A psychometric and behavioural analysis of mobile gambling
The British population are increasingly using mobile devices (e.g. smartphones, tablets) to gamble. The empirical work in this thesis looks at how the interaction of gambling’s schedule of reinforcement and mobile device behaviours accelerate the acquisition of learned maladaptive behaviours. The fi...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/41063/ |
| _version_ | 1848796188429516800 |
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| author | James, Richard J.E. |
| author_facet | James, Richard J.E. |
| author_sort | James, Richard J.E. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The British population are increasingly using mobile devices (e.g. smartphones, tablets) to gamble. The empirical work in this thesis looks at how the interaction of gambling’s schedule of reinforcement and mobile device behaviours accelerate the acquisition of learned maladaptive behaviours. The first four chapters report psychometric modelling of gambling prevalence data to understand problem gambling further and identify key indicators relevant to associative processes in gambling behaviour. Chapter 2 reports a taxometric analysis of problem gambling assessment data to test whether these screens measure a dimensional or latent class model, finding stronger support for the latter. However, this only identified a small taxon consisting of around 5% of gamblers endorsing more than one problem gambling symptom. Chapter 3 reports the use of latent class analysis to examine distinct subtypes of responding to different screens, findings a common three-class model that showed signs of a mixed latent structure: the same taxon as Chapter 2 was observed, but the three classes showed little overlap in symptom count. Chapter 4 reports further work modelling the sociodemographic characteristics of these different subgroups. Together the data from these chapters were used help to identify indicators of those most likely to a) be most susceptible to gambling harm and b) common to all problem gamblers. In Chapter 5 a Monte Carlo analysis was conducted to understand the efficacy of taxometric procedures on binary variables, before replicating the taxometric analysis reported in Chapter 2 using dichotomous variables and extending the work to the South Oaks Gambling Screen. The indicators derived from these chapters were then used in laboratory and field studies to study mobile gambling behaviour. The laboratory study in Chapter 6 manipulated two behavioural processes, trial spacing and partial reinforcement, that are relevant to mobile gambling behaviour, showing how a mobile-like schedule is related to increased perseverance and loss-chasing. The same paradigm was used to deliver an experiment on participants’ mobile phones in a field environment in Chapter 7. They further demonstrate that a mobile style schedule of reinforcement is associated with considerable persistence in the face of mounting losses, as participants continued to persevere in the face of losses despite a free choice to cease playing. Finally in the discussion I apply the key themes of the thesis to in-play betting, a form of play that has been heavily promoted alongside mobile gambling, and to an understanding of behavioural addictions. |
| first_indexed | 2025-11-14T19:44:01Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-41063 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:44:01Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-410632025-02-28T13:42:04Z https://eprints.nottingham.ac.uk/41063/ A psychometric and behavioural analysis of mobile gambling James, Richard J.E. The British population are increasingly using mobile devices (e.g. smartphones, tablets) to gamble. The empirical work in this thesis looks at how the interaction of gambling’s schedule of reinforcement and mobile device behaviours accelerate the acquisition of learned maladaptive behaviours. The first four chapters report psychometric modelling of gambling prevalence data to understand problem gambling further and identify key indicators relevant to associative processes in gambling behaviour. Chapter 2 reports a taxometric analysis of problem gambling assessment data to test whether these screens measure a dimensional or latent class model, finding stronger support for the latter. However, this only identified a small taxon consisting of around 5% of gamblers endorsing more than one problem gambling symptom. Chapter 3 reports the use of latent class analysis to examine distinct subtypes of responding to different screens, findings a common three-class model that showed signs of a mixed latent structure: the same taxon as Chapter 2 was observed, but the three classes showed little overlap in symptom count. Chapter 4 reports further work modelling the sociodemographic characteristics of these different subgroups. Together the data from these chapters were used help to identify indicators of those most likely to a) be most susceptible to gambling harm and b) common to all problem gamblers. In Chapter 5 a Monte Carlo analysis was conducted to understand the efficacy of taxometric procedures on binary variables, before replicating the taxometric analysis reported in Chapter 2 using dichotomous variables and extending the work to the South Oaks Gambling Screen. The indicators derived from these chapters were then used in laboratory and field studies to study mobile gambling behaviour. The laboratory study in Chapter 6 manipulated two behavioural processes, trial spacing and partial reinforcement, that are relevant to mobile gambling behaviour, showing how a mobile-like schedule is related to increased perseverance and loss-chasing. The same paradigm was used to deliver an experiment on participants’ mobile phones in a field environment in Chapter 7. They further demonstrate that a mobile style schedule of reinforcement is associated with considerable persistence in the face of mounting losses, as participants continued to persevere in the face of losses despite a free choice to cease playing. Finally in the discussion I apply the key themes of the thesis to in-play betting, a form of play that has been heavily promoted alongside mobile gambling, and to an understanding of behavioural addictions. 2017-07-12 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/41063/1/Richard%20James%20Thesis.pdf James, Richard J.E. (2017) A psychometric and behavioural analysis of mobile gambling. PhD thesis, University of Nottingham. gambling; mobile gambling; problem gambling; addiction; behaviour; taxometrics; latent class analysis |
| spellingShingle | gambling; mobile gambling; problem gambling; addiction; behaviour; taxometrics; latent class analysis James, Richard J.E. A psychometric and behavioural analysis of mobile gambling |
| title | A psychometric and behavioural analysis of mobile gambling |
| title_full | A psychometric and behavioural analysis of mobile gambling |
| title_fullStr | A psychometric and behavioural analysis of mobile gambling |
| title_full_unstemmed | A psychometric and behavioural analysis of mobile gambling |
| title_short | A psychometric and behavioural analysis of mobile gambling |
| title_sort | psychometric and behavioural analysis of mobile gambling |
| topic | gambling; mobile gambling; problem gambling; addiction; behaviour; taxometrics; latent class analysis |
| url | https://eprints.nottingham.ac.uk/41063/ |