Search

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K -means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

Most researchers have estimated the edge weights for relative importance networks using a well-established measure of general dominance for multiple regression. This approach has several desirable properties including edge weights that represent R ² contributions, in-degree centralities that correspond to R ² for each item when using other items as predictors, and strong replicability. We endorse the continued use of relative importance networks and believe they have a valuable role in network psychometrics. However, to improve their utility, we introduce a modified approach that uses best-subsets regression as a preceding step to select an appropriate subset of predictors for each item. The benefits of this modification include: (a) computation time savings that can enable larger relative importance networks to be estimated, (b) a principled approach to edge selection that can significantly improve specificity, (c) the provision of a signed network if desired, (d) the potential use of the best-subsets regression approach for estimating Gaussian graphical models, and (e) possible generalization to best-subsets logistic regression for Ising models. We describe, evaluate, and demonstrate the proposed approach and discuss its strengths and limitations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Symptoms often play an important role in the scientific inquiry of psychological disorders and have been theorized to play a functional role in the disorders themselves. However, little is known about the course of specific symptoms and individual differences in course. Understanding the course of specific symptoms and factors influencing symptom course can inform psychological theory and future research on course and treatment., Methods: The current study examined alcohol use disorder (AUD) criteria to explore how etiologically relevant covariates differentially affected the course of individual criteria. The study examined 34,653 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC), to analyze the extent to which AUD symptom course is predicted by alcohol consumption patterns, family history of alcoholism, the presence of internalizing and externalizing disorders, and race., Results: The course of all AUD criteria was significantly influenced by these predictors, with the magnitude of the influence varying across different criteria and different aspects of the course (i.e., onset, persistence, recurrence). The strength of the relationship is partially related to the theoretical proximity of a given covariate to AUD symptomatology, with heavy drinking being the strongest and family history of AUD being the weakest. The course of all criteria was strongly associated with the prevalence of the criterion in the overall sample., Conclusions: The course of AUD criteria is heterogeneous, appearing to be influenced by conceptually proximal predictors, the prevalence of the criterion, and perhaps an underlying common factor. Diagnostic accuracy may be improved by including a criterion related to alcohol consumption. Future work should include exploring the interchangeability of criteria and alternative operationalization of them., (© 2023 Research Society on Alcohol.)

The Ising model has received significant attention in network psychometrics during the past decade. A popular estimation procedure is IsingFit, which uses nodewise l 1 -regularized logistic regression along with the extended Bayesian information criterion to establish the edge weights for the network. In this paper, we report the results of a simulation study comparing IsingFit to two alternative approaches: (1) a nonregularized nodewise stepwise logistic regression method, and (2) a recently proposed global l 1 -regularized logistic regression method that estimates all edge weights in a single stage, thus circumventing the need for nodewise estimation. MATLAB scripts for the methods are provided as supplemental material. The global l 1 -regularized logistic regression method generally provided greater accuracy and sensitivity than IsingFit, at the expense of lower specificity and much greater computation time. The stepwise approach showed considerable promise. Relative to the l 1 -regularized approaches, the stepwise method provided better average specificity for all experimental conditions, as well as comparable accuracy and sensitivity at the largest sample size., (© 2022. The Psychonomic Society, Inc.)

The modularity index (Q) is an important criterion for many community detection heuristics used in network psychometrics and its subareas (e.g., exploratory graph analysis). Some heuristics seek to directly maximize Q, whereas others, such as the walktrap algorithm, only use the modularity index post hoc to determine the number of communities. Researchers in network psychometrics have typically not employed methods that are guaranteed to find a partition that maximizes Q, perhaps because of the complexity of the underlying mathematical programming problem. In this paper, for networks of the size commonly encountered in network psychometrics, we explore the utility of finding the partition that maximizes Q via formulation and solution of a clique partitioning problem (CPP). A key benefit of the CPP is that the number of communities is naturally determined by its solution and, therefore, need not be prespecified in advance. The results of two simulation studies comparing maximization of Q to two other methods that seek to maximize modularity (fast greedy and Louvain), as well as one popular method that does not (walktrap algorithm), provide interesting insights as to the relative performances of the methods with respect to identification of the correct number of communities and the recovery of underlying community structure., (© 2022. The Psychonomic Society, Inc.)

Spectral clustering is a well-known method for clustering the vertices of an undirected network. Although its use in network psychometrics has been limited, spectral clustering has a close relationship to the commonly used walktrap algorithm. In this article, we report results from simulation experiments designed to evaluate the ability of spectral clustering and the walktrap algorithm to recover underlying cluster (or community) structure in networks. The salient findings include: (a) the recovery performance of the walktrap algorithm can be improved by using K-means clustering instead of hierarchical clustering; (b) K -means and K -median clustering led to comparable recovery performance when used to cluster vertices based on the eigenvectors of Laplacian matrices in spectral clustering; (c) spectral clustering using the unnormalized Laplacian matrix generally yielded inferior cluster recovery in comparison to the other methods; (d) when the correct number of clusters was provided for the methods, spectral clustering using the normalized Laplacian matrix led to better recovery than the walktrap algorithm; and (e) when the correct number of clusters was not provided, the walktrap algorithm using the Q w modularity index was better than spectral clustering using the eigengap heuristic at determining the appropriate number of clusters. Overall, both the walktrap algorithm and spectral clustering of the normalized Laplacian matrix are effective for partitioning the vertices of undirected networks, with the latter performing better in most instances. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Common outputs of software programs for network estimation include association matrices containing the edge weights between pairs of symptoms and a plot of the symptom network. Although such outputs are useful, it is sometimes difficult to ascertain structural relationships among symptoms from these types of output alone. We propose that matrix permutation provides a simple, yet effective, approach for clarifying the order relationships among the symptoms based on the edge weights of the network. For directed symptom networks, we use a permutation criterion that has classic applications in electrical circuit theory and economics. This criterion can be used to place symptoms that strongly predict other symptoms at the beginning of the ordering, and symptoms that are strongly predicted by other symptoms at the end. For undirected symptom networks, we recommend a permutation criterion that is based on location theory in the field of operations research. When using this criterion, symptoms with many strong ties tend to be placed centrally in the ordering, whereas weakly-tied symptoms are placed at the ends. The permutation optimization problems are solved using dynamic programming. We also make use of branch-search algorithms for extracting maximum cardinality subsets of symptoms that have perfect structure with respect to a selected criterion. Software for implementing the dynamic programming algorithms is available in MATLAB and R. Two networks from the literature are used to demonstrate the matrix permutation algorithms., (© 2021. The Psychometric Society.)

Recently, (as measured by advances in quantitative methodology) Borsboom (2008) has proposed network analysis as an alternative to the latent variable model as a method for understanding the potentially complex relationships among a system of variables. Over the past five to eight years, there has been a veritable explosion in methodology and its application in social, behavioral, and health sciences (citation). This current issue contains nine articles and three commentaries on new methodology, critiques, and the evaluation of current techniques surrounding psychometric network models. In this introduction, I provide a brief overview of these papers and some suggestions on potential ways in which to move the field forward.

This reply addresses the commentary by Epskamp et al. (in press) on our prior work, of using fixed marginals for sampling the data for testing hypothesis in psychometric network application. Mathematical results are presented for expected column (e.g., item prevalence) and row (e.g., subject severity) probabilities under three classical sampling schemes in categorical data analysis: (i) fixing the density, (ii) fixing either the row or column marginal, or (iii) fixing both the row and column marginal. It is argued that, while a unidimensional structure may not be the model we want, it is the structure we are confronted with given the binary nature of the data. Interpreting network models in the context of this artifactual structure is necessary, with preferred solutions to be expanding the item sets of disorders and moving away from the use of binary data and their associated constraints.

This article develops a class of models called sender/receiver finite mixture exponential random graph models (SRFM-ERGMs). This class of models extends the existing exponential random graph modeling framework to allow analysts to model unobserved heterogeneity in the effects of nodal covariates and network features without a block structure. An empirical example regarding substance use among adolescents is presented. Simulations across a variety of conditions are used to evaluate the performance of this technique. We conclude that unobserved heterogeneity in effects of nodal covariates can be a major cause of misfit in network models, and the SRFM-ERGM approach can alleviate this misfit. Implications for the analysis of social networks in psychological science are discussed.

Psychiatric diagnostic systems, such as The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), use expert consensus to determine diagnostic criteria sets and rules (DCSRs), rather than exploiting empirical techniques to arrive at optimal solutions (OS). Our project utilizes complete enumeration (i.e., generating all possible subsets of item combinations A and B with all possible thresholds, T) to evaluate all possible DCSRs given a set of relevant diagnostic data. This method yields the entire population distribution of diagnostic classifications (i.e., diagnosis of the disorder versus no diagnosis) produced by a set of dichotomous predictors (i.e., diagnostic criteria). Once unique sets are enumerated, optimization on some predefined correlate or predictor will maximally separate diagnostic groups on one or more, disorder-specific "outcome" criteria. We used this approach to illustrate how to create a common Substance Use Disorder (SUD) DCSR that is applicable to multiple substances. We demonstrate the utility of this approach with respect to alcohol use disorder and Cannabis Use Disorder (CUD) using DSM-5 criteria as input variables. The optimal SUD solution with a moderate or above severity grading included four criteria (i.e. 1) having a strong urge or craving for the substance (CR), 2) failure to fulfill major role obligations at work school or home (FF), 3) continued use of the substance despite social or interpersonal problems caused by the substance use (SI) and 4) physically hazardous use (HU)) with a diagnostic threshold of two. The derived DCSR was validated with known correlates of SUD and performed as well as DSM-5. Our findings illustrate the value of using an empirical approach to what is typically a subjective process of choosing criteria and algorithms that is prone to bias. The optimization of diagnostic criteria can reduce criteria set sizes, resulting in decreased research, clinician, and patient burden., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The problem of comparing the agreement of two n × n matrices has a variety of applications in experimental psychology. A well-known index of agreement is based on the sum of the element-wise products of the matrices. Although less familiar to many researchers, measures of agreement based on within-row and/or within-column gradients can also be useful. We provide a suite of MATLAB programs for computing agreement indices and performing matrix permutation tests of those indices. Programs for computing exact p-values are available for small matrices, whereas resampling programs for approximate p-values are provided for larger matrices.

Forbes, Wright, Markon, and Krueger (2017) make a compelling case for proceeding cautiously with respect to the overinterpretation and dissemination of results using the increasingly popular approach of creating "networks" from co-occurrences of psychopathology symptoms. We commend the authors on their initial investigation and their utilization of cross-validation techniques in an effort to capture the stability of a variety of network estimation methods. Such techniques get at the heart of establishing "reproducibility," an increasing focus of concern in both psychology (e.g., Pashler & Wagenmakers, 2012) and science more generally (e.g., Baker, 2016). However, as we will show, the problem is likely worse (or at least more complicated) than they initially indicated. Specifically, for multivariate binary data, the marginal distributions enforce a large degree of structure on the data. We show that some expected measurements-such as commonly used centrality statistics-can have substantially higher values than what would usually be expected. As such, we propose a nonparametric approach to generate confidence intervals through Monte Carlo simulation. We apply the proposed methodology to the National Comorbidity Survey - Replication, provided by Forbes et al., finding that the many of the results are indistinguishable from what would be expected by chance. Further, we discuss the problem of multiple testing and potential issues of applying methods developed for 1-mode networks (e.g., ties within a single set of observations) to 2-mode networks (e.g., ties between 2 distinct sets of entities). When taken together, these issues indicate that the psychometric network models should be employed with extreme caution and interpreted guardedly. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

Mixture modeling is a popular technique for identifying unobserved subpopulations (e.g., components) within a data set, with Gaussian (normal) mixture modeling being the form most widely used. Generally, the parameters of these Gaussian mixtures cannot be estimated in closed form, so estimates are typically obtained via an iterative process. The most common estimation procedure is maximum likelihood via the expectation-maximization (EM) algorithm. Like many approaches for identifying subpopulations, finite mixture modeling can suffer from locally optimal solutions, and the final parameter estimates are dependent on the initial starting values of the EM algorithm. Initial values have been shown to significantly impact the quality of the solution, and researchers have proposed several approaches for selecting the set of starting values. Five techniques for obtaining starting values that are implemented in popular software packages are compared. Their performances are assessed in terms of the following four measures: (1) the ability to find the best observed solution, (2) settling on a solution that classifies observations correctly, (3) the number of local solutions found by each technique, and (4) the speed at which the start values are obtained. On the basis of these results, a set of recommendations is provided to the user.

For 30 years, the adjusted Rand index has been the preferred method for comparing 2 partitions (e.g., clusterings) of a set of observations. Although the index is widely used, little is known about its variability. Herein, the variance of the adjusted Rand index (Hubert & Arabie, 1985) is provided and its properties are explored. It is shown that a normal approximation is appropriate across a wide range of sample sizes and varying numbers of clusters. Further, it is shown that confidence intervals based on the normal distribution have desirable levels of coverage and accuracy. Finally, the first power analysis evaluating the ability to detect differences between 2, different adjusted Rand indices is provided. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)