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Development and validation of a measure of organizational capacity for implementing youth development programs

Author

Listed:
  • Dymnicki, Allison
  • Neiman, Samantha
  • Joshi, Aasha
  • Johnson, Tasha
  • Hodgman, Sarah

Abstract

Accumulating evidence indicates that incorporating youth development (YD) principles, strategies, and supports into an organization promotes positive adult and youth outcomes. However, few validated measures assess this type of capacity. The YMCA commissioned a study to validate its Capacity Assessment for Youth Development Programming (Y-CAP), which examines the organizational infrastructure required to implement YD programs and processes in seven areas. Survey development was an iterative process informed by existing frameworks, instruments, and pilot testing of items. The Y-CAP was reviewed and revised three times prior to this study, with a final round of revisions made at the start of the validation phase as a result of thorough content, survey methodology, and psychometrics reviews. The revised Y-CAP was completed by 123 YMCA implementation teams. Rasch analyses were used to determine the extent to which validity evidence supports the use and interpretation of the Y-CAP scores. Convergent validity was assessed by comparing Y-CAP scales to the Algorhythm staff survey for youth-serving organizations, and focus groups informed the consequential validity of the Y-CAP. The results provide strong evidence for the reliability and validity of the Y-CAP, which can be used to guide continuous quality improvement initiatives that support capacity and functioning in youth-serving organizations and programs.

Suggested Citation

  • Dymnicki, Allison & Neiman, Samantha & Joshi, Aasha & Johnson, Tasha & Hodgman, Sarah, 2021. "Development and validation of a measure of organizational capacity for implementing youth development programs," Evaluation and Program Planning, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:epplan:v:86:y:2021:i:c:s0149718921000112
    DOI: 10.1016/j.evalprogplan.2021.101916
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    1. DeBoer, Larry & McNamara, Kevin T. & Cranfield, John & Graham, Thea, 2000. "Legislator Influence and Public School Finance," The Review of Regional Studies, Southern Regional Science Association, vol. 30(2), pages 117-135, Fall.
    2. David Andrich, 1978. "A rating formulation for ordered response categories," Psychometrika, Springer;The Psychometric Society, vol. 43(4), pages 561-573, December.
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