• To understand the uses of various data analysis techniques, such as analytic induction, constant comparison, thematic analysis, critical analysis, narrative analysis, phenomenological analysis, and policy analysis.
  • To recognize why and when various data analysis techniques are most appropriate.
  • To understand approaches and evolutions of topic selection and why this is important to building congruence in research.
  • To understand how congruence is achieved in fitting together the research questions, data gathering, data analysis, literature review, and conclusion making.
  • To relate ones’ sense of conviction, mission, and cultural awareness to the research approach.
  • To know why one must sometimes turn around and collect more data.
  • To understand selected interpretive stances, e.g., phenomenology, symbolic interactionism, and semiotics, and their roles in shaping data analysis techniques.
  • To be able to use critical interpretive stances, such as critical women, race, and class studies.
  • To think imaginatively about the nature of the conclusions of one’s dissertation in light of one’s mission and audience.
  • To be familiar with a variety of conceptual bases of research, e.g., behavioristic, idealistic, essentialistic, existentialistic, pragmatic, critical, humanistic.
  • Be able to explain one’s ontological bearings, methodological approaches, and epistemological understandings and the relationship of those aspects to one’s own research.
  • To master criteria of good research and to be able to analyze research according to those essential criteria.
  • To explore online and social media as viable ways of advancing one’s research.

Instructional Methods

The course will consist of a blend of lecture, individual reflection and group work. The main focus of this seminar is on participants’ research, especially data analysis, and how to get it done. Participants will move forward through a series of five exercises designed to advance one’s dissertation completion. Resources include one’s own data, online texts, various online materials, lecture presentations and peer collaboration. Moreover, emphasis will be placed on a wide assortment of online resources, some unfamiliar to participants.


Regular grading system, based upon participants’ self-evaluation, peer evaluation and instructor evaluation of research plan and mastery of course material. All evaluation questions concern the extent to which one’s dissertation research has been improved.


  1. Complete the personal skills section of the course, consisting of five exercises.
  2. Read and review a pertinent data analysis related book, such as one from the list above.
  3. Describe the data collection and data analysis procedures, techniques, and methods of your dissertation. Show the congruence between data collection and data analysis. Justify why those techniques are appropriate, sufficient, and logical. Present in a paper not to exceed 10 pages. Also present to the class for not more than one half hour, including questions.
  4. Present an agenda for building research expertise, 5 to 10 pages. The agenda is to include sections regarding resources (e.g., human, material, virtual) learning objectives, anticipated benchmarks (e.g., gaining funding, publishing in particular journals, leadership opportunities in professional and/or scholarly associations, international development plans, etc.) and a timeline for completion.