ABSTRACT
Engineering education tends to default to “simple approaches” rooted in statistical training and a classical approach to null hypothesis significance testing dating back over 100 years. These approaches often limit the kinds of questions that can be asked and can obscure insights that would have pushed forward new knowledge. With more recent developments in statistical theory and computational techniques, there are opportunities to represent each participant and their nuanced experiences more accurately in engineering education – thus providing insights that may have otherwise been missed. Other fields have utilized emerging methods to make more robust claims and more comprehensively represent complex phenomena. Intended for audience with familiarity in quantitative research, this chapter discusses advanced considerations for quantitative research in four areas: (1) study design, (2) data collection and preparation, (3) data analysis, and (4) data equity. In each of the data analysis areas, the chapter provides a description of a method, the strengths and limitations of the approach, starting resources, and a relevant example. The overarching goal of the chapter is to help engineering education researchers explore new methods, questions, and areas of their work, thereby opening opportunities for new directions in engineering education research.
