Maarten Marsman

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Verified

Maarten verified their affiliation via an institutional email.

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• PhD
• Assistant Professor at University of Amsterdam

Purpose: Research on alcohol use disorder has exclusively focused on either its symptom-level mechanisms--the network perspective-- or sociodemographic determinants--epidemiological research. Moreover, such research failed to stratify analyses for important person-level factors (e.g., sex or ethnicity). Here, we combine network and epidemiological...

Psychometric networks have become a popular tool for multivariate data analysis in psychology and the social sciences. Researchers conceptualize a construct as a network of variables, interpreting the presence or absence of a network edge (i.e., conditional independence) and the strength of the present edges (i.e., the strength of the partial assoc...

Multivariate analysis using graphical models is rapidly gaining ground in psychology. In particular, Markov random field (MRF) graphical models have become popular because their graph structure reflects the conditional associations between psychological variables. Despite the fact that most psychological variables are assessed on an ordinal scale,...

Multivariate analysis of psychological variables using graphical models has become a standard analysis in the psychometric literature. Most cross-sectional measures are either binary or ordinal, and the methodology for inferring the structure of networks of binary and ordinal variables is developing rapidly. In practice, however, research questions...

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain...

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where the two popula...

Background and Hypothesis Psychotic disorders (PDs) have huge personal and societal impact, and efforts to improve outcomes in patients are continuously needed. Environmental risk factors (ERFs), especially modifiable risk factors, are important to study because they pose a target for intervention and prevention. No studies have investigated ERFs,...

In recent years, there has been a growing call to advance psychological theorizing through formal modeling. We answer this by introducing a methodology for developing psychological theories using probabilistic network models (PNMs). Originating in statistical mechanics, PNMs describe networks of interacting elements and have already shaped prominen...

Network psychometrics has become an important method for studying the network structure of multivariate psychological data. Accurate recovery of the underlying network structure is paramount and requires that the models are appropriate for the data at hand. Traditionally, Gaussian graphical models for continuous data and Ising models for binary dat...

Paranormal beliefs encompass a wide variety of phenomena, including the existence of supernatural entities such as ghosts and witches, as well as extraordinary human abilities such as telepathy and clairvoyance. In the current study, we used a nationally representative sample ( N = 2534 ) to investigate the presence and correlates of paranormal bel...

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying...

In fMRI research, graphical models are used to uncover complex patterns of relationships between brain regions. Connectivity-based fMRI studies typically analyze nested data; raw observations, e.g., BOLD responses, are nested within participants, which are nested within populations, e.g., healthy controls. Often studies ignore the nested structure...

Psychometric network estimation often relies on 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, w...

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal struc...

The Ising model is one of the most popular models in network psychometrics. However, statistical analysis of the Ising model is difficult due to the presence of its intractable normalizing constant in the probability function. As a result, maximum likelihood estimation using the exact likelihood is only possible for small graphs, and approximation...

Network psychometrics models psychological constructs as interconnected variables. Rather than treating variables as independent entities, network analysis views them as nodes in a system that interact with each other; their interactions yield partial associations. Recently, researchers have emphasized the use of Bayesian methods in graphical model...

This review explores the current landscape of network estimation methods in the context of fMRI-based network neuroscience, focusing on static undirected network analysis. We focused on papers published in a single year (2022), and characterised what we consider the fundamental building blocks of network analysis: sample size, network size, associa...

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying...

Network psychometrics models psychological constructs as interconnected variables. Rather than treating variables as independent entities, network analysis views them as nodes in a system that interact with each other; their interactions yield partial associations. Recently, researchers have empha-sized the use of Bayesian methods in graphical mode...

Bayesian analysis methods provide a significant advancement in network psychometrics, allowing researchers to use the edge inclusion Bayes factor for testing conditional independence between pairs of variables in the network. Using this methodology requires setting prior distributions on the network parameters and on the network’s structure. Howeve...

Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as nodes, and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for prop...

Bayesian analysis methods provide a significant advancement in network psychometrics, allowing researchers to use the edge inclusion Bayes factorfor testing conditional independence between pairs of variables in the network. Using this methodology requires prior distributions on the network parameters and on the network’s structure. However, the im...

The Ising model is one of the most popular models in network psychometrics. Analysis of the Ising model, however, is difficult because of the presence of an intractable normalizing constant in its probability function. As a result, exact estimation using maximum likelihood is only possible for small graphs. For larger graphs approximation methods a...

Network psychometrics is a recent approach to studying psychological constructs as interconnected variables. Rather than treating variables as independent entities, network analysis or graphical modeling views them as nodes in a system that interact with each other; their interactions yield partial associations. Recently, researchers have emphasize...

Network models have gained significant popularity in the field of psychology over the past decade. The Ising model specifically is widely used for analyzing binary cross-sectional data. This paper extends the Ising model to incorporate a temporal dimension. The proposed time-dependent Ising model is based on Fortuin and Kasteleyn’s (1972) theory of...

The multibridge package allows a Bayesian evaluation of informed hypotheses ${\mathscr{H}}_{r}$ H r applied to frequency data from an independent binomial or multinomial distribution. multibridge uses bridge sampling to efficiently compute Bayes factors for the following hypotheses concerning the latent category proportions 𝜃 : (a) hypotheses that...

Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal struc...

Multivariate analysis using graphical models is rapidly gaining ground in psychology. In particular, Markov Random Field (MRF) graphical models have become popular because their graph structure reflects the conditional associations between psychological variables. Despite the fact that most psychological variables are assessed on an ordinal scale,...

Network psychometrics is a new direction in psychological research that conceptualizes multivariate data as interacting systems. Variables are represented as nodes and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model...

Purpose: Research on alcohol use disorder has exclusively focused on either its symptom-level mechanisms--the network perspective-- or sociodemographic determinants--epidemiological research. Moreover, such research failed to stratify analyses for important person-level factors (e.g., sex or ethnicity). Here, we combine network and epidemiological...

The Ising model is a graphical model that has played an essential role in network psychometrics. It has been used as a theoretical model to conceptualize psychological concepts and as a statistical model to analyze psychological data. Using graphical models such as the Ising model to analyze psychological data has been heavily critiqued since these...

One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t-test. However, frequentist t-tests do not quantify evidence and require various assumption tests. Recently popularized Bayesian t-tests do quantify evidence, but these were developed for scenarios where the two populat...

This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from in...

The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian appro...

Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we incor...

Network models like the Ising model are increasingly used in psychological research. In a recent article published in this journal, Brusco et al. (2019) provide a critical assessment of the conditions that underlie the Ising model and the eLasso method that is commonly used to estimate it. In this commentary, we show that their main criticisms are...

Equal parameter estimates across subgroups is a substantial requirement of statistical tests. Ignoring subgroup differences poses a threat to study replicability, model specification, and theory development. Structural change tests are a powerful statistical technique to assess parameter invariance. A core element of those tests is the empirical fl...

Editors’ introduction to the special issue “Network psychometrics in action”: Methodological innovations inspired by empirical problems.

This chapter is about two recently published algorithms that can be used to sample from conditional distributions. We show how the efficiency of the algorithms can be improved when a sample is required from many conditional distributions. Using real-data examples from educational measurement, we show how the algorithms can be used to sample from in...

Hypotheses concerning the distribution of multinomial proportions typically entail exact equality constraints that can be evaluated using standard tests. Whenever researchers formulate inequality constrained hypotheses, however, they must rely on sampling-based methods that are relatively inefficient and computationally expensive. To address this p...

Twentieth century theory formation in human intelligence was dominated by factor theories; network theories will dominate the twenty first. Network theories answer a broad call for formal theories in psychological science, provide a strong approach to an idiographic science, and create an opportunity to study the developmental mechanisms of human's...

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use...

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 con...

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. How...

The multibridge R package allows a Bayesian evaluation of informed hypotheses H_r applied to frequency data from an independent binomial or multinomial distribution. multibridge uses bridge sampling to efficiently compute Bayes factors for the following hypotheses concerning the latent category proportions theta: (a) hypotheses that postulate equal...

Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inference concerning the importance of the predictors. H...

This chapter provides an introduction to Bayesian inference using Markov Chain Monte Carlo (MCMC) methods. We focus on two popular MCMC methods: Metropolis-Hastings and the Gibbs sampler. A Metropolis-Hastings algorithm developed by Marsman et al. (Sci Rep 5:9050, 1–7, 2015) will be used to illustrate how MCMC can be done for a wide range of models...

Twentieth century theory formation in human intelligence was dominated by factor theories; network theories will dominate the twenty first. Network theories answer a broad call for formal theories in psychological science, provide a strong approach to an idiographic science, and create an opportunity to study the developmental mechanisms of human's...

Alcohol use disorder is argued to be a highly complex disorder influenced by a multitude of factors on different levels. Common research approaches fail to capture this breadth of interconnecting symptoms. To address this gap in theoretical assumptions and methodological approaches, we used a network analysis to assess the interplay of alcohol use...

Full text: The preprint is available here: https://doi.org/10.31234/osf.io/e96m8. The final version of this manuscript is published in General and Specific Mental Abilities: https://www.cambridgescholars.com/product/978-1-5275-3310-3. Summary: Human intelligence is a puzzling concept. There is no consensus on the definition of intelligence, not ev...

Equal parameter estimates across subgroups - measurement invariance - is a substantial requirement of statistical tests. Ignoring subgroup differences poses a major threat to study replicability, model specification, and theory development. One powerful statistical method that allows testing for measurement invariance is structural change tests. A...

The Ising model is one of the most widely analyzed graphical models in network psychometrics. Unfortunately, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian...

How confident are researchers in their own claims? Augustus De Morgan suggested that researchers may initially present their conclusions modestly, but afterwards use them as if they were a “moral certainty”. To prevent this from happening, De Morgan proposed that whenever researchers make a claim, they accompany it with a number that reflects their...

People’s choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network app...

People's choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network app...

Hypotheses concerning the distribution of multinomial proportions typically entail exact equality constraints that can be evaluated using standard tests. Whenever researchers formulate inequality constrained hypotheses, however, they must rely on sampling-based methods that are relatively inefficient and computationally expensive. To address this p...

The Savage-Dickey density ratio is a specific expression of the Bayes factor when testing a precise (equality constrained) hypothesis against an unrestricted alternative. The expression greatly simplifies the computation of the Bayes factor at the cost of assuming a specific form of the prior under the precise hypothesis as a function of the unrest...

The Gaussian graphical model (GGM) has become a popular tool for analyzing networks of psychological variables. In a recent paper 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 conce...

Items in a test are often used as a basis for making decisions and such tests are therefore required to have good psychometric properties, like unidimensionality. In many cases the sum score is used in combination with a threshold to decide between pass or fail, for instance. Here we consider whether such a decision function is appropriate, without...

Geary puts forward an appealing argument for the consideration of mitochondrial functioning as a candidate for a formative g Geary (2019); it is also an ambitious argument [...]

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from 2 separate large samples (total N > 15,000) were then...

Despite an ongoing stream of lamentations, many empirical disciplines still treat the p value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has...

The Savage-Dickey density ratio is a specific expression of the Bayes factor when testing a precise (equality constrained) hypothesis against an unrestricted alternative. The expression greatly simplifies the computation of the Bayes factor at the cost of assuming a specific form of the prior under the precise hypothesis as a function of the unrest...

Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a specific selection of relevant predictors; in the second stage, the regression coefficients of the winning model are used for prediction and for inference concerning the importance of the predictors. H...

The Ising model is a model for pairwise interactions between binary variables that has become popular in the psychological sciences. It has been first introduced as a theoretical model for the alignment between positive (1) and negative (-1) atom spins. In many psychological applications, however, the Ising model is defined on the domain {0, 1} ins...

L’analyse de variance (ANOVA) est la procédure standard utilisée pour l’inférence statistique dans les plans factoriels. En règle générale, les analyses de variance sont exécutées à l’aide de statistiques fréquentistes, où les valeurs p déterminent la significativité statistique en termes de « tout ou rien ». Ces dernières années, l’approche bayési...

To what extent are research results influenced by subjective decisions that scientists make as they design studies? Fifteen research teams independently designed studies to answer five original research questions related to moral judgments, negotiations, and implicit cognition. Participants from two separate large samples (total N > 15,000) were th...

Bayesian inference for rank-order problems is frustrated by the absence of an explicit likelihood function. This hurdle can be overcome by assuming a latent normal representation that is consistent with the ordinal information in the data: the observed ranks are conceptualized as an impoverished reflection of an underlying continuous scale, and inf...

Observing exclusively positive associations among a set of variables (i.e., a positive manifold) is a robust finding in many areas in psychology. These positive associations can be explained by positing an underlying common cause or, alternatively, through positive direct effects among the variables. Recently, the Kruis-Maris model has been propose...

Network models like the Ising model are increasingly used in psychological research. In a recent article published in this journal, Brusco, Steinley, Hoffman, Davis-Stober, and Wasserman (in press) provide a critical assessment of the conditions that underlie the Ising model and the eLasso method that is commonly used to estimate it. In this commen...

The Ising model is a graphical model that has played an essential role in the field of network psychometrics, where it has been used as a theoretical model to re-conceptualize psychometric concepts and as a statistical model for the analysis of psychological data. But in network psychometrics, the psychological data that are analyzed often come fro...

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to th...

The positive manifold of intelligence has fascinated generations of scholars in human ability. In the past century, various formal explanations have been proposed, including the dominant g factor, the revived sampling theory, and the recent multiplier effect model and mutualism model. In this article, we propose a novel idiographic explanation. We...

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The predominant approach to jointly account for response times and accuracies are statistical models. Substantive approaches such as the diffusion model, on the other hand...

In network psychometrics undirected graphical models—such as the Ising model from statistical physics—are used to characterize the manifest probability distribution of psychometric data. In practice, we often find that it is extremely difficult to apply graphical models as the Ising model to educational data because (i) the model’s likelihood is im...

Despite an ongoing stream of lamentations, many empirical disciplines still treat the p-value as the sole arbiter to separate the scientific wheat from the chaff. The continued reign of the p-value is arguably due in part to a perceived lack of workable alternatives. In order to be workable, any alternative methodology must be (1) relevant: it has...

Undirected graphical models have many applications in such areas as machine learning, image processing, and, recently, psychology. Psychopathology in particular has received a lot of attention, where symptoms of disorders are assumed to influence each other. One of the most relevant questions practically is on which symptom (node) to intervene to h...

We propose to use the squared multiple correlation coefficient as an effect size measure for experimental analysis-of-variance designs and to use Bayesian methods to estimate its posterior distribution. We provide the expressions for the squared multiple, semipartial, and partial correlation coefficients corresponding to four commonly used analysis...

Cognitive Bias Modification (CBM) refers to a family of interventions targeting substance-related cognitive biases, which have been found to play a role in the maintenance of addictive behaviors. In this study, we conducted a Bayesian meta-analysis of individual patient data from studies investigating the effects of CBM as a behavior change interve...

In this paper we study the statistical relations between three latent trait models for accuracies and response times: the hierarchical model (HM) of van der Linden (Psychometrika 72(3):287–308, 2007), the signed residual time model (SM) proposed by Maris and van der Maas (Psychometrika 77(4):615–633, 2012), and the drift diffusion model (DM) as pro...

We propose to use the squared multiple correlation coeffi?cient as an eff?ect size measure for experimental ANOVA designs, and to use Bayesian methods to estimate its posterior distribution. We provide the expressions for the squared multiple, semipartial, and partial correlation coe?fficients corresponding to four commonly used ANOVA designs and i...

There are many situations in which researchers perform multiple hypothesis tests simultaneously. It is important that the results of these tests are cor- rected for multiplicity. If this correction is not performed, it is likely that some null hypotheses will be falsely rejected. There are various different methods for performing multiplicity corre...

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing...

This paper introduces JASP, a free graphical software package for basic statistical procedures such as t tests, ANOVAs, linear regression models, and analyses of contingency tables. JASP is open-source and differentiates itself from existing open-source solutions in two ways. First, JASP provides several innovations in user interface design; specif...

In solving multiplication problems, children use both fast retrieval-based processes and slower computational processes. In the current study, we explore the possibility of disentangling these strategies using information contained in the observed response latencies using a method that is applicable in large data sets. We used a tree-based item res...

Steinley, Hoffman, Brusco, and Sher (2017) proposed a new method for evaluating the performance of psychological network models: fixed-margin sampling. The authors investigated LASSO regularized Ising models (eLasso) by generating random datasets with the same margins as the original binary dataset, and concluded that many estimated eLasso paramete...

The Ising model is a widely used model for multivariate binary data. It has been first introduced as a theoretical model for the alignment between positive (+1) and negative (-1) atom spins, but is now estimated from data in many applications. A popular way to estimate the Ising model is the pseudo-likelihood approach which reduces estimation to a...

Current developments in the statistics community suggest that modern statistics education should be structured holistically, i.e., by allowing students to work with real data and answer concrete statistical questions, but also by educating them about alternative statistical frameworks, such as Bayesian statistics. In this article, we describe how w...

This paper discusses a Metropolis-Hastings algorithm developed by Marsman, Maris, Bechger, and Glas (2015). The algorithm is derived from first principles, and it is proven that the algorithm becomes more efficient with more data and meets the growing demands of large scale educational measurement.

We describe a general method that allows experimenters to quantify the evidence from the data of a direct replication attempt given data already acquired from an original study. These so-called replication Bayes factors are a reconceptualization of the ones introduced by Verhagen and Wagenmakers (Journal of Experimental Psychology: General, 143(4),...

Across the social sciences, researchers have overwhelmingly used the classical statistical paradigm to draw conclusions from data, often focusing heavily on a single number: p. Recent years, however, have witnessed a surge of interest in an alternative statistical paradigm: Bayesian inference, in which probabilities are attached to parameters and m...

This paper discusses a Metropolis-Hastings algorithm developed by \citeA{MarsmanIsing}. The algorithm is derived from first principles, and it is proven that the algorithm becomes more efficient with more data and meets the growing demands of large scale educational measurement.

The Ising model was originally developed to model magnetisation of solids in statistical physics. As a network of binary variables with the probability of becoming ’active’ depending only on direct neighbours, the Ising model appears appropriate for many other processes. For instance, it was recently applied in psychology to model co-occurrences of...

The Ising model was originally developed to model magnetisation of solids in statistical physics. As a network of binary variables with the probability of becoming 'active' depending only on direct neighbours, the Ising model appears appropriate for many other processes. For instance, it was recently applied in psychology to model co-occurrences of...