Framework of the dimensions of scale-up explaining why effect sizes might decrease as scale increases¶

Theory · All theories
Evidence · 2 claims (2 for) · 1 study (1 causal), q3 · 1 of 1 report an effect size · 2 claims rest on one study

Description¶

The article develops a framework of the dimensions of scale-up, an explanatory account of why intervention effect sizes might shrink as interventions move from small studies to broader adoption. The authors state: "we develop a framework of the dimensions of scale-up to explain why effect sizes might decrease as scale increases." They then use this framework to organize a comparison of causal estimates from the present state-level study with those from earlier, smaller studies of Pre-K Mathematics, and report the observed decline as consistent with it.

Design Implications¶

Context¶

Requirements¶

• Requires causal estimates from studies at multiple points along the scale-up sequence (pilot, efficacy, effectiveness, scale-up) so that effect sizes can be compared across scale.

Constraints¶

• As presented, the framework is used interpretively to explain an observed decline in effect sizes; the available text does not print a test of the framework's dimensions.

Target Learners¶

• Low-income children of pre-K age in California pre-K programs

Target Learning Objectives¶

• Early mathematics ability

Claims¶

• Pre K Math Effect Sizes Decreased Over Scale Up [+M]
• Pre-K Mathematics remains effective at the state scale, with a positive and statistically significant effect of .30 on the ECLS-B Mathematics Assessment [+W]

Related Theories¶

Examples¶

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Key Sources¶

• Jaime Thomas, Thomas D. Cook, Alice Klein, Prentice Starkey, Lydia DeFlorio. (2018). The Sequential Scale-Up of an Evidence-Based Intervention: A Case Study. Evaluation Review, vol. 42, no. 3. https://www.mathematica.org/publications/the-sequential-scale-up-of-an-evidence-based-intervention-a-case-study