Document Type
Article
Date of Original Version
2016
Abstract
Background
Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing.
Purpose
A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory.
Method
This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel.
Results
Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions.
Conclusion
Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.
Citation/Publisher Attribution
Brick, L.A. D., Velicer, W. F., Redding, C. A., & Rossi, J. S. Extending Theory-Based Quantitative Predictions to New Health Behaviors. International Journal of Behavioral Medicine, 23(2), 123-134.
Available at: http://dx.doi.org/10.1007/s12529-015-9506-y
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This is a pre-publication author manuscript of the final, published article.
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