Cheung, T., & Dehipawala, S., & Taibu, R., & Shekoyan, V. (2017, October), Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students  Paper presented at 2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania. 10.18260/1-2--29384

\bibitem{asee_peer_29384}
  Cheung, T., \& Dehipawala, S., \& Taibu, R., \& Shekoyan, V. (2017, October), \emph{Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students}
  Paper presented at 2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania.
  10.18260/1-2--29384

Tak Cheung, sunil Dehipawala, Rex Taibu, and Vazgen Shekoyan.  "Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students".  2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania, 2017, October.  ASEE Conferences, 2017.  https://peer.asee.org/29384 Internet.  02 May, 2024

\bibitem{asee_peer_29384}
  Tak Cheung, sunil Dehipawala, Rex Taibu, and Vazgen Shekoyan.
  "Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students".
  \emph{2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania, 2017, October}.
  ASEE Conferences, 2017.
  https://peer.asee.org/29384 Internet.  02 May, 2024

@INPROCEEDINGS{asee_peer_29384
author = "Tak Cheung, sunil Dehipawala, Rex Taibu, and Vazgen Shekoyan"
title = "Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students"
booktitle = "2017 Mid-Atlantic Section Fall Conference"
year = "2017"
month = "October"
address = "Penn State University - Berks Campus - Reading, Pennsylvania"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/29384}
number = {10.18260/1-2--29384}
}

TY  - CPAPER
AB  - The latent variable modeling technique in education assessment is illustrated using problem solving examples in first semester physics for engineering and technology students.   The regression modeling of data in the Newton second law laboratory class has equivalence to an analysis with the internal force serving as the latent variable.   A path diagram would contain the internal force as a latent variable which manifest its influence on the observed datasets, namely, acceleration and force.    Similarly, the pre-score and post-score data collected for education assessment can be studied with latent variable modeling to broaden the scope beyond regression which requires an independent variable.  The numerical and flexibility advantages of latent variable modeling versus regression modeling in education assessment are presented.    Free LISREL student version working details and free confirmatory factor analysis Microsoft Excel software from university faculty are presented.   The adaptation of Queensborough Community College Physics faculty education research into Queensborough NSF-REU projects for engineering students is discussed.
AU  - Tak Cheung
AU  - sunil Dehipawala
AU  - Rex Taibu
AU  - Vazgen Shekoyan
CY  - Penn State University - Berks Campus - Reading, Pennsylvania
DA  - 2017/10/06
PB  - ASEE Conferences
TI  - Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students
UR  - https://peer.asee.org/29384
DO  - 10.18260/1-2--29384
ER  -