Biblio
This tutorial will introduce participants to Grounded Theory, which is a qualitative framework to discover new theory from an empirical analysis of data. This form of analysis is particularly useful when analyzing text, audio or video artifacts that lack structure, but contain rich descriptions. We will frame Grounded Theory in the context of qualitative methods and case studies, which complement quantitative methods, such as controlled experiments and simulations. We will contrast the approaches developed by Glaser and Strauss, and introduce coding theory - the most prominent qualitative method for performing analysis to discover Grounded Theory. Topics include coding frames, first- and second-cycle coding, and saturation. We will use examples from security interview scripts to teach participants: developing a coding frame, coding a source document to discover relationships in the data, developing heuristics to resolve ambiguities between codes, and performing second-cycle coding to discover relationships within categories. Then, participants will learn how to discover theory from coded data. Participants will further learn about inter-rater reliability statistics, including Cohen's and Fleiss' Kappa, Krippendorf's Alpha, and Vanbelle's Index. Finally, we will review how to present Grounded Theory results in publications, including how to describe the methodology, report observations, and describe threats to validity.
This paper concerns the role of human errors in the field of Early Risk assessment in Software Project Management. Researchers have recently begun to focus on human errors in early risk assessment in large software projects; statistics show it to be major components of problems in software over 80% of economic losses are attributed to this problem. There has been comparatively diminutive experimental research on the role of human errors in this context, particularly evident at the organizational level, largely because of reluctance to share information and statistics on security issues in online software application. Grounded theory has been employed to investigate the main root of human errors in online security risks as a research methodology. An open-ended question was asked of 103 information security experts around the globe and the responses used to develop a list of human errors causes by open coding. The paper represents a contribution to our understanding of the causes of human errors in information security contexts. It is also one of the first information security research studies of the kind utilizing Strauss and Glaser's grounded theory approaches together, during data collection phases to achieve the required number of participants' responses and is a significant contribution to the field.
This paper concerns the role of human errors in the field of Early Risk assessment in Software Project Management. Researchers have recently begun to focus on human errors in early risk assessment in large software projects; statistics show it to be major components of problems in software over 80% of economic losses are attributed to this problem. There has been comparatively diminutive experimental research on the role of human errors in this context, particularly evident at the organizational level, largely because of reluctance to share information and statistics on security issues in online software application. Grounded theory has been employed to investigate the main root of human errors in online security risks as a research methodology. An open-ended question was asked of 103 information security experts around the globe and the responses used to develop a list of human errors causes by open coding. The paper represents a contribution to our understanding of the causes of human errors in information security contexts. It is also one of the first information security research studies of the kind utilizing Strauss and Glaser's grounded theory approaches together, during data collection phases to achieve the required number of participants' responses and is a significant contribution to the field.