Data-Based Decision Making and Accountability
What the questions test
Data-based decision making is the spine of school psychology practice: every recommendation should trace back to a defensible data set. Know RIOT (Review records, Interview, Observe, Test) as the framework for gathering information across sources and methods; triangulating across RIOT reduces the risk of single-source error.
Core concepts: baseline, target behavior, goal, progress monitoring, and decision rules. Universal screening (often three times yearly) identifies risk; diagnostic assessment confirms; progress-monitoring tools (CBM, direct behavior ratings) repeatedly sample the skill. Reliability and validity determine whether a tool can support high-stakes decisions, and the standard error of measurement reminds you that a score is an interval, not a point.
Traps
- Percentile ≠ percentage correct. A 25th percentile score means 25% of the norm group scored at or below it.
- Grade-equivalent scores are not growth rates; they compress at the tails and should not measure progress.
- Making a Tier 3 decision from one data point; require a trend of at least three to four consecutive points.
- Treating improvement as proof of cause; watch maturation, history, and regression to the mean.
- Confusing a benchmark/criterion with a norm-referenced score.
Rule of thumb / worked example
Slope = (most recent score − first score) ÷ number of weeks. A student's oral reading fluency rises from 40 to 58 wcpm over 10 weeks: slope = 1.8 wcpm/week. If the goal slope was 1.5, the intervention is working - keep it. If four consecutive points fall below the aimline, change the intervention (not the goal), but first verify fidelity.