Added ITS design elements collectively provide a strong causal warrant when predictions are complex, results cohere with them, and no alternative fits the same pattern¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Added design elements collectively provide a strong causal warrant when the predictions they engender are complex, the results cohere with the predictions, and no alternative can fit the same pattern of predictions even if it can fit some of them. → Manyee Wong 2015

Evidence¶

Manyee Wong 2015¶

Manyee Wong, Thomas D. Cook, Peter M. Steiner. (2015). Adding Design Elements to Improve Time Series Designs: No Child Left Behind as an Example of Causal Pattern-Matching. Journal of Research on Educational Effectiveness, vol. 8, no. 2. https://www.mathematica.org/publications/adding-design-elements-to-improve-time-series-designs-no-child-left-behind-as-an-example-of-causal doi:10.1080/19345747.2013.878011

q1 · i? · theoretical · r2

The authors' theoretical argument for a pattern-matching standard of causal warrant in augmented ITS designs: complexity of predictions, coherence of results with predictions, and the failure of alternatives to fit the full pattern.

"No added design element is perfect by itself, but we argue that they collectively provide a strong causal warrant when the predictions they engender are complex, the results “cohe re” with the predictions, and no alternative can fit the same pattern of predictions even if it can fit some of them."

Discussion¶

Related Claims¶

• Adding multiple design elements to the basic ITS structure promotes causal inference by limiting alternative interpretations, demonstrated with No Child Left Behind — a narrower finding that bears on this claim