Data Analysis
The data gathered in this study were coded and analyzed using an inductive approach. Categories and thematic connections were derived from the data, using a spiraling reduction process as described by qualitative researchers including Creswell (1998), LeCompte and Preissle (1993), and Miles and Huberman (1994). I combed through the total data set in several iterative stages:
I entered every raw data source into the computer in text form so I would have searchable electronic versions of every data element. I also printed every file so I would have a hard copy version for more convenient viewing. Employing the constant comparative method originally devised by Glaser and Straus (described in Maykut & Morehouse, 1994), I penciled keywords and phrases in the margins of every portion of data, including typed survey results, transcribed interviews, typed fieldnotes, transcribed videotapes, and other artifacts; using a pencil allowed me the freedom to backtrack and revise my wording in light of newer evidence as it emerged. The first stage of data analysis elicited themes identified by keywords such as “oral,” “print,” “icons,” and “collaboration,” and key phrases such as “hypermedia functions,” “hypermedia elements,” and “hypermedia processes.”
After every germane unit of data (defined as a participant quotation, a transcribed unit, or a unified portion of an artifact) had been coded with a keyword, I typed these keywords into lists and began sorting them into categories related to each of my research questions -- in this case, “How does hypermedia function as a language form in the creation of a webzine?” (Other research questions included, “What did the e-zeen project mean to the student editors?”; “What processes are involved in the creation of a webzine?”; “How did the e-zeen project affect the seventh-grade language arts curriculum?”; and “What roles can computers play in the classroom?” Findings from the last question are reported in Eagleton & Hamilton, 2001b; all others are discussed in Eagleton, 1999b.) After numerous brainstorming sessions and several additional runs through the data, many of these original themes were blended, renamed, and reorganized into more permanent categories.
I felt that saturation had been achieved when each relevant data bit had been successfully grouped into one or more thematic categories and when the leftover data bits were determined to be irrelevant to the research questions at hand. I did not conduct member checks due to the onset of the summer holidays and the resulting unavailability of the participants; however, I feel confident that the data were sufficiently triangulated due to the large volume that was gathered and the variety of sources utilized.
When I determined that the new categories were internally consistent and fairly stable, I scrolled through the source data on the computer and copied and pasted each unit of meaning into new word-processing files named “transmediation,” “cueing systems,” “hypermedia genres,” and so on. I inserted every positive and negative instance of each theme into these categorical files and then developed outlines for each category, sequenced the data bits, and printed each new file so it would be easily accessible during writing.
Following are examples of the emic approach I used to categorize the data related to the roles of computers in the classroom (Table A, described in Eagleton & Hamilton, 2001b) and the affective needs of seventh graders (Table B, described in the Case Studies section of this article and in Eagleton, 1999b).
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Roles of Computers in the Classroom
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Affective Needs of Seventh Graders
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From “Making It Come to Life on the Computer: Toward an Understanding of Hypermedia Literacy” by Maya B. Eagleton.
Reading Online, www.readingonline.org
Posted July 2002