scholarly journals Cluster Analysis Research on Consumers’ Perceived Recommendation Trust

2021 ◽  
Vol 9 (2) ◽  
pp. 119-134
Author(s):  
Olasupo Sule ◽  
◽  
Bheki Surian ◽  
Keyword(s):  
Cluster Analysis

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.

When cluster analysis fails: method and theory in the search for alcoholic subtypes

Addiction ◽  
1997 ◽  
Vol 92 (12) ◽  
pp. 1665-1666
Author(s):  
Thomas F. Babor
Keyword(s):  
Cluster Analysis

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.

A cluster analysis study onCinnamomum from Kerala, India

1991 ◽  
Vol 102 (3-4) ◽  
pp. 167-175
Author(s):  
Shylaja Ravindran ◽  
R. Balakrishnan ◽  
K. S. Manilal ◽  
P. M. Ravindran
Keyword(s):  
Cluster Analysis ◽  
Analysis Study
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