In philosophical logic, a certain family of model constructions has received particular attention. Prominent examples are the cumulative hierarchy of well-founded sets, and Kripke’s least fixed point models of grounded truth. I develop a general formal theory of groundedness and explain how the well-founded sets, Cantor’s extended numbersequence and Kripke’s concepts of semantic groundedness are all instances of the general concept, and how the general framework illuminates these cases. Then, I develop a new approach to a grounded theory of proper classes. However, the general concept of groundedness does not account for the philosophical significance of its paradigm instances. Instead, I argue, the philosophical content of the cumulative hierarchy of sets is best understood in terms of a primitive notion of ontological priority. Then, I develop an analogous account of Kripke’s models. I show that they exemplify the in-virtue-of relation much discussed in contemporary metaphysics, and thus are philosophically significant. I defend my proposal against a challenge from Kripke’s “ghost of the hierarchy”.

Factors which influence uptake and impact of research • findings from Northern Ireland Health and Social Care System. Tile Payback Framework provides a model by which the totality of impact from specific research or research programmes can be categorized within six domains; Knowledge production, research in the research system, informing policy and decision making, behavioural change, factors influencing utilisation and health service economic benefits. I have utilised the framework to study factors influencing Payback from research completed within the Belfast Trust (2004 - 2007). A web-based survey of 132 projects was conducted, and Payback from individual projects was scored using a simple scoring system previously employed in a study of Payback in Hong Kong and an additional novel Conceptual Equivalent Scale based on pre-defined descriptors of achievement in each of the Payback domains. A purposive sample of six projects was selected for detailed case study analysis of factors Influencing Payback, explored with key project informants. A realist approach was adopted to identify key learning points transferable to research funders and host organisations. Availability of funding, protected time following project completion to allow knowledge transfer and multidisciplinary engagement were key factors identified which influenced Payback across domains.

Within epidemiological and clinical research, missing data are a common issue which are often inappropriately handled in practice. Multiple imputation (MI) is a popular tool used to 'fill in' partially observed data with plausible values drawn from an appropriate imputation distribution. Software generally implements MI under the assumption that data are 'missing at random' (MAR) i.e. that the missing mechanism is not dependent on the missing data conditional on the observed data. This is a strong inherently untestable assumption, and if incorrect can result in misleading inferences. The sensitivity of inferences to this assumption needs to be assessed by exploring the alternative assumption that missing data are 'missing not at random' (MNAR) i.e. even conditioned on the observed data, the probability of missing observations depends on their unseen, underlying values. Broadly there are two ways to frame, and perform sensitivity analyses (SA) to accomplish this: using a pattern mixture model or a selection model. Motivated by a cancer dataset, we develop a novel pattern mixture approach to collecting and incorporating in the analysis prior information elicited from experts. We demonstrated the inferential validity of our approach by simulation. Our second example is an individual patient meta-analysis of sudden infant death syndrome studies. We extended existing multilevel MI software to perform SA for the risk of bed sharing in these complex data. Inferences were found to be robust. Finally we considered a proposal of Carpenter et al. (2007) for SA after MI by reweighting. We developed a modification, which dramatically improves its performance in small data sets. The routine use of SA in applied research is held back by the lack of practical methodology and examples. This thesis addresses these issues, and so lowers the barrier to the widespread adoption of SA.

Call record data describe the process of survey recruitment, and include the timing and outcome of interviewers' contact attempts. These are a convenient source of auxiliary information which can potentially be used to correct for nonresponse bias in household surveys. However, call records are distinct from traditional forms of auxiliary information: they are not a fixed characteristic of the household; and by their nature they are longitudinal and non-rectangular. These issues complicate their use, and existing applications of call records to post-survey adjustments are limited. I delineate three potential uses of call record data: (i) as predictor variables in a model of the response outcome; (ii) as predictors in a model for the survey items; and (iii) to use in a joint model for the survey variables and response process. Approaches (i) and (ii) are implemented under the 'missing at random' framework of design-based weighting and multiple imputation respectively. Related to (i), I investigate sequence analysis as a potentially more efficient way of summarising complex call records. Approach (iii) leads to an event-history model for the response process. This is estimated jointly with incomplete survey data, and in doing so the missing at random assumption is relaxed. The analysis is applied to data from a current, large-scale household survey: The Irish Longitudinal Study on Ageing. Incorporating call record data improved post-survey adjustments, but only for a limited number of variables, particularly estimates related to marital status. Throughout the analysis I offer methodological insights which have implications beyond the setting of nonresponse adjustments, most notably, the role of costs in the sequence analysis algorithm and the question of imputing in a hierarchical setting when the number of level-one units is unknown. Future research should explore approaches which utilise call record data to minimise bias during fieldwork.

This thesis presents a model of evaluator behaviour in usability evaluations and describes an evaluator centred approach to analytical usability evaluation assessment. Usability evaluations involving multiple evaluators often produce inconsistent results, even when using the same usability evaluation method. This would suggest that individual evaluator judgement is influenced by resources other than those provided by the usability evaluation method. This research sought to discover factors external to usability evaluation methods that influence evaluators in their decision making during usability evaluations, and thus, explaining such inconsistencies between evaluators. The research involved conducting two large analytical evaluations using multiple evaluators and subjecting the results to falsification testing to validate evaluation predictions. Extended Structured Problem Report Formats were specifically designed for usability problem reporting to enable the confident coding of all possible prediction types. The problem reports required explanation for decisions made in problem discovery and analysis thus aiding the identification of factors influencing evaluator judgement. The results show evaluation methods provide little support in usability problem discovery and analysis; hence evaluators rely on a variety of individual knowledge resources such as technical, design, domain and user knowledge for example. Such knowledge resources were not common to all evaluators. The absence, or inappropriate perception of different knowledge resources resulted in missed or inappropriately analysed candidate usability problems. The results provide a model of evaluator behaviour that explains why some evaluators discover usability problems that others fail to discover, and how evaluators make appropriate and inappropriate decisions about candidate usability problems that result in true and false positive, and true and false negative predictions.

This study aimed to explain the prevailing situation of research culture in a state-run Pakistani university by identifying and analysing research-related cultural factors (e.g. ideas, beliefs, values, and assumptions etc.) characterising it. The study also examined the ways in which the influence of these factors condition academics’ research practices. Moreover, the contribution of academics’ research practices in maintaining or modifying existing research culture was also explored. A combination of Archer’s social realist framework of cultural analysis and Evans’s model of researcher development was used to meet the needs of the study. The latter was used as a thinking tool to point out various aspects of the complex phenomenon of research culture from the literature produced in different academic areas within the wider field of higher education while the former provides the overall theoretical basis for conceptualising the phenomenon as well as analysing the data. In line with critical realism, the data about different aspects of the phenomenon was collected from two social sciences faculties of University X. Multiple tools were used including audio recording of semi-structured interviews of twenty-two academics with diverse research experiences, numeral data of questionnaires gathered from 70 academics, and the written texts in the form of relevant policy documents. The study identified seven sets of prominent research-related cultural factors namely; aspects of academics’ job, natural and social sciences divide, utility of research, choices of research strategy, research-related skills, intellectual engagement, and research productivity/outputs that characterised the research related cultural system of the university. Most of the cultural factors entail constraining causal influences on academics’ research practices as they were in the relationship of contingent/competitive contradiction which indicates a low level of integration in the research culture of the university. The study also found that the majority of constraining cultural factors were reproduced after the socio-cultural interaction occurred during 2008-11. However, the emergence of three cultural factors present in discourses about research-led teaching, quality of research outputs and research related skills was evident which suggested slight increase in the level of cultural integration during this period. The detailed analysis of the existing situation of the university may serve as a resource for its leadership of the university to adopt appropriate policies to promote research culture in the university, especially in social sciences faculties. The theoretically driven concept of research culture (based on Archer’s approach)used for this study may also help other researchers and academics investigate this phenomenon in other universities.

Missing data are often imputed with plausible values when various analyses are performed. One popular approach employed to impute data is multiple imputation, which requires specification of a suitable imputation model. This thesis investigates the impact on multiply imputed hierarchical datasets when the imputation model is misspecified. The first issue studied is the presence of omitted variable bias. The same issue is then studied with a focus on the use of multiple imputation for creating synthetic data to protect data confidentiality. Here, the quality of multiply imputed datasets is studied not only through performance of various analysis models, but also, risks of disclosure for sensitive data. With the help of simulation studies and a longitudinal dataset from establishments in Germany, the detrimental effect of such model misspecification is evaluated, and recommendations are made for users of multiple imputation for both missing and synthetic data. The second issue investigated is model misspecification due to incorrect modelling of the shape of the error term. Existing methods for robust regression and alternatives to the normal distribution are compared within the synthetic data context only. Results from simulation studies and data on household wealth in the UK are used to identify appropriate methods for multiple imputation in such a scenario.

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In this thesis I discuss how the phenomena of indexicality and enregisterment (Silverstein 2003; Agha 2003) can be observed and studied in historical contexts via the use of historical textual data. I present a framework for the study of historical enregisterment which compares data from corpora of both nineteenth-century and modern Yorkshire dialect material, and the results of an online survey of current speakers so as to ascertain the validity of the corpus data and to use ‘the present to explain the past’ (Labov 1977:226). This framework allows for the identification of enregistered repertoires of Yorkshire dialect in both the twenty-first and nineteenth centuries. This is achieved by combining elicited metapragmatic judgements and examples of dialect features from the online survey with quantitative frequency analysis of linguistic features from Yorkshire dialect literature and literary dialect (Shorrocks 1996) and qualitative metapragmatic discourse (Johnstone et al 2006) from sources such as dialect dictionaries, dialect grammars, travel writing, and glossaries. I suggest that processes of enregisterment may operate along a continuum and that linguistic features may become ‘deregistered’ as representative of a particular variety; I also suggest that features may become ‘deregistered’ to the point of becoming ‘fossil forms’, which is more closely related to Labov’s (1972) definition of the ultimate fate of a linguistic stereotype. I address the following research questions: 1. How was the Yorkshire dialect enregistered in the nineteenth century? 2. How is Yorkshire dialect enregistered in the present day? 3. How do these compare, and how might we account for the results of this comparison? In so doing, I highlight that we can gain insights into the social value of linguistic features in historical contexts.

This study explores professional practice and examines an approach to research that could be useful for the practitioner in developing and extending their practice. The existence of artistry is recognised within professional practice (Schon 1983), and is important in making professional judgements (Fish and Coles 1998, pp. 28-53, de Cossart and Fish 2005). Therefore, as in the methodology proposed by Fish (1998), the artistic/holistic paradigm was adopted because this specifically enables the exploration of professional artistry and is suited to insider practitioner research. The study critically appraised the use of the proposed artistic/holistic paradigm. A case study approach was used in which the researcher was the case. A portrait of an episode in clinical practice was produced, followed by a critical appraisal of this portrait. These then became the portrait of research practice, which was equally appraised. This mirrors the process seen within the arts in which critical appreciation is a reflective process, deriving its rigour from the discipline and connoisseurship of the critic. The results demonstrate that the artistic/holistic paradigm is well suited to continuing professional development, both individually and corporately. The proposed paradigm does enable the recognition and exploration of professional artistry, both within clinical and research practice. Professional practice has a moral foundation and it was shown that this must be openly recognised if meaningful professional development is to occur. Evidencebased medicine, which is founded on the technical-rational view of practice, was shown to be insufficient for the professional's ongoing development. This, and similar work, will impact and contribute to the ongoing evolution of the traditions of the profession.