TY - JOUR
T1 - Using topological data analysis in social science research
T2 - 126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019
AU - Godwin, Allison
AU - Thielmeyer, Aaron Robert Hamilton
AU - Rohde, Jacqueline Ann
AU - Verdín, Dina
AU - Benedict, Brianna Shani
AU - Baker, Rachel Ann
AU - Doyle, Jacqueline
N1 - Funding Information:
This work was supported through funding by the National Science Foundation CAREER Grant No. 1554057. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. The authors wish to thank the STRIDE team and survey participants for their engagement with this study.
Funding Information:
Dina Verdín is a Ph.D. Candidate in Engineering Education and M.S. student in Industrial Engineering at Purdue University. She completed her B.S. in Industrial and Systems Engineering at San José State University. Dina is a 2016 recipient of the National Science Foundation’s Graduate Research Fellowship and an Honorable Mention for the Ford Foundation Fellowship Program. Her research interest focuses on changing the deficit base perspective of first-generation college students by providing asset-based approaches to understanding this population. Dina is interested in understanding how first-generation college students author their identities as engineers and negotiate their multiple identities in the current culture of engineering.
Funding Information:
Jacqueline A. Rohde is a graduate student at Purdue University as the recipient of an NSF Graduate Research Fellowship. Her research interests in engineering education include the development student identity and attitudes, with a specific focus on the pre-professional identities of engineering undergraduates who join nonindustry occupations upon graduation.
Publisher Copyright:
© American Society for Engineering Education, 2019.
PY - 2019/6/15
Y1 - 2019/6/15
N2 - This research paper describes a new statistical method for engineering education, Topological Data Analysis (TDA), and considers the important decisions made during analysis and their impact on the quality of the results. We also describe why this new method may provide novel ways of understanding multidimensional data for student attitudes, beliefs, and mindsets. In this paper, we discuss the considerations that researchers must understand in conducting TDA with engineering education data. In the data analysis, a researcher must choose a filtering method, clustering method, number of filter slices (n), overlap in data, and cut height (ε) for each dimension. The importance and effect on the consistency and quality of the data differ for each decision. Some have a large impact on the results of the analysis (e.g., cut height [ε]), while others have a moderate impact on the resulting map appearance but not key structural features identified (e.g., number of filter slices [n]). We illustrate these methodological decisions as well as the results of TDA and its usefulness for engineering education using data from a project investigating first-year engineering students' underlying attitudes, beliefs, and mindsets to characterize the latent diversity of these students. A paper-and-pencil survey was administered to 3,855 students at 32 ABET accredited institutions across the U.S. in Fall 2017. After cleaning the data using attention checks within the survey, 3,711 student responses were examined for validity evidence. Exploratory factor analysis (for newly developed scales) and confirmatory factor analysis (for existing scales) were conducted. The resulting factors with strong validity evidence and high variability among engineering students were used in the TDA to map students' latent diversity. The results of this map indicate six distinct data progressions as well as a sparse group of students whose responses were not similar to the majority of the dataset. This work illustrates the opportunities for using TDA and provides a discussion of the different researcher decisions that are involved in this statistical technique.
AB - This research paper describes a new statistical method for engineering education, Topological Data Analysis (TDA), and considers the important decisions made during analysis and their impact on the quality of the results. We also describe why this new method may provide novel ways of understanding multidimensional data for student attitudes, beliefs, and mindsets. In this paper, we discuss the considerations that researchers must understand in conducting TDA with engineering education data. In the data analysis, a researcher must choose a filtering method, clustering method, number of filter slices (n), overlap in data, and cut height (ε) for each dimension. The importance and effect on the consistency and quality of the data differ for each decision. Some have a large impact on the results of the analysis (e.g., cut height [ε]), while others have a moderate impact on the resulting map appearance but not key structural features identified (e.g., number of filter slices [n]). We illustrate these methodological decisions as well as the results of TDA and its usefulness for engineering education using data from a project investigating first-year engineering students' underlying attitudes, beliefs, and mindsets to characterize the latent diversity of these students. A paper-and-pencil survey was administered to 3,855 students at 32 ABET accredited institutions across the U.S. in Fall 2017. After cleaning the data using attention checks within the survey, 3,711 student responses were examined for validity evidence. Exploratory factor analysis (for newly developed scales) and confirmatory factor analysis (for existing scales) were conducted. The resulting factors with strong validity evidence and high variability among engineering students were used in the TDA to map students' latent diversity. The results of this map indicate six distinct data progressions as well as a sparse group of students whose responses were not similar to the majority of the dataset. This work illustrates the opportunities for using TDA and provides a discussion of the different researcher decisions that are involved in this statistical technique.
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M3 - Conference article
AN - SCOPUS:85078756408
SN - 2153-5965
JO - ASEE Annual Conference and Exposition, Conference Proceedings
JF - ASEE Annual Conference and Exposition, Conference Proceedings
Y2 - 15 June 2019 through 19 June 2019
ER -