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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]

To achieve scientific progress in terms of building a cumulative body of knowledge, careful attention to benchmarking is of the utmost importance, requiring that proposals of new methods are extensively and carefully compared with their best predecessors, and existing methods subjected to neutral comparison studies. Answers to benchmarking questions should be evidence‐based, with the relevant evidence being collected through well‐thought‐out procedures, in reproducible and replicable ways. In the present paper, we review good research practices in benchmarking from the perspective of the area of cluster analysis. Discussion is given to the theoretical, conceptual underpinnings of benchmarking based on simulated and empirical data in this context. Subsequently, the practicalities of how to address benchmarking questions in clustering are dealt with, and foundational recommendations are made based on existing literature. This article is categorized under:Fundamental Concepts of Data and Knowledge > Data ConceptsFundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > Structure Discovery and Clustering [ABSTRACT FROM AUTHOR]

Deterministic blockmodelling is a well‐established clustering method for both exploratory and confirmatory social network analysis seeking partitions of a set of actors so that actors within each cluster are similar with respect to their patterns of ties to other actors (or, in some cases, other objects when considering two‐mode networks). Even though some of the historical foundations for certain types of blockmodelling stem from the psychological literature, applications of deterministic blockmodelling in psychological research are relatively rare. This scarcity is potentially attributable to three factors: a general unfamiliarity with relevant blockmodelling methods and applications; a lack of awareness of the value of partitioning network data for understanding group structures and processes; and the unavailability of such methods on software platforms familiar to most psychological researchers. To tackle the first two items, we provide a tutorial presenting a general framework for blockmodelling and describe two of the most important types of deterministic blockmodelling applications relevant to psychological research: structural balance partitioning and two‐mode partitioning based on structural equivalence. To address the third problem, we developed a suite of software programs that are available as both Fortran executable files and compiled Fortran dynamic‐link libraries that can be implemented in the R software system. We demonstrate these software programs using networks from the literature. [ABSTRACT FROM AUTHOR]

Most partitioning methods used in psychological research seek to produce homogeneous groups (i.e., groups with low intra‐group dissimilarity). However, there are also applications where the goal is to provide heterogeneous groups (i.e., groups with high intra‐group dissimilarity). Examples of these anticlustering contexts include construction of stimulus sets, formation of student groups, assignment of employees to project work teams, and assembly of test forms from a bank of items. Unfortunately, most commercial software packages are not equipped to accommodate the objective criteria and constraints that commonly arise for anticlustering problems. Two important objective criteria for anticlustering based on information in a dissimilarity matrix are: a diversity measure based on within‐cluster sums of dissimilarities; and a dispersion measure based on the within‐cluster minimum dissimilarities. In many instances, it is possible to find a partition that provides a large improvement in one of these two criteria with little (or no) sacrifice in the other criterion. For this reason, it is of significant value to explore the trade‐offs that arise between these two criteria. Accordingly, the key contribution of this paper is the formulation of a bicriterion optimization problem for anticlustering based on the diversity and dispersion criteria, along with heuristics to approximate the Pareto efficient set of partitions. A motivating example and computational study are provided within the framework of test assembly. [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]

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]

Abstract: We present a statistical methodology for clustering decision makers according to similar choice behavior. We apply a p‐median clustering algorithm that identifies an “exemplar” for each cluster, a decision maker who best represents that cluster. We demonstrate that information about group behavior can be inferred by examining the behavior of each cluster's exemplar. The method is exploratory, providing information about the prevalence of decision strategies without researchers needing to specify candidate strategies a priori. The method is also very general and can be applied to a wide range of decision‐making data structures. We illustrate our method by re‐analyzing two existing choice data sets. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The emergence of Gaussian model-based partitioning as a viable alternative to K-means clustering fosters a need for discrete optimization methods that can be efficiently implemented using model-based criteria. A variety of alternative partitioning criteria have been proposed for more general data conditions that permit elliptical clusters, different spatial orientations for the clusters, and unequal cluster sizes. Unfortunately, many of these partitioning criteria are computationally demanding, which makes the multiple-restart (multistart) approach commonly used for K-means partitioning less effective as a heuristic solution strategy. As an alternative, we propose an approach based on iterated local search (ILS), which has proved effective in previous combinatorial data analysis contexts. We compared multistart, ILS and hybrid multistart-ILS procedures for minimizing a very general model-based criterion that assumes no restrictions on cluster size or within-group covariance structure. This comparison, which used 23 data sets from the classification literature, revealed that the ILS and hybrid heuristics generally provided better criterion function values than the multistart approach when all three methods were constrained to the same 10-min time limit. In many instances, these differences in criterion function values reflected profound differences in the partitions obtained. [ABSTRACT FROM AUTHOR]

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem ( MSMCSP). A recently published branch-and-bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch-and-bound approach. [ABSTRACT FROM AUTHOR]

Background Variability in sensitivity to the acute effects of alcohol is an important risk factor for the development of alcohol use disorder ( AUD). The most commonly used retrospective self-report measure of sensitivity, the Self-Rating of the Effects of Alcohol (SRE) form, queries a limited number of alcohol effects and relies on respondents' ability to recall experiences that might have occurred in the distant past. Here, we investigated the construct validity of an alternative measure that queries a larger number of alcohol effects, the Alcohol Sensitivity Questionnaire ( ASQ), and compared it to the SRE in predicting momentary subjective responses to an acute dose of alcohol. Methods Healthy young adults ( N = 423) completed the SRE and the ASQ and then were randomly assigned to consume either alcohol or a placebo beverage (between-subjects manipulation). Stimulation and sedation (Biphasic Alcohol Effects Scale) and subjective intoxication were measured multiple times after drinking. Results Hierarchical linear models showed that the ASQ reliably predicted each of these outcomes following alcohol but not placebo consumption, provided unique prediction beyond that associated with differences in recent alcohol involvement, and was preferred over the SRE (in terms of model fit) in direct model comparisons of stimulation and sedation. Conclusions The ASQ compared favorably with the better-known SRE in predicting increased stimulation and reduced sedation following an acute alcohol challenge. The ASQ appears to be a valid self-report measure of alcohol sensitivity and therefore holds promise for identifying individuals at-risk for AUD and related problems. [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]

Aims This study used a person-centered approach to test whether drinking motive typologies could be identified. Design Longitudinal study of college students within the Intensive Multivariate Prospective Alcohol College- Transitions ( IMPACTS) data set. Setting University campus in the United States. Participants University students (baseline n reporting alcohol motives = 2158; baseline age = 18.60 years old). Measurements The Drinking Motives Questionnaire- Revised ( DMQ-R). Findings Using Steinley & Brusco's cluster analysis approach [based on the theoretical ratio expected between the within sum of squares and the total sum of squares when the data are divided into two clusters when no cluster structure is present; the cut-off for the ratio is 0.25 for uniform (multivariate uniform) distributions and 0.36 for normal (multivariate normal) distributions], we examined whether there was evidence for distinct clusters of individuals that differed on their overall level of motives to drink. We tested the fit of a one-group (cluster) solution compared to multi-cluster solutions. Both cross-sectionally and prospectively, the data could not be partitioned into two or more clusters [regardless of whether the cut-off assuming a multivariate uniform distribution (i.e. 0.25) or the more liberal multivariate normal distribution (i.e. 0.36) was used]. These findings showed that enhancement and coping drinkers do not form two distinct groups but, rather, these motives exist on a continuum such that individuals who are high in one internal motive tend to be high in the other motive. Conclusions Coping and enhancement drinkers do not form two distinct groups. Variable-centered approaches to drinking motives may be a better alternative to classifying all drinkers as either enhancement or coping drinkers for both clinical and research endeavors. [ABSTRACT FROM AUTHOR]

Objective: How to best classify concerns related to eating, weight, and shape (CREWS) in men remains an open question. Research on men considering CREWS during different developmental periods could be particularly informative. Method: Focusing on one potentially dynamic developmental period, this study charts the course of CREWS in men over the college years. Latent class/latent transition analysis identified typologies of weight- and shape-influenced self judgment, limiting attempts, fasting, overeating, binge eating, self-induced vomiting, and laxative or diuretic abuse for 1,025 men over the four traditional college years. Results: Three classes emerged: (1) no obvious pathological eating-related concerns (61-65%); (2) a high likelihood of limiting attempts and a moderately high likelihood of overeating (31-34%); (3) pervasive bulimic-like concerns (4-6%). Class membership was highly stable across assessment occasions. Discussion: The results contribute to the growing literature on empirically derived classifications of CREWS and indicate that for many men CREWS are a chronic presence during the college years. © 2011 by Wiley Periodicals, Inc. (Int J Eat Disord 2012; 45:768-775) [ABSTRACT FROM AUTHOR]

Abstract: To date, the vast majority of cluster analysis applications in OM research have relied on traditional hierarchical (e.g., Ward''s algorithm) and nonhierarchical (e.g., K-means algorithms) methods. Although these venerable methods should continue to be employed effectively in the OM literature, we also believe there is a significant opportunity to expand the scope of clustering methods to emergent techniques. We provide an overview of some alternative clustering procedures (including advantages and disadvantages), identify software programs for implementing them, and discuss the circumstances where they might be employed gainfully in OM research. The implementation of emergent clustering methods in the OM literature should enable researchers to offer implications for practice that might not have been uncovered with traditional methods. [Copyright &y& Elsevier]

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]

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]

Background: Research has documented normative patterns of personality change during emerging and young adulthood that reflect decreases in traits associated with substance use, such as impulsivity. However, evidence suggests variability in these developmental changes. Methods: This study examined trajectories of impulsivity and their association with substance use and related problems from ages 18 to 35. Analyses were based on data collected from a cohort of college students ( N = 489), at high and low risk for AUDs, first assessed as freshmen at a large, public university. Results: Mixture modeling identified five trajectory groups that differed in baseline levels of impulsivity and developmental patterns of change. Notably, the trajectory group that exhibited the sharpest declines in impulsivity tended to display accelerated decreases in alcohol involvement from ages 18 to 25 compared to the other impulsivity groups. Conclusion: Findings highlight the developmental nature of impulsivity across emerging and young adulthood and provide an empirical framework to identify key covariates of individual changes of impulsivity. [ABSTRACT FROM AUTHOR]

The article presents a study that develops two new heuristics for subset selection in polynomial regression based on tabu and variable neighborhood researches. It says that the procedures incorporate an explanatory neighborhood search method that simplify the escape from optimal subset. It adds that there are applications where the inclusion/exclusion of variables necessitates inclusion/exclusion of other variables which call for further studies.

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]

The kappa coefficient is one of the most widely used measures for evaluating the agreement between two raters asked to assign N objects to one of K nominal categories. Weighted versions of kappa enable partial credit to be awarded for near agreement, most notably in the case of ordinal categories. An exact significance test for weighted kappa can be conducted by enumerating all rater agreement tables with the same fixed marginal frequencies as the observed table, and accumulating the probabilities for all tables that produce a weighted kappa index that is greater than or equal to the observed measure. Unfortunately, complete enumeration of all tables is computationally unwieldy for modest values of N and K. We present an implicit enumeration algorithm for conducting an exact test of weighted kappa, which can be applied to tables of non-trivial size. The algorithm is particularly efficient for 'good' to 'excellent' values of weighted kappa that typically have very small p-values. Therefore, our method is beneficial for situations where resampling tests are of limited value because the number of trials needed to estimate the p-value tends to be large. [ABSTRACT FROM AUTHOR]

This paper synthesizes the results, methodology, and research conducted concerning the K-means clustering method over the last fifty years. The K-means method is first introduced, various formulations of the minimum variance loss function and alternative loss functions within the same class are outlined, and different methods of choosing the number of clusters and initialization, variable preprocessing, and data reduction schemes are discussed. Theoretic statistical results are provided and various extensions of K-means using different metrics or modifications of the original algorithm are given, leading to a unifying treatment of K-means and some of its extensions. Finally, several future studies are outlined that could enhance the understanding of numerous subtleties affecting the performance of the K-means method. [ABSTRACT FROM AUTHOR]

The article discusses several papers published within the issue including the classic notions of blockmodeling, the predicting structure and attributes in social networks, and an application of predicting climate models.