Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are m...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference Ročník 2016; s. 5360
Hlavní autori:	Hogan, Bernie, Melville, Joshua R, Philips, 2nd, Gregory Lee, Janulis, Patrick, Contractor, Noshir, Mustanski, Brian S, Birkett, Michelle
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.05.2016
Predmet:	Social networks D.2.1 Requirements/Specifications: Elicitation methods E.1 Data Structures: Graphs and networks H.5.3 Group and Organization Interfaces: Web-based interaction Participant-aided sociograms
On-line prístup:	Zistit podrobnosti o prístupe
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within-subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
DOI:	10.1145/2858036.2858368