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RSCH 8310 Walden Qualitative Reasoning and Analysis Coding Discussion

RSCH 8310 Walden Qualitative Reasoning and Analysis Coding Discussion

Question Description

To prepare for this Discussion:

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  • Review the chapters in the Saldaña text found in this week’s Learning Resources.
  • Review the Introduction to Coding and From Content to Coding media programs in the Learning Resources.
  • Refer back to your observational field notes from the Scholars of Change Videos from Weeks 1–4.
  • Choose one of the four Scholars of Change videos and refer to your field notes from your observation.
  • Access the transcript you downloaded for the media program of the Scholars of Change video you selected for this Discussion.
  • Begin to code the transcript and the observational field notes of the Scholar of Change Video you chose. (Note: You will only need one or two codes for this Discussion, although more are acceptable.)

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By Day 3

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Post a brief description of the video you chose. Next, include an example of one or two codes and provide quotes from your notes or transcript to support your example. Finally, explain your reasoning for this coding.

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Be sure to support your main post and response post with reference to the week’s Learning Resources and other scholarly evidence in APA style.

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By Day 5

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Respond to one of your colleagues’ posts and explain how your colleague’s codes are similar to or different from yours.

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For Introduction (Please Paraphrase)

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Coding collaboratively

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Writers of joint research projects advocate that coding in these cases can and should be a collaborative effort (Erickson & Stull, 1998; Guest & MacQueen, 2008; Schreier, 2012). Multiple minds bring multiple ways of analyzing and interpreting the data: “a research team builds codes and coding builds a team through the creation of shared interpretation and understanding of the phenomenon being studied” (Weston et al., 2001, p. 382). Provocative questions get posed for consideration that could possibly generate new and richer codes (Olesen, Droes, Hatton, Chico, & Schatzman, 1994). Ultimately, team members must coordinate and insure that their sometimes individual coding efforts harmonize, particularly if a central database and multi-user CAQDAS system are employed. MacQueen, McLellan-Lemal, Bartholow, and Milstein (2008, p. 132) strongly advise that one member of the team be assigned primary responsibility as “codebook editor” – the one who creates, updates, revises, and maintains the master list for the group.

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Those conducting action or community-based research can invite the study’s participants/stakeholders themselves into the analytic process as a collaborative venture to provide a sense of ownership and investment in data analysis and its consequent recommendations for social change (Stringer, 2014). Northcutt and McCoy (2004) label focus group development of their own categories of interest “affinities.” Children and adolescents, too, can be taught to investigate and analyze issues that relate to their social worlds (Alderson, 2008; Heiligman, 1998; Warren, 2000). Haw and Hadfield (2011) and Heath, Hindmarsh, and Luff (2010) hold “data sessions” where informed colleagues and sometimes participants themselves are invited to preview and review video fragments from fieldwork to collaboratively interrogate and discuss relevant multiple dimensions of the research issues suggested. This dialogic exchange of ideas in a workshop and collegial atmosphere attunes the research team to new and varying perspectives before more intensive scrutiny and formal video analysis begin.

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Team members can both code their own and others’ data gathered in the field to cast a wider analytic net and provide a “crowd-sourcing reality check” for each other. For these types of collaborative ventures, intercoder agreement or interpretive convergence (the percentage at which different coders agree and remain consistent with their assignment of particular codes to particular data) is an important part of the process (for formulas and discussions see Bernard, 2011, pp. 447–9; Boyatzis, 1998, pp. 144–59; DeCuir-Gunby, Marshall & McCulloch, 2011; Hruschka et al., 2004; and Krippendorff, 2009). There is no standard or base percentage of agreement among qualitative researchers, but the 80–90% range seems a minimal benchmark to those most concerned with an evidentiary statistic. Selected CAQDAS programs include such measures as the kappa coefficient, Pearson’s r, and other coding comparison queries as calculation functions for intercoder agreement.

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Some methodologists question the utility and application of intercoder agreement for qualitative data analysis since the entire process is an interpretive enterprise. Thus, research teams may wish to dispense with such quantitative measures altogether and rely on intensive group discussion, “dialogical intersubjectivity,” coder adjudication, and simple group consensus as an agreement goal (Brinkmann & Kvale, 2015; Harry et al., 2005; Sandelowski & Barroso, 2007).

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Coding by committee can range from a time-saving democratic effort, to a frustrating enterprise filled with road blocks, depending on the amount and complexity of data and – to be honest – the researcher personalities involved. Group dynamics suggest that a team meeting regularly to collectively code data should consist of no more than five people. More than five individuals makes problem-solving and decision-making exponentially more difficult. It may also be wise to develop strategies and contingency plans ahead of time for what to do in case coding progress stalls or if professional disagreements occur and an executive decision needs to be made. I myself prefer to be the “lone wolf coder” when it comes to working with colleagues on a research project, but my team members are given copies of my coded data to review at all stages, and are encouraged to function as rigorous examiners and auditors of my analyses.

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References

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Saldaña, J. (2016). The coding manual for qualitative researchers (3rd ed.). Thousand Oaks, CA: Sage Publications.

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  • Chapter 1, “An Introduction to Codes and Coding” (pp. 1–42)
  • Chapter 2, “Writing Analytic Memos About Narrative and Visual Data” (pp. 43–65)

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Laureate Education (Producer). (2016). From content to coding [Video file]. Baltimore, MD: Author.

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Laureate Education (Producer). (2016). Introduction to coding [Video file]. Baltimore, MD: Author.

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