Mirabelli, J., & Jensen, K., & Vohra, S., & Johnson, E. (2022, August), Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention  Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41122

\bibitem{asee_peer_41122}
  Mirabelli, J., \& Jensen, K., \& Vohra, S., \& Johnson, E. (2022, August), \emph{Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention}
  Paper presented at 2022 ASEE Annual Conference \& Exposition, Minneapolis, MN.
  10.18260/1-2--41122

Joseph Mirabelli, Karin Jensen, Sara Vohra, and Eileen Johnson.  "Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention".  2022 ASEE Annual Conference & Exposition, Minneapolis, MN, 2022, August.  ASEE Conferences, 2022.  https://peer.asee.org/41122 Internet.  25 Apr, 2025

\bibitem{asee_peer_41122}
  Joseph Mirabelli, Karin Jensen, Sara Vohra, and Eileen Johnson.
  "Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention".
  \emph{2022 ASEE Annual Conference \& Exposition, Minneapolis, MN, 2022, August}.
  ASEE Conferences, 2022.
  https://peer.asee.org/41122 Internet.  25 Apr, 2025

@INPROCEEDINGS{asee_peer_41122
author = "Joseph Mirabelli, Karin Jensen, Sara Vohra, and Eileen Johnson"
title = "Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention"
booktitle = "2022 ASEE Annual Conference \& Exposition"
year = "2022"
month = "August"
address = "Minneapolis, MN"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/41122}
number = {10.18260/1-2--41122}
}

TY  - CPAPER
AB  - This theory paper on quantitative methods focuses on the use of Exploratory Factor Analysis (EFA) in engineering education research. EFA techniques are fundamental, widespread, and powerful quantitative tools with applications to scale creation, model design, and understanding measures with complicated covariances. Despite the prevalence of EFA methods and tools in engineering education research, best practices and decisions associated with conducting EFA analyses are poorly defined and standards between disciplines often differ. For instance, when determining which items to retain in a large-item scale, there is not a consensus among researchers on cutoff values for factor loadings, or on which order multiple factors below cutoffs should be dropped. To the knowledge of the authors, no set of standards exist or have been adopted from another field for conducting EFA methods in engineering education. Defining these standards can be of significant value to the field, especially to those who are new to engineering education research. In a recent analysis of a novel, 81-item scale with N=624 responses, our team recognized this as an opportunity to compare multiple approaches to conducting an EFA. We performed multiple item retention methods following four different algorithmic procedures, such as identifying an order to removing under-loading versus cross-loading items, and a fifth synthetic procedure using the results from the first four. We then compared the results of performing a full EFA analysis with these procedures, including goodness of fit measures and factor structure. These five analyses were performed such that multiple approaches would triangulate towards strong items and latent factors and therefore yield a more reliable identification of the factor structure. With the intent of generating a discussion of EFA standards and practices within engineering education, we present observations and findings from the different algorithmic procedures of item retention on a large, 81-item scale. The EFA techniques performed average to 51 items and 10 factors retained from the full survey. After presenting the results of these methods, we offer reflections upon our analysis techniques and our suggestions for future best practices in EFA work. We discuss the implications of undergoing such exhaustive analysis processes on both the reliability of quantitative analyses in engineering education research and on the value of comparing similar analysis techniques in teams conducting research collaboratively.
AU  - Joseph Mirabelli
AU  - Karin Jensen
AU  - Sara Vohra
AU  - Eileen Johnson
CY  - Minneapolis, MN
DA  - 2022/08/23
PB  - ASEE Conferences
TI  - Exploring the Exploratory Factor Analysis: Comparisons and Insights from Applying Five Procedures to Determining EFA Item Retention
UR  - https://peer.asee.org/41122
DO  - 10.18260/1-2--41122
ER  -