“Differences in Print and Screen Reading in Graduate Students”

by Lauren J. Short | Xchanges 14.1, Spring 2019

Methods

Participants

Six graduate students participated in my data collection process. One participant is a PhD student in the Composition and Rhetoric program (Claudia), one is an MA candidate in Linguistics (Gertrude), one is an MFA in Fiction (Phil), one is a PhD student in Economics (Courtney), one is a PhD candidate in Economics (David), and one is a PhD candidate in Natural Resources and Earth Systems Science (Amanda). My respondent from the MA program in Linguistics is also a multilingual speaker. Four respondents were female and two were male. All respondents were Caucasian and native speakers of English, with the exception of Gertrude. Participants were allowed to choose whether to be referred to by their first name or by a pseudonym.

Procedure

Participants were asked a series of 12 interview questions (Appendix A) about their reading strategies on print and on screen. Since the term “strategy” is somewhat vague, I provided participants with a list of common reading strategies before they began the study. This list includes: underlining and/or highlighting portions of text; taking margin notes; creating annotated bibliographies; taking notes in separate locations; using sticky notes; glancing through the table of contents; reading through headings; identifying the thesis/main points; using symbols as markers of important points (like stars); creating indexes; using apps like Notability or iAnnotate; and choice of screen to read upon when reading digitally (computer, tablet).

The interview process generally took about 15 minutes per person. Claudia, Gertrude, and Phil were interviewed in person, while I recorded their responses, and later transcribed the material collected. Courtney, David, and Amanda were distributed the same interview questions in a digital word processing document and asked to type their responses directly.

Analytical Methods

I first employed in vivo coding (Saldaña, 2009), which led to a final discourse structure and analysis (Gee, 1999). In vivo coding is “the practice of assigning a label to a section of data, such as an interview transcript, using a word or short phrase taken from that section of data” (Given, 2008). The aim of in vivo coding is to stay as close as possible to the participants’ own words. These methods were useful to me because the in vivo coding led me to connect patterns between linking concepts that interview participants identified in their responses. Furthermore, discourse analysis led me to draw conclusions that weren’t explicitly stated in respondents’ words.