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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.

What is Measurement?

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:

  • best practices in test development
  • careful item writing
  • accessible content and administration procedures
  • accurate calibration and scoring and
  • understandable score reporting.

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.

Blog

Calculating Implicit Association Test Scores

I wrote a couple years ago about the limitations of implicit association tests (IAT) for measuring racial bias. Their reliability (test-retest) and validity (correlations with measures of overt bias) are surprisingly low, considering the popularity of the tests. At the time, I couldn’t find an explanation of how IAT scores are calculated (I didn’t look …

Differential Item Functioning in the Smarter Balanced Test

In class last fall, we reviewed the Smarter Balanced (SB) technical report for examples of how validity evidence is collected and documented, including through differential item functioning (DIF) analysis. I teach and research DIF, but I don’t often inspect operational results from a large-scale standardized test. Results for race/ethnicity showed a few unexpected trends. Here’s …