Corinne Huggins-Manley
Professor | University of Florida
Gainesville, FL, UNITED STATES
Corinne Huggins-Manley studies quantitative methods for measuring and analyzing educational phenomena.
Biography
Corinne Huggins-Manley is a professor in Educational Research, Measurement, and Evaluation. Her research is focused on educational measurement, particularly with respect to issues of validity and fairness. She has published research on developments in fairness as a lack of statistical bias (e.g., differential item functioning; population invariance of equating) as well as on broader issues of fair measurement and construct validity. She is the principal investigator and co-principal Investigator on three large measurement grants from the Institute of Education Sciences and the National Science Foundation.
Areas of Expertise (3)
Fairness in Testing
Educational Measurement
Statistical Bias in Testing
Articles (3)
A Nonparametric Composite Group DIF Index for Focal Groups Stemming from Multicategorical Variables
Journal of Educational MeasurementHuggins-Manley, et al.
2024-05-12
The purpose of this study is to develop a nonparametric DIF method that (a) compares focal groups directly to the composite group that will be used to develop the reported test score scale, and (b) allows practitioners to explore for DIF related to focal groups stemming from multicategorical variables that constitute a small proportion of the overall testing population. We propose the nonparametric root expected proportion squared difference (REPSD) index that evaluates the statistical significance of composite group DIF for relatively small focal groups stemming from multicategorical focal variables, with decisions of statistical significance based on quasi-exact p values obtained from Monte Carlo permutations of the DIF statistic under the null distribution.
Semisupervised Learning Method to Adjust Biased Item Difficulty Estimates Caused by Nonignorable Missingness in a Virtual Learning Environment
Educational and Psychological ManagementKang Xue, et al.
2021-06-04
In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log file data.
Unsupported Causal Inferences in the Professional Counseling Literature Base
Journal of Counseling and DevelopmentA. Corinne Huggins-Manley, et al.
2021-06-08
At the heart of many counseling research interests and questions is a desire to understand causal relationships between variables. However, inferring causation from correlational studies ranges from difficult to impossible, and researchers have found that various literature bases contain large proportions of studies that draw unsupported causal inferences.
