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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 R2 contributions, in-degree centralities that correspond to R2 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. The standard approach for fitting a relative importance network is to take each node of the network in turn as the dependent variable and run an all-possible-subsets regression using all of the other nodes as independent variables. With this approach, nodes are included as variables in the regression models even if they have little or no explanatory value. We propose a modification of the standard approach whereby best-subsets regression is used to select subsets of independent variables prior to running the all-possible-subsets regressions that estimate the relative importance network. The results of a simulation study showed that this modified approach leads to better recovery of the population-model relative importance network structure. The practical value of the modified approach is that a more parsimonious and interpretable network is obtained [ABSTRACT FROM AUTHOR]

During the past 5 to 10 years, an estimation method known as eLasso has been used extensively to produce symptom networks (or, more precisely, symptom dependence graphs) from binary data in psychopathological research. The eLasso method is based on a particular type of Ising model that corresponds to binary pairwise Markov random fields, and its popularity is due, in part, to an efficient estimation process that is based on a series of l1-regularized logistic regressions. In this article, we offer an unprecedented critique of the Ising model and eLasso. We provide a careful assessment of the conditions that underlie the Ising model as well as specific limitations associated with the eLasso estimation algorithm. This assessment leads to serious concerns regarding the implementation of eLasso in psychopathological research. Some potential strategies for eliminating or, at least, mitigating these concerns include (a) the use of partitioning or mixture modeling to account for unobserved heterogeneity in the sample of respondents, and (b) the use of co-occurrence measures for symptom similarity to either replace or supplement the covariance/correlation measure associated with eLasso. Two psychopathological data sets are used to highlight the concerns that are raised in the critique. We present the first principled critique of the Ising model and the associated eLasso method. The Ising model is a form of a pairwise Markov random field that is used to model binary data. The current approach utilizes a set of logistic regressions to obtain parameter estimates, with the final model being chosen with the so-called eLasso method to mitigate against overfitting. Throughout, we highlight why application of this model to data with traits that are commonly found in psychopathological data may be problematic due to issues such as sample size, density, asymmetric parameter weights, and so on. Specifically, the necessary sample size for fitting these models is dependent on the density (e.g., the number of connections) of the network, with a strong preference being given to networks that are not dense. Unfortunately, several (if not the vast majority of) psychopathology disorders have been conceptualized as cohesive traits, and as such, methods used to measure them assume that all of the items arise from a common factor. This standard assumption leads to items that are highly intercorrelated, resulting in networks that are dense—exactly the situation that is not desired for the predominant estimation techniques of the Ising model. In turn, we recommend a partitioning approach that may help address some of these issues. Beyond this issue, we assess the appropriateness of averaging parameter asymmetric parameter estimates to achieve symmetric parameter estimates—a practice that could result in the loss of information. Finally, we show that it is likely that much of the data analyzed with these methods do not arise from the same homogeneous population; as such, we introduce a partitioning method to aid in determining if several "subnetworks" are being averaged together when they are fit together as one big network, resulting in global findings that are not consistent with any local structures. [ABSTRACT FROM AUTHOR]

Increasingly, the structure of mental disorders has been studied in the form of a network, characterizing how symptoms or criteria interact with and influence each other. Many studies of psychiatric symptoms and diagnostic criteria employ community or population-based surveys using co-occurrence of the symptoms/criteria to form the networks. However, given the overall low prevalence rates of mental disorders and their symptoms in the general population, most of those surveyed may not exhibit or endorse any symptoms and yet are often included in network analyses. Consequently, because network models are built on associations between symptoms/criteria, much of the observed variability is driven by individuals who are asymptomatic. Using data from the National Epidemiological Survey of Alcohol and Related Conditions (NESARC) Wave 2 and NESARC-III, we explore the effect of these "asymptomatic" observations on the estimated relations among diagnostic criteria of alcohol use disorder to determine the effects of such observations on estimated networks. We do so using the eLasso tool, as well as with traditional measures of correlation between binary variables (the Φ coefficient and odds ratio). We find that when the proportion of asymptomatic individuals are systematically culled from the sample, the estimated pairwise relations are often significantly affected, even changing signs in some cases. Our findings indicate that researchers should carefully consider the population(s) included in their sample and the implications it has on their interpretations of pairwise similarity estimates and resulting generalizability and reproducibility of estimates of network structures. (PsycINFO Database Record (c) 2019 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

Network models of the symptoms of psychological disorders provide a novel lens for examining comorbidity. Viewing symptoms as causal entities in their own right, researchers can attempt to identify specific symptoms that "bridge" diagnostic entities, providing a more granular perspective on comorbidity than the one provided by analysis at the syndromal level. Such analyses could help identify transdiagnostic targets for both research and clinical interventions. Although promising conceptually, extant work using this approach often uses structured diagnostic interview data that employ "skip outs," branching logic conditional on responses to gating questions (which may be criterial or risk markers). We demonstrate that skip outs, where screening items are asked for each disorder before assessing the remaining symptoms, can produce significant problems in interpretation of comorbidity between symptoms and, hence, transdiagnostic network models. The nature and extent of this problem is explored, and suggestions for future network studies of comorbidity are provided. (PsycINFO Database Record (c) 2018 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Medical over- and underutilization are central problems that stand in the way of delivering optimal health care. As a result, one important question is how people decide to take action, versus not, when it comes to their health. The present article proposes and validates a new measure that captures the extent to which individuals are "medical maximizers" who are predisposed to seek health care even for minor problems, versus "medical minimizers" who prefer to avoid medical intervention unless it is necessary. Method: Studies 1-3 recruited participants using Amazon's Mechanical Turk. Study 1 conducted exploratory factor analysis (EFA) to identify items relevant to the proposed construct. In Study 2 confirmatory factor analysis (CFA) was conducted on the identified items, as well as tests of internal, discriminant, and convergent validity. Study 3 examined test-retest reliability of the scale. Study 4 validated the scale in a non-Internet sample. Results: EFA identified 10 items consistent with the proposed construct, and subsequent CFA showed that the 10 items were best understood with a bifactor model that assessed a single underlying construct consistent with medical maximizing-minimizing, with 3 of the 10 items cross-loading on another independent factor. The scale was distinct from hypochondriasis, distrust in medicine, health care access, and health status, and predicted self-reported health care utilization and a variety of treatment preferences. Conclusions: Individuals have general preferences to maximize versus minimize their use of health care, and these preferences are predictive of health care utilization and treatment preferences across a range of health care contexts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Arguments are proposed that researchers using longitudinal data should consider more and less complex statistical model alternatives to their initially chosen techniques in an effort to "right-size" the model to the data at hand. Such model comparisons may alert researchers who use poorly fitting, overly parsimonious models to more complex, better-fitting alternatives and, alternatively, may identify more parsimonious alternatives to overly complex (and perhaps empirically underidentified and/or less powerful) statistical models. A general framework is proposed for considering (often nested) relationships between a variety of psychometric and growth curve models. A 3-step approach is proposed in which models are evaluated based on the number and patterning of variance components prior to selection of better-fitting growth models that explain both mean and variation-covariation patterns. The orthogonal free curve slope intercept (FCSI) growth model is considered a general model that includes, as special cases, many models, including the factor mean (FM) model (McArdle & Epstein, 1987), McDonald's (1967) linearly constrained factor model, hierarchical linear models (HLMs), repeatedmeasures multivariate analysis of variance (MANOVA), and the linear slope intercept (linearSI) growth model. The FCSI model, in turn, is nested within the Tuckerized factor model. The approach is illustrated by comparing alternative models in a longitudinal study of children's vocabulary and by comparing several candidate parametric growth and chronometric models in a Monte Carlo study. [ABSTRACT FROM AUTHOR]

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple clusters, the procedure is extended to determine the number of clusters. Through a series of simulations, the proposed methodology is shown to outperlbrm several other commonly used procedures for determining both the presence of clusters and their number. [ABSTRACT FROM AUTHOR]

McLachlan (2011) and Vermunt (2011) each provided thoughtful replies to our original article (Steinley & Brusco, 2011). This response serves to incorporate some of their comments while simultaneously clarifying our position. We argue that greater caution against overparamaterization must be taken when assuming that clusters ,are highly elliptical in nature. Specifically, users of mixture model clustering techniques should be wary of overreliance on fit indices, and the importance of cross-validation is highlighted. Additionally, we note that K-means clustering is part of a larger family of discrete partitioning algorithms, many of which are designed to solve problems identical to those for which mixture modeling approaches are often touted. [ABSTRACT FROM AUTHOR]

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison, 2002). Focus is given to the multivariate normal distribution, and 9 separate decompositions (i.e., class structures) of the covariance matrix are investigated. To provide a link to the current literature, comparisons are made with K-means clustering in 3 detailed Monte Carlo studies. The findings have implications for applied researchers in that mixture-model clustering techniques performed best when the covariance structure and number of clusters were known. However, as the information about the shape and number of clusters became unknown, degraded performance was observed for both K-means clustering and mixture-model clustering. [ABSTRACT FROM AUTHOR]

The validity of the abuse-dependence distinction within alcohol use disorders (AUDs) has been increasingly questioned on psychometric and conceptual grounds. Two types of findings are often cited as support for the validity of this distinction: (a) Dependence is more persistent than abuse, and (b) dependence is more highly cornorbid with other Axis I and Axis II disorders than is abuse. Using data from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), we examined the extent to which the current diagnostic algorithm (3 of 7 dependence criteria for a diagnosis of dependence; 1 of 4 abuse criteria for a diagnosis of abuse if dependence criteria are not met) produces this pattern of findings independent of item set. Analyses in which all 330 permutations of the II AUD criteria were partitioned into a 4-item abuse set and a 7-item dependence set were conducted to examine the relevance of the criteria sets to estimates of persistence and comorbidity independent of criteria. Regardless of the criteria used, the dependence set (i.e., 3/7 criteria) always and substantially outperformed the abuse set (1/4) with respect to both persistence and comorbidity. These data indicate that chronicity and comorbidity are flawed indicators for the abuse-dependence distinction (and likely other conditions in which hierarchical decision rules are used). In addition, our analyses show that the current set of criteria defining alcohol dependence and abuse are not optimal. [ABSTRACT FROM AUTHOR]

Structural balance theory (SBT) has maintained a venerable status in the psychological literature for more than 5 decades. One important problem pertaining to SBT is the approximation of structural or generalized balance via the partitioning of the vertices of a signed graph into K clusters. This K-balance partitioning problem also has more general psychological applications associated with the analysis of similarity/dissimilarity relationships among stimuli. Accordingly, K-balance partitioning can be gainfully used in a wide variety of SBT applications, such as attraction and child development, evaluation of group membership, marketing and consumer issues, and other psychological contexts not necessarily related to SBT. We present a branch-and-bound algorithm for the K-balance partitioning problem. This new algorithm is applied to 2 synthetic numerical examples as well as to several real-world data sets from the behavioral sciences literature. [ABSTRACT FROM AUTHOR]

The p-median clustering model represents a combinatorial approach to partition data sets into disjoint, nonhierarchical groups. Object classes are constructed around exemplars, that is, manifest objects in the data set, with the remaining instances assigned to their closest cluster centers. Effective, state-of-the-art implementations of p-median clustering are virtually unavailable in the popular social and behavioral science statistical software packages. We present p-median clustering, including a detailed description of its mechanics and a discussion of available software programs and their capabilities. Application to a complex structured data set on the perception of food items illustrates p-median clustering. [ABSTRACT FROM AUTHOR]

The study of confusion data is a well established practice in psychology. Although many types of analytical approaches for confusion data are available, among the most common methods are the extraction of 1 or more subsets of stimuli, the partitioning of the complete stimulus set into distinct groups, and the ordering of the stimulus set. Although standard commercial software packages can sometimes facilitate these types of analyses, they are not guaranteed to produce optimal solutions. The authors present a MATLAB *.m file for preprocessing confusion matrices, which includes fitting of the similarity-choice model. Two additional MATLAB programs are available for optimally clustering stimuli on the basis of confusion data. The authors also developed programs for optimally ordering stimuli and extracting subsets of stimuli using information from confusion matrices. Together, these programs provide several pragmatic alternatives for the applied researcher when analyzing confusion data. Although the programs are described within the context of confusion data, they are also amenable to other types of proximity data. [ABSTRACT FROM AUTHOR]

Using the cluster generation procedure proposed by D. Steinley and R. Henson (2005), the author investigated the performance of K-means clustering under the following scenarios: (a) different probabilities of cluster overlap; (b) different types of cluster overlap; (c) varying samples sizes, clusters, and dimensions; (d) different multivariate distributions of clusters; and (e) various multidimensional data structures. The results are evaluated in terms of the Hubert--Arabie adjusted Rand index, and several observations concerning the performance of K-means clustering are made. Finally, the article concludes with the proposal of a diagnostic technique indicating when the partitioning given by a K-means cluster analysis can be trusted. By combining the information from several observable characteristics of the data (number of clusters, number of variables, sample size, etc.) with the prevalence of unique local optima in several thousand implementations of the K-means algorithm, the author provides a method capable of guiding key data-analysis decisions. [ABSTRACT FROM AUTHOR]

This article provides an investigation of cluster validation indices that relates 4 of the indices to the L. Hubert and P. Arabie (1985) adjusted Rand index--the cluster validation measure of choice (G. W. Milligan & M. C. Cooper, 1986). It is shown how these other indices can be "roughly" transformed into the same scale as the adjusted Rand index. Furthermore, in-depth explanations are given of why classification rates should not be used in cluster validation research. The article concludes by summarizing several properties of the adjusted Rand index across many conditions and provides a method for testing the significance of observed adjusted Rand indices. [ABSTRACT FROM AUTHOR]

The popular K-means clustering method, as implemented in 3 commercial software packages (SPSS, SYSTAT, and SAS), generally provides solutions that are only locally optimal for a given set of data. Because none of these commercial implementations offer a reasonable mechanism to begin the K-means method at alternative starting points, separate routines were written within the MATLAB (Math-Works, 1999) environment that can be initialized randomly (these routines are provided at the end of the online version of this article in the PsycARTICLES database). Through the analysis of 2 empirical data sets and 810 simulated data sets, it is shown that the results provided by commercial packages are most likely locally optimal. These results suggest the need for some strategy to study the local optima problem for a specific data set or to identify methods for finding "good" starting values that might lead to the best solutions possible. [ABSTRACT FROM AUTHOR]

In recent years, trajectory approaches to characterizing individual differences in the onset and course of substance involvement have gained popularity. Previous studies have sometimes reported 4 prototypic courses: (a) a consistently "low" group, (b) an "increase" group, (c) a "decrease" group, and (d) a consistently "high" group. Although not always recovered, these trajectories are often found, despite these studies varying in the ages of the samples studied and the duration of the observation periods employed. Here, the authors examined the consistency with which these longitudinal patterns of heavy drinking were recovered in a series of latent class growth analyses that systematically varied the age of the sample at baseline, the duration of observation, and the number and frequency of measurement occasions. Data were drawn from a 4-year, 8-wave panel study of college student drinking (N = 3,720). Despite some variability across analyses, there was a strong tendency for these prototypes to emerge regardless of the participants' age at baseline and the duration of observation. These findings highlight potential problems with commonly employed trajectory-based approaches and the need to not over-reify these constructs. [ABSTRACT FROM AUTHOR]

Although college women are known to be at high risk for eating-related problems, relatively little is known about how various aspects of concerns related to eating, weight, and shape are patterned syndromally in this population. Moreover, the extent to which various patterns represent stable conditions or transitory states during this dynamic period of development is unclear. The present study used latent class and latent transition analysis (LCA/LTA) to derive syndromes of concerns related to eating, weight, and shape and movement across these syndromes in a sample of 1,498 women ascertained as first-time freshmen and studied over 4 years. LCA identified 5 classes characterized by (a) no obvious pathological eating-related concerns (prevalence: 28%-34%); (b) a high likelihood of limiting attempts (prevalence: 29%-34%); (c) a high likelihood of overeating and binge eating (prevalence: 14%-18%); (d) a high likelihood of limiting attempts and overeating or binge eating (prevalence: 14%-17%); and (e) pervasive bulimiclike concerns (prevalence: 6%-7%). Membership in each latent class tended to be stable over time. When movement occurred, it tended to be to a less severe class. These findings indicate that there are distinct, prevalent, and relatively stable forms of eating-related concerns in college women. [ABSTRACT FROM AUTHOR]

Mental disorders are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of mental disorders, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting construct multidimensionality with alcohol use disorder (AUD). To do so, we used a large sample of college heavy drinkers (N = 909) for whom AUD symptomology was thoroughly assessed (87 items) and a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional AUD model with one item per symptom criterion fit the data well, whereas a unidimensional model with all items fit the data poorly. Starting with an 11-item AUD model, model fit decreased and the variability in factor loadings increased as additional items were added to the model. Additionally, multidimensional models outperformed unidimensional ones in terms of variance explained in theoretically relevant external criteria. All told, we converged on a hierarchically organized model of AUD with three broad, transcriterial dimensions that reflected tolerance, withdrawal, and loss of control. In addition to introducing a hierarchical model of AUD, we propose that thorough assessment of psychological constructs paired with serious consideration of alternative, multidimensional structures can move past the deadlock of their unidimensional representations. (PsycInfo Database Record (c) 2021 APA, all rights reserved). [ABSTRACT FROM AUTHOR]

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).

Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple clusters, the procedure is extended to determine the number of clusters. Through a series of simulations, the proposed methodology is shown to outperform several other commonly used procedures for determining both the presence of clusters and their number.