Related Experiment Video
Updated: Mar 12, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
Meeting the Challenges of Longitudinal Cluster-Based Trials in Schools: Lessons From the Chicago Trial of Positive
Kendra M Lewis1, David L DuBois2, Peter Ji3
11 Agriculture and Natural Resources, University of California, Davis, CA, USA.
Abstract:
We describe challenges in the 6-year longitudinal cluster randomized controlled trial (CRCT) of Positive Action (PA), a social-emotional and character development (SECD) program, conducted in 14 low-income, urban Chicago Public Schools. Challenges pertained to logistics of study planning (school recruitment, retention of schools during the trial, consent rates, assessment of student outcomes, and confidentiality), study design (randomization of a small number of schools), fidelity (implementation of PA and control condition activities), and evaluation (restricted range of outcomes, measurement invariance, statistical power, student mobility, and moderators of program effects). Strategies used to address the challenges within each of these areas are discussed. Incorporation of lessons learned from this study may help to improve future evaluations of longitudinal CRCTs, especially those that involve evaluation of school-based interventions for minority populations and urban areas.
More Related Videos
07:20Author Spotlight: Repetitive Transcranial Magnetic Stimulation Combined with Movement Observation in Cerebral Palsy
Published on: August 9, 2024
08:05Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
Related Concept Videos
Community Based Intervention
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
Group Design
Longitudinal Research
Longitudinal Studies
Cluster Sampling Method
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Robbers Cave