Population recovery capabilities of 35 cluster analysis methods

Overall, John E.; Gibson, Janet M.; Novy, Diane M.

doi:10.1002/1097-4679(199307)49:4<459::aid-jclp2270490402>3.0.co;2-p

Cited by 86 publications

(45 citation statements)

References 17 publications

(7 reference statements)

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“…This two-stage clustering procedure (hierarchical and non-hierarchical methods used in tandem) produced enhanced cluster recovery and were found to be robust with respect to different types of error (Milligan, 1980). In the hierarchical cluster analysis, we used Ward's minimum variance methods applied to squared Euclidean distance indices, because a simulation study suggested this combination to be superior (Overall et al 1993). We chose two stopping rules for determining the number of clusters.…”

Section: Group Classificationmentioning

confidence: 99%

Risk and Resilience Factors among Italian Municipal Police Officers Exposed to Critical Incidents

Prati

Pietrantoni

2009

J Police Crim Psych

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Police officers are exposed to critical events as part of their duty which can affect their well-being. The present study examines the relationships between risk and protective factors and health outcome following critical incident exposure among police officers. Person-oriented analyses were applied to questionnaire data from a sample of 509 Italian municipal police officers. Two groups with different patterns of risk and protective factors were found by cluster analysis. One group (resilient officers) had higher levels of protective factors such as self-esteem and social support and lower levels of risk factors such as peritraumatic distress and perceived threat in comparison to the other group (non resilient police officers). The resilient group reported fewer traumatic stress reactions than the non resilient group in spite of a similar degree of exposure to critical incidents and better health than expected in spite of severe abuse. The two groups differed according to sleeping pills use but not for alcohol and smoking habits. Risk and protective factors could offer an explanation of resilience as an outcome.

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Section: Group Classificationmentioning

confidence: 99%

Risk and Resilience Factors among Italian Municipal Police Officers Exposed to Critical Incidents

Prati

Pietrantoni

2009

J Police Crim Psych

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“…In this case, the dissimilarity between any pair of students was defined in terms of the city-block metric (as the sum of the absolute differences between their scores on the six scales). Using artificial data sets, Overall, Gibson, and Novy (1993) found that complete linkage clustering and the city-block metric achieved better recovery of latent clusters than all other combinations of clustering methods and metrics.…”

Section: Conceptions Of Effective Tutoring 12mentioning

confidence: 99%

Student and tutor perceptions of effective tutoring in distance education

2009

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Questionnaire responses of 457 students and 602 tutors were used to investigate conceptions of a 'good tutor'. In each case, factor analysis identified scales that reflected key constructs; cluster analysis identified subgroups with different patterns of scale scores; and discriminant analysis determined the scales that contributed the most to differences among the clusters.Both sets of data yielded conceptions of tutoring that were described as task-oriented and student-oriented, respectively. The students' data yielded an additional, career-oriented conception. The tutors' data yielded two additional conceptions that were described as knowledge-oriented and impersonal, respectively. The distribution of the tutors' conceptions (but not that of the students' conceptions) varied across different faculties, suggesting that tutors from different disciplines have different beliefs about effective tutoring. The study suggests that both tutors and students would benefit from having a better appreciation of the importance of support in facilitating learning.

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“…Ward's algorithm is a hierarchical agglomerative procedure that has been shown to be one of the better clustering methods in several simulation studies (Blashfield, 1976;Milligan, 1980;Milligan & Cooper, 1987;Milligan & Hirtle, 2003;Overall, Gibson, & Novy, 1993). Since hierarchical cluster analyses are generally sensitive to the input order of the data (e.g., Podani, 1997), we used the SPSS add-on PermuCLUSTER (van der Kloot, Spanns, & Heiser, 2005) to compare the cluster solutions obtained from 100 random permutations of the input order.…”

Section: Time Series-based Typologymentioning

confidence: 99%

Detecting longitudinal patterns of daily smoking following drastic cigarette reduction

Hoeppner

Goodwin

Velicer

et al. 2008

Addictive Behaviors

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To enhance prolonged smoking cessation or reduction, a better understanding of the process of change is needed. This study examines daily smoking rates following the end of an intensive smoking reduction program originally designed to evaluate the relationship of tobacco biomarkers with reduced levels of smoking. A novel pattern-oriented approach called time-series-based typology is used to detect homogeneous smoking patterns in time-intensively (i.e., 40 occasions) observed smokers (n=57), who were predominantly Caucasian (94.7%), male (52.6%), and on average 47.9 years old (SD=11.3). The majority of the smokers exhibited a change in their daily smoking behavior over the course of 40 days with 47.4% increasing and 40.4% decreasing the number of cigarettes smoked per day, which is contrary to the results a group level approach would have found. Very few smokers (12.3%) maintained their average smoking rate, and exhibited an externally controlled smoking pattern. Trajectory type could be predicted by temporally proximal motivation and selfefficacy variables ((F(4, 106)=3.46, p=.011, η 2 =.115), underscoring their importance in maintaining reduced smoking rates. Time series-based typology demonstrated good sensitivity to the identification of meaningfully different trajectories. Keywordstime series-based typology; time series analysis; harm reduction; longitudinal smoking patterns; idiographic research A new wave of behavior change intervention research is beginning that seeks to usher a new generation of more effective, far-reaching, and sustainable interventions into existence (Orleans, 2005; S. D. Solomon, 2005). The creation and dissemination of innovative methodological tools that allow for the analysis of complex longitudinal data is an important part of this new initiative. In this paper, we present an innovative approach to longitudinal data that is based on traditionally used analyses and is thus very accessible to applied researchers. In particular, our paper focuses on detecting longitudinal patterns of daily smoking following drastic cigarette reduction. With most cigarette smokers relapsing within three months of a cessation attempt (Fiore, Smith, Jorenby, & Baker, 1994;Shiffman, 1993), there is a need to improve our understanding of the process of behavior change following smoking reduction Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. and cessation and underlying relapse. To this end, our paper presents the largest time series study of smoking behaviors to date. It is also our starting point for showcasing an innovative and easy to use analytical to...

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Population recovery capabilities of 35 cluster analysis methods

Cited by 86 publications

References 17 publications

Risk and Resilience Factors among Italian Municipal Police Officers Exposed to Critical Incidents

Risk and Resilience Factors among Italian Municipal Police Officers Exposed to Critical Incidents

Student and tutor perceptions of effective tutoring in distance education

Detecting longitudinal patterns of daily smoking following drastic cigarette reduction

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