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Minimize RSR Award Detail

Research Spending & Results

Award Detail

Awardee:WAKE FOREST UNIVERSITY HEALTH SCIENCES
Doing Business As Name:Wake Forest University School of Medicine
PD/PI:
  • Edward Ip
  • (336) 716-9833
  • eip@wakehealth.edu
Award Date:07/23/2021
Estimated Total Award Amount: $ 284,994
Funds Obligated to Date: $ 284,994
  • FY 2021=$284,994
Start Date:08/15/2021
End Date:07/31/2024
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Partially Ordered Item Response Modeling for Longitudinal and Multivariate Data
Federal Award ID Number:2120174
DUNS ID:937727907
Program:Methodology, Measuremt & Stats
Program Officer:
  • Cheryl Eavey
  • (703) 292-7269
  • ceavey@nsf.gov

Awardee Location

Street:Medical Center Blvd
City:Winston-Salem
State:NC
ZIP:27157-1023
County:Winston Salem
Country:US
Awardee Cong. District:05

Primary Place of Performance

Organization Name:Wake Forest University School of Medicine
Street:Medical Center Blvd
City:Winston-Salem
State:NC
ZIP:27157-0001
County:Winston Salem
Country:US
Cong. District:10

Abstract at Time of Award

This research project will develop methods for longitudinal and multidimensional models with partially ordered sets of responses or posets. Precise and valid measurement of psychological and social constructs are key to progress in the social sciences. However, many psychological constructs such as personality, self-efficacy, and emotional intelligence are not directly observed. These constructs are measured indirectly through questionnaire instruments with a designated response format, such as multiple-choice questions. Scores are assigned to individuals in a way that communicates quantitative information about the construct. In many measurement situations, however, scoring is a challenge and there may be no best answer. The resulting responses often result in data that are a mix of ranked responses and responses that cannot be ranked. Such a structure forms a partially ordered set or poset. This project will advance measurement science by including poset response as a new category of response format. Publicly available software will be developed. The project will train and mentor graduate students with the support of an established psychometric graduate program at the University of North Carolina. Collaborative activities with the Educational Testing Service will broaden the impact of this project. This research project will develop a flexible class of measurement and predictive models for poset responses within complex settings that include multivariate, multidimensional, and longitudinal data. Because of the mix of different measurement scales, posets are notoriously challenging to model. This has been reflected in the predominantly non-model-based methods that have so far been used for handling posets; e.g., collapsing categories or summarizing by weighted means. The project's approach to poset measurement will be based on the theory of latent variable modeling. This method will enable researchers to test and falsify the model to advance the science. The poset model will allow measurement errors to be quantified, a feature that is often lacking in other methods. The model will be validated using simulation experiments and real-world applications across a broad range of areas, including cognitive tests, posets derived from latent class or cluster analyses, situational judgment tests, and attitudinal surveys. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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