Quality and rigor of the concept mapping methodology: A pooled study analysis
The use of concept mapping in research and evaluation has expanded dramatically over the past 20 years. Researchers in academic, organizational, and community-based settings have applied concept mapping successfully without the benefit of systematic analyses across studies to identify the features of a methodologically sound study. Quantitative characteristics and estimates of quality and rigor that may guide for future studies are lacking. To address this gap, we conducted a pooled analysis of 69 concept mapping studies to describe characteristics across study phases, generate specific indicators of validity and reliability, and examine the relationship between select study characteristics and quality indicators. Individual study characteristics and estimates were pooled and quantitatively summarized, describing the distribution, variation and parameters for each. In addition, variation in the concept mapping data collection in relation to characteristics and estimates was examined. Overall, results suggest concept mapping yields strong internal representational validity and very strong sorting and rating reliability estimates. Validity and reliability were consistently high despite variation in participation and task completion percentages across data collection modes. The implications of these findings as a practical reference to assess the quality and rigor for future concept mapping studies are discussed.
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- Trochim, William M. K., 1989. "Concept mapping : Soft science or hard art?," Evaluation and Program Planning, Elsevier, vol. 12(1), pages 87-110, January.
- Dumont, Jeanne, 1989. "Validity of multidimensional scaling in the context of structured conceptualization," Evaluation and Program Planning, Elsevier, vol. 12(1), pages 81-86, January.
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- Joanna Sale & Kevin Brazil, 2004. "A Strategy to Identify Critical Appraisal Criteria for Primary Mixed-Method Studies," Quality & Quantity: International Journal of Methodology, Springer, vol. 38(4), pages 351-365, August.
- J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
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