Beyond “what works”: A mixed‐methods study of intervention effect modifiers in the Good Behavior Game

This mixed‐methods study examines two moderators of the impact of the Good Behavior Game—implementation variability, participant risk status, and the interaction between them—as predictors of behavioral and academic outcomes. Quantitative data from 38 primary schools were utilized, with outcome data...

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Bibliographic Details
Published in:Psychology in the schools 2020-02, Vol.57 (2), p.222-246
Main Authors: Ashworth, Emma, Humphrey, Neil, Lendrum, Ann, Hennessey, Alexandra
Format: Article
Language:English
Subjects:
At risk populations
At Risk Students
Attention
Behavior modification
Behavior Problems
Classroom Techniques
Classrooms
cumulative risk
Data
Disruptive behaviour
Educational Games
Elementary School Students
Elementary schools
Fidelity
good behavior game
Intervention
mixed methods
Mixed methods research
Moderators
Prosocial Behavior
Reading Achievement
Risk behavior
Scores
Student Behavior
Teachers
Variability
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Summary:This mixed‐methods study examines two moderators of the impact of the Good Behavior Game—implementation variability, participant risk status, and the interaction between them—as predictors of behavioral and academic outcomes. Quantitative data from 38 primary schools were utilized, with outcome data collected at baseline and 2‐year follow‐up. Behavior (disruptive behavior, prosocial behavior, and concentration problems) was assessed via the Teacher Observation of Classroom Adaptation Checklist. Reading attainment was assessed via national teacher assessment scores, and the Hodder Group Reading Test. Implementation fidelity/quality data were collected via independent observations. Participant risk status was modeled using a cumulative risk index. Multilevel modeling revealed that higher levels of fidelity/quality were associated with improved overall reading scores (d = 0.203–225), but worsening disruptive behavior among high‐risk students (d = 0.560). Thematic analysis of qualitative interview data collected from 20 teachers identified six groups of at‐risk students who were perceived to experience differential effects, and five key mechanisms underpinning these.
ISSN:0033-3085
1520-6807
DOI:10.1002/pits.22312