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Contemporary philosophy of the social sciences is dominated by methodological individualism. Intentional agency is assumed to be conceptually and explanatorily prior to social facts and social practices. In particular, it is generally thought that denials of methodological individualism are bound to include ontologically unnatural and, thereby, unacceptable views. This dissertation provides a comprehensive criticism of this orthodoxy. Part I argues that social facts do not have to be understood as aggregates of actions and attitudes of essentially asocial individuals. Rather, the construction of social facts requires that acting as a member of a group rather than as a disparate individual is a fundamental building block of social reality and social facts. This idea is explicated in the anti-individualistic terms of the theory of collective intentionality. Part II tackles the accusation that the theory of collective intentionality is indefensibly anti-naturalistic in the sense that its picture of humans is essentially incompatible with evolutionary biology. This accusation is answered in terms of detailed analyses of evolutionary models of human sociality and empirical studies of the nature of social action. Part II concludes that it is actually the methodologically individualistic picture of social action as strategic individual action that is unacceptable. The theory of collective intentionality is compatible with and supported by scientific naturalism. Part III, then, defends full-blown methodological holism. It is argued that intentional action and agency as we know them actually require that individual agents (qua agents and not qua physical objects) are essentially constituted by social practices. Intentional action must be explained and understood in terms of social practices. However, this view is argued to be perfectly naturalistic both in the sense of not assuming any ontologically suspect entities and in the sense of being supported by the natural sciences. Indeed, it is the individualistic orthodoxy that has to apply unnatural notions.

In recent years there has been an increased interest in computational social sciences, digital humanities and political sciences to perform automated quantitative narrative analysis (QNA) of text in large scale, by studying actors, actions and relations in a given narration. Social scientists have always relied on news media content to study opinion biases and extraction of socio-historical relations and events. Yet in order to perform analysis they had to face labour-intensive coding where basic narrative information was manually extracted from text and annotated by hand. This PhD thesis addresses this problem using a big-data approach based on automated information extraction using state of the art Natural Language Processing, Text mining and Artificial Intelligence tools. A text corpus is transformed into a semantic network formed of subject-verb-object (SVO) triplets, and the resulting network is analysed drawing from various theories and techniques such as graph partitioning, network centrality, assortativity, hierarchy and structural balance. Furthermore we study the position of actors in the network of actors and actions; generate scatter plots describing the subject/object bias, positive/ negative bias of each actor; and investigate the types of actions each actor is most associated with. Apart from QNA, SVO triplets extracted from text can also be used to summarize documents. Our findings are demonstrated on two different corpora containing English news articles about US elections and Crime and a third corpus containing ancieilt folklore stories from the Gutenberg Project. Amongst potentially interesting findings we found the 2012 US elections campaign was very much focused on 'Economy' and 'Rights'; and overall, the media reported more frequently positive statements for the Democrats than the Republicans. In the Crime study we found that the network identified men as frequent perpetrators, and women and children as victims, of violent crime. A network approach to text based on semantic graphs is a promising approach to analyse large corpora of texts and, by retaining relational information pertaining to actors and objects, this approach can reveal latent and hidden patterns, and therefore has relevance in the social sciences and humanities.

Existing information regarding methodological journeys is predominantly restricted to autobiographical accounts, which may limit understanding, particularly in relation to the development, and significance, of methodological consciousness. This thesis explores the methodological journeys of doctoral social sciences researchers in the UK, in the early stages of their research career. It investigates how they speak of their perceptions of their individual journeys, and the potential relationships between their life histories and educational experiences and the epistemological, ontological and methodological assumptions they hold. The research design consisted of life history interviews with 9 researchers. In addition, 6 of the researchers were then involved in collective biography discussions. Information from these interviews was developed into a series of narrative accounts, which illustrate the complexity of the individual journeys. The analysis of the narrative information also included attempts to approach the information through both an interpretivist and post-structuralist lens, and privileged the value of embracing multiple perspectives. The accounts of the researchers indicated that through their journeys, their philosophical assumptions may be understood as a socially constructed product of their life histories and academic experiences. The journeys were characterised as a series of social experiences which challenged and reinforced individual assumptions. The presentation of these journeys was seen as connected to social context and the nature of the assumptions held by the individual. Experiences of post graduate research training were presented as having the potential to unlock the methodological consciousness required to re-frame these experiences, improve understanding and resolve methodological conflict. This research presents an original contribution to knowledge through its approach to the exploration and illustration of methodological journeys. It has implications for post graduate programmes in the social sciences and makes a case for the need for these to embrace methodological diversity and introduce paradigmatic understandings through teaching and supervision. In addition it asserts that individual researchers may benefit from directly engaging in aspects of reflexivity and contextualisation of their assumptions.

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Thesis consists of 78 previously published items. 78 parts in 3 boxes.

This thesis makes a distinctive contribution to debates about how to include and quality assess 'qualitative' research in systematic reviews. It analyses sets of quality criteria, assesses the impact of study quality on findings and compares 'quantitative' and 'qualitative' perspectives on quality. The research consists of a review of the literature and three new methodological studies. The first study surveyed and evaluated quality assessment tools, the second analysed the development of a new tool, and the third examined the relationship between the quality of 'qualitative' research and the findings of systematic reviews. A large number of different quality criteria have been proposed for 'qualitative' research but assessment tools represent 'good practice' guides rather than aids to distinguish between 'good' and 'bad' studies. Continuous funding, a policy-focussed context, and a multi-disciplinary team which viewed research questions as drivers for quality assessment were important factors for developing a unique tool which did help to distinguish between studies. There was no straightforward relationship between study quality and the findings of reviews. However, excluding lower quality studies had little impact on review findings. Studies which made the biggest contribution to reviews were those with appropriate methods for the review question and findings displaying conceptual depth. In contrast to procedures for 'quantitative' research, engaging with study findings as well as study methods is important for assessing fully the quality of 'qualitative' research. This thesis generates important empirical evidence for debates about how to assess the quality of 'qualitative' research. It shows how standard quality assessment protocols need to be altered better to fit 'qualitative' research, reveals how study quality can impact on review findings and demonstrates some problems with the terms 'qualitative' and 'quantitative'. Future debate in this area should focus on how to identify reliable answers to questions about intervention process, context and need.

Statistical models are often used as the defining basis of measurement in social science. However, important issues such as uncertainty, model criticism and the impact of model assumptions are often neglected. These issues are explored in the context of three leading cases of model-based measurement. The relative index of inequality (RII) is used to measure socioeconomic inequality in health outcomes. A new definition of the RII is introduced, in place of the standard formulation based on linear regression. Non-linear outcome rates are accommodated, and estimation using cubic splines fitted by maximum penalized likelihood is developed. The modeling approach taken naturally handles standardizing variables such as age. Bootstrap estimation of variability is pursued. Simulations and a real data example demonstrate a reduction in bias, at the cost of some increase in variance, when incidence rates are non-linear. Social mobility can be measured using an index of mobility, which summarizes a mobility table in a single number. A certain index, based on the supposition of an underlying continuous-time Markov process, satisfies criteria set out in the literature. The 'embedding problem' is explored to assess when it is valid to apply this index. A sufficient condition for the distribution of a likelihood ratio test statistic is derived, and used to develop confidence intervals. In an example using French data, the index is used to caution against the conclusions of the popular UNIDIFF model. Electoral transitions between parties can be measured via a transition table of votes. The aggregated compound multinomial model for such a table appears rigid. Flexibility is introduced in two ways: the log-linear dependence on covariates is relaxed to one involving thin plate splines, and a new model with a general covariate structure is introduced. However, little improvement is offered in the example of describing the dependence of the 2005 British general election results on the 2001 results. This is illustrated using novel plots of residuals.

The purpose of this thesis is to demonstrate the progress that has been made towards the goal of producing a prototype computer model of Abell's Theory of Comparative Narratives, and subsequently, designing metrics to rigorously measure Abell's concept of 'closeness' of texts. The production of such a model does not simply involve the mechanical (though distinctly non-trivial) transference of Abell's theory from paper to machine; various facets of the theory are not of a sufficiently high specification for a computer model and the fulfilment of such a computer model requires attention to these areas, specifically: i) a repeatable method of comparing the structures of individual events; ii) a consistent procedure of comparing the overall structure of a pair of texts, following on from Abell's basic concept of paths of social determination. iii) metrics to demonstrate that the solutions proposed do indeed address the shortcomings of Abell's theory. In order to preserve the qualitative nature of the theory and to demonstrate its potential real-world uses, the computer model attempts to avoid complex mathematics as far as possible and to produce transparent, non-expert results.

Government social researchers are a group of civil servants who have been overlooked in the existing literature on policy-making in the UK. Their role is particularly intriguing in policy areas relating to environment, food, and energy policy. In these domains, researchers in Science and Technology Studies have argued that policy-makers hold flawed assumptions about citizens’ views and likely actions, contributing to an image of UK policy institutions as overly technocratic and resistant to change. In this context, this thesis aims to understand changes in social research capacity and influence in the Department for Environment, Food and Rural Affairs (DEFRA) and the Department of Energy and Climate Change (DECC) between 2001 and 2015. Based on an analysis of more than 200 documents and 46 interviews with civil servants and external researchers, this thesis illuminates the growth of social research capacity and influence within DEFRA and DECC, since these departments were formed in 2001 and 2008 respectively. The first two empirical chapters (4 & 5) explain how social research capacity expanded within specific institutional, political, and epistemic contexts, through changes in how actors perceived the meanings, roles, and value of social research. It is shown that, contrary to what has been implied by recent literature, DEFRA and DECC are epistemically diverse and dynamic: they house multiple and conflicting epistemic perspectives which are reshaped over time. Moreover, social researchers are committed to performing a ‘challenge function’, whereby they question assumptions, values, and the framing of ideas. Indeed, such challenging has been important in shaping the capacity for social research within these departments. Social researchers’ ‘challenge function’ has also contributed to their gaining greater influence in DEFRA and DECC. Considering policy areas from each department in depth, Chapters 6 & 7 show that social researchers have enabled both ‘single-loop’ and ‘double-loop’ learning. As a result, in both departments social researchers have had some success in encouraging their colleagues to develop and test out policy ideas with the help of empirical research about citizens’ perspectives and everyday lives. While social researchers’ 'challenge function' is a significant policy learning mechanism, it has also been inhibited in various ways within these departments. The thesis concludes that their challenge function could be strengthened if social researchers gain greater representation in the senior civil service and more institutional recognition of their expert knowledge relating to a policy area (besides their skills). Moreover, better interdisciplinary collaboration is needed early on in policy development processes. Such changes have the potential to improve both the effectiveness and democratic legitimacy of policy-making within DEFRA and DECC.