Role of Stressful Life Events in Homelessness: An Intragroup Analysis

The present paper describes the results of an analysis of individual differences—using multidimensional strategies—in stressful life event data collected from a representative sample of homeless people (N = 289) in Madrid, Spain, which revealed the existence of three subgroups within the sample. Each subgroup can be defined by the following differentiating characteristics: The first cluster (n = 124) was characterized by economic problems; the second (n = 80) by health problems, alcohol abuse, and death of one or both parents; and the third cluster (n = 50) by an accumulation of stressful life in childhood events and alcohol abuse. In order to define the subgroups, the article also examines the differences found in other variables such as total duration of homelessness, mental and physical health status, and social support availability, among others. The existence of such subgroups within the homeless population emphasizes the importance of designing different interventions for each of these groups, adapted to their diverse needs.     

Optimal Partitioning of a Data Set Based on the p-Median Model

Although the K-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The p-median model is an especially well-studied clustering problem that requires the selection of p objects to serve as cluster centers. The objective is to choose the cluster centers such that the sum of the Euclidean distances (or some other dissimilarity measure) of objects assigned to each center is minimized. Using 12 data sets from the literature, we demonstrate that a three-stage procedure consisting of a greedy heuristic, Lagrangian relaxation, and a branch-and-bound algorithm can produce globally optimal solutions for p-median problems of nontrivial size (several hundred objects, five or more variables, and up to 10 clusters). We also report the results of an application of the p-median model to an empirical data set from the telecommunications industry.     

How Many Times and How Many Ways: The Impact of Number of Nonsuicidal Self‐Injury Methods on the Relationship Between Nonsuicidal Self‐Injury Frequency and Suicidal Behavior

Several variables have been proposed as heavily influencing or explaining the association between nonsuicidal self‐injury (NSSI) and suicidal behavior. We propose that increased comfort with bodily harm may serve as an incrementally valuable variable to consider. We sought to indirectly test this possibility by examining the moderating role of number of NSSI methods utilized on the relationship between NSSI frequency and lifetime number of suicide attempts, positing that increased variability in methods would be indicative with a greater general comfort with inflicting harm upon one's own body. In both a large sample of emerging adults (n = 1,317) and a subsample with at least one prior suicide attempt (n = 143), results were consistent with our hypothesis. In both samples, the interaction term was significant, with the relationship between NSSI frequency and suicidal behavior increasing in magnitude from low to mean to high levels of NSSI methods. Although frequency of NSSI is robustly associated with suicidal behavior, the magnitude of that relationship increases as an individual engages in a wider variety of NSSI methods. We propose that this may be due to an increased comfort with the general concept of damaging one's own body resulting from a broader selection of methods for self‐harm.     

A square root method of selecting a minimum set of variables in multiple regression: I. The method

An extension of Dwyer's “square root” method has been made to the problem of selecting a minimum set of variables in a multiple regression problem. The square root method of selection differs from the Wherry-Doolittle method primarily in that (1) the computations required are more compact, (2) anF ratio criterion is used which leads to the selection of fewer variables. The method provides solutions for the problems of test selection, item analysis, analysis of variance with disproportionate frequencies, and other problems requiring the rejection of superfluous variables. In a subsequent article a worked example will be given, and the square root and Wherry-Doolittle methods compared.     

A variable-selection heuristic for K-means clustering

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.     

A cognitive and an affective dimension of alexithymia in six languages and seven populations

The Dutch Bermond–Vorst Alexithymia Questionnaire (BVAQ) is translated into various languages. The aim of this research was to establish the factor structure of subscales on seven cultural groups. The BVAQ consists of five subscales of eight items each: Emotionalising, Fantasising, Analysing, Identifying, and Verbalising emotions. The BVAQ was administered to a group of Dutch students (n=375), a group of English students (n=175), a group of Australian students, university employees and visitors (n=216), a group of French speaking Belgian students (n=175), a group of Italian people (n=791; a mix of various clinical groups), a group of Polish people (n=427; also a mix of various clinical groups) and a group of Russian people (n=141; general population). The hypothesised two-factor structure of an affective alexithymia dimension (Emotionalising, Fantasising) and a cognitive alexithymia dimension (Insight and Verbalising), with “Analysing emotions” loading on both factors, was clearly supported by confirmatory factor analyses (CFA). Both orthogonal and oblique principal components analyses (PCA), without restriction concerning the number of factors, provided the same two-factor solution in all groups explaining between 55% and 64% of the variance. Oblique rotation further demonstrated that the correlations between these two factors were low in all populations. The combined CFA and PCA results, therefore, indicated that a model with two independent factors has to be preferred over the model assuming two correlated factors.     

Control Options in Managing Strategic Decision-making Processes

Abstract

Top decision making processes are generally depicted as chaotic, unstructured, incremental or spasmodic, and protracted. In this article it is shown, based on data from 25 strategic decision processes from 12 organizations in three industry sectors, that, within certain limits, there certainly are control options in strategic decision-making processes. In addition, their use tums out to be profitable. The analyses of patterns in the use of control dimensions in the 25 cases leads to a typology of four types of decision models or strategies: the neo-rational model (n = 3), the bureaucratic model (n = 4), the arena model (n = 8) and the open-end model (n = 10). This empirical typology confirms earlier typologies based on literature.

The use of control strategies is shown to vary across industry sectors and topics of decisions and have adifferential effect. It is concluded that context and content variables have either a selection effect (some strategies do not occur in some sectors) or a optimization effect (some strategies are more effective than others in a given context).     

Multiple goals,motivation and academic learning

Background: The type of academic goals pursued by students is one of the most important variables in motivational research in educational contexts. Although motivational theory and research have emphasised the somewhat exclusive nature of two types of goal orientation (learning goals versus performance goals), some studies (Meece, 1994; Seifert, 1995, 1996) have shown that the two kinds of goals are relatively complementary and that it is possible for students to have multiple goals simultaneously, which guarantees some flexibility to adapt more efficaciously to various contexts and learning situations. Aim: The principal aim of this study is to determine the academic goals pursued by university students and to analyse the differences in several very significant variables related to motivation and academic learning. Sample: Participants were 609 university students (74% women and 26% men) who filled in several questionnaires about the variables under study. Method: We used cluster analysis ('quick cluster analysis' method) to establish the different groups or clusters of individuals as a function of the three types of goals (learning goals, performance goals, and social reinforcement goals). By means of MANOVA, we determined whether the groups or clusters identified were significantly different in the variables that are relevant to motivation and academic learning. Lastly, we performed ANOVA on the variables that revealed significant effects in the previous analysis. Results: Using cluster analysis, three groups of students with different motivational orientations were identified: a group with predominance of performance goals (Group PG: n = 230), a group with predominance of multiple goals (Group MG: n = 238), and a group with predominance of learning goals (Group LG: n = 141). Conclusions: Groups MG and LG attributed their success more to ability, they had higher perceived ability, they took task characteristics into account when planning which strategies to use in the learning process, they showed higher persistence, and used more deep learning strategies than did the students with predominance of performance goals (Group PG). On the other hand, Groups MG and PG took the evaluation criteria more into account when deciding which strategies to use in order to learn, and they attributed their failures more to luck than did Group LG. Students from Group MG attributed their success more to effort than did the other two groups and they attained higher achievement than Group PG. Group LG tended to attribute their failures more to lack of effort than did the other two groups.     

Expectations and Attributions in Social Anxiety Disorder: Diagnostic Distinctions and Relationship to General Anxiety and Depression

Contemporary cognitive models suggest that social anxiety disorder arises from a number of cognitive factors, including tendencies to form pessimistic (rather than optimistic) attributions and expectations for socially-related events. These models also assume that the strengths of such attributions and expectations are more closely linked with social anxiety than with general anxiety or depression. To test these assumptions, a battery of self-report measures was completed by participants with a primary diagnosis of generalized social anxiety disorder (n?=?75), panic disorder with agoraphobia (n?=?44), or post-traumatic stress disorder (n?=?59). To examine differences on these cognitive variables, group comparisons were performed controlling for general anxiety, depression and medication status. Social anxiety disorder, compared with panic disorder with agoraphobia and post-traumatic stress disorder, was characterized by lower expectations for positive social events and higher expectations for negative social events. There was no difference among the groups on expectations for non-social positive or negative events. Stable and global attributions for social negative events were more closely associated with social anxiety disorder than with panic disorder with agoraphobia and post-traumatic stress disorder. Correlational analyses also revealed specific relationships among social-cognitive measures and social anxiety, even after controlling for general anxiety and depression. The results are consistent with cognitive models of social anxiety disorder.     

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.     

A model-based cluster analysis of social experiences in clinically anxious youth: links to emotional functioning

Social difficulties are commonly associated with anxiety disorders in youth, yet are not well specified in the literature. The aim of this study was to identify patterns of social experiences in clinically anxious children and examine the associations with indices of emotional functioning. A model-based cluster analysis was conducted on parent-, teacher-, and child-reports of social experiences with 64 children, ages 7–12 years (M = 8.86 years, SD = 1.59 years; 60.3% boys; 85.7% Caucasian) with a primary diagnosis of separation anxiety disorder, social phobia, and/or generalized anxiety disorder. Follow-up analyses examined cluster differences on indices of emotional functioning. Findings yielded three clusters of social experiences that were unrelated to diagnosis: (1) Unaware Children (elevated scores on parent- and teacher-reports of social difficulties but relatively low scores on child-reports, n = 12), (2) Average Functioning (relatively average scores across all informants, n = 44), and (3) Victimized and Lonely (elevated child-reports of overt and relational victimization and loneliness and relatively low scores on parent- and teacher-reports of social difficulties, n = 8). Youth in the Unaware Children cluster were rated as more emotionally dysregulated by teachers and had a greater number of diagnoses than youth in the Average Functioning group. In contrast, the Victimized and Lonely group self-reported greater frequency of negative affect and reluctance to share emotional experiences than the Average Functioning cluster. Overall, this study demonstrates that social maladjustment in clinically anxious children can manifest in a variety of ways and assessment should include multiple informants and methods.     

Work Alienation as an Individual‐Difference Construct for Predicting Workplace Adjustment: A Test in Two Samples1

This study represents the first attempt to examine the validity of work alienation as a general attitude toward the work domain. As hypothesized, hierarchical regression analyses of data from 2 employee samples (n= 99 and n= 250) indicated that work alienation explained incremental variance in selected workplace adjustment variables (i. e., job involvement, affective organizational commitment, affective occupational commitment, overall job satisfaction, and volitional absence) beyond the variance accounted for by work conscientiousness (i. e., dependability and achievement orientation) and by variables used to control for sources of self‐report variance (i. e., self‐deception and negative affectivity). These results support the legitimacy of work alienation as an individual‐difference construct associated with work‐related adjustment.     

Congruence and temporal stability of multimiethod profiles: A new pair of personality variables

Congruence and temporal stability of multimethod profiles within the individual subject were studied as proposed new personality variables in a pool of seven small groups of graduate students in clinical psychology (N= 51) Sex differences were minimal A Stability Index was derived from (a) Q correlation stability coefficients obtained from the single method profiles of an inventory, self-ratings, and mean ratings by others, or (b) absolute difference scores between the composite profiles on occasion 1 and occasion 2 A Congruence Index was derived from (a) Q correlation coefficients obtained from each pair of profiles measured by the three methods for each subject, or (b) mean absolute difference scores obtained from each pair of method profiles These moderately correlated indices were used as dependent variables in stepwise multiple regression analyses, in which scores on a profile of 21 personality variables served as independent variables Although the difference score method was at least as effective as the correlational method in predicting congruence, the Q correlational method was superior in all other analyses conducted in this study Persons high in temporal stability seem to maintain an intellectually searching and interpersonally affiliative approach to the environment, showing little hostility and little tendency to seek change in current life circumstances The highly congruent individual seeks a positive nurturant interaction with other persons, gains pleasure from both sensory and interpersonal experiences, and shows little discontent with present life circumstances     

An analysis of Guttman's simplex

Applying a Spearman formula for factor loadings to a variant of the diagonal method, the Guttman simplex model is factored algebraically inton/2 additive factors. The finding that communalities can be discovered such that the rank of a simplex becomesn/2 is contradictory to Guttman's contention that the minimal rank isn — 2. Certain matrices of 4 and 5 variables presented by Guttman as simplexes, can, in general, be considered 2-factor matrices, easily analyzed to simple structure without rotation. One example of 6 variables is factored by the method described to a 3-factor structure.Prepared under Contract 816(02) between the Office of Naval Research and Washington University and presented in part at a meeting of the Southern Society for Philosophy and Psychology, St. Louis, Missouri, March 27, 1959. Permission is granted for reproduction, translation, publication, use, and disposal in whole and in part by or for the United States Government.     

Personality factors as predictors of pathological gambling

The aim of this study was to investigate the relationship between different personality variables and pathological gambling (PG). The NEO-FFI and measures of impulsivity and sensation-seeking were administered to a sample of pathological gamblers (n = 90) and to a contrast group of non-pathological gamblers (n = 66) matched on sex and age. Gender, age, education level and the personality variables were entered into crude and adjusted logistic regression analyses with PG-status as the dependent variable. The results showed that educational level and all personality variables were significant predictors of PG in the crude analyses, however only four of the 12 significant predictor variables (Neuroticism, Openness, Impulsivity, and need for Stimulus Intensity) remained significant in the adjusted analysis. All predictor variables accounted for 71% of the variance in PG-status. Clinical implications of the findings are discussed.     

Evidence for Discontinuity Patterns Across the Stages of Change: A Role for Attitudinal Ambivalence

Cross-sectional analyses of the transtheoretical ("stages of change") model (TTM) can provide valuable information concerning the variables that are likely to shift individuals from one stage to the next. The present study was designed to investigate the roles of theory of planned behaviour (TPB) variables and attitudinal ambivalence across the stages of change. Two samples derived from the general population completed measures of stage, TPB and attitudinal ambivalence with respect to consuming a low-fat diet (n = 148) and eating five portions of fruit and vegetables per day (n = 151). Consistent with previous research, the present study found linear increases in TPB variables across the stages of change. However, as predicted, attitudinal ambivalence showed discontinuity patterns, displaying a quadratic relationship with the stages of change, such that least ambivalence was experienced in the precontemplation and maintenance stages. Discussion of the findings focuses on implications for the development of theory-driven interventions.     

Helping Those Who Help Themselves: Does Counseling Enhance Retention?

This study examined how academic distress changed over the course of counseling and predicted retention. The sample comprised students receiving services at the campus counseling center (n = 404), students from a psychology department subject pool (n = 311), and students from the general campus population (n = 75,748). The analyses performed included t test, analysis of covariance, chi‐square test, and logistical regression. The results suggested that students whose academic distress did not decrease had lower retention rates than both clients whose academic distress improved and the general student body.     

Cluster analysis in community research: Epistemology and practice

Cluster analysis refers to a family of methods for identifying cases with distinctive characteristics in heterogeneous samples and combining them into homogeneous groups. This approach provides a great deal of information about the types of cases and the distributions of variables in a sample. This paper considers cluster analysis as a quantitative complement to the traditional linear statistics that often characterize community psychology research. Cluster analysis emphasizes diversity rather than central tendency. This makes it a valuable tool for a wide range of familiar problems in community research. A number of these applications are considered here, including the assessment of change over time, network composition, network density, person-setting relationships, and community diversity. A User's Guide section is included, which outlines the major decisions involved in a basic cluster analyses. Despite difficulties associated with the identification of optimal cluster solutions, carefully planned, theoretically informed application of cluster analysis has much to offer community researchers. Editor's note: Dr. Edward Seidman served as action editor for this article while serving as Associate Editor for Methodology.     

The contribution of an orthogonal multiple factor solution to multiple correlation

A method is indicated by which multiple factor analysis may be used in determining a number,r, and then in selectingr predicting variables out ofn variables so that each of the remainingn-r variables may be predicted almost as well from ther variables as it could be predicted from all then—1 variables.