scholarly journals Examining adults’ participant roles in cyberbullying

2019 ◽  
Vol 36 (11-12) ◽  
pp. 3362-3370 ◽  
Author(s):  
Lucy R. Betts ◽  
Thom Baguley ◽  
Sarah E. Gardner
Keyword(s):  
Cluster Analysis ◽  
Group Membership ◽  
Participant Roles

Adults’ participant roles in cyberbullying remain unclear. Two hundred and sixty-four (163 female and 87 male) 18- to 74-year-olds from 31 countries completed measures to assess their experiences of, and engagement in, 5 cyberbullying types for up to 9 media. Cluster analysis identified two distinct groups: rarely victim and bully (85%) and frequently victim and occasional bully. Sex and age predicted group membership: Females and older participants were more likely to belong to the rarely victim and bully group, whereas males and younger participants were more likely to belong to the frequently victim and occasional bully group. The findings have implications for anti-cyberbullying interventions and how behaviors are interpreted online.

Judging a book by its cover

2015 ◽  
Vol 5 (1) ◽  
pp. 23-48 ◽  
Author(s):  
Peter Dixon ◽  
Marisa Bortolussi ◽  
Blaine Mullins
Keyword(s):  
Cluster Analysis ◽  
Science Fiction ◽  
Linear Models ◽  
Group Membership ◽  
Signaling System ◽  
Similarity Structure ◽  
Book Covers

In this experiment, we investigated whether book covers can signal sub-genre information to knowledgeable readers. Self-identified science-fiction fans and mystery fans sorted 80 randomly selected book covers from each of those genres into groups of their own devising. The sorts were used to identify similarity among books, and that similarity structure was used to measure similarity among subjects. Cluster analysis was then used to find groups of subjects who sorted similarly. Linear models were demonstrated that group membership was related to the knowledge subjects reported about the genres. This pattern of results supports the view that book covers constitute an implicit signaling system between publishers and experienced readers of a fictional genre.

Cluster Analysis of Marketing Data Examining On-line Shopping Orientation

2011 ◽  
pp. 208-225 ◽  
Author(s):  
Kevin E. Voges ◽  
Nigel K.L. Pope ◽  
Mark R. Brown
Keyword(s):  
Cluster Analysis ◽  
Rough Sets ◽  
Group Membership ◽  
Simple Extension ◽  
Classification Problems ◽  
Cluster Membership ◽  
Shopping Orientation ◽  
On Line ◽  
Marketing Data ◽  
Sets Theory

Cluster analysis is a common market segmentation technique, usually using k-means clustering. Techniques based on developments in computational intelligence are increasingly being used. One such technique is the theory of rough sets. However, previous applications have used rough sets techniques in classification problems, where prior group membership is known. This chapter introduces rough clustering, a technique based on a simple extension of rough sets theory to cluster analysis, and applicable where group membership is unknown. Rough clustering solutions allow multiple cluster membership of objects. The technique is demonstrated through the analysis of a data set containing scores on psychographic variables, obtained from a survey of shopping orientation and Web purchase intentions. The analysis compares k-means and rough clustering approaches. It is suggested that rough clustering can be considered to be extracting concepts from the data. These concepts can be valuable to marketers attempting to identify different segments of consumers.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

2020 ◽  
pp. 1-25
Author(s):  
Matthew L. Hall ◽  
Stephanie De Anda
Keyword(s):  
Cluster Analysis ◽  
Exposure Assessment ◽  
Hard Of Hearing ◽  
Assessment Tool ◽  
Communication Mode ◽  
Developmental Potential ◽  
Language Access ◽  
Retest Reliability ◽  
Language Exposure ◽  
Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

Social representations of human rights violations: Further evidence

2001 ◽  
Vol 60 (2) ◽  
pp. 89-98 ◽  
Author(s):  
Alain Clémence ◽  
Thierry Devos ◽  
Willem Doise
Keyword(s):  
Cluster Analysis ◽  
Human Rights ◽  
Costa Rica ◽  
Young People ◽  
Social Representations ◽  
Social Reality ◽  
Questionnaire Study ◽  
Human Rights Violations ◽  
High Tolerance ◽  
Individual Actions

Social representations of human rights violations were investigated in a questionnaire study conducted in five countries (Costa Rica, France, Italy, Romania, and Switzerland) (N = 1239 young people). We were able to show that respondents organize their understanding of human rights violations in similar ways across nations. At the same time, systematic variations characterized opinions about human rights violations, and the structure of these variations was similar across national contexts. Differences in definitions of human rights violations were identified by a cluster analysis. A broader definition was related to critical attitudes toward governmental and institutional abuses of power, whereas a more restricted definition was rooted in a fatalistic conception of social reality, approval of social regulations, and greater tolerance for institutional infringements of privacy. An atypical definition was anchored either in a strong rejection of social regulations or in a strong condemnation of immoral individual actions linked with a high tolerance for governmental interference. These findings support the idea that contrasting definitions of human rights coexist and that these definitions are underpinned by a set of beliefs regarding the relationships between individuals and institutions.

How Do People Share Their Positive Events?

2016 ◽  
Vol 37 (4) ◽  
pp. 250-259 ◽  
Author(s):  
Cara A. Palmer ◽  
Meagan A. Ramsey ◽  
Jennifer N. Morey ◽  
Amy L. Gentzler
Keyword(s):  
Cluster Analysis ◽  
Communication Technology ◽  
Big Five ◽  
Well Being ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Positive Events ◽  
Growing Body ◽  
Sharing Patterns ◽  
Practical Implications

Abstract. Research suggests that sharing positive events with others is beneficial for well-being, yet little is known about how positive events are shared with others and who is most likely to share their positive events. The current study expanded on previous research by investigating how positive events are shared and individual differences in how people share these events. Participants (N = 251) reported on their likelihood to share positive events in three ways: capitalizing (sharing with close others), bragging (sharing with someone who may become jealous or upset), and mass-sharing (sharing with many people at once using communication technology) across a range of positive scenarios. Using cluster analysis, five meaningful profiles of sharing patterns emerged. These profiles were associated with gender, Big Five personality traits, narcissism, and empathy. Individuals who tended to brag when they shared their positive events were more likely to be men, reported less agreeableness, less conscientiousness, and less empathy, whereas those who tended to brag and mass-share reported the highest levels of narcissism. These results have important theoretical and practical implications for the growing body of research on sharing positive events.

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