@inproceedings{8c47ff2b7b584689b6123677d631f3d4,
title = "Using Bayesian analysis to refine the measurement of the innovative capacities of engineers",
abstract = "In this research-to-practice paper we provide the context for our research and review our process for conducting our engineering innovativeness factor analysis using Bayesian statistical approaches rather than using the traditional principal components analysis (PCA). Our purpose in discussing this analysis approach is to demonstrate that we found the Bayesian analysis approach to be a superior way to refine factors and reduce survey items when developing our instrument for assessment of engineering innovativeness characteristics.",
author = "Ferguson, {Daniel M.} and Navin, {Diane D.} and Thompson, {Michael L.} and Phillips, {Amy D.} and Ohland, {Matthew W.} and Jablokow, {Kathryn W.}",
note = "Funding Information: Engineering Innovativeness research was supported by Venture Well, the National Science Foundation REE Grants #1264901 and #1264769 and the Procter & Gamble Co. (sponsors). Publisher Copyright: {\textcopyright} 2018 IEEE.; 48th Frontiers in Education Conference, FIE 2018 ; Conference date: 03-10-2018 Through 06-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/FIE.2018.8658698",
language = "English (US)",
series = "Proceedings - Frontiers in Education Conference, FIE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Frontiers in Education",
address = "United States",
}