When Data Challenges Theory
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When Data Challenges Theory
Author: Davide Garassino
language: en
Publisher: John Benjamins Publishing Company
Release Date: 2022-02-15
This volume offers a critical appraisal of the tension between theory and empirical evidence in research on information structure. The relevance of ‘unexpected’ data taken into account in the last decades, such as the well-known case of non-focalizing cleft sentences in Germanic and Romance, has increasingly led us to give more weight to explanations involving inferential reasoning, discourse organization and speakers’ rhetorical strategies, thus moving away from ‘sentence-based’ perspectives. At the same time, this shift towards pragmatic complexity has introduced new challenges to well-established information-structural categories, such as Focus and Topic, to the point that some scholars nowadays even doubt about their descriptive and theoretical usefulness. This book brings together researchers working in different frameworks and delving into cross-linguistic as well as language-internal variation and language contact. Despite their differences, all contributions are committed to the same underlying goal: appreciating the relation between linguistic structures and their context based on a firm empirical grounding and on theoretical models that are able to account for the challenges and richness of language use.
Qualitative Research with Socio-Technical Grounded Theory
This book is a timely and practical guide to conducting qualitative research with a socio-technical approach. It covers the foundations of research including research design, research philosophy, and literature review; describes qualitative data collection, qualitative data preparation and filtering; explains qualitative data analysis using the techniques of socio-technical grounded theory (STGT); and presents the advanced techniques of qualitative theory development using emergent or structured modes. It provides guidance on evaluating qualitative research application and outcomes; and explores the possible role of Artificial Intelligence (AI) in qualitative research in the future. The book is structured into five parts. Part I – Introduction includes three chapters that serve to provide: an overview of the book in Chapter 1; a brief history of the origins and evolution of the GT methods in Chapter 2; and an introduction to STGT in Chapter 3. Part II – Foundations of Research includes three chapters that cover: the building blocks of empirical research through a simple yet powerful approach to designing research methods (the research design canvas) in Chapter 4; the fundamental concepts of research philosophy in Chapter 5; and the myriad of literature review methods including those suited to STGT in Chapter 6. Part III – Qualitative Data Collection and Analysis includes four chapters that explain: the key concepts related to collecting qualitative data in Chapter 7; techniques used for collecting qualitative data in Chapter 8; how to go about preparing and filtering qualitative data in Chapter 9; and the qualitative data analysis procedures of open coding, constant comparison, and memoing in Chapter 10. Part IV – Theory Development includes two chapters that explain: what is considered theory (or theoretical outcomes) in Chapter 11; and the advanced STGT steps of theory development in Chapter 12. Eventually, Part V – Evaluation and Future Directions includes two chapters that: present the evaluation guidelines for assessing STGT applications and outcomes in Chapter 13; and explore new opportunities in qualitative research using large language models in Chapter 14. This book enables new and experienced researchers in modern as well as traditional disciplines to conduct rigorous qualitative research on socio-technical topics in the digital world. They will be able to approach qualitative research with confidence and produce valuable research outcomes in the form of rich descriptive findings, taxonomies, theoretical models, theoretical frameworks, preliminary and mature theories, recommendations, and guidelines, all grounded in empirical evidence.
Meeting the Challenges of Data Quality Management
Author: Laura Sebastian-Coleman
language: en
Publisher: Academic Press
Release Date: 2022-01-25
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses. - Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today's digitally interconnected world - Explores the five challenges in relation to organizational data, including "Big Data," and proposes approaches to meeting them - Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations - Provides Data Quality practitioners with ways to communicate consistently with stakeholders