Literature DB >> 32425216

A Propensity Score Method for Investigating Differential Item Functioning in Performance Assessment.

Michelle Y Chen1, Yan Liu2, Bruno D Zumbo2.   

Abstract

This study introduces a novel differential item functioning (DIF) method based on propensity score matching that tackles two challenges in analyzing performance assessment data, that is, continuous task scores and lack of a reliable internal variable as a proxy for ability or aptitude. The proposed DIF method consists of two main stages. First, propensity score matching is used to eliminate preexisting group differences before the test, ideally creating equivalent groups as in a randomized experimental study. Then, linear mixed effects models are adopted to perform DIF analysis based on the matched data set. We demonstrate this propensity DIF method using a high-stakes functional English language proficiency test. DIF due to education was investigated in the writing component, which consists of two continuously scored performance-based tasks. Although the proposed method is demonstrated in the context of language testing, it can be applied to other types of performance assessments.
© The Author(s) 2019.

Keywords:  differential item functioning (DIF); mixed effects model; performance assessment; propensity score matching; validation; writing assessment

Year:  2019        PMID: 32425216      PMCID: PMC7221496          DOI: 10.1177/0013164419878861

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  11 in total

1.  Statistical aspects of the analysis of data from retrospective studies of disease.

Authors:  N MANTEL; W HAENSZEL
Journal:  J Natl Cancer Inst       Date:  1959-04       Impact factor: 13.506

2.  Are Single-Item Global Ratings Useful for Assessing Health Status?

Authors:  Cathaleene Macias; Paul B Gold; Dost Öngür; Bruce M Cohen; Trishan Panch
Journal:  J Clin Psychol Med Settings       Date:  2015-10-22

3.  A Systematic Review of Propensity Score Methods in the Social Sciences.

Authors:  Felix J Thoemmes; Eun Sook Kim
Journal:  Multivariate Behav Res       Date:  2011-02-07       Impact factor: 5.923

4.  Just one question: If one question works, why ask several?

Authors:  Ann Bowling
Journal:  J Epidemiol Community Health       Date:  2005-05       Impact factor: 3.710

5.  The Satisfaction With Life Scale.

Authors:  E Diener; R A Emmons; R J Larsen; S Griffin
Journal:  J Pers Assess       Date:  1985-02

6.  Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies.

Authors:  Paul R Rosenbaum
Journal:  Biometrics       Date:  2007-06       Impact factor: 2.571

7.  Centering predictor variables in cross-sectional multilevel models: a new look at an old issue.

Authors:  Craig K Enders; Davood Tofighi
Journal:  Psychol Methods       Date:  2007-06

8.  Impact of multiple matched controls on design sensitivity in observational studies.

Authors:  Paul R Rosenbaum
Journal:  Biometrics       Date:  2013-02-04       Impact factor: 2.571

9.  Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model Averaged Causal Effects.

Authors:  Corwin Matthew Zigler; Francesca Dominici
Journal:  J Am Stat Assoc       Date:  2014-01-01       Impact factor: 5.033

10.  A comparison of 12 algorithms for matching on the propensity score.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2013-10-07       Impact factor: 2.373

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.