Achievement gap estimates are widely used to measure the effectiveness and fairness of the education system, so their accuracy and unbiasedness are necessary for appropriate conclusions¶

Claim · All claims
Evidence · 1 study · theoretical r1 · q1
Currency · newest study 2018 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Because gap estimates are used to measure the effectiveness and fairness of the education system over time and across schooling, having estimates that are accurate and unbiased is necessary for drawing appropriate conclusions. → James Soland 2018

Evidence¶

James Soland 2018¶

James Soland. (2018). The achievement gap or the engagement gap? Investigating the sensitivity of gaps estimates to test motivation. Applied Measurement in Education 31(4), 312-323. https://www.nwea.org/research/publication/the-achievement-gap-or-the-engagement-gap-investigating-the-sensitivity-of-gaps-estimates-to-test-motivation/ doi:10.1080/08957347.2018.1495213

q1 · i? · theoretical · r1

Abstract-level framing argument from the article's introduction of why gap estimates matter: they are used "to measure the effectiveness and fairness of the education system" at points in time, over decades, and as children progress through school. No data are reported in the available text.

"Gap estimates are often used to measure the effectiveness and fairness of the education system at a given point in time, over the course of decades, and as children progress through school."

Discussion¶

Related Claims¶

• The developed MIRT equating methods behave as unbiased, effective, and consistent estimators of transformation parameters — related
• Stage 1 (after the first unit review assignment) is the optimal point for identifying at-risk students, balancing timeliness, accuracy, and fairness — related
• Standard errors sometimes differ across estimators, so policy conclusions from clustered education RCTs could be sensitive to the choice of estimator — related
• The article investigates whether achievement gap estimates are sensitive to test motivation, framing the question as achievement gap versus engagement gap — a narrower finding that bears on this claim
• A semi-supervised deep learning architecture recovers the ability distribution of anchor students more accurately than direct 2PL-IRT fitting under simulated nonignorable missingness — related
• Basic conclusions about male-female achievement gaps and changes in gaps as students progress through school may change when models account for test effort — related