Advanced Search »

Newsletter


	We expect major site improvements soon. To be informed of important news about our site, enter your email here. Of course, you can always unsubscribe later. Your address will not be released to others.

Unsubscribe »

National Science Foundation Award #0536728

CAREER: Expanding the Applicability, Utility and Popularity of Item Response Theory Models for Unfolding

Investigator(s):

James Roberts (PI)

Sponsor:

Georgia Tech Research Corporation - GA Institute of Technology, GA 30332 4043850866

Start Date/Expiration Date

2005-06-01 to 2006-05-31 (amended 2005-07-07)

Awarded Amount to Date:

$76,274

Abstract: This research program will develop and apply new measurement methodology in the domain of item response theory (IRT) models for unfolding. An unfolding IRT model is a probabilistic model that simultaneously estimates each respondent's latent trait along with the characteristics of each item using the responses to a test or questionnaire. In contrast to traditional IRT models that implement cumulative item response functions to measure traits like ability and proficiency, unfolding IRT models incorporate single-peaked, nonmonotonic item response functions to measure constructs like attitudes, preferences and individual locations within certain developmental processes that occur in stages. This research program will expand the applicability, utility and popularity of a particular unfolding IRT model for polytomous responses known as the generalized graded unfolding model (GGUM). These goals will be achieved through an integrated sequence of educational and research projects. Research projects will include: 1) An investigation of alternative indices of model, item and person fit that can be utilized with the GGUM - the results will enable practitioners to better identify when the GGUM is and is not appropriate for a given set of item responses. 2) An exploration of Bayesian estimates of GGUM parameters derived from a Markov chain Monte Carlo method - this technique will potentially improve the accuracy of GGUM parameter estimates in situations with sparse data and will better represent the uncertainty inherent in those parameter estimates. 3) The development of a new multidimensional extension of the GGUM - this model will allow for the measurement of multidimensional latent traits that are of interest to social scientists in many substantive areas like psychology, marketing, political science, etc. 4) The development of a new mixture model that is based on the GGUM response function - this mixture model will estimate latent traits of conscientious respondents while probabilistically culling unconscientious respondents from the measurement portion of the algorithm. The educational component of this program includes a series of both introductory and intensive workshops on the application and benefits of the GGUM family of models. Workshops will be developed and presented to applied measurement practitioners across the U.S. and Europe. The refinement and subsequent distribution of free, user-friendly computer software that estimates GGUM parameters will complement these educational activities. Recent psychometric research suggests that responses to typical attitude and preference questionnaires are more appropriately described by unfolding models rather than cumulative models. Indeed, the use of cumulative models in these situations can lead to invalid measures. Unfolding IRT models will promote more valid measurement in these situations while also providing other benefits commonly associated with cumulative IRT models. These include the ability to build item banks that maintain a common scale of measurement, the ability to estimate the precision of each respondent's latent trait estimate, and the ability to measure attitudes, preferences and other constructs in the social sciences using computerized adaptive testing methods. Consequently, the results from this research can improve the quality of measurements that are developed in many social science disciplines while simultaneously expanding applied measurement practices in ways that are both technically sound and practical. The educational component of this program will provide measurement practitioners with the requisite knowledge and tools to effectively use this growing methodology.

NSF Org:

SES - Division of Social and Economic Sciences

Award Number:

0536728

Award Instrument:

Continuing grant

Program Manager:

Cheryl L. Eavey
SES Division of Social and Economic Sciences
SBE Directorate for Social, Behavioral & Economic Sciences

NSF Program(s):

METHOD, MEASURE & STATS

Field Application(s):

Program Reference Code(s):

FACULTY EARLY CAREER DEVELOPMENT PROGRAM, 1045
PECASE- eligible, 1187
UNASSIGNED, 0000

Program Element Code(s):

1333