Student Assessment and Data
Domain 3 covers assessment and data literacy: selecting and interpreting tests, using multiple data sources, and evaluating whether interventions worked. The exam favors practical interpretation over psychometric theory, but you must know reliability, validity, and score types.
Core rules and concepts
- Reliability is consistency; validity is whether a score supports the intended inference. A test can be reliable but invalid for a given use.
- Standard scores compare performance to a norm group. Percentile rank is the percentage of the norm group at or below the score-not the percentage correct.
- Stanines range from 1 to 9 with a mean of 5; standard scores often have a mean of 100 and SD of 15.
- Use confidence intervals and the standard error of measurement; a single score is an estimate, not a precise label.
- Grade equivalents are easily misread and should not be used to place students or compare across tests.
- Use multiple data points: grades, attendance, behavior referrals, universal screening, state tests, and stakeholder perception.
Worked example
A parent says, 'My child scored in the 85th percentile, so she got 85% right.' The counselor explains that the 85th percentile means the student performed at or above 85% of the norm group, not 85% correct. The counselor also checks the confidence interval and the subtest pattern, then compares the score with classroom work and teacher ratings before recommending advanced placement. The reasoning: percentile is a rank, the SEM shows estimate error, and high-stakes decisions require multiple sources.
Traps that cost marks
- Treating grade equivalents as grade-level mastery.
- Comparing percentile ranks from different tests as if they were the same scale.
- Ignoring the standard error of measurement when scores are close.
- Selecting a test only because it is familiar, without checking validity for the intended purpose.
- Using one screening score to make a special education decision.