Pure mathematics

Mathematics

Statistics

Psychiatry

Mathematics education

Machine learning

Empirical research

Psychology

Economics

Psychological intervention

Class size

Data science

Educational research

Computer science

Schema (genetic algorithms)

Management science

Field (mathematics)

Scalability

Database

Educational Researcher

Researchers commonly interpret effect sizes by applying benchmarks proposed by Jacob Cohen over a half century ago. However, effects that are small by Cohen’s standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In this article, I present five broad guidelines for interpreting effect sizes that are applicable across the social sciences. I then propose a more structured schema with new empirical benchmarks for interpreting a specific class of studies: causal research on education interventions with standardized achievement outcomes. Together, these tools provide a practical approach for incorporating study features, costs, and scalability into the process of interpreting the policy importance of effect sizes.

Interpreting Effect Sizes of Education Interventions