Average Effect Size (AES) Quality Indexing

In Simple Words

Think of inspecting a used car's health by looking at ten individual parts like tires, battery, and brakes separately—each test might not be conclusive on its own. Combining all ten tests into a single standardized average overall index provides a clear, statistically reliable rating of vehicle quality.

Research question

Evaluators evaluating school quality across small sample sizes often face low statistical power and false-positive risks when analyzing multiple correlated indicators individually.

Participants

N = 300 target secondary schools serving voucher applicants in Bogotá, Colombia.

Study Design

Joint estimation technique scaling multiple peer quality indicators into standardized deviation units to compute aggregated Average Effect Sizes while accounting for within-subject outcome correlations.

Results

Combining multiple peer quality indicators (mean college entrance exam scores, dropout rates, remedial programs, matriculation fees) into an AES index revealed that voucher winners attended schools with statistically significant lower observable peer quality across academic indicators.

Takeaway

Aggregate correlated school quality metrics into a standardized Average Effect Size index to increase statistical power when evaluating small institutional sample sizes.

Read alongside the research

Plain-English glossary

Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.

Statistically significant

A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.

ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.

American Statistical Association · Statement on p-values ↗Read more about statistically significant →

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