scholarly journals Using Social Network Analysis to Evaluate Academic Assistance Networks in a Holistic Education Secondary School

2018 ◽  
Vol 4 (1) ◽  
pp. 25-41
Author(s):  
R. Renee Setari ◽  
Anthony Philip Setari
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Secondary School ◽  
Multiple Regression ◽  
Holistic Education ◽  
Quadratic Assignment ◽  
Academic Assistance ◽  
Social Network Data ◽  
Academic Help

One goal of Erdkinder schools is for students and teachers to provide academic assistance to their peers, particularly to less-knowledgeable ones. However, traditional educational evaluations do not provide a means to investigate the exchange of academic help. This study piloted the use of social network analysis to describe academic assistance relationships within a Montessori secondary school. Using a network survey, social network data concerning the exchange of academic help were collected from 23 students and 8 teachers. The results show that while students provide help to both fellow students and teachers, teachers are the main source of assistance for students. In some subjects, a few students and teachers neither provided nor received assistance, indicating another area for improvement. The results of a multiple regression quadratic assignment procedure (multiple regression-QAP) show that for most subjects, their willingness to help others was not significantly influenced by their own personal level of knowledge. Thus, more-knowledgeable individuals do not provide more assistance to less-knowledgeable peers. To adhere to Erdkinder principles, this school should encourage more-knowledgeable students to recognize their responsibility to help others and to actually help those who need support. This pilot yielded valuable information, and social network analysis warrants further study within holistic education.

Social Network Analysis in Marketing

E-Marketing ◽  
2012 ◽  
pp. 185-197
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Social Network Analysis in Marketing

2011 ◽  
pp. 271-283
Author(s):  
Przemyslaw Kazienko ◽  
Piotr Doskocz ◽  
Tomasz Kajdanowicz
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relationships ◽  
Network Data ◽  
Social Network Data ◽  
The Social ◽  
Targeted Marketing ◽  
Potential Customers ◽  
Demographic Features

The chapter describes a method how to perform a classification task without any demographic features and based only on the social network data. The concept of such collective classification facilitates to identify potential customers by means of services used or products purchased by the current customers, i.e. classes they belong to as well as using social relationships between the known and potential customers. As a result, a personalized offer can be prepared for the new clients. This innovative marketing method can boost targeted marketing campaigns.

Do clusters create shared value? A social network analysis of the motor valley case

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fernando G. Alberti ◽  
Federica Belfanti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Impact ◽  
Statistical Significance ◽  
Research Question ◽  
Quadratic Assignment ◽  
Shared Value ◽  
Content Type ◽  
Cluster Development

Purpose This paper aims to contribute to the debate about creating shared value (CSV) and clusters, by shedding light on how clusters might generate shared value, i.e. cause social and business benefits, hence focusing on the following research question “do clusters create shared value?” Design/methodology/approach The study relied on social network analysis methods and techniques. Data have been collected from both primary and secondary sources, in the empirical context of the Motor Valley cluster in Emilia-Romagna. The authors computed three independent and four dependent variables to operationalize the concept of cluster development and shared value creation. A multiple regression quadratic assignment procedure and, more specifically, the most accurate model of that procedure, that is the double semi-partialling method, has been carried out to answer the research question. Finally, empirical evidence has been complemented with other cluster-level data recently collected by the Italian Cluster Mapping project. Findings The findings confirm how the development of the Motor Valley cluster in Emilia-Romagna contributed to the creation of economic and social growth opportunities for all the actors. The study shows that clusters do create shared value and the chosen cluster development variables do explain much of the business and social impact variables at a very high statistical significance level. Originality/value The paper contributes to the under-explored research on clusters and CSV with a very first attempt in providing quantitative evidence of the phenomenon.

Application of Computer System in the Study of Sociology

2014 ◽  
Vol 926-930 ◽  
pp. 1680-1683
Author(s):  
Ying Ming Xu ◽  
Shu Juan Jin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computer Algorithms ◽  
Useful Knowledge ◽  
Social Network Data ◽  
Analysis Task ◽  
Original Analysis ◽  
The Social ◽  
The Relationship

With the development of information technology, more and more data about social to be collected. If we can analyze them effectively, it will help people to understand sociological understanding, promoting the development of social science. But the increasing amount of data and analysis to put forward a huge challenge. Now the social networks have already surpassed the processing ability of the original analysis means, must use a more effective tool to complete the analysis task. The computer as a way of helping people from massive data to find the potential useful knowledge tools, play an important role in many fields. Social network analysis, also known as link mining, refers to the handling of the relationship between social network data in the computer method. In this paper, the methods of computer and the social network analysis was introduced in this paper and the computer algorithms are summarized in the application of social network analysis.

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members

2017 ◽  
Vol 14 (5) ◽  
pp. 360-367 ◽  
Author(s):  
Megan S. Patterson ◽  
Patricia Goodson
Keyword(s):  
Physical Activity ◽  
Eating Disorders ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Body Dissatisfaction ◽  
Group Dynamics ◽  
Exercise Behavior ◽  
Quadratic Assignment ◽  
Compulsive Exercise

Background:Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people.Methods:SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET.Results:QAP regression analysis revealed a statistically significant model (R2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model.Discussion:In our sample, women who are connected to “important” or “powerful” people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Data Preprocessing for Dynamic Social Network Analysis

2013 ◽  
pp. 25-39 ◽  
Author(s):  
Preeti Gupta ◽  
Vishal Bhatnagar
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Preprocessing ◽  
Original Data ◽  
Network Data ◽  
Data Set ◽  
Social Network Data ◽  
Dynamic Social Network ◽  
The Social

The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to illustrate the importance of data preprocessing for social network analysis.

An Introduction to Social Network Analysis for Personality and Social Psychologists

2017 ◽  
Vol 8 (4) ◽  
pp. 442-453 ◽  
Author(s):  
Allan Clifton ◽  
Gregory D. Webster
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Groups ◽  
Network Data ◽  
Personality Psychology ◽  
Social Network Data ◽  
And Behavior

Social network analysis (SNA) is a methodology for studying the connections and behavior of individuals within social groups. Despite its relevance to social and personality psychology, SNA has been underutilized in these fields. We first examine the paucity of SNA research in social and personality journals. Next we describe methodological decisions that must be made before collecting social network data, with benefits and drawbacks for each. We discuss common SNAs and give an overview of software available for SNA. We provide examples from the literature of SNA for both one-mode and two-mode network data. Finally, we make recommendations to researchers considering incorporating SNA into their research.

scholarly journals Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Haiyan Liu ◽  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Relations ◽  
Research Question ◽  
Psychological Research ◽  
Personality Psychology ◽  
Analysis Techniques ◽  
Social Network Data ◽  
Social Entities

Whether birds of a feather flock together or opposites attract is a classical research question in social and personality psychology. In most existing studies, correlation-based techniques are commonly used to study the similarity/dissimilarity among social entities. Social network data comprises two primary components: actors and the possible social relations between them. It, therefore, has observations on both the dyads with and without social relations. Because of the availability of the baseline group (dyads without social relations), it is possible to contrast the two groups of dyads using social network analysis techniques. This study aims to illustrate how to use social network analysis techniques to address psychological research questions. Specifically, we will investigate how the similarity or dissimilarity of actor's characteristics relates to the likelihood for them to build social relations. By analyzing a college friendship network, we found the quadratic relations between personality similarity and friendship. Both very similar and very dissimilar personalities boost friendship among college students.

scholarly journals Acute Medical Unit Healthcare workers’ interaction in a developing country: Social Network Analysis

2021 ◽  
Author(s):  
Jihane Belayachi ◽  
Sarah Benammi ◽  
Rhita Nechba Bennis ◽  
Naoufel Madani ◽  
Redouane Abouqal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Quadratic Assignment ◽  
P Value ◽  
Moderate Degree ◽  
Head Nurse ◽  
Medical Unit ◽  
Acute Medical Unit ◽  
Index Value

INTRODUCTIONSocial network analysis is used to increase the awareness of leaders about the power of networks, to further catalyze relationships and connections, and to strengthen the capacity of the network to act collectively. We focus on understanding and measuring the communication patterns in an acute medical unit team. We sought to use Social Network Analysis to describe the patterns of communications in teamwork of an acute medical unit.METHODSNetwork Analysis was conducted to examine network structure in 58 teamwork professional communication in an AMU. Team members reported the frequency (0 to 10+ times) of professional discussion with every other coworker during the last 48-hoursk, density, degree and betweenness centralization, degree and betweenness centrality; and homophily were calculated. P-value was obtained based on 1000 quadratic assignment procedure QAP permutations of the network. The network analysis was used to construct network maps using multidimentional scaling and generates a visual representation of networks through network diagrams.RESULTSthere were 460 connections (density=28%). The whole network has a moderate degree centralization (37%) and lower betweenness centralization (8%). Three senior physicians, the head nurse, the physiotherapist, the medical secretary and the archive manager were most central in network. There was evidence regarding heterophily in a network indicated by high level of E-I index value 0.34 (P<0.01, by QAP).CONCLUSIONSNA provided a description of patterns of communications in teamwork in an acute medical unit. We used SNA statistics to reveal variation in patterns of team communication and teammate interconnectedness by shift.

scholarly journals Estimating uncertainty and reliability of social network data using Bayesian inference

2015 ◽  
Vol 2 (9) ◽  
pp. 150367 ◽  
Author(s):  
Damien R. Farine ◽  
Ariana Strandburg-Peshkin
Keyword(s):  
Social Network ◽  
Bayesian Inference ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Potential Method ◽  
Network Analyses ◽  
Social Network Data ◽  
Global Properties ◽  
Uncertainty Estimates ◽  
High Level

Social network analysis provides a useful lens through which to view the structure of animal societies, and as a result its use is increasingly widespread. One challenge that many studies of animal social networks face is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in estimating the rates of association or interaction between individuals. We present a method based on Bayesian inference to incorporate uncertainty into network analyses. We test the reliability of this method at capturing both local and global properties of simulated networks, and compare it to a recently suggested method based on bootstrapping. Our results suggest that Bayesian inference can provide useful information about the underlying certainty in an observed network. When networks are well sampled, observed networks approach the real underlying social structure. However, when sampling is sparse, Bayesian inferred networks can provide realistic uncertainty estimates around edge weights. We also suggest a potential method for estimating the reliability of an observed network given the amount of sampling performed. This paper highlights how relatively simple procedures can be used to estimate uncertainty and reliability in studies using animal social network analysis.

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