The Generalizability of Motivation Filtering in Improving Test Score Validity

Abstract

Accountability for educational quality is a priority at all levels of education. Low-stakes testing is one way to measure the quality of education that students receive and make inferences about what students know and can do. Aggregate test scores from low-stakes testing programs are suspect, however, to the degree that these scores are influenced by low test-taker effort. This study examined the generalizability of a recently developed technique called motivation filtering, whereby scores for students of low motivation are systemically filtered from test data to determine aggregate test scores that more accurately reflect student performance and that can be used for reporting purposes. Across assessment tests in five different content areas, motivation filtering was found to consistently increase mean test performance and convergent validity.