Related Experiment Video
Updated: Jun 29, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
Program Evaluation's Path to Greater Policy Relevance: Learning From Rossi's Iron Laws
1School of Public Policy, University of Maryland, College Park, MD, USA.
Abstract:
In a 1987 article, Peter R. Rossi promulgated "The Iron Law of Evaluation and Other Metallic Rules." The Metallic Laws were meant as an informal (and humorous) overstatement of the weakness of contemporary evaluations of social programs. Rossi' s underlying worry was not so much about the state of evaluation technology in the abstract, but, rather, in its inability to advance our broad understanding of social problems and what to do about them---in other words, to make evaluation policy relevant. Rossi attributed the continuing failure to develop successful "large-scale social programs" to the failure to build a strong knowledge base for this kind of "social engineering." The qualities of studies that enable such accumulated learning are variously labeled "external validity," "generalizability," "applicability," or "transferability." This Special Issue includes five papers that seek to explore and apply this understanding.
More Related Videos
06:05The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
14:43A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Related Concept Videos
Law of Effect
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
Models, Theories, and Laws
Behaviorism
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
Lazarus's Cognitive Appraisal Theory
Primary Appraisal:...
Ethics in Research
Purposive Learning