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High-Stakes Testing Case Study: A Latent Variable Approach for Assessing Measurement and Prediction Invariance

Published online by Cambridge University Press:  01 January 2025

Steven Andrew Culpepper,
Herman AguinisOpen the ORCID record for Herman Aguinis[Opens in a new window],
Justin L. Kern and
Roger Millsap
Steven Andrew Culpepper
Affiliation:
University of Illinois at Urbana–Champaign
Herman Aguinis*
Affiliation:
George Washington University
Justin L. Kern
Affiliation:
University of Illinois at Urbana–Champaign
Roger Millsap
Affiliation:
Arizona State University
*
Correspondence should be made to Steven Andrew Culpepper, Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USA. Email: sculpepp@illinois.edu
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Abstract

The existence of differences in prediction systems involving test scores across demographic groups continues to be a thorny and unresolved scientific, professional, and societal concern. Our case study uses a two-stage least squares (2SLS) estimator to jointly assess measurement invariance and prediction invariance in high-stakes testing. So, we examined differences across groups based on latent as opposed to observed scores with data for 176 colleges and universities from The College Board. Results showed that evidence regarding measurement invariance was rejected for the SAT mathematics (SAT-M) subtest at the 0.01 level for 74.5% and 29.9% of cohorts for Black versus White and Hispanic versus White comparisons, respectively. Also, on average, Black students with the same standing on a common factor had observed SAT-M scores that were nearly a third of a standard deviation lower than for comparable Whites. We also found evidence that group differences in SAT-M measurement intercepts may partly explain the well-known finding of observed differences in prediction intercepts. Additionally, results provided evidence that nearly a quarter of the statistically significant observed intercept differences were not statistically significant at the 0.05 level once predictor measurement error was accounted for using the 2SLS procedure. Our joint measurement and prediction invariance approach based on latent scores opens the door to a new high-stakes testing research agenda whose goal is to not simply assess whether observed group-based differences exist and the size and direction of such differences. Rather, the goal of this research agenda is to assess the causal chain starting with underlying theoretical mechanisms (e.g., contextual factors, differences in latent predictor scores) that affect the size and direction of any observed differences.

Keywords

measurement invarianceprediction invarianceinstrumental variableshigh-stakes testing

Information

Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 1 , 15 March 2019, pp. 285 - 309
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-9649-2) contains supplementary material, which is available to authorized users.

Roger Millsap passed away unexpectedly on May 9, 2014 due to a brain hemorrhage. This article is the product of our collective work involving conceptualization, data collection and analysis, and writing. We dedicate the article to him.

We thank Alberto Maydeu-Olivares, a Psychometrika associate editor, and two anonymous reviewers for their excellent recommendations, which allowed us to improve our manuscript in a substantial manner.

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