Multiple-design-element interrupted time series evaluation¶
Research Method · All research methods
Evidence · 3 claims (3 for) · 1 study (1 theoretical),q1· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
Because the basic single-group ITS depends on extrapolating the pretest functional form as a counterfactual, an assumption the authors call problematic, evaluators should add multiple design elements to the basic ITS structure. Each element limits alternative interpretations, and together the elements provide a strong causal warrant when predictions are complex and results cohere with them.
Accounts¶
- Strengthen short interrupted time series evaluations by adding multiple design elements rather than relying on the basic single-group ITS structure: Because the basic single-group ITS depends on extrapolating the pretest functional form as a counterfactual, an assumption the authors call problematic, evaluators should add multiple design elements to the basic ITS structure. Each element limits alternative interpretations, and together the elements provide a strong causal warrant when predictions are complex and results cohere with them. (Manyee Wong et al. (2015))
- Causal pattern-matching as a warrant standard for augmented interrupted time series designs: The article frames causal inference in augmented ITS designs as a pattern-matching exercise: multiple design elements each generate predictions, and the causal claim is warranted when the predictions are complex, the observed results "cohere" with the predictions, and no alternative explanation can fit the same pattern of predictions even if it fits some of them. No single element is sufficient; the warrant is collective. The framework is illustrated with No Child Left Behind. (Manyee Wong et al. (2015))
Claims¶
- Adding multiple design elements to the basic ITS structure promotes causal inference by limiting alternative interpretations, demonstrated with No Child Left Behind [+W]
- 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 [+W]
- Ambiguous preintervention functional forms and other factors affecting posttest means and slopes are common in ITS applications [+W]
Related Research Methods¶
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Key Sources¶
- 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
- 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
Page metadata
| Field | Value |
|---|---|
type |
research-method |
id |
multiple-design-element-interrupted-time-series-evaluation |
title |
Multiple-design-element interrupted time series evaluation |
description |
Because the basic single-group ITS depends on extrapolating the pretest functional form as a counterfactual, an assumption the authors call problematic, evaluators should add multiple design elements to the basic ITS structure. |
status |
review |
moved_from |
theory/causal-pattern-matching-its-warrant-framework |
generated |
by process:settle-candidates on 2026-10-09 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — manyee-wong-2015 |
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