Welcome to Θ – b, a site dedicated to improving the field of educational and psychological measurement through the sharing of instructional resources, research findings, and statistical tools.
This site is maintained by Tony Albano, Associate Professor in the School of Education at the University of California, Davis. More here.
Measurement, in the fields of education and psychology, is the process of gathering information and making inferences about human attributes like knowledge, achievement, and motivation.
Measurement is concerned with the interaction between person and item, sometimes expressed as Θ – b. Good measurement optimizes this interaction via:
The ideal result is actionable information that supports decisions regarding things like academic abilities, preparation for college, and qualifications for a career.
If you’re liking what you hear, check out my intro measurement book. It’s free online and as a PDF, and will soon be available for print on demand.
Differential item functioning (DIF) with intersectional groups or interaction effects requires lots more statistical comparisons than traditional groupings do. In large-scale, high-stakes testing programs, we usually look at A grouping variable with $k = 5$ levels produces $k – 1$ DIF comparisons per item (each group level compared with the reference level), so the traditional …
Continue reading “Omnibus tests of differential item functioning”
A friend shared an article from Times Union (paywalled) claiming that more students pass after NY adjusts test scoring. More students passed this year’s math and English tests, but many of them didn’t have to do as well as last year to be named proficient. After the passing score was adjusted, more than half of …
Continue reading “Did More Students Pass NY Tests Because the State Lowered Cut Scores?”
The National Council on Measurement in Education (NCME) held its annual conference in April, so I’ve had a couple months to ruminate on what I learned there. Here are some notes from my notes. References Albano, T., French, B. F., & Vo, T. T. (2024). Traditional vs intersectional DIF analysis: Considerations and a comparison using …
