Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis

Tsair-Wei Chien; Shih-Bin Su

doi:doi:10.11648/j.acm.s.2017060401.15

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Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis

Tsair-Wei Chien, Shih-Bin Su

Published in Applied and Computational Mathematics (Volume 6, Issue 4-1)

Received: 20 December 2016 Accepted: 9 January 2017 Published: 24 January 2017

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Abstract

Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.

Published in	Applied and Computational Mathematics (Volume 6, Issue 4-1) This article belongs to the Special Issue Some Novel Algorithms for Global Optimization and Relevant Subjects
DOI	10.11648/j.acm.s.2017060401.15
Page(s)	55-63
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Social Network Analysis, Structure Holes, Chi-Square Statistics, Middle East Respiratory Syndrome, Amoebiasis

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APA Style

Tsair-Wei Chien, Shih-Bin Su. (2017). Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Applied and Computational Mathematics, 6(4-1), 55-63. https://doi.org/10.11648/j.acm.s.2017060401.15

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ACS Style

Tsair-Wei Chien; Shih-Bin Su. Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Appl. Comput. Math. 2017, 6(4-1), 55-63. doi: 10.11648/j.acm.s.2017060401.15

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AMA Style

Tsair-Wei Chien, Shih-Bin Su. Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Appl Comput Math. 2017;6(4-1):55-63. doi: 10.11648/j.acm.s.2017060401.15

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@article{10.11648/j.acm.s.2017060401.15,
  author = {Tsair-Wei Chien and Shih-Bin Su},
  title = {Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis},
  journal = {Applied and Computational Mathematics},
  volume = {6},
  number = {4-1},
  pages = {55-63},
  doi = {10.11648/j.acm.s.2017060401.15},
  url = {https://doi.org/10.11648/j.acm.s.2017060401.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2017060401.15},
  abstract = {Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.},
 year = {2017}
}

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TY  - JOUR
T1  - Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis
AU  - Tsair-Wei Chien
AU  - Shih-Bin Su
Y1  - 2017/01/24
PY  - 2017
N1  - https://doi.org/10.11648/j.acm.s.2017060401.15
DO  - 10.11648/j.acm.s.2017060401.15
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 55
EP  - 63
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.s.2017060401.15
AB  - Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.
VL  - 6
IS  - 4-1
ER  -

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Author Information

Tsair-Wei Chien

Research Departments, Chi-Mei Medical Center, Tainan, Taiwan; Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan
Shih-Bin Su

Department of Occupation Medicine, Chi-Mei Medical Center, Tainan, Taiwan

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APA Style

Tsair-Wei Chien, Shih-Bin Su. (2017). Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Applied and Computational Mathematics, 6(4-1), 55-63. https://doi.org/10.11648/j.acm.s.2017060401.15

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ACS Style

Tsair-Wei Chien; Shih-Bin Su. Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Appl. Comput. Math. 2017, 6(4-1), 55-63. doi: 10.11648/j.acm.s.2017060401.15

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AMA Style

Tsair-Wei Chien, Shih-Bin Su. Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis. Appl Comput Math. 2017;6(4-1):55-63. doi: 10.11648/j.acm.s.2017060401.15

Copy | Download

@article{10.11648/j.acm.s.2017060401.15,
  author = {Tsair-Wei Chien and Shih-Bin Su},
  title = {Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis},
  journal = {Applied and Computational Mathematics},
  volume = {6},
  number = {4-1},
  pages = {55-63},
  doi = {10.11648/j.acm.s.2017060401.15},
  url = {https://doi.org/10.11648/j.acm.s.2017060401.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2017060401.15},
  abstract = {Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Using Structure Holes for Determining Key Factors: An Illustration of Reporting Eradication of Amoebiasis
AU  - Tsair-Wei Chien
AU  - Shih-Bin Su
Y1  - 2017/01/24
PY  - 2017
N1  - https://doi.org/10.11648/j.acm.s.2017060401.15
DO  - 10.11648/j.acm.s.2017060401.15
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 55
EP  - 63
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.s.2017060401.15
AB  - Background: Many researches aim to determine key factors affecting their concerns of interest using traditional statistical techniques, such as logistical or linear regressions. Social network analysis (SNA) is a newly novel way determining key roles through the use of network and graph theories recently. An example of commonly visualized through SNA is the disease transmission path of Middle East respiratory syndrome (MERS). Purpose: To determine key roles using structure holes of SNA for further improvement, and to show the SNA advantage over traditional classic test theory. Methods: Data were records regarding 443 adult mentally retarded residents who were infected with amoebiasis and distributed in 10 houses in past 10 years. A series of intensive mass screenings and treatment interventions were conducted. Structure holes were applied to verify the efficacy of determining key roles and strong associations for the domains of interest in a network and compared with the result obtained from the traditional Chi-square statistics. Results: The classification of key roles in a network (e.g., with which year the residency room with amoebiasis cases has strongly association) can be effectively discriminated through the structure holes of SNA. Though the result is similar to the traditional Chi-square statistics, the structure holes can release much more useful and valuable information for further investigation. Conclusions: Because of advances in computer technology, the number of healthcare studies for the group classification and association assertion continues to increase and benefit comparisons of data if structure holes of SNA are applied.
VL  - 6
IS  - 4-1
ER  -

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