network scale-up method

2004 ◽  
Vol 1 (2) ◽  
pp. 395-405
Author(s):  
Silvia Snidero ◽  
Roberto Corradetti ◽  
Dario Gregori
Keyword(s):  
Social Network ◽  
Scale Up ◽  
Monte Carlo Simulation Study ◽  
The Social ◽  
Network Scale ◽  
Ideal Number ◽  
Unbiased Estimates ◽  
The Mean ◽  
Ml Estimator ◽  
Biased Estimates

The network scale-up method is a social network estimator for the size of hidden or hard-to-count subpopulations. These estimators are based on a simple model which have however strong assumptions. The basic idea is that the proportion of the mean number of people known by respondent in a subpopulation E of T of size e is the same of the proportion that the e subpopulation E forms in general population T of size t: mc = t , where c is the number of persons known by each respondent and m is the mean number of persons known by each respondent in the subpopulation E. The persons known by every subject is called the "social network", and its size is c, estimated by several estimators proposed in the recent literature. In this paper we present a Monte Carlo simulation study aimed at understanding the behavior of the scale-up method type estimators under several conditions. The first goal was to understand what would be the ideal number of subpopulations of known size to be used in planning the research. The second goal was to analyze what happens when we use overlapped subpopulations. Our results showed that with the scale-up estimator we always obtain biased estimates for any number of subpopulations employed in estimates. With the Killworth's ML estimator, the improvement of scale-up method, we have substantially unbiased estimates under any condition. Also in case of overlapping, and increasing the degree of it among subpopulations, bias raises with scale-up method, instead it remains close to zero with ML estimator.

The Network Scale-Up Method

2020 ◽  
pp. 152-169
Author(s):  
Tyler H. McCormick
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Count Data ◽  
Scale Up ◽  
Entire Population ◽  
Relational Data ◽  
Sampling Techniques ◽  
Probability Sampling ◽  
Network Scale ◽  
Standard Probability

The network scale-up method is one of a series of methods that leverage a respondent’s social network to more effectively capture information about specific groups or about the population as a whole. The network scale-up method works with questions that are known as aggregated relational data (ARD). These questions take the form “How many Xs do you know?” That is, ARD are count data consisting of the number of connections between a respondent and individuals with a specific characteristic. Critically, ARD do not involve observing any links in the network and are collected using standard probability sampling techniques. The main focus of this chapter is estimating the size of a group of individuals using ARD and the network scale-up method. As the name implies, the method uses information for survey respondents’ social networks to “scale up” to an entire population.

#SoMe4Surgery: from inception to impact

2020 ◽  
Vol 6 (2) ◽  
pp. 72-82 ◽  
Author(s):  
Rebecca C Grossman ◽  
Douglas Graham Mackenzie ◽  
Deborah S Keller ◽  
Nicola Dames ◽  
Perbinder Grewal ◽  
...  
Keyword(s):  
Social Media ◽  
Social Network ◽  
Online Community ◽  
Healthcare Professionals ◽  
The Social ◽  
History Of ◽  
The Mean ◽  
Network Application ◽  
Impact Phase ◽  
Geographical Locations

Objectives and settingThe aim of this study was to create a hashtag #SoMe4Surgery on the social network application Twitter, and to examine the natural history of the resulting online community.Design and outcome measuresA prospective, four-stage framework was proposed and used: (1) inception phase (connection): users were actively invited to participate; (2) dissemination phase (contagion): several tweetchats were designed, scheduled and run; (3) adherence phase (feedback): Twitonomy and NodeXL summaries were regularly posted on Twitter; and (4) impact phase (outcomes): abstracts and manuscripts, and related projects on Twitter. Tweets, influencers and interactions were analysed, and a brief survey was shared with participants to assess demographics and motivations of social media use.ParticipantsUsers engaging with the #SoMe4Surgery hashtag.ResultsUsers of the hashtag came from a wide variety of specialties and geographical locations, as well as varying in age and stage of training. The inception of #SoMe4Surgery was followed by an increase in the follower count and impressions of users. A total of 675 tweeters posted 11 855 tweets with 30 122 retweets between 28 July and 27 December inclusive. There were new contributors and activity throughout the period. There were many more retweeters than accounts posting original content. Over a 10-day period ending on 31 December 2018, the number of followers of the 10 most influential accounts was higher than the number of followers of the 10 most engaging accounts (p=0.002). The mean (SD) number of tweetchat participants was 121 (64), who posted 719 (365) tweets and had a potential reach of 3 825 155 (1 887 205) accounts. Spin-off projects included surveys from low and medium-income countries, subspecialised hashtags, presentations and one publication.ConclusionsThe creation of a cohesive online surgical community may allow a flattened hierarchy, with increased engagement between surgeons, other healthcare professionals, researchers and patients.

scholarly journals Breaking the paradigm: Optimized Case Finding multiplies tuberculosis detection among key populations in Ukraine

2021 ◽  
Vol 15 (09.1) ◽  
pp. 75S-81S
Author(s):  
Liliia Masiuk ◽  
Olga Denisiuk ◽  
Evgenia Geliukh ◽  
Zahedul Islam ◽  
Garry Aslanyan ◽  
...  
Keyword(s):  
Social Network ◽  
General Population ◽  
People Who Inject Drugs ◽  
Scale Up ◽  
Case Finding ◽  
Key Populations ◽  
Case Detection Rate ◽  
Tb Treatment ◽  
The Social ◽  
Close Contacts

Introduction: In 2018, there were 3 million “missed” tuberculosis (TB) cases globally, much of which was disproportionally concentrated among key populations. To enhance TB case-finding, an Optimized Case Finding (OCF) strategy involving all contacts within the social network of an index TB case was introduced in five regions of Ukraine. We assessed TB detection and linkage to TB treatment using OCF in key populations. Methodology: A cohort study using routine programme data (July 2018 – March 2020). OCF empowers the index TB case to identify and refer up to eight close contacts within his/her social network for TB investigations. Results: Of 726 index TB cases in key populations, 6998 close contacts were referred for TB investigations and 275 were diagnosed with TB (183 drug-sensitive and 92 drug-resistant TB). The TB case detection rate was 3930/100,000 and the Numbers Needed to Investigate to detect one TB case was 25. TB was most frequent among people who inject drugs and homeless groups. Compared to TB detection using routine household case finding within the general population (1090/100,000), OCF was 3.6-fold more effective and when compared to passive case finding in the general population (60/100,000), OCF was 66 times more effective. 99% (273) of TB patients were linked to care and initiated TB treatment. Conclusions: The OCF strategy among key populations is highly effective in detecting TB cases and linking them to care. We advocate to scale-up this case finding strategy in Ukraine and beyond.

scholarly journals Social Network Influences on Non-Agricultural Employment Quality for Part-Time Peasants: A Case Study of Sichuan Province, China

2019 ◽  
Vol 11 (15) ◽  
pp. 4134 ◽  
Author(s):  
Kaijing Xue ◽  
Dingde Xu ◽  
Shaoquan Liu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Education Level ◽  
Network Heterogeneity ◽  
Agricultural Employment ◽  
The Social ◽  
Network Scale ◽  
Part Time ◽  
Indirect Impacts ◽  
Employment Quality

In recent years, the issue of employment quality for workers has received increasing attention from the government and academia. As a social resource, a social network can provide people with social support and help job seekers find better jobs by transmitting the information on job opportunities. However, currently, there are few empirical studies on employment quality from the perspective of social networks. Based on data from 194 samples from 400 households in Sichuan Province in 2015, this paper constructs an employment quality index system from the six dimensions of labor wages, working time, employment stability, employment environment, career development, and social security. In addition, from the perspective of the structural features and the overall characteristics of the social network, OLS (Ordinary Least Squares) and the path analysis model are used to quantitatively explore the mechanisms of action paths of the social network in terms of the non-agricultural employment quality of part-time peasants. The results show that: (1) the social network scale and the relative network of part-time peasants are found to positively affect employment quality. (2) Age, gender, and education level have indirect impacts on the employment quality loop through network heterogeneity and network scale. In addition, network heterogeneity and health status indirectly impact employment quality through a network scale. (3) By synthesizing the direct and indirect impacts, the comprehensive impacts of each factor on employment quality, in decreasing order, are: village–county distance > village terrain > family population > network scale > education level > skill > network heterogeneity > health status > age > gender. The results suggest that we should pay attention to the role of social network resources in improving employment quality, and should implement various measures to expand peasants’ social networks, so as to achieve high-quality employment.

scholarly journals Scientist as Subject: How Researcher Behaviors Influence Psychological Knowledge

2019 ◽  
Author(s):  
Nicholas William Fox
Keyword(s):  
Social Network ◽  
Knowledge Production ◽  
Scientific Literature ◽  
Scale Up ◽  
Research Practices ◽  
Direct Estimate ◽  
Questionable Research Practices ◽  
Gini Coefficients ◽  
Network Scale ◽  
Psychological Knowledge

Background:Interacting with the published literature (“knowledge consumption”) and publishing new scientific findings (“knowledge production”) are two key moments in the scientist’s search for truth, and bias in either of these can distort what is known about an area of research. This dissertation details three studies conducted on researchers in psychology that together provide evidence of scientists’ behaviors influencing these key moments of knowledge production and knowledge consumption. Methods:Psychologists were recruited to participate in each study (N = 215 and N = 587). Studies used custom web tools and social network methods to collect unique datasets on psychologists’ social networks and how they approach the scientific literature. The analytic approach differed based on each study. For studies on knowledge consumption, Gini coefficients and measures of unpredictability were calculated to better understand the dynamics of the published literature. For studies on knowledge production, the generalized network scale up method was used to estimate the size of the population of current users of questionable research practices, and regression was used to better understand the relationship between attitudes and stigma against certain psychologists.Results:The presence of download counts (an operationalization of influential metadata) with scientific literature resulted in larger inequality of downloads, meaning potential readers were more likely to download articles that had been previously downloaded by others. Download count presence also resulted in a higher unpredictability of success. The proportion of psychologists who currently use questionable research practices was estimated as 18.18% by direct estimate and 24.4% by the social network scale up estimate. Finally, these researchers were found to be a stigmatized sub-population of psychologists, which could either help or hinder efforts to reduce this population size.Conclusions:There is evidence that psychologists may inadvertently bias the knowledge they generate and consume in several different ways. While this dissertation focused specifically on psychologists, there is potential for this work to be applied in other areas of scientific inquiry. These findings highlight the importance of understanding the scientist as a means of better understanding the science.

scholarly journals Magnitude of Sexual Behaviours Among College Students In The Eighth Macro-Region of Iran By Self-Reported and Network Scale-Up

2021 ◽  
Author(s):  
Mohadeseh Balvardi ◽  
Nasim Dehdashti ◽  
Zahra Imani Ghoghary ◽  
Fatemeh Alavi-Arjas ◽  
Mojtaba Keikha
Keyword(s):  
Sexual Behaviors ◽  
Direct Method ◽  
Medical Science ◽  
Scale Up ◽  
Cross Sectional Study ◽  
Self Report ◽  
Cross Sectional ◽  
Heterosexual Intercourse ◽  
Network Scale ◽  
The Mean

Abstract Background This study was designed to directly and indirectly estimate the prevalence of sexual behaviors among students of medical science universities in the eighth Macro- region of Iran. Methods This cross-sectional study was performed on 3900 students from Kerman and Sistan and Baluchestan provinces in 2019. The data were collected using direct (i.e., self-report of their own behaviors) and indirect (NSU: Network scale-up) methods. Results The mean (SD) age of students was 22.45 (3.25). The prevalence of heterosexual intercourse in return for money, extramarital heterosexual intercourse, masturbation, sexting, porn watching, homosexuality and abortion based on NSU method was 6.0%, 8.5%, 19.5%, 9.1%, 22.9%, 2.4% and 0.5% respectively. Corresponding figures of the direct method were 5.7%, 5.8% 18.6%, 9.7%, 23.1%, 2.1% and 0.9% respectively. Conclusion Sexual behaviors like porn watching, masturbation and sexting can harm the youth, family and society. The youth should be given training to correctly react to sexual situations.

The social network

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

The Multilevel p2 Model

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Network Data ◽  
Mcmc Algorithm ◽  
Independent Observation ◽  
Single Observation ◽  
Multiple Networks ◽  
Social Network Data ◽  
Multiple Observations ◽  
Practical Support ◽  
The Social

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

ПРАГМАТИЧНИЙ ПОТЕНЦІАЛ НАТИВНОЇ РЕКЛАМИ: ФОРМИ, ТРЕНДИ (НА ПРИКЛАДІ НАТИВНОГО КОНТЕНТУ В СОЦІАЛЬНІЙ МЕРЕЖІ FACEBOOK ЩОДО БРЕНДА ФАКУЛЬТЕТУ ЖУРНАЛІСТИКИ ЗНУ)

2020 ◽  
pp. 113
Author(s):  
V. Kovpak ◽  
N. Trotsenko
Keyword(s):  
Social Network ◽  
Professional Training ◽  
Educational Process ◽  
Communication Strategy ◽  
Student Centered ◽  
Teacher Student ◽  
The Social ◽  
National University ◽  
High Level ◽  
The Impact

<div><p><em>The article analyzes the peculiarities of the format of native advertising in the media space, its pragmatic potential (in particular, on the example of native content in the social network Facebook by the brand of the journalism department of ZNU), highlights the types and trends of native advertising. The following research methods were used to achieve the purpose of intelligence: descriptive (content content, including various examples), comparative (content presentation options) and typological (types, trends of native advertising, in particular, cross-media as an opportunity to submit content in different formats (video, audio, photos, text, infographics, etc.)), content analysis method using Internet services (using Popsters service). And the native code for analytics was the page of the journalism department of Zaporizhzhya National University on the social network Facebook. After all, the brand of the journalism department of Zaporozhye National University in 2019 celebrates its 15th anniversary. The brand vector is its value component and professional training with balanced distribution of theoretical and practical blocks (seven practices), student-centered (democratic interaction and high-level teacher-student dialogue) and integration into Ukrainian and world educational process (participation in grant programs).</em></p></div><p><em>And advertising on social networks is also a kind of native content, which does not appear in special blocks, and is organically inscribed on one page or another and unobtrusively offers, just remembering the product as if «to the word». Popsters service functionality, which evaluates an account (or linked accounts of one person) for 35 parameters, but the main three areas: reach or influence, or how many users evaluate, comment on the recording; true reach – the number of people affected; network score – an assessment of the audience’s response to the impact, or how far the network information diverges (how many share information on this page).</em></p><p><strong><em>Key words:</em></strong><em> nativeness, native advertising, branded content, special project, communication strategy.</em></p>

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