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McNeish, Daniel; Peña, Armando; Vander Wyst, Kiley B.; Ayers, Stephanie L.; Olson, Micha L.; Shaibi, Gabriel Q. – Prevention Science, 2023
Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post hoc adjustments to…
Descriptors: Prevention, Intervention, Growth Models, Program Effectiveness
McNeish, Daniel; Peña, Armando; Vander Wyst, Kiley B.; Ayers, Stephanie L.; Olson, Micha L.; Shaibi, Gabriel Q. – Grantee Submission, 2021
Growth mixture models (GMMs) are applied to intervention studies with repeated measures to explore heterogeneity in the intervention effect. However, traditional GMMs are known to be difficult to estimate, especially at sample sizes common in single-center interventions. Common strategies to coerce GMMs to converge involve post-hoc adjustments to…
Descriptors: Prevention, Intervention, Growth Models, Program Effectiveness
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Ormshaw, Michael James; Kokko, Sami Petteri; Villberg, Jari; Kannas, Lasse – Health Education, 2016
Purpose: The purpose of this paper is to utilise the collective opinion of a group of Finnish experts to identify the most important learning outcomes of secondary-level school-based health education, in the specific domains of physical activity and nutrition. Design/ Methodology/ Approach: The study uses a Delphi survey technique to collect the…
Descriptors: Health, Outcomes of Education, Health Education, Literacy