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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]

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]

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]

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]

In this commentary, we use " I p i -factor" when referring to the theoretical construct, and "general factor of psychopathology" when referring to a statistical instantiation of the I p i -factor (Watts, Poore, & Waldman, [82]). The leftmost column includes a list of each psychopathology indicator used to test a general factor of psychopathology, with its use in each study denoted with an X. The rightmost column displays the percent of studies included in the review that included each psychopathology indicator. As such, bifactor models appear at face value to offer something appealing to psychopathology researchers: the ability to model general and specific sources of variance in psychopathology (Brandes, Herzhoff, Smack, & Tackett, [12]). For instance, although all indicators are allowed to load onto a general factor in a bifactor model, there are a number of bifactor models of psychopathology with general factors that are overrepresented by some forms of psychopathology, and underrepresented (or even unrepresented or negatively indicated) by others. [Extracted from the article]

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]

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]

We propose a hybrid two-group classification method that integrates linear discriminant analysis, a polynomial expansion of the basis (or variable space), and a genetic algorithm with multiple crossover operations to select variables from the expanded basis. Using new product launch data from the biochemical industry, we found that the proposed algorithm offers mean percentage decreases in the misclassification error rate of 50%, 56%, 59%, 77%, and 78% in comparison to a support vector machine, artificial neural network, quadratic discriminant analysis, linear discriminant analysis, and logistic regression, respectively. These improvements correspond to annual cost savings of $4.40–$25.73 million. [ABSTRACT FROM AUTHOR]

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]

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]

We consider problems where relationships between two sets (or modes) of objects are available in the form of a binary matrix with elements of 1 (0) indicating a bond (lack of a bond) between corresponding row and column objects. The goal is to establish a partition of the row objects and, simultaneously, a partition of the column objects to form blocks that consist of either exclusively 1s or exclusively 0s to the greatest extent possible. Thistwo-mode blockmodeling problemarises in several scientific domains. In the social sciences, two-mode blockmodeling is particularly relevant for social network analysis, where the goal is to simultaneously partition a set of individuals and another set of objects (e.g., events they attended, organizations they are affiliated with, etc.). The inherent computational challenge of simultaneously constructing partitions for two distinct sets of objects has fostered a reliance on heuristics for two-mode blockmodeling. We offer an exact algorithm and demonstrate its efficacy in a simulation study. Two applications to real-world networks are also provided. [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]

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]

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]

Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the within-cluster regression models from the error explained by the clustering process. In some cases, most of the variation in the response variable is explained by clustering the objects, with little additional benefit provided by the within-cluster regression models. Accordingly, there is tremendous potential for overfitting with clusterwise regression, which is demonstrated with numerical examples and simulation experiments. To guard against the misuse of clusterwise regression, we recommend a benchmarking procedure that compares the results for the observed empirical data with those obtained across a set of random permutations of the response measures. We also demonstrate the potential for overfitting via an empirical application related to the prediction of reflective judgment using high school and college performance measures. [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]

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]

The article discusses a study related to use of Cohen's κ for community detection in social networks and to measure interrater agreement. Topics discussed include traditional goal of identifying clusters that are internally cohesive, testing the algorithm on a variety of simulated and real networks within community structure, and use of this methodology to identify friend groups within the school.