Does panel conditioning affect data quality in ego-centered social network questions?

2019 ◽  
Vol 56 ◽  
pp. 45-54 ◽  
Author(s):  
Henning Silber ◽  
Jette Schröder ◽  
Bella Struminskaya ◽  
Volker Stocké ◽  
Michael Bosnjak
Keyword(s):  
Social Network ◽  
Data Quality ◽  
Panel Conditioning

Tailoring Data Quality Models Using Social Network Preferences

2009 ◽  
pp. 152-166 ◽  
Author(s):  
Ismael Caballero ◽  
Eugenio Verbo ◽  
Manuel Serrano ◽  
Coral Calero ◽  
Mario Piattini
Keyword(s):  
Social Network ◽  
Data Quality ◽  
Quality Models

scholarly journals Panel Conditioning in the U.S. Consumer Expenditure Survey

2021 ◽  
Vol 37 (1) ◽  
pp. 53-69
Author(s):  
Stephanie Eckman ◽  
Ruben Bach
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Consumer Expenditure ◽  
Consumer Expenditure Survey ◽  
Panel Conditioning ◽  
Interview Survey ◽  
The U.S ◽  
Over Time ◽  
Analyze Data

Abstract The U.S. Consumer Expenditure Interview Survey asks many filter questions to identify the items that households purchase. Each reported purchase triggers follow-up questions about the amount spent and other details. We test the hypothesis that respondents learn how the questionnaire is structured and underreport purchases in later waves to reduce the length of the interview. We analyze data from 10,416 four-wave respondents over two years of data collection. We find no evidence of decreasing data quality over time; instead, panel respondents tend to give higher quality responses in later waves. The results also hold for a larger set of two-wave respondents.

scholarly journals Academic Social Network Sites: Opportunities and Challenges

2018 ◽  
Vol 7 (3.13) ◽  
pp. 133 ◽  
Author(s):  
Mumtazimah Mohammad ◽  
Yuzarimi M. Lazim ◽  
Suharmili Rosle
Keyword(s):  
Social Network ◽  
Data Quality ◽  
Social Networking ◽  
Social Networking Sites ◽  
Social Network Sites ◽  
Efficient Algorithms ◽  
Comprehensive Investigation ◽  
Scholarly Community ◽  
Academic Social Network ◽  
Modern Information

Academic Social Networking Sites (ASNs) have become one of the most important platforms in modern information and education industries. It will definitely become an essential mechanism to communicate, connect and collaborate among the scholarly community. In this paper, we provide a comprehensive investigation of ASNs such as Academia, ResearchGate, Mendeley and Zotero, and identify the features of ASNs. We also discuss the opportunities for ASNs and provide the challenges. Challenges related to ASNs are: collecting relevant data and identifying data quality, finding evaluation metrics and also developing efficient algorithms. Disciplinary differences in domain area and academic position become the opportunities why academicians use ASNs.  

Use of Apache Flume in the Big Data Environment for Processing and Evaluation of the Data Quality of the Twitter Social Network

2018 ◽  
pp. 314-326
Author(s):  
Gladys-Alicia Tenesaca-Luna ◽  
Diego Imba ◽  
María-Belén Mora-Arciniegas ◽  
Verónica Segarra-Faggioni ◽  
Ramiro Leonardo Ramírez-Coronel
Keyword(s):  
Big Data ◽  
Social Network ◽  
Data Quality ◽  
Data Environment

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

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