This thesis is original in the sense that it examines a relatively new method of collecting data using Social Network Sites, and explores the feasibility of this method for survey research providing valuable data regarding health behaviours among young Saudis. Aims: The primary aim of this study is to assess the feasibility of Social Network Sites (SNS) as a mode of collecting data for health behavioural surveys by comparing data collected from an SNS-based questionnaire with data collected from a traditional paper questionnaire using the same variables. It also aims to provide descriptive information about health behaviours among young people in Saudi Arabia. Methods: The study demonstrated the Young Risky Behaviour Survey 'YRBS' questionnaire cross-sectionaly by two modes for different participants groups. Each mode of participant's recruitment is considered as a sub-study. The first one is the 'offline study' where participants were recruited randomly from schools and universities in Saudi Arabia. The second sub-study is the 'Online study' where participants were recruited using Facebook fan page and Facebook ads. Results from both sub-studies were analysed and compared in order to answer the research questions. Results: The main results of reported risky health behaviours indicated that 15% of participants are current smokers, 41% drank fizzy drinks (4-6 times a week) and 94% did not meet the recommended level of physical activity. Comparison between SNS and PAP indicated that SNS is an effective tool for collecting health behaviour data and yielded sufficient sample size with a wider geographical reach. Moreover, mode effect on reporting risky behaviours was only significant in 8 out of 25 risky behaviours. Conclusion: Comparisons between the approaches suggest few notable differences, and both methods have produced similar figures regarding health behaviours. Though the limitations of both methods are also discussed in this thesis, the results support the argument that SNS can be a very useful tool leading to representable data.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:695519 |
| Date | January 2015 |
| Creators | Alshaikh, Fahdah |
| Contributors | Rawaf, Salman ; Majeed, Azeem |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/40910 |
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