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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Ising model has received significant attention in network psychometrics during the past decade. A popular estimation procedure is IsingFit, which uses nodewise l1-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 l1-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 l1-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 l1-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. [ABSTRACT FROM AUTHOR]

Objective: To develop a shortened form of the Young Adult Alcohol Problems Screening Test1 (YAAPST; original length = 27 items) using a novel combinatorial approach. Participants: 489 college freshmen, half of whom were above average risk for alcohol use disorder based upon family history, attending a large, Midwestern University followed prospectively for 16–17 years across 7 measurement occasions (waves). Methods: Using an exhaustive combinatorial approach, we evaluated all possible combinations of questions and questionnaire length. Each candidate short-form was evaluated using part-whole correlations, alpha coefficients, and polyserial correlations with DSM-III criteria. Results: The 12-item scale, the Brief-YAAPST, was chosen as the best candidate short-form as this scale had the fewest items while maintaining strong reliability and validity. Conclusions: Complete enumeration optimization effectively produced a 12-item short form with minimal loss of reliability and validity making the Brief-YAAPST a desirable assessment alternative when there are concerns about participant or client burden. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Using complete enumeration (e.g., generating all possible subsets of item combinations) to evaluate clustering problems has the benefit of locating globally optimal solutions automatically without the concern of sampling variability. The proposed method is meant to combine clustering variables in such a way as to create groups that are maximally different on a theoretically sound derivation variable(s). After the population of all unique sets is permuted, optimization on some predefined, user-specific function can occur. We apply this technique to optimizing the diagnosis of Alcohol Use Disorder. This is a unique application, from a clustering point of view, in that the decision rule for clustering observations into the "diagnosis" group relies on both the set of items being considered and a predefined threshold on the number of items required to be endorsed for the "diagnosis" to occur. In optimizing diagnostic rules, criteria set sizes can be reduced without a loss of significant information when compared to current and proposed, alternative, diagnostic schemes. [ABSTRACT FROM AUTHOR]

K-means inverse regression was developed as an easy-to-use dimension reduction procedure for multivariate regression. This approach is similar to the original sliced inverse regression method, with the exception that the slices are explicitly produced by a K-means clustering of the response vectors. In this article, we propose K-medoids clustering as an alternative clustering approach for slicing and compare its performance to K-means in a simulation study. Although the two methods often produce comparable results, K-medoids tends to yield better performance in the presence of outliers. In addition to isolation of outliers, K-medoids clustering also has the advantage of accommodating a broader range of dissimilarity measures, which could prove useful in other graphical regression applications where slicing is required. [ABSTRACT FROM AUTHOR]

This article addresses the issue of assigning items to different test dimensions (e.g., determining which dimension an item belongs to) with cluster analysis. Previously, hierarchical methods have been used (Roussos et al. 1997); however, the findings here suggest that an iterative reallocation partitioning (IRP) algorithm provides interpretively similar solutions and statistically better solutions to the problem. More importantly, it is shown that the inherent nature of locally optimal solutions in the IRP algorithm leads to a method that aids in determining the appropriateness of performing a cluster analysis—a feature that is lacking in the standard hierarchical methods currently in the literature. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including optimal clustering; intuitionistic fuzzy sets; and ultrametrics and symbolic maps.

We present a procedure (RAUS) for residual analysis of a dissimilarity matrix whereby unidimensional scaling is successively applied to the absolute value of residuals. A key advantage of RAUS is that the efficient Defays formulation of unidimensional scaling can be used for the fitting of each scale. An example using U.S. Supreme Court voting data is provided to illustrate the interpretation of successive scales. A simulation study was performed to evaluate RAUS. [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]

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. [ABSTRACT FROM AUTHOR]

Affinity propagation is a message‐passing‐based clustering procedure that has received widespread attention in domains such as biological science, physics, and computer science. However, its implementation in psychology and related areas of social science is comparatively scant. In this paper, we describe the basic principles of affinity propagation, its relationship to other clustering problems, and the types of data for which it can be used for cluster analysis. More importantly, we identify the strengths and weaknesses of affinity propagation as a clustering tool in general and highlight potential opportunities for its use in psychological research. Numerical examples are provided to illustrate the method. [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]

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. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including traditional Dendritic Cell Algorithm (DCA), missing data in affine subspaces, and potential mislabeled group data.

Two expectations of the adjusted Rand index (ARI) are compared. It is shown that the expectation derived by Morey and Agresti (1984, Educational and Psychological Measurement, 44, 33) under the multinomial distribution to approximate the exact expectation from the hypergeometric distribution (Hubert & Arabie, 1985, Journal of Classification, 2, 193) provides a poor approximation, and, in some cases, the difference between the two expectations can increase with the sample size. Proofs concerning the minimum and maximum difference between the two expectations are provided, and it is shown through simulation that the ARI can differ significantly depending on which expectation is used. Furthermore, when compared in a hypothesis testing framework, multinomial approximation overly favours the null hypothesis. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles in the issue on topics including Bayesian techniques, support vector machine (SVM), and hierarchical clustering.

Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date. [ABSTRACT FROM PUBLISHER]

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]

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. [ABSTRACT FROM AUTHOR]

Objective: The findings of previous research that examined relationships between popularity and alcohol use in adolescents have been mixed, and few hypotheses have proposed mechanisms for this relationship. The current study expands on previous literature (a) by examining a possible mechanism that can explain the relation between popularity and alcohol use (home access to alcohol) and (b) by using another sociometric measure ("betweenness"), beyond popularity, that may relate more to home alcohol access.Method: Using network-level data from adolescents in 9th-11th grades in eight schools within two in-home waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examined two sociometric measures of social status: popularity (number of schoolmates who nominated participants as a friend) and betweenness (level of ties participants have to multiple social subgroups within a network).Results: Betweenness, but not popularity, related to later alcohol use. Having home access to alcohol positively related to later alcohol use, and having friends with home access to alcohol negatively related to later alcohol use. Alcohol access was also related to later sociometric status. Friends' alcohol access negatively related to later betweenness, and personal alcohol access moderated other pathways predicting betweenness.Conclusions: Betweenness appears to play a unique role in the association between social status and alcohol use in adolescent social networks. This is potentially tied to specific ways in which adolescents may be able to access alcohol (through home or through friends with access at home). More research is necessary to examine the ways in which multiple sociometric statuses relate to the contexts in which adolescents access and use alcohol. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This can lead to the overextraction of latent classes and the attribution of substantive meaning to these spurious classes. The goals of this study are (1) to explore the performance of the Bayesian information criterion, sample-adjusted BIC, and bootstrap likelihood ratio test in growth mixture modeling analysis with nonnormal distributed outcome variables and (2) to examine the effects of nonnormal time invariant covariates in the estimation of the number of latent classes when outcome variables are normally distributed. For both of these goals, we will include nonnormal conditions not considered previously in the literature. Two simulation studies were conducted. Results show that spurious classes may be selected and optimal solutions obtained in the data analysis when the population departs from normality even when the nonnormality is only present in time invariant covariates. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue including the model-based clustering, the development of a regression mixture approach and the advancement of regression model-based clustering.

Several reports published in the current issue of the journal are presented including on density sensitive hierarchical clustering method, exponential power depth as a measure of statistical depth to aid in classification and alternative methodology for disambiguating inherent chaining.

An introduction is presented in which the editor discusses reports within the issue, including topics on the cognitive diagnosis model in educational assessment, principal components analysis, and analysis of the history of website visits.

It is common knowledge that mixture models are prone to arrive at locally optimal solutions. Typically, researchers are directed to utilize several random initializations to ensure that the resulting solution is adequate. However, it is unknown what factors contribute to a large number of local optima and whether these coincide with the factors that reduce the accuracy of a mixture model. A real-data illustration and a series of simulations are presented that examine the effect of a variety of data structures on the propensity of local optima and the classification quality of the resulting solution. We show that there is a moderately strong relationship between a solution that has a high proportion of local optima and one that is poorly classified. [ABSTRACT FROM PUBLISHER]

Some individuals “change” more dramatically than others when intoxicated, and the nature and magnitude of these changes can result in harmful outcomes. This study utilized reports (N = 374) of participants’ “typical” five-factor model (FFM) characteristics across sober and intoxicated states and assessed the degree to which these reports could be grouped into meaningful clusters, as well as the association of cluster membership with negative alcohol-related consequences. Results from finite mixture model clustering revealed a four cluster solution. Cluster 1, “Hemingway,” was the largest and defined by intoxication-related decreases in Conscientiousness and Intellect that were below average; Cluster 2, “Mary Poppins,” was defined by being high in Agreeableness when sober, decreasing less than average in Conscientiousness and Intellect and increasing more than average in Extraversion when drunk; Cluster 3, “Mr. Hyde,” reported larger drunk decreases in Conscientiousness and Intellect and smaller increases in Extraversion; Cluster 4, “The Nutty Professor,” was defined by being low in Extraversion when sober, increasing more than average in Extraversion and decreasing less than average in Conscientiousness when drunk. Cluster membership was associated with experiencing more alcohol consequences. These results support use of the FFM to characterize clinically meaningful subgroups of sober-to-drunk differences in trait expression. [ABSTRACT FROM AUTHOR]

One of the most important distinctions that must be made in clustering research is the difference between models (or problems) and the methods for solving those problems. Nowhere is this more evident than with the evaluation of the popular affinity propagation algorithm (apcluster.m), which is a MATLAB implementation of a neural clustering method that has received significant attention in the biological sciences and other disciplines. Several authors have undertaken comparisons of apcluster.m with methods designed for models that fall within the class of uncapacitated facility location problems (UFLPs). These comparative models include the p-center (or K-center) model and, more importantly, the p-median (or K-median) model. The results across studies are conflicting and clouded by the fact that, although similar, the optimization model underlying apcluster.m is slightly different from the p-median model and appreciably different from the pcenter model. In this paper, we clarify that apcluster.m is actually a heuristic for a 'maximization version' of another model in the class of UFLPs, which is known as the simple plant location problem (SPLP). An exact method for the SPLP is described, and the apcluster.m program is compared to a fast heuristic procedure (sasplp.m) in both a simulation experiment and across numerous datasets from the literature. Although the exact method is the preferred approach when computationally feasible, both apcluster.m and sasplp.m are efficient and effective heuristic approaches, with the latter slightly outperforming the former in most instances. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented on topics including clustering of time series data; approach to data reduction in the context of classification; and construction of cognitive diagnostic tests.

Building on Brusco and Steinley (2008), a computationally efficient stepwise optimal heuristic is provided for maximizing the adjusted Rand index (Hubert and Arabie 1985). The proposed algorithm is different than other methods for estimating the maximum value for the adjusted Rand index (e.g., Messatfa 1992) in that it does not rely on mathematical programming; consequently, problems of much larger size can be handled. Using the proposed method, various characteristics of the adjusted Rand index are explored and presented. [ABSTRACT FROM AUTHOR]

The minimum-diameter partitioning problem ( MDPP) seeks to produce compact clusters, as measured by an overall goodness-of-fit measure known as the partition diameter, which represents the maximum dissimilarity between any two objects placed in the same cluster. Complete-linkage hierarchical clustering is perhaps the best-known heuristic method for the MDPP and has an extensive history of applications in psychological research. Unfortunately, this method has several inherent shortcomings that impede the model selection process, such as: (1) sensitivity to the input order of the objects, (2) failure to obtain a globally optimal minimum-diameter partition when cutting the tree at K clusters, and (3) the propensity for a large number of alternative minimum-diameter partitions for a given K. We propose that each of these problems can be addressed by applying an algorithm that finds all of the minimum-diameter partitions for different values of K. Model selection is then facilitated by considering, for each value of K, the reduction in the partition diameter, the number of alternative optima, and the partition agreement among the alternative optima. Using five examples from the empirical literature, we show the practical value of the proposed process for facilitating model selection for the MDPP. [ABSTRACT FROM AUTHOR]

Although socializing effects of friends' drinking on adolescent drinking behavior have been firmly established in previous literature, study results on the importance of gender, as well as the specific role that gender may play in peer socialization, are very mixed. Given the increasing importance of gender in friendships (particularly opposite-sex friendships) during adolescence, it is necessary to better understand the nuanced roles that gender can play in peer socialization effects on alcohol use. In addition, previous studies focusing on the interplay between individual gender and friends' gender have been largely dyadic; less is known about potential gendered effects of broader social networks. The current study sought to further investigate potential effects of gender on friends' influence on adolescent drinking behavior with particular emphasis on the number of same-sex and opposite-sex friends within one's friendship network, as well as closeness to these friends. Using Waves I and II of the saturated sample of the National Longitudinal Study of Adolescent Health (Add Health), adolescent friendship networks were used to calculate the mean drinking behaviors of adolescent friends. Multi-level models estimated the effects of individual drinking behaviors, friend drinking behaviors, and school-level drinking behaviors on adolescent drinking 1 year later, as well as moderating effects of gender composition of friendship groups and male and female friend closeness on the relationship between friends' drinking behaviors and adolescent drinking behavior. Results documented that gender composition of friendship groups did not influence the effect of friends' drinking on individual drinking 1 year later. However, closeness to friends did influence this relationship. As closeness to male friends decreased, the influence of their drinking behavior increased, for both boys and girls. A similar effect was found for female friends, but only for boys. Female friend closeness did not affect the relationship between peer alcohol socialization and girls' alcohol use. The findings indicate that the role of gender on alcohol socialization may be more complex than previously thought, particularly when examining the potential role that alcohol use may play as a mechanism for social bonding within opposite-sex friendships and same-sex male friendships. [ABSTRACT FROM AUTHOR]

Error variance in structural models is often specified as a conditional variance associated with a manifest variable or as a regression path from a standardized error variance. This paper describes scenarios in which specification of both terms is useful: (1) Corrections for attenuation in single indicator factor models; (2) Tests of the equality in the proportion of explained variance across multiple dependent variables in regression models; (3) Factor models with two indicators; and (4) Tests of invariance in the proportion of measurement error variance across occasions in growth curve models. Examples of such models using both simulated and real-world data are presented. [ABSTRACT FROM AUTHOR]

A measure of “clusterability” serves as the basis of a new methodology designed to preserve cluster structure in a reduced dimensional space. Similar to principal component analysis, which finds the direction of maximal variance in multivariate space, principal cluster axes find the direction of maximum clusterability in multivariate space. Furthermore, the principal clustering approach falls into the class of projection pursuit techniques. Comparisons are made with existing methodologies both in a simulation study and analysis of real-world data sets. Furthermore, a demonstration of how to interpret the results of the principal cluster axes is provided on the analysis of Supreme Court voting data and similarities between the interpretation of competing procedures (e.g., factor analysis and principal component analysis) are provided. In addition to the Supreme Court analysis, we analyze several data sets often used to test cluster analysis procedures, including Fisher's Iris data, Agresti's Crab data, and a data set on glass fragments. Finally, discussion is provided to help determine when the proposed procedure will be the most beneficial to the researcher. [ABSTRACT FROM AUTHOR]

There are a number of important problems in quantitative psychology that require the identification of a permutation of the n rows and columns of an n × n proximity matrix. These problems encompass applications such as unidimensional scaling, paired-comparison ranking, and anti-Robinson forms. The importance of simultaneously incorporating multiple objective criteria in matrix permutation applications is well recognized in the literature; however, to date, there has been a reliance on weighted-sum approaches that transform the multiobjective problem into a single-objective optimization problem. Although exact solutions to these single-objective problems produce supported Pareto efficient solutions to the multiobjective problem, many interesting unsupported Pareto efficient solutions may be missed. We illustrate the limitation of the weighted-sum approach with an example from the psychological literature and devise an effective heuristic algorithm for estimating both the supported and unsupported solutions of the Pareto efficient set. [ABSTRACT FROM AUTHOR]

Clusterwise p* models are developed to detect differentially functioning network models as a function of the subset of observations being considered. These models allow the identification of subgroups (i.e., clusters) of individuals who are 'structurally' different from each other. These clusters are different from those produced by standard blockmodeling of social interactions in that the goal is not necessarily to find dense subregions of the network; rather, the focus is finding subregions that are functionally different in terms of graph structure. Furthermore, the clusterwise p* approach allows for local estimation of network regions, avoiding some of the common degeneracy problems that are rampant in p* (e.g., exponential random graph) models. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 487-496, 2011 [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]

Aims Alcohol tolerance is a hallmark indicator of alcohol dependence. Even so, the allure of peers' admiration for having the ability to drink heavily may lead some adolescents and young adults to practice, or 'train', to increase their tolerance (particularly at US colleges, where heavy drinking is highly prevalent and central to the social culture). This is a potential health hazard that has not been documented empirically. Thus, we initiated a study of tolerance 'training' and its association to risky and heavy drinking. Design, setting and participants A cross-sectional online survey of 990 college student life-time drinkers at a large Midwestern US university. Findings Of the sample, 9.9% ( n = 97) reported deliberately 'training' to increase tolerance. On average, they reported increasing from approximately seven to 10 US standard drinks in a night prior to 'training' to 12-15 drinks at the end of 'training,' over approximately 2-3 weeks' duration. Although the proportion of frequent binge drinking among 'non-trainers' (34.4%) was similar to national rates, 'trainers' were much more likely to be frequent bingers (76.3%; OR = 6.15). Conclusions A number of students report deliberately inducing alcohol tolerance, probably directly increasing the risk for alcohol poisoning and other acute harms and/or dependence. This phenomenon might additionally be applicable to other populations, and deserves further study and attention as a potential personal and public health risk. Prevention efforts might aim to reduce the perceived importance of heavy-drinking abilities. [ABSTRACT FROM AUTHOR]

Variable selection in multiple regression requires identifying the best subset from among a set of candidate predictors. In the case of polynomial regression, the variable selection process can be further complicated by the need to obtain subsets that are hierarchically well formulated. We present a branch-and-bound algorithm for selection of the best hierarchically well-formulated subset in second-order polynomial regression. We apply the new algorithm to a well-known data set from the regression literature and compare the results with those obtained from a branch-and-bound algorithm that does not impose the hierarchical constraints. This comparison reveals that the hierarchical constraints yield only a small penalty in explained variation. We offer Fortran and MATLAB implementations of the branch-and-bound algorithms as supplemental materials associated with this work. [ABSTRACT FROM AUTHOR]

The implementation of multiobjective programming methods in combinatorial data analysis is an emergent area of study with a variety of pragmatic applications in the behavioural sciences. Most notably, multiobjective programming provides a tool for analysts to model trade offs among competing criteria in clustering, seriation, and unidimensional scaling tasks. Although multiobjective programming has considerable promise, the technique can produce numerically appealing results that lack empirical validity. With this issue in mind, the purpose of this paper is to briefly review viable areas of application for multiobjective programming and, more importantly, to outline the importance of cross-validation when using this method in cluster analysis. [ABSTRACT FROM AUTHOR]

This paper develops a new procedure, called stability analysis, for K-means clustering. Instead of ignoring local optima and only considering the best solution found, this procedure takes advantage of additional information from a K-means cluster analysis. The information from the locally optimal solutions is collected in an object by object co-occurrence matrix. The co-occurrence matrix is clustered and subsequently reordered by a steepest ascent quadratic assignment procedure to aid visual interpretation of the multidimensional cluster structure. Subsequently, measures are developed to determine the overall structure of a data set, the number of clusters and the multidimensional relationships between the clusters. [ABSTRACT FROM AUTHOR]

This research note focuses on a problem where the cluster sizes for two partitions of the same object set are assumed known; however, the actual assignments of objects to clusters are unknown for one or both partitions. The objective is to find a contingency table that produces maximum possible agreement between the two partitions, subject to constraints that the row and column marginal frequencies for the table correspond exactly to the cluster sizes for the partitions. This problem was described by H. Messatfa ( Journal of Classification, 1992, pp. 5–15), who provided a heuristic procedure based on the linear transportation problem. We present an exact solution procedure using binary integer programming. We demonstrate that our proposed method efficiently obtains optimal solutions for problems of practical size. [ABSTRACT FROM AUTHOR]

A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these procedures are demonstrated in a simulation study, showing favorable results when compared with existing standardization methods. A detailed demonstration of the weighting and selection procedure is provided for the well-known Fisher Iris data and several synthetic data sets. [ABSTRACT FROM AUTHOR]

An important manufacturing cell formation problem requires permutations of the rows (parts) and columns (machines) of a part-machine incidence matrix such that the reordered matrix exhibits a block-diagonal form. Numerous objective criteria and algorithms have been proposed for this problem. In this paper, a new perspective is offered that is based on the relationship between the consecutive ones property associated with interval graphs and Robinson structure within symmetric matrices. This perspective enables the cell formation problem to be decomposed into two permutation subproblems (one for rows and one for columns) that can be solved optimally using dynamic programming or a branch-and-bound algorithm for matrices of nontrivial size. A simulated annealing heuristic is offered for larger problem instances. Results pertaining to the application of the proposed methods for a number of problems from the literature are presented. [ABSTRACT FROM AUTHOR]

K-means clustering is arguably the most popular technique for partitioning data. Unfortunately, K-means suffers from the well-known problem of locally optimal solutions. Furthermore, the final partition is dependent upon the initial configuration, making the choice of starting partitions all the more important. This paper evaluates 12 procedures proposed in the literature and provides recommendations for best practices. [ABSTRACT FROM AUTHOR]

Similarities between latent class models with K classes and linear factor models with K—1 factors are investigated. Specifically, the mathematical equivalence between the covariance structure of the two models is discussed, and a Monte Carlo simulation is performed using generated data that represents both latent factors and latent classes with known amounts of overlap. It is shown that, under certain conditions, the distribution of factor scores can be related to the continuity of the latent space via tests of multimodality as suggested by McDonald (1967). [ABSTRACT FROM AUTHOR]

An introduction is presented in which discusses several articles in the issue on topics including a latent structural model (LSM) for polytomous attributes; the important topic of cluster validation; and cognitive diagnosis modelling.