensuing decades, little has improved. We aim to chart a better path forward for psychological theory by revisiting Meehl’s criticisms, his proposed solution, and the reasons his soluti…
A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function …
Moderated Network Models
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of…
Interpreting the Ising Model: The Input Matters
While it is possible to transform the parameters of the Ising model in one domain to obtain a statistically equivalent model in the other domain, the parameters in the two versions of the Ising model lend themselves to different interpretations and imply different dynamics, when studying the Ising model as a dynamical system. In this tutorial paper, we provide an accessible discussion of the interpretation of threshold and interaction parameters …
Latent Variable Models and Networks: Statistical Equivalence and Testability
Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have e…
MGM: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
We present the R-package mgm for the estimation of both stationary and time-varying Mixed Graphical Models and mixed Vector Autoregressive models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since mixed types of variables (continuous, count, categorical) are ubiquitous in datasets in many disciplines. In addition, we extend both models to the time-varying case in which the true model chan…
How well do network models predict observations? On the importance of predictability in network models
Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been paid to how well they predict observations. This is despite the fact that predictability is crucial for judging the practical relevance of edges: for instance in clinical practice, predictability of a symptom indicates whether an intervention on tha…
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases…
An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of…
Association of Symptom Network Structure With the Course of Depression
This study reports that symptom networks of patients with MDD are related to longitudinal course: persisters exhibited a more densely connected network at baseline than remitters. More pronounced associations between symptoms may be an important determinant of persistence in MDD.
State of the aRt personality research: A tutorial on network analysis of personality data in R
A new method for constructing networks from binary data
Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for th…
Deconstructing the construct: A network perspective on psychological phenomena
Simpson's paradox in psychological science: A practical guide
The direction of an association at the population-level may be reversed within the subgroups comprising that population-a striking observation called Simpson's paradox. When facing this pattern, psychologists often view it as anomalous. Here, we argue that Simpson's paradox is more common than conventionally thought, and typically results in incorrect interpretations-potentially with harmful consequences. We support this claim by reviewing result…
Qgraph: Network Visualizations of Relationships in Psychometric Data
We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can b…
Comorbidity: A network perspective
The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory , in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a ra…
State of the aRt personality research: A tutorial on network analysis of personality data in R
Causal inference: Principles unifying experimental and observational accounts
Causal reasoning is closely related to interventions. We believe that if A causes B , then a change in A leads to a change in the probability distribution of B . This interventionist view of causality is natural to many, in particular to social scientists, because it is close to the standard paradigm of experimentation. This has led some to believe that the only meaningful way to obtain any causal claim is by experimentation. Here we suggest that…
Comorbidity: A network perspective
The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory , in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a ra…
Qgraph: Network Visualizations of Relationships in Psychometric Data
We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can b…
Deconstructing the construct: A network perspective on psychological phenomena
Simpson's paradox in psychological science: A practical guide
The direction of an association at the population-level may be reversed within the subgroups comprising that population-a striking observation called Simpson's paradox. When facing this pattern, psychologists often view it as anomalous. Here, we argue that Simpson's paradox is more common than conventionally thought, and typically results in incorrect interpretations-potentially with harmful consequences. We support this claim by reviewing result…
A new method for constructing networks from binary data
Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for th…
Association of Symptom Network Structure With the Course of Depression
This study reports that symptom networks of patients with MDD are related to longitudinal course: persisters exhibited a more densely connected network at baseline than remitters. More pronounced associations between symptoms may be an important determinant of persistence in MDD.
State of the aRt personality research: A tutorial on network analysis of personality data in R
How well do network models predict observations? On the importance of predictability in network models
Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been paid to how well they predict observations. This is despite the fact that predictability is crucial for judging the practical relevance of edges: for instance in clinical practice, predictability of a symptom indicates whether an intervention on tha…
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases…
An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of…
MGM: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
We present the R-package mgm for the estimation of both stationary and time-varying Mixed Graphical Models and mixed Vector Autoregressive models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since mixed types of variables (continuous, count, categorical) are ubiquitous in datasets in many disciplines. In addition, we extend both models to the time-varying case in which the true model chan…
Network analysis of multivariate data in psychological science
In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. This Primer provides an anatomy of these techniques, describes the current state of the art and discu…
Invisible Hands and Fine Calipers: A Call to Use Formal Theory as a Toolkit for Theory Construction
In recent years, a growing chorus of researchers has argued that psychological theory is in a state of crisis: Theories are rarely developed in a way that indicates an accumulation of knowledge. Paul Meehl raised this very concern more than 40 years ago. Yet in the ensuing decades, little has improved. We aim to chart a better path forward for psychological theory by revisiting Meehl’s criticisms, his proposed solution, and the reasons his soluti…
A Tutorial on Estimating Time-Varying Vector Autoregressive Models
Time series of individual subjects have become a common data type in psychological research. These data allow one to estimate models of within-subject dynamics, and thereby avoid the notorious problem of making within-subjects inferences from between-subjects data, and naturally address heterogeneity between subjects. A popular model for these data is the Vector Autoregressive (VAR) model, in which each variable is predicted by a linear function …
Moderated Network Models
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research, this is often implausible. In this article, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of…
Interpreting the Ising Model: The Input Matters
While it is possible to transform the parameters of the Ising model in one domain to obtain a statistically equivalent model in the other domain, the parameters in the two versions of the Ising model lend themselves to different interpretations and imply different dynamics, when studying the Ising model as a dynamical system. In this tutorial paper, we provide an accessible discussion of the interpretation of threshold and interaction parameters …
Latent Variable Models and Networks: Statistical Equivalence and Testability
Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have e…
Relations between Networks, Regression, Partial Correlation, and the Latent Variable Model
The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent article in this journal, Forbes, Wright, Markon, and Krueger (FWMK) voiced the concern that GGMs that are estimated from partial correlations wrongfully remove the variance that is shared by its constituents. If true, this concern has grave consequences for the application of GGMs. Indeed, if partial correlations only captur…
Comparing network structures on three aspects: A permutation test.
Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to com…
Network Inference With the Lasso
Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often used to obtain edges in a network, and the underlying distribution of lasso estimates is discontinuous and has probability one at zero when the estimate is zero, obtaining p-values and confidence intervals is problematic. It is also not always desirable to…
Nodewise Parameter Aggregation for Psychometric Networks
Psychometric networks can be estimated using nodewise regression to estimate edge weights when the joint distribution is analytically difficult to derive or the estimation is too computationally intensive. The nodewise approach runs generalized linear models with each node as the outcome. Two regression coefficients are obtained for each link, which need to be aggregated to obtain the edge weight (i.e., the conditional association). The nodewise …
Non-Stationarity in Time-Series Analysis: Modeling Stochastic and Deterministic Trends
Time series analysis is increasingly popular across scientific domains. A key concept in time series analysis is stationarity, the stability of statistical properties of a time series. Understanding stationarity is crucial to addressing frequent issues in time series analysis such as the consequences of failing to model non-stationarity, how to determine the mechanisms generating non-stationarity, and consequently how to model those mechanisms (i…
Causal inference: Principles unifying experimental and observational accounts
Causal reasoning is closely related to interventions. We believe that if A causes B , then a change in A leads to a change in the probability distribution of B . This interventionist view of causality is natural to many, in particular to social scientists, because it is close to the standard paradigm of experimentation. This has led some to believe that the only meaningful way to obtain any causal claim is by experimentation. Here we suggest that…
ReMoDe – Recursive modality detection in distributions of ordinal data
The detection of the number of modes in distributions of ordinal data is relevant for applied researchers across disciplines, from uncovering polarization to detecting incidence groups in clinical symptom scales. Yet, established modality detection methods are either purely descriptive or not developed for ordinal data. In the present paper, we attempt to fill this gap by proposing a recursive modality detection method (ReMoDe) which detects mode…
Mental Health Research Topics (20 works) · Computer Science (19 works) · Mathematics (16 works) · Artificial Intelligence (15 works) · Functional Brain Connectivity Studies (13 works) · Complex Network Analysis Techniques (11 works) · Machine learning (11 works) · Econometrics (10 works) · Psychology (10 works) · Statistics (10 works)