DDS-7250

Gournelos, T. (2019). Doing academic research: A practical guide to research methods and analysis. Taylor & Francis Group.
https://ebookcentral.proquest.com/lib/nu/detail.action?docID=5773107

Key chapters:
Chapter 2: 2-9
Chapter 4: 19-24

This resource provides practical guidance for conducting academic research, including methods, analysis, and step-by-step strategies for researchers.

Williamson, K. (2002). Research methods for students, academics, and professionals: Information management and systems. Elsevier Science & Technology.
https://ebookcentral.proquest.com/lib/nu/detail.action?docID=1640195

Key chapters:
Chapter 4: Ethics in Research (67-81)
Chapter 7: Experimental Research Designs (125-146)

This resource explores various research methodologies with a focus on ethical considerations and experimental design, making it essential for information management and systems research.

Vahidov, R. (2012). Design-type research in information systems: Findings and practices. IGI Global. https://doi.org/10.4018/978-1-4666-0131-4

Key chapters:
Chapter 1: Design (1-24)
Chapter 4: Traditional Science vs. Design-Type Research (76-93)
Chapter 5: Design-Type Research in Information Systems (94-101)

This book highlights the differences between traditional scientific approaches and design-type research, offering valuable insights into its application in information systems.

Fiesler, C. (2019). Ethical considerations for research involving (speculative) public data. Proc. ACM Hum.-Comput. Interact., 3(GROUP), Article 249, 13 pages. https://doi.org/10.1145/3370271

This article explores ethical challenges and considerations when using public or speculative datasets in research, emphasizing the implications for human-computer interaction studies.

Kremer-Herman, N. (2017). Ethical considerations of doing research at scale. In Practice and Experience in Advanced Research Computing 2017: Sustainability, Success, and Impact (PEARC '17). Association for Computing Machinery, New York, NY, USA, Article 61, 1–3.

This resource discusses the ethical complexities involved in conducting large-scale research, focusing on data collection, analysis, and the broader impacts on privacy and sustainability.

Saltz, J. S., & Dewar, N. (2019). Data science ethical considerations: A systematic literature review and proposed project framework. Ethics Inf Technol, 21, 197–208. https://doi.org/10.1007/s10676-019-09502-5

This study presents a comprehensive review of ethical issues in data science and offers a structured framework for addressing these concerns in project development and execution.

Burns, C., Thomason, J., & Tansey, W. (2020). Interpreting black box models via hypothesis testing. In Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference (FODS '20). Association for Computing Machinery, New York, NY, USA, 47–57. https://doi.org/10.1145/3412815.3416889

This paper discusses methods for interpreting complex black box models in data science through the application of hypothesis testing, bridging theoretical and practical perspectives.

Erdogmus, H. (2022). Bayesian hypothesis testing illustrated: An introduction for software engineering researchers. ACM Comput. Surv., 55(6), Article 119, 28 pages. https://doi.org/10.1145/3533383

This article provides a comprehensive introduction to Bayesian hypothesis testing, tailored for software engineering researchers seeking to apply these concepts in their work.

Garton, E. O., Ratti, J. T., & Giudice, J. H. (2004, In Press). Research and experimental design. In Braun, C. (Ed.), Techniques for wildlife investigations and management.

This chapter covers essential methodologies for research and experimental design, specifically within the context of wildlife investigations and management techniques.

Schumacher, A. (n.d.). Hypothesis Testing: Bayesian or Frequentist? [Video]. YouTube.
https://www.youtube.com/watch?v=qwlFuj8J774



This video provides an accessible comparison between Bayesian and frequentist approaches to hypothesis testing, focusing on their differences and applications.

Ayra, N. (2023). Hypothesis testing vs hypothesis generation. KD Nuggets.
https://www.kdnuggets.com/2023/02/hypothesis-testing-data-science.html#:~:text=Hypothesis%20Testing%20is%20when%20the,no%20effect%20on%20the%20population

This article explains the distinctions between hypothesis testing and hypothesis generation, highlighting their roles in data science.

Xu, Y., & Goodacre, R. (2018). On splitting training and validation set: A comparative study of cross-validation, bootstrap, and systematic sampling for estimating the generalization performance of supervised learning. Journal of Analysis and Testing, 2(3), 249–262. https://doi.org/10.1007/s41664-018-0068-2

This study compares different strategies for splitting datasets to evaluate machine learning models, providing practical guidance for improving generalization performance.

McGowan, L. D., Peng, R. D., & Hicks, S. C. (2021). Design principles for data analysis. https://doi.org/10.1080/10618600.2022.2104290

This resource outlines essential principles for designing robust and interpretable data analyses, targeting researchers and data scientists.

Coleman, T., Peng, W., & Mentch, L. (2022). Scalable and efficient hypothesis testing with random forests. Journal of Machine Learning Research, 23(1), Article 170, 35 pages. https://dl.acm.org/doi/10.5555/3586589.3586759

This paper presents novel approaches to hypothesis testing using random forests, emphasizing scalability and computational efficiency.

Jain, R. (n.d.). Performance evaluation of computer systems. Retrieved from https://www.cse.wustl.edu/~jain/books/perf_sli.htm

This classic resource offers an in-depth exploration of performance evaluation techniques for computer systems, covering both theoretical and practical aspects.