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AN INTEGRATED MULTIPLE STATISTICAL TECHNIQUE FOR PREDICTING POST-SECONDARY EDUCATIONAL DEGREE OUTCOMES BASED PRIMARILY ON VARIABLES AVAILABLE IN THE 8TH GRADE

Nicholls, Gillian M. (2009) AN INTEGRATED MULTIPLE STATISTICAL TECHNIQUE FOR PREDICTING POST-SECONDARY EDUCATIONAL DEGREE OUTCOMES BASED PRIMARILY ON VARIABLES AVAILABLE IN THE 8TH GRADE. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

There is a class of complex problems that may be too complicated to solve by any single analytical technique. Such problems involve so many measurements of interconnected factors that analysis with a single dimension technique may improve one aspect of the problem while overall achieving little or no improvement. This research examines the utility of modeling a complex problem with multiple statistical techniques integrated to analyze different types of data. The goal was to determine if this integrated approach was feasible and provided significantly better results than a single statistical technique. An application in engineering education was chosen because of the availability and comprehensiveness of the NELS:88 longitudinal dataset. This dataset provided a huge number of variables and 12,144 records of actual students progressing from 8th grade to their final educational outcomes 12 years later. The probability of earning a Science, Technology, Engineering, or Mathematics (STEM) degree is modeled using variables available in the 8th grade as well as standardized test scores. The variables include demographic, academic performance, and experiential measures. Extensive manipulation of the NELS:88 dataset was conducted to identify the student outcomes, prepare the set of covariates for modeling, and determine when the students' final outcome status occurred. The integrated models combined logistic regression, survival analysis, and Receiver Operating Characteristics (ROC) Curve analysis to predict obtaining a STEM degree vs. other outcomes. The results of the integrated models were compared to actual outcomes and the results of separate logistic regression models. Both sets of models provided good predictive accuracy. The feasibility of integrated models for complex problems was confirmed. The integrated approach provided less variability in incorrect STEM predictions, but the improvement was not statistically significant. The main contribution of this research is designing the integrated model approach and confirming its feasibility. Additional contributions include designing a process to create large multivariate logistic regression models; developing methods for extensive manipulation of a large dataset to adapt it for new analytical purposes; extending the application of logistic regression, survival analysis, and ROC Curve analysis within educational research; and creating a formal definition for STEM that can be statistically verified.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Nicholls, Gillian M.gmn3@pitt.eduGMN3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWolfe, Harveyhwolfe@pitt.eduHWOLFE
Committee MemberBlock, Henry Whwb@stat.pitt.eduHWB
Committee MemberNeedy, Kim Lkimneedy@pitt.eduKIMNEEDY
Committee MemberShuman, Larry Jshuman@pitt.eduSHUMAN
Committee MemberBesterfield-Sacre, Marymbsacre@pitt.eduMBSACRE
Date: 28 January 2009
Date Type: Completion
Defense Date: 1 July 2008
Approval Date: 28 January 2009
Submission Date: 21 November 2008
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Industrial Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: CIRP; National Education Longitudinal Study; Post-secondary education; SME; SMET
Other ID: http://etd.library.pitt.edu/ETD/available/etd-11212008-141246/, etd-11212008-141246
Date Deposited: 10 Nov 2011 20:05
Last Modified: 15 Nov 2016 13:51
URI: http://d-scholarship.pitt.edu/id/eprint/9752

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