June 24, 2017
June 24, 2017
June 28, 2017
Educational Research and Methods
The purpose of this research paper is to discuss the application of cluster analysis within engineering education research, the variations in cluster analysis methods, and the impact of this analysis on the process of selecting participants for interviews. Cluster analysis can be used to frame how research participants are selected for qualitative or mixed methods studies when saturation of viewpoints within the participant population is desired. Cluster analysis has been used successfully within many fields to group data based on similar attributes. Within engineering education, researchers have utilized clustering techniques to group participants that have similar traits, beliefs, ideas, backgrounds, demographics, and other measures.
As new methods are incorporated into engineering education research practices, it is important to understand their underlying assumptions, boundary conditions, and limitations. This is particularly important in cluster analysis since not all clustering techniques yield the same response from the same data set. This paper will describe commonly used techniques in data science, and discuss their suitableness for different data sets within the context of engineering education research. We will then describe in detail the three clustering techniques we have used for participant selection: k-means, Ward’s, and Complete-Link.
This work is situated within a larger, explanatory mixed methods project focused on understanding how undergraduates conceptualize their identities as researchers and their engineering epistemic beliefs. We will highlight the nature of the quantitative data, cluster analyses, and characteristics of the resulting clusters. Responses to anchored survey items were collected from participants from five institutions varying in size, type, and location within the United States. This data set includes 45 anchored items probing participants’ epistemic beliefs and need for cognitive closure. Average scores for six factors based on underlying theoretical constructs were calculated for each participant. Four of these factors had high reliability measures and were used for cluster analysis (Discomfort with Ambiguity, Closed-Mindedness, Certainty of Knowledge, and Sources of Knowledge). All three clustering techniques that were applied resulted in two distinct groups of participants. The number of participants in each cluster varied between techniques with k-means creating similarly sized clusters (n1 = 58, n2 = 51), Ward’s slightly less similarly sized (n1 = 63, n2 = 46), and Complete Link with least similar sizes (n1 = 83, n2 = 26). For all cluster solutions, the two clusters differed on the constructs of Closed-Mindedness and Certainty of Knowledge. Cluster solutions using k-means and Ward’s method showed differences between clusters on the Sources of Knowledge construct whereas Complete Link did not indicate this difference. Regardless of the technique, clusters did not differ on the Discomfort with Ambiguity construct with relatively high scores (average around 5 out of 7, where 7 represented “Strongly Agree”).
Results from these cluster analyses will be used to characterize participants for the qualitative phase of the project which will examine students’ perceptions of research and their epistemic beliefs within the context of research. Selecting participants based on cluster analyses will allow for theoretical sampling, which is appropriate for our grounded theory approach.
Ehlert, K. M., & Faber, C. J., & Kennedy, M. S., & Benson, L. (2017, June), Utilizing Cluster Analysis of Close-Ended Survey Responses to Select Participants for Qualitative Data Collection Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/29099
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