BUiD Library: INF 531 Informatics Research Methods: Reading list

INF 531 Informatics Research Methods

Department: MSc Cybersecurity Science and Applications / MSc in Informatics

Module Description: This module provides a comprehensive introduction to the methodologies and skills required to conduct Informatics research. It focuses on understanding the nature of Informatics and Informatics research, formulating research questions, and developing hypotheses. This module will delve into defining research objectives and scope and selecting suitable research methods and tools. It also provides a significant focus on research evaluation and assessment, such as criteria for assessing Informatics research, validity, reliability, and generalizability of research findings, and assessing research impact and significance. The module offers practical research advice and numerous essential research skills, including reading, reviewing, presenting, and writing.

Module texts

There is no textbook. We will rely on web resources and papers. Below are recommended readings.

Olsson, H. H. (2018). 'Challenges and strategies for undertaking continuous experimentation to embedded systems: industry and research perspectives', in Agile Processes in Software Engineering and Extreme Programming: 19th International Conference, XP 2018, Porto, Portugal, May 21–25, 2018, Proceedings (Vol. 314, p. 277). Springer.
Kitchenham, B. & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report.
Kohavi, R. & Longbotham, R. (2017). 'Online controlled experiments and a/b testing', in Encyclopedia of machine learning and data mining. Springer US, pp. 922-929.
Hair, J. F., Hult, G. T. M., Ringle, C. M. & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Al-Emran, M., Mezhuyev, V. & Kamaludin, A. (2018). 'PLS-SEM in information systems research: a comprehensive methodological reference', in International Conference on Advanced Intelligent Systems and Informatics. Springer, pp. 644-653.
Ringle, C. M. & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: the importance-performance map analysis. Industrial Management & Data Systems, vol. 116(9), pp. 1865-1886.
Buchert, T. et al. (2015). A survey of general-purpose experiment management tools for distributed systems. Future Generation Computer Systems, vol.45, pp. 1-12.
Tedre, M. & Moisseinen, N. (2014). Experiments in computing: a survey. The Scientific World Journal.
Gustedt, J., Jeannot, E. & Quinson, M. (2009). Experimental methodologies for large-scale systems: a survey. Parallel Processing Let, vol. 19(3), pp. 399-418.
Kohavi, R., Longbotham, R., Sommerfield, D. & Henne, R. M. (2009). Controlled experiments on the web: survey and practical guide. Data Mining and Knowledge Discovery, vol. 18(1), pp. 140-181.
Feitelson, D. G. (2005). Experimental computer science: the need for a cultural change.
Kitchenham, B. A., Pfleeger, S. L., Pickard, L. M., Jones, P.W., Hoaglin, D. C., El Emam, K. & Rosenberg, J. (2002). Preliminary guidelines for empirical research in software engineering. IEEE Transactions in Software Engineering, vol. 28(8), pp. 721-734.
Neideen, T. & Brasel, K. (2007). Understanding statistical tests. Journal of surgical education, vol. 64(2), pp. 93-96.
Introduction to Hypothesis testing http://wise.cgu.edu/hypomod/
Introduction to Mathematical Arguments http://math.berkeley.edu/~hutching/teach/proofs.pdf