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The « Concept mapping » methodology: a review of users’ evaluative comments. A Case of Misconceived Mapping ?

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Presentation done at the AEA annual meeting 2012. Minneapolis, October 26.

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The « Concept mapping » methodology: a review of users’ evaluative comments. A Case of Misconceived Mapping ?

  1. 1. The « Concept mapping »methodology: a review of users’ evaluative comments Christian Dagenais, Ph.D. Université de Montréal Valéry Ridde, Ph.D. Université de Montréal Normand Péladeau, Ph.D. Provalis Research AEA annual meeting 2012 Minneapolis, October 26
  2. 2. The « Concept mapping » methodology• Developed by W. M. K. Trochim• Based on the active participation of stakeholders• Process qualitative data using multivariate statistical analysis (MDS & HCA)• Presents results in graph format
  3. 3. Objective Review advantages and limitations of the“Concept Mapping” methodology identified in published studies
  4. 4. Search strategy (in short)• Studies published between 1989 and 2012• Publication about Concept Mapping methodology developped by W. Trochim• General boolean query: "concept mapping" AND ("multidimensional scaling" OR "cluster analysis")• Sources – Citation index: Web of Science – Databases: ERIC, PsycINFO, MEDLINE, Social Work Abstracts, Sociological Abstracts – Search engine : Google Scholar• 190 articles included• Exclusion criteria: Trochim, Rosas or Kane as authors
  5. 5. Intended use of Concept Mapping 60 50 40 30Number of articles 20 10 0 Logic models Planification Outcome Needs assessment Concept definition Theory creation Instruments Other development evaluation development
  6. 6. Evaluative comments formulated by users About DefinitionRessources needed Includes necessary time, money, equipment or human resources necessaryBenefits for participants eg: sense of cohesion, belonging, discussion and sharing of opinions and ideasProcess Understanding or appreciation of the participation process, logistics and use of softwareResults Interpretation, relevance, and usefulness of the resultsParticipants’ perspective Results reflect participants’ reality and are presented in their own wordsMethodological The usefulness of the method application, its flexibility, the choice of research topic and/or participantsStatistical The validity or limitations of statistical procedures
  7. 7. Evaluative comments (a)100 90 80 70 60 Positive 50 Negative 40 30 20 10 n=20 n=12 n=70 n=0 n=28 n=27 n=35 n=16 n=33 n=0 0 Ressources needed Benefits for participants Process Results Participants perspective
  8. 8. Evaluative comments (b) Evaluative Comments (b)100 90 80 70 60 50 Positive Negative 40 30 20 10 0 n=30 n=28 n=3 n=9 Methodological Statistical
  9. 9. Evaluative comments made by the users About Definition Positive NegativeBenefits for participants eg: sense of cohesion, belonging, discussion and 70 (82) 0 (0) sharing of opinions and ideasParticipants’ perspective Results reflect participants reality and are presented 33 (44) 0 (0) in their own wordsRessources needed Includes time, money, equipment or human 20 (32) 12 (6) resources necessaryProcess Includes problems related to participation (fatigue, 28 (43) 27 (35) understanding or appreciation of the process), logistics and use of softwareResults About the interpretation, relevance, usefulness of the 35 (54) 16 (19) results and the ability to propose concrete actionsGeneralizability The extension of results and conclusions from a CM 1 (1) 57 (64) project to other population or the population at largeMethodological The ease of application of the method, its flexibility, 30 (46) 28 (29) the choice of research topic or participantsStatistical The validity or limitations of statistical procedures 3 (5) 9 (11)
  10. 10. The CM Statistical Procedure
  11. 11. The CM Statistical Procedure
  12. 12. The CM Statistical Procedure
  13. 13. The CM Statistical Procedure
  14. 14. The CM Statistical Procedure
  15. 15. The CM Statistical Procedure
  16. 16. The The Statistical Procedure CM CM Statistical Procedure
  17. 17. The Logical & Expert Argument
  18. 18. The Logical & Expert Argument
  19. 19. The Logical & Expert Argument
  20. 20. The Logical & Expert Argument
  21. 21. The Logical & Expert Argument
  22. 22. The Logical & Expert Argument
  23. 23. The Logical & Expert Argument••
  24. 24. Are we really talking about slight movements?
  25. 25. The Logical & Expert Argument
  26. 26. The Logical & Expert Argument
  27. 27. The Logical & Expert Argument
  28. 28. The Logical & Expert Argument
  29. 29. The Logical & Expert Argument
  30. 30. The Logical & Expert Argument
  31. 31. The Logical & Expert Argument
  32. 32. The Logical & Expert Argument
  33. 33. The Logical & Expert Argument
  34. 34. The Logical & Expert Argument
  35. 35. The Logical & Expert ArgumentWhat Kruskal & Wish really suggest
  36. 36. The Mathematical Argument
  37. 37. The Mathematical ArgumentTraditional CM (Concept Systems) Computation of clustering solutions for 2 to 30 clusters.Alternate Computation of Clusters4. Computation of two indicators of “goodness of fit” • Percentage of pairings represented by clusters • Number of potentially misclassified statements
  38. 38. The Mathematical Argument
  39. 39. The Mathematical Argument
  40. 40. The Mathematical Argument
  41. 41. The Mathematical Argument
  42. 42. The Mathematical Argument
  43. 43. The Mathematical ArgumentDefining misclassified statements
  44. 44. The Mathematical Argument
  45. 45. The Mathematical Argument
  46. 46. The Mathematical Argument
  47. 47. The Mathematical Argument
  48. 48. The Mathematical Argument
  49. 49. The Mathematical Argument
  50. 50. The Social Validity Argument
  51. 51. The Social Validity Argument
  52. 52. The Social Validity Argument
  53. 53. The Social Validity Argument
  54. 54. The Social Validity Argument
  55. 55. The Social Validity Argument
  56. 56. The Social Validity ArgumentSocial Validation ProcedureThree studies Dagenais & Hackett (2008) – Literacy: 12 items in 9 clusters Jean et al (2007) – Rural Living: 8 items in 8 clusters Kane & Trochim (2007) – Non profit: 17 items in 8 clustersSubjects 34 graduate students – University of Montreal (Psychology & Social Medicine)
  57. 57. The Social Validity Argument
  58. 58. The Social Validity Argument
  59. 59. ••••
  60. 60. Alternate Graphical Approaches Carter, Chicca Enyedy, Goodyear & Arcinue (2009). Concept mapping of the events supervisees find helpful in group supervision. Training and Education in Professional Psychology, 3 (1), 1-9.
  61. 61. Alternate Graphical Approaches
  62. 62. Alternate Graphical Approaches
  63. 63. Alternate Graphical Approaches
  64. 64. Alternate Graphical Approaches
  65. 65. Alternate Graphical Approaches Alternate Graphical Approaches
  66. 66. Thank you!

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