Network and school variations in adolescents’ health behaviour and educational attainment: a multilevel analysis of US data

Gerogiannis, George (2023) Network and school variations in adolescents’ health behaviour and educational attainment: a multilevel analysis of US data. PhD thesis, University of Glasgow.

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This thesis develops a statistical methodology for an important area of social network modelling, that of the effects that an individual’s social network can have on the individual’s propensity to engage in an array of different acts, which has been a public health concern in many societies and is increasingly becoming important in the commercial world as access to such data is becoming increasingly available and can be used to maximise profits. The majority of studies that investigate this phenomenon estimate the fixed effects of network statistics on an individual’s propensity to engage in a certain behaviour and are based on network health data. The process thought to generate this phenomenon is typically modelled with a univariate Bernoulli generalised linear model, which simplifies the network component present in the process by summarising it with statistics, a procedure which induces a loss of information. Over the past 20 years, statistical methodology has been developed to remedy this issue with the use of a Bernoulli generalised linear mixed model which explicitly accounts for the network components by modelling them as random effects. The work presented in this thesis provides several novel contributions to these approaches

• The first of which is the development of a multivariate model that extends the multiple membership multiple classification model proposed by Browne et al. (2001).

• The second is the development of a multivariate model that considers a spatio-network interaction involving the sets of spatial and network random effects, as it may be of interest to study whether friendship effects differ depending on the areal unit in which an individual lives.

• The third concerns the development of a software package that will enable researchers to implement the models developed in this thesis.

These novel contributions are achieved through the use of Bayesian hierarchical models with estimation performed with Markov chain Monte Carlo simulation.

Item Type: Thesis (PhD)
Qualification Level: Doctoral
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HM Sociology
L Education > L Education (General)
R Medicine > RA Public aspects of medicine
Colleges/Schools: College of Social Sciences
Supervisor's Name: Tranmer, Professor Mark, Lee, Professor Duncan and Moore, Professor Laurence
Date of Award: 2023
Depositing User: Theses Team
Unique ID: glathesis:2023-83799
Copyright: Copyright of this thesis is held by the author.
Date Deposited: 04 Sep 2023 10:12
Last Modified: 04 Sep 2023 10:16
Thesis DOI: 10.5525/gla.thesis.83799

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