Different athletes cope differently during a sport competition: a cluster analysis of coping

This study aimed at examining the ways in which different athletes may combine the use of several coping strategies and at testing whether perceived goal attainment, affective states, and experience of control would differ across distinctive profiles of coping. A sample of 151 French-Canadian athletes out of 200 who were approached by a research assistant completed l’Inventaire des Strat gies de Coping en Comp tition Sportive (Gaudreau & Blondin, 2002a), the PANAS ( Watson, Clark, & Tellegen, 1988), and a series of items measuring experience of control and perceived goal attainment. Results of a cluster analysis indicated that athletes could be classified in four groups according to their in-competition use of coping strategies. After controlling for athletes’ self-referenced goal attainment, their experience of control, positive affective state, and anger-dejection state differed significantly (p<0.008) across the four profiles of coping. Athletes who have used high level of task-oriented coping in combination with low level of disengagement-oriented coping reported better goal and psychological adjustment than athletes who have used high level of disengagement-oriented coping in combination with low level of task-oriented coping. Overall, these findings suggested that research should move from coping strategies to coping profiles in order to provide a deeper understanding of how different individuals cope with stress.     

Short-term predictive validity of cluster analytic and dimensional classification of child behavioral adjustment in school

A constructive debate over the classification of child psychopathology can be stimulated by investigating the validity of different classification approaches. We examined and compared the short-term predictive validity of cluster analytic and dimensional classifications of child behavioral adjustment in school using the Behavior Assessment System for Children (BASC; [Reynolds, C. R., and Kamphaus, R. W. (1992). Behavior Assessment System for Children (BASC). Circle Pines, MN: AGS.]) Teacher Rating Scales for Children (TRS-C). We cluster analyzed 14 dimensional scores of children's behavior in first grade and identified seven clusters. Then we examined the predictive power of the dimensions and the clusters using a variety of school outcomes in second grade, including academic achievement scores in reading and math, absenteeism, discipline reports, and participation rates in pre-referral intervention. Both methods significantly predicted all school outcomes with similar magnitudes of effect sizes, but the dimensional approach was more powerful in predicting the outcomes except pre-referral intervention. Moreover, coherent group differences in the clusters were identified with respect to the school outcomes. This latter finding may suggest differentiated prognoses of cluster membership. The implications for future validation studies of alternative classification systems of child behavioral adjustment are discussed.     

Identifying child temperament types using cluster analysis in three samples

Although previous research has explored the existence of temperament clusters in children, these studies have used single samples, reducing generalizability, and broad temperament constructs to identify clusters, obscuring fine-grained dimensional differences. To address these limitations, the current study identified a common cluster structure across two diverse samples of preschool children using the same fine-grained temperament measure (ns = 96, 187), then verified the cluster structure on a large, nationally-representative sample (n = 757). A consistent six-cluster solution was identified across the three samples: Unregulated, Regulated, High Reactive, Bold, Average, and Well-Adjusted. Demonstration of a replicable typology advances the understanding of temperament in childhood, and provides a parsimonious way to communicate complex information about a child’s temperament to parents and caregivers.     

The estimation of ultrametric and path length trees from rectangular proximity data

A least-squares algorithm for fitting ultrametric and path length or additive trees to two-way, two-mode proximity data is presented. The algorithm utilizes a penalty function to enforce the ultrametric inequality generalized for asymmetric, and generally rectangular (rather than square) proximity matrices in estimating an ultrametric tree. This stage is used in an alternating least-squares fashion with closed-form formulas for estimating path length constants for deriving path length trees. The algorithm is evaluated via two Monte Carlo studies. Examples of fitting ultrametric and path length trees are presented.G. De Soete is Aspirant of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek at the University of Ghent, Belgium. W. S. DeSarbo and J. D. Carroll are Members of Technical Staff at AT&T Bell Laboratories, Murray Hill, N.J. G. W. Furnas is Member of Technical Staff at Bell Communications Research, Murray Hill, N.J.     

Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data

A reparameterization of a latent class model is presented to simultaneously classify and scale nominal and ordered categorical choice data. Latent class-specific probabilities are constrained to be equal to the preference probabilities from a probabilistic ideal-point or vector model that yields a graphical, multidimensional representation of the classification results. In addition, background variables can be incorporated as an aid to interpreting the latent class-specific response probabilities. The analyses of synthetic and real data sets illustrate the proposed method.The authors thank Yosiho Takane, the editor and referees for their valuable suggestions. Authors are listed in reverse alphabetical order.     

Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables

In the application of clustering methods to real world data sets, two problems frequently arise: (a) how can the various contributory variables in a specific battery be weighted so as to enhance some cluster structure that may be present, and (b) how can various alternative batteries be combined to produce a single clustering that best incorporates each contributory set. A new method is proposed (SYNCLUS, SYNthesizedCLUStering) for dealing with these two problems.We wish to thank Anne Freeny and Deborah Art for their computer assistance, and Ed Fowlkes for his helpful technical discussion. We would also like to acknowledge the insightful and helpful comments from the editor and reviewers.     

The a priori defended: a defense of the generality argument

One of Laurence BonJour’s main arguments for the existence of the a priori is an argument that a priori justification is indispensable for making inferences from experience to conclusions that go beyond experience. This argument has recently come under heavy fire from Albert Casullo, who has dubbed BonJour’s argument, “The Generality Argument.” In this paper I (i) defend the Generality Argument against Casullo’s criticisms, and (ii) develop a new, more plausible, version of the Generality Argument in response to some other objections of my own. Two of these objections stem out of BonJour’s failing to fully consider the importance of the distinction between being justified in believing that an inference is good and being justified in making an inference. The final version of the argument that I develop sees the Generality Argument as one part of a cumulative case argument for the existence of a priori justification, rather than as a stand-alone knock-down argument.     

Publication bias in research synthesis: sensitivity analysis using a priori weight functions

Publication bias, sometimes known as the "file-drawer problem" or "funnel-plot asymmetry," is common in empirical research. The authors review the implications of publication bias for quantitative research synthesis (meta-analysis) and describe existing techniques for detecting and correcting it. A new approach is proposed that is suitable for application to meta-analytic data sets that are too small for the application of existing methods. The model estimates parameters relevant to fixed-effects, mixed-effects or random-effects meta-analysis contingent on a hypothetical pattern of bias that is fixed independently of the data. The authors illustrate this approach for sensitivity analysis using 3 data sets adapted from a commonly cited reference work on research synthesis (H. M. Cooper & L. V. Hedges, 1994).     

The relationship between maladaptive personality functioning and problematic technology use in adolescence: A cluster analysis approach

In the last two decades, scientific research has explored the problematic use of internet, videogames and mobile phones. However, there is still little consistent knowledge regarding the co-occurrence of problematic technology use and the role of maladaptive personality characteristics in adolescence. The present study aimed to investigate adolescents' styles of technology use with a cluster analysis approach focusing on personality functioning. The sample comprised 408 Italian adolescents (46.3% males) aged 11 to 18 years (M _age = 13.80; SD = 2.08). Data were collected using the Internet Addiction Test, the Videogame Dependency Scale, the Test of Mobile-Phone Dependence Brief Form and the Personality Inventory for DSM5 Brief Form. Results provided a four-cluster solution based on the co-occurrence of problematic technology use. The four clusters were labeled as follows: cluster 1: “Above average internet and mobile-phone use”; cluster 2: “Below average technology use”; cluster 3: “Above average videogame use”; and cluster 4: “Problematic technology use.” Analyses on demographic variables (e.g., gender and age) demonstrated significant differences between the four groups. Adolescents with high levels of problematic technology use reported greater overall personality dysfunction than the other three groups. This finding supported our hypothesis on maladaptive personality functioning in adolescents at risk for addiction. Finally, the Antagonism domain played a specific role in differentiating the severity of adolescents' involvement in technology use. Further studies are needed to confirm our findings and to plan preventive interventions as well as therapeutic treatments.     

Behavioral and cognitive-affective features of stuttering in preschool-age children: Regression and exploratory cluster analyses

PurposeThe purpose of this study was to investigate associations among behavioral and cognitive-affective features of stuttering in preschool-age children who stutter, and the extent to which participants may or may not cluster together based on multiple indices of stuttering.MethodsParticipants were 296 preschool-age children who stutter (mean age 47.9 months). Correlation and regression analyses, as well as k-means cluster analyses were conducted between and among several indices of stuttering: frequency of stuttering- and non-stuttering-like disfluencies (SLDs and NSLDs), ratios of repetitions and prolongations/blocks out of total number of SLDs, associated nonspeech behaviors, duration of stuttering events, KiddyCAT scores (Vanryckeghem & Brutten, 2007), and a TOCS parent-rated scale (Gillam et al., 2009).ResultsFor preschool-age children who stutter, most indices of overt stuttering behaviors were intercorrelated (e.g., more SLDs were associated with higher ratio of repetitions). Self-reported KiddyCAT scores (Vanryckeghem & Brutten, 2007) were largely not significantly associated with stuttering. Cluster analyses yielded two participant groupings: a larger group with less prominent stuttering features and a smaller group with more prominent features.ConclusionsThis study contributes to an increasingly comprehensive and nuanced understanding of the heterogeneous features of stuttering and their development in preschool-age children. Findings show strong intercorrelations between measures of stuttering behaviors, but more tenuous relationships between behaviors and cognitive-affective reactions to stuttering. Exploration of clusters of characteristics within this population revealed potential opportunities for future research.     

The long-term effect of relational information in classification learning

This study examines the long-term effect of mutual information in the learning of Shepardian classifications. Mutual information is a measure of the complexity of the relationship between features because it quantifies how the features relate to each other. For instance, in various categorisation models, Type VI concepts—originally studied by Shepard, Hovland, and Jenkins (1961)—are unanimously judged to be the most complex kind of 3-D Boolean concepts. This has been largely confirmed by empirical data. Yet, it is apparently inconsistent with the fact that this concept entails the greatest amount of mutual information of all the 3-D Boolean concepts. The present study was aimed at verifying whether individuals can use relational information, in the long run, to devise easier strategies for category learning. Subject performance was measured repeatedly for 1 hour on either successive Type VI concepts (using different features between problems) or successive Type IV concepts. The results showed that shortly after the second problem, Type VI concepts became easier to learn than Type IV ones. The gap between the mean per-problem error rates of the two concepts continued to increase as the number of problems increased. Two other experiments tended to confirm this trend. The discussion brings up the idea of combining different metrics in categorisation models in order to include every possible way for subjects to simplify the categorisation process.     

Communality of a variable: Formulation by cluster analysis

The communality of a variable represents the degree of its generality acrossn – 1 behaviors. Domain-sampling principles provide a fundamental conception and definition of the communality. This definition may be alternatively stated in eight different ways. Three definitions lead to precise formulas that determine thetrue value of the communality: (i) from thek necessary and sufficient dimensions derived by iterated factoring, (ii) from then – 1 remaining variable-domains, and (iii) fromk' multiple clusters of then variables. Seven definitions provide approximation formulas: (i) one from thek dimensions as initially factored, (ii) one from then – 1 remaining variables, and (iii) five from a single cluster. Rank of the matrix is not a desiratum in some definitions. Using an example designed by Guilford to illustrate multiple-factor analysis, applications of the formulas based on the three precise definitions recover the true communalities, and five approximation formulas each gives values closer than thead hoc estimates usually employed in factor analysis.The writer wishes to express his indebtedness to C. F. Wrigley and H. Kaiser for their many helpful constructive criticisms.     

Applying Cluster Analysis to Define a Typology of Chronic Fatigue Syndrome in a Medically-Evaluated,Random Community Sample

This study involved a randomly selected, medically-evaluated, community-based sample of 166 individuals with chronic fatigue. Participants diagnosed with chronic fatigue syndrome and medically-explained chronic fatigue reported significantly more severe fatigue following exertion than the idiopathic chronic fatigue group, and participants with medically-explained chronic fatigue also reported significantly more severe fatigue following exertion than the psychiatrically-explained chronic fatigue group. A cluster analysis was performed to define a typology of chronic fatigue symptomatology for participants diagnosed with chronic fatigue syndrome. Three clusters emerged. Cluster 1 contained only one participant with chronic fatigue syndrome and was characterized by relatively low post-exertional fatigue. Cluster 2 contained a small proportion of individuals with chronic fatigue syndrome and was characterized by most severe post-exertional fatigue and most improvement in fatigue following rest. Cluster 3 contained the highest proportion of individuals with chronic fatigue syndrome, and was characterized by high post-exertional fatigue and fatigue not alleviated by rest.     

Development of classification: The use of similarity and dimensional relations

Findings about perceptual development indicate that overall similarity is the primary perceptual relation by which young children compare complex objects. Traditional studies of classification, however, did not focus on children's organizational use of holistic relations but rather on their ability to classify by dimensions or criterial attributes. The results from such traditional studies suggest that young children are deficient classifiers. The present research investigated the possibility, contrary to the traditional view, that 4- to 6-year-old children are competent and systematic classifiers at least by overall similarity. In three experiments, preschoolers and kindergarteners classified various sets of multidimensional stimuli that could be organized into categories by overall similarity or by dimensional attributes. Consistent with the research in perceptual development, the children were highly attentive to overall similarity. However, the preschoolers in particular showed marked difficulty in using this relation to form categories of more than two objects. The children's difficulties were highly reminiscent of traditional claims about early classification. Analyses of the classification strategies used by the children, however, suggest that even the youngest children understood the purpose of a classification. The developmentla changes appear to be in the ability to execute a classification. Importantly, type of classification strategy was independent of type of category organization. Individual children used the same strategies both when classifying by overall similarity and by dimensional attributes. These results strongly suggest that it is the classification skills themselves, and not just the ability to classify by particular relations, that change with age.     

The use of visual information in natural scenes

Despite the complexity and diversity of natural scenes, humans are very fast and accurate at identifying basic-level scene categories. In this paper we develop a new technique (based on Bubbles, Gosselin & Schyns, 2001a; Schyns, Bonnar, & Gosselin, 2002) to determine some of the information requirements of basic-level scene categorizations. Using 2400 scenes from an established scene database (Oliva & Torralba, 2001), the algorithm randomly samples the Fourier coefficients of the phase spectrum. Sampled Fourier coefficients retain their original phase while the phase of nonsampled coefficients is replaced with that of white noise. Observers categorized the stimuli into 8 basic-level categories. The location of the sampled Fourier coefficients leading to correct categorizations was recorded per trial. Statistical analyses revealed the major scales and orientations of the phase spectrum that observers used to distinguish scene categories.