ERIC Number: EJ1330065
Record Type: Journal
Publication Date: 2022
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0731-1745
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Available Date: N/A
Disrupted Data: Using Longitudinal Assessment Systems to Monitor Test Score Quality
An, Lily Shiao; Ho, Andrew Dean; Davis, Laurie Laughlin
Educational Measurement: Issues and Practice, v41 n1 p28-32 Spr 2022
Technical documentation for educational tests focuses primarily on properties of individual scores at single points in time. Reliability, standard errors of measurement, item parameter estimates, fit statistics, and linking constants are standard technical features that external stakeholders use to evaluate items and individual scale scores. However, these cross-sectional, "point-in-time" features can mask threats to the validity of score interpretations, including those for aggregate scores and trends over time. We use test score data collected before and during the COVID-19 pandemic to show that longitudinal analyses, not just point-in-time analyses, are necessary to detect threats to desired inferences. We propose that educational agencies require and vendors include longitudinal data features, including "match rates" and correlations, as standard exhibits in technical documentation.
Descriptors: Documentation, Scores, Evaluation Methods, Longitudinal Studies, Tests, Test Items, Test Validity, Test Interpretation, Data Collection, COVID-19, Pandemics, Inferences, Correlation
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www-wiley-com.bibliotheek.ehb.be/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
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