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Using Concept Mapping as a Research Approach: Collecting, Analyzing, and Visualizing Data

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Do you plan to actively involve participants in program development, theory development, program planning, and/or needs assessment process? Do you plan to understand, address, and conceptualize complex issue from stakeholders’, program beneficiaries’ practitioners’, policy makers’, and/or community’s perspectives? Concept may be an appropriate research approach. The Concept Mapping approach primarily involves involving participants to generate, sort and rate statements related to a research/evaluation phenomenon, and conducting statistical analyses such as Hierarchical Cluster Analysis and Multidimensional Scaling so as to develop visual representation that reflects the sorted and rated statements.

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Using Concept Mapping as a Research Approach: Collecting, Analyzing, and Visualizing Data

  1. 1. Using Concept Mapping as a Research Approach COLLECTING, ANALYZING, AND VISUALIZING DATA Philip Adu, Ph.D. Methodology Expert National Center for Academic & Dissertation Excellence (NCADE) The Chicago School of Professional Psychology
  2. 2. Outline 1. What is concept mapping approach? 2. When to use a concept mapping approach 3. Concept mapping research examples 4. Concept mapping process (six phases) 5. Concept mapping software
  3. 3. What is Concept Mapping Approach? • Involving participants to generate, sort and rate statements • Conducting statistical analyses such as Hierarchical Cluster Analysis and Multidimensional Scaling • Developing a visual representation that reflects the sorted and rated statements. (Kane & Trochim, 2007)
  4. 4. When to use a Concept Mapping Approach • Plan a program • Evaluate a program • Conduct a needs assessment • Seek participants’ perspectives • Develop a theory or model • Develop an instrument (Johnsen, Biegel & Shafran,1998)
  5. 5. Concept Mapping Research Examples Aarons GA, Wells RS, Zagursky K, Fettes DL, Palinkas LA. (2009). Implementing Evidence-Based Practice in Community Mental Health Agencies: A Multiple Stakeholder Analysis. American Journal of Public Health, 2087-2095. doi:http://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.2009.161711 Bedi R. (2006). Concept Mapping the Client’s Perspective on Counseling Alliance Formation Journal of Counseling Psychology, 53(1)26-35. doi:http://psycnet.apa.org/journals/cou/53/1/26/ Behar, L. B., & Hydaker, W.M. (2009). Defining community readiness for the implementation of a system of care. Administration and Policy in Mental Health and Mental Health Services Research, 36(6)381-92. doi:http://www.ncbi.nlm.nih.gov/pubmed/19526338 Biegel, D. E., Johnsen, J. A., & Shafran, R. (1997). Overcoming barriers faced by African-American families with a family member with mental illness. Family Relations, 46(2)163-178. doi:http://www.jstor.org/stable/10.2307/585040 White, K. S., & Farrell, A. D. (1998). Structure of anxiety symptoms in urban children: Competing factor models of the revised children's manifest anxiety scale. Journal Of Consulting And Clinical Psychology, 69(2)333-337. doi:http://psycnet.apa.org/journals/ccp/69/2/333/ For more examples, go to: http://www.conceptsystems.com/bibliographies/
  6. 6. Resources 1. http://screencast.com/t/Mjg5NDIzYmMt 2. http://screencast.com/t/MTM1MGYxNjY 3. http://screencast.com/t/Mjk0OWRl 4. http://screencast.com/t/YzkwNmE3OG 5. http://screencast.com/t/ZjI3MTJlMz 6. http://screencast.com/t/Y2ZlYTQ2MD Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Concept Mapping Book Concept Mapping Dissertation Concept Mapping Videos
  7. 7. Concept Mapping Process (Six Phases) Phase 1: Generating Statements • Step 1: Brainstorming ideas • Step 2: Compiling and refining the statements Phase 2: Sorting Statements Generated • Step 1: Preparing for the sorting activity • Step 2: Sorting the statements Phase 3: Rating Statements • Step 1: Preparing for the rating activity • Step 2: Rating the statements Phase 4: Analyzing Sorting Data • Step 1: Conducting Multidimensional Scaling (MDS) • Step 2: Conducting Hierarchical Cluster Analysis (HCA) • Step 3: Creating a scatter plot based on the coordinates generated • Step 4: Deciding on the number of clusters Phase 5: Analyzing Rating Data • Step 1: Determining average ratings for each statement • Step 2: Merging mean ratings onto the statements • Step 3: Conducting additional statistical analyses Phase 6: Visualizing the Findings • Step 1: Preparing a Point Map, Cluster Map, and Rating Map (Kane & Trochim, 2007)
  8. 8. Phase 1: Generating Statements Step 1: Brainstorming ideas Focus Statement (Contains instructions of the kinds of ideas needed and what is expected of participants/researcher) Step 2: Compiling and Refining the statements Through • Interviews • Observations • Focus groups • Document analysis By the researcher Could be organized: On site or Online By stakeholders Project Goal/Purpose Focus Prompt (a phrase that guides the construction of the statements) Guided by (Kane & Trochim, 2007)
  9. 9. Phase 1: Generating Statements Step 1: Brainstorming ideas Focus Statement “State actions dissertation advisors/chairs and advisees could take to help advises to successfully complete their dissertation” Step 2: Compiling and Refining the statements • Document analysis • Critical review of the literature Project Goal/Purpose “Connection between doctoral advising and successful completion of doctoral dissertation” Focus Prompt “To ensure successful completion of doctoral dissertation, ...” Guided by Results • Five doctoral advising components 1. Selection process 2. Advising approach 3. Advisor-advisee relationship 4. Roles, responsibilities, and expectations 5. Power relations Results 40 statements e.g. • “Advisors should be ready to listen to the concerns of their students” • “Students have to respect the authority of their advisors” (Kane & Trochim, 2007)
  10. 10. Phase 2: Sorting Statements Generated Step 1: Preparing for the sorting activity Focus Statement/Prompt (Contains directions about how the statements should be sorted) Step 2: Sorting the statements Could be organized: Onsite Online (Participants are expected to sort the statements in a way that make sense to them) Guided by Cluster 1 Cluster 3 Cluster 2 Mail (Kane & Trochim, 2007)
  11. 11. Phase 2: Sorting Statements Generated Step 1: Preparing for the sorting activity Online Using SurveyMonkey Focus Statement/Prompt • Creating a short video to: • Introduce participants to concept mapping • Inform them what they are expected to do (Adu, 2011, p. 182 )
  12. 12. Phase 3: Rating Statements Step 1: Preparing for the rating activity Focus Statement/Prompt (Contains directions about how the statements should be rated) Step 2: Rating the statements Could be organized: Onsite Online (Participants are expected to rate the statements based on the focus statement/prompt) Guided by Normally, two rating criteria Examples Difficulty Vs. importance Feasibility Vs. Relevance Availability Vs. Relevance Urgency Vs Feasibility Frequency Vs. Severity Affordability Vs. Necessity Knowledgeable Vs. Responsiveness Usability Vs. Adoption (Kane & Trochim, 2007)
  13. 13. Phase 3: Rating Statements Step 1: Preparing for the rating activity Online Using SurveyMonkey Focus Statement/Prompt • Asking participants to rate the statements based on: • Difficulty of implementing ... • Importance of contributing to the ... (Adu, 2011, p. 186 ) Difficulty Importance 1 = Relatively easy, 2 = Somewhat difficult, 3 = Moderately difficult, 4 = Very difficult, and 5 = Extremely difficult 1 = Relatively unimportant, 2 = Somewhat important, 3 = Moderately important, 4 = Very important, and 5 = Extremely important
  14. 14. Phase 4: Analyzing Sorting Data Step 1: Conducting Multidimensional Scaling (MDS)  It is about transforming individual sorting data into a two dimensional scale/map x y 3.2 4.5 4.1 2.9 6.1 4.7 4.5 5.6 5.5 7.8 Sorting data x, y coordinates MDS creates x and y coordinates for each sorting statements Videos:  http://screencast.com/t/Mjg5NDIzYm Mt  http://screencast.com/t/MTM1MGYx NjY BY: Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Introduction to Multidimensional Scaling
  15. 15. Phase 4: Analyzing Sorting Data Step 2: Conducting Hierarchical Cluster Analysis (HCA) x, y coordinates x y 3.2 4.5 4.1 2.9 6.1 4.7 4.5 5.6 5.5 7.8  It helps to generate a cluster of statements that represent the sorting data  You determine the number of clusters you want  The output displays statements in each cluster  x,y coordinates are used to conduct the HCA HCA results Video:  http://screencast.com/t/Mjk0OWRl BY: Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Creating cluster membership and cluster tree (dendrogram) Cluster Membership
  16. 16. Phase 4: Analyzing Sorting Data Step 3: Creating a scatter plot based on the coordinates generated x, y coordinates Scatter plot x y 3.2 4.5 4.1 2.9 6.1 4.7 4.5 5.6 5.5 7.8 The coordinates are used to create a scatter plot which displays the distance between statements  It is a cumulative representation of how participants sorted the statements Video:  http://screencast.com/t/YzkwNmE3O G BY: Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Creating scatter plot
  17. 17. Phase 4: Analyzing Sorting Data Step 4: Deciding on the number of clusters Things to consider • Proximity of the sorting statements (i.e. display of the scatter plot) • Hierarchical Cluster Analysis (HCA) results • Content of each sorting statement Cluster Membership
  18. 18. Phase 5: Analyzing Rating Data Step 1: Determining average ratings for each statement Step 2: Merging mean ratings onto the statements Video:  http://screencast.com/t/ZjI3MTJlMz BY: Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Computing mean ratings and merge them onto the statements Merge
  19. 19. Phase 5: Analyzing Rating Data Step 3: Conducting additional statistical analyses Video:  http://screencast.com/t/Y2ZlYTQ2MD BY: Reagan Curtis, Ph.D. Professor and Chair Department of Learning Sciences & Human Development West Virginia University Additional Statistical Analyses  Rating Map (i.e. Scatter plot of rating data with two rating criteria such as ‘Importance Vs. feasibility’ ratings)
  20. 20. Phase 6: Visualizing the Findings (Adu, 2011, p. 52 ) Cluster Map (i.e. Scatter plot of sorting data with cluster of statements)
  21. 21. Phase 6: Visualizing the Findings Go-zone graph Rating Map (i.e. Scatter plot of rating data with two rating criteria such as ‘Importance Vs. difficulty’ ratings) (Adu, 2011, p. 63 )
  22. 22. Phase 6: Visualizing the Findings Difficulty Importance 1 = Relatively easy, 2 = Somewhat difficult, 3 = Moderately difficult, 4 = Very difficult, and 5 = Extremely difficult 1 = Relatively unimportant, 2 = Somewhat important, 3 = Moderately important, 4 = Very important, and 5 = Extremely important (Adu, 2011, p. 65 )
  23. 23. Phase 6: Visualizing the Findings Importance Difficulty 1 = Relatively unimportant, 2 = Somewhat important, 3 = Moderately important, 4 = Very important, and 5 = Extremely important 1 = Relatively easy, 2 = Somewhat difficult, 3 = Moderately difficult, 4 = Very difficult, and 5 = Extremely difficult (Adu, 2011, p. 65 )
  24. 24. Using Concept Mapping Software http://www.conceptsystems.com/groupconceptmapping 3 in 1 Concept Mapping Software 1. Collect data  Generate ideas/statements  Sort statements  Rate statements 2. Analyze sorting and rating data  Multidimensional scaling  Hierarchical cluster Analysis  Means for each rating statement 3. Visualize data  Point Map (i.e. scatter plot of sorting data)  Cluster Map (i.e. Scatter plot of sorting data with cluster of statements)  Rating Map (i.e. Scatter plot of rating data with two rating criteria such as ‘Importance Vs. feasibility’ ratings)
  25. 25. Summary Concept Mapping Process Phase 1: Generating Statements Phase 2: Sorting Statements Generated Phase 3: Rating Statements Phase 4: Analyzing Sorting Data Phase 5: Analyzing Rating Data Phase 6: Visualizing the Findings
  26. 26. Adu, P. K. (2011). Conceptualizing doctoral advising from professors' and doctoral students' perspectives using concept mapping. (Order No. 3531922, West Virginia University). ProQuest Dissertations and Theses, , 210. Retrieved from http://search.proquest.com/docview/1221263677?accountid=34120. (1221263677). Johnsen, J. A., Biegel, D. E., & Shafran, R.. (1998). Concept mapping in mental health: Uses and adaptations. Evaluation and Program Planning, 23(1)67-75. doi:http://www.sciencedirect.com/science/article/pii/S0149718999000385 Kane, M., & Trochim, W.M.K. (2007). Concept mapping for planning and evaluation. In L. Bickman and D.J. Rog (Eds.), Applied Social Research Methods Series: Vol. 50. Thousand Oaks, CA: Sage Publications. References

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