Distribution of sexual health knowledge and attitudes in adolescent social networks: social network analysis of data from the STIs and Sexual Health feasibility study

This study is on effects of student’s centred method of learning (cooperative learning method) on sexual health knowledge and attitudes of adolescents in Anambra State secondary schools. The inability of adolescents to acquire adequate knowledge regarding sexual health could be due to the method of teaching used which are mainly techer-centred methods such as lecture, discussion, Socratic, demonstration teaching methods among others. Therefore, students’ centered method is recommended in today’s teaching and learning and cooperative learning method is one of them. The main purpose of the study was to determine the mean scores of adolescents in Anambra State secondary schools exposed to cooperative learning method on sexual health knowledge and attitudes and by comparing the scores with that of control group. The study was guided by six research questions and eight hypotheses. Quasi experimental research design was used and the population consisted of 20,342 senior secondary two (SS2) students in Anambra State. One hundred and fourty SS2 students comprised sample for the study using simple random sampling technique without replacement. Four secondary schools in Anambra state were sampled. Then one intact class was sampled from each of the sampled schools using simple random sampling technique without replacement. Two schools were exposed with cooperative learning method and the other two schools were used as control group for six weeks. The instruments for data collection were Sexual Health knowledge Test (SHKT) and Sexual Health Attitudes Questionnaire (SHAQ). Analyses of data were carried out using Mean, Standard Deviation and Analysis of Covariance (ANCOVA). The findings of the study among others revealed that cooperative learning method improved sexual health knowledge and attitudes of the subjects as depicted by the positive gained mean scores recorded more than the control group. Male adolescents exposed to cooperative learning method had better sexual health knowledge gained mean score (X = 37.27) than their female counterparts (X = 32.46) exposed to the same method. But the female adolescents’ standard deviation showed better homogeneity of responses than their male counterparts. Female adolescents exposed to cooperative learning method had better sexual health attitude gained mean score (X = 11.38) and their standard deviation showed better homogeneity of responses than their male counterparts exposed to the same method (X = 9.37). The effect of cooperative learning method on the mean sexual health knowledge and attitudes scores of adolescents in Anambra state secondary schools differ significantly using their post-test mean scores P < 0.05. On the contrary, the effect of cooperative learning method on the mean sexual health knowledge and attitudes scores of male and female adolescents in Anambra state secondary schools did not differ significantly using their post-test mean scores P > 0.05. It was recommended among other things that there is need to include cooperative learning method in handling sexual health education in the school curriculum at all levels of education in Nigeria.

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Introduction

The Oxford Handbook of Social Networks ◽

10.1093/oxfordhb/9780190251765.013.1 ◽

2020 ◽

pp. xxii-13

Author(s):

Ryan Light ◽

James Moody

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Bibliometric Analysis ◽

Social Life ◽

New Directions ◽

Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

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The Oxford Handbook of Social Networks

10.1093/oxfordhb/9780190251765.001.0001 ◽

2020 ◽

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Social Life ◽

Information Science ◽

Computational Social Science ◽

The Social ◽

Diverse Field ◽

The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

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Social Network Analysis Visualization

Advances in Marketing, Customer Relationship Management, and E-Services - Handbook of Research on Integrating Social Media into Strategic Marketing ◽

10.4018/978-1-4666-8353-2.ch015 ◽

2015 ◽

pp. 257-270

Author(s):

Mohana Shanmugam ◽

Yusmadi Yah Jusoh ◽

Rozi Nor Haizan Nor ◽

Marzanah A. Jabar

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Online Social Network ◽

Future Research ◽

Research Gaps ◽

The Social ◽

Network Studies ◽

Academic Study

The social network surge has become a mainstream subject of academic study in a myriad of disciplines. This chapter posits the social network literature by highlighting the terminologies of social networks and details the types of tools and methodologies used in prior studies. The list is supplemented by identifying the research gaps for future research of interest to both academics and practitioners. Additionally, the case of Facebook is used to study the elements of a social network analysis. This chapter also highlights past validated models with regards to social networks which are deemed significant for online social network studies. Furthermore, this chapter seeks to enlighten our knowledge on social network analysis and tap into the social network capabilities.

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The Mathematics of Social Network Analysis: Metrics for Academic Social Networks

International Journal of Computer Applications Technology and Research ◽

10.7753/ijcatr0412.1003 ◽

2015 ◽

Vol 4 (12) ◽

pp. 889-893 ◽

Cited By ~ 4

Author(s):

Tasleem Arif

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Academic Social Networks ◽

Network Analysis Metrics

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Predicting Violent Victimization Using Social Network Analysis From Police Data

Violence and Victims ◽

10.1891/vv-d-19-00037 ◽

2021 ◽

Vol 36 (3) ◽

pp. 436-454

Author(s):

Andrew M. Fox ◽

Kenneth J. Novak ◽

Tinneke Van Camp ◽

Chadley James

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Violent Crime ◽

Violent Victimization ◽

Crime Victimization ◽

Effective Prevention ◽

Police Data ◽

Assault Victims

Extant research suggests that membership in crime networks explains vulnerability to violent crime victimization. Consequently, identifying deviant social networks and understanding their structure and individual members' role in them could provide insight into victimization risk. Identifying social networks may help tailor crime prevention strategies to mitigate victimization risks and dismantle deviant networks. Social network analysis (SNA) offers a particular means of comprehending and measuring such group-level structures and the roles that individuals play within them. When applied to research on crime and victimization, it could provide a foundation for developing precise, effective prevention, intervention, and suppression strategies. This study uses police data to examine whether individuals most central to a deviant social network are those who are most likely to become victims of violent crime, and which crime network roles are most likely to be associated with vulnerability to violent victimization. SNA of these data indicates that network individuals who are in a position to manage the flow of information in the network (betweenness centrality), regardless of their number of connections (degree centrality), are significantly more likely to be homicide and aggravated assault victims. Implications for police practice are discussed.

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Entanglement, Materiality and the Social Organisation of Construction Workers in Classical Athens

Ancient Greek History and Contemporary Social Science ◽

10.3366/edinburgh/9781474421775.003.0019 ◽

2018 ◽

pp. 512-528

Author(s):

Diane Harris Cline

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Network Theory ◽

Actor Network Theory ◽

Fifth Century ◽

Actor Network ◽

The Social ◽

Building Program

This chapter views the “Periclean Building Program” through the lens of Actor Network Theory, in order to explore the ways in which the construction of these buildings transformed Athenian society and politics in the fifth century BC. It begins by applying some Actor Network Theory concepts to the process that was involved in getting approval for the building program as described by Thucydides and Plutarch in his Life of Pericles. Actor Network Theory blends entanglement (human-material thing interdependence) with network thinking, so it allows us to reframe our views to include social networks when we think about the political debate and social tensions in Athens that arose from Pericles’s proposal to construct the Parthenon and Propylaea on the Athenian Acropolis, the Telesterion at Eleusis, the Odeon at the base of the South slope of the Acropolis, and the long wall to Peiraeus. Social Network Analysis can model the social networks, and the clusters within them, that existed in mid-fifth century Athens. By using Social Network Analysis we can then show how the construction work itself transformed a fractious city into a harmonious one through sustained, collective efforts that engaged large numbers of lower class citizens, all responding to each other’s needs in a chaine operatoire..

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Local Community Extraction in Social Network Analysis

Social Media Mining and Social Network Analysis ◽

10.4018/978-1-4666-2806-9.ch011 ◽

2013 ◽

pp. 172-181

Author(s):

Xianchao Zhang ◽

Liang Wang ◽

Yueting Li ◽

Wenxin Liang

Keyword(s):

Social Networks ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Local Community ◽

Linear Time ◽

Complete Information ◽

Global Community ◽

Local Algorithms ◽

Community Extraction

To identify global community structures in networks is a great challenge that requires complete information of graphs, which is infeasible for some large networks, e.g. large social networks. Recently, local algorithms have been proposed to extract communities for social networks in nearly linear time, which only require a small part of the graphs. In local community extraction, the community extracting assignments are only done for a certain subset of vertices, i.e., identifying one community at a time. Typically, local community detecting techniques randomly start from a vertex and gradually merge neighboring vertices one-at-a-time by optimizing a measure metric. In this chapter, plenty of popular methods are presented that are designed to obtain a local community for a given graph.

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