Researching New Literacies

Design, Theory, and Data in Sociocultural Investigation

by Michele Knobel (Volume editor) Colin Lankshear (Volume editor)
©2017 Textbook X, 254 Pages


This book provides an expansive guide for designing and conducting robust qualitative research across a diverse range of purposes concerned with understanding new literacies in theory and in practice. It is based on the idea that one of the best ways of learning how to do good research is by closely following the approaches taken by excellent researchers. This volume brings together a group of internationally reputed qualitative researchers who have investigated new literacies from a sociocultural perspective. These contributors offer "under the hood" accounts of how they have adapted existing research approaches and, where appropriate, developed new ones to frame their research theoretically and conceptually, collected and analyzed their data, and discussed their analytic results in order to achieve their research purposes. Each chapter, based on a substantial and successful study undertaken by the researchers, addresses the research process from one or more of the following emphases: theory and design, data collection, and data analysis and interpretation. Core elements discussed in each chapter include research purposes and questions; theoretical and conceptual framing; data collection and analysis; research findings and implications; and limitations, glitches, and difficulties experienced in the research process.

Table Of Contents

  • Cover
  • Title
  • Copyright
  • About the author(s)/editor(s)
  • About the book
  • This eBook can be cited
  • Table of Contents
  • Acknowledgments
  • Chapter One: Researching New Literacies: Addressing the Challenges of Initial Research Training (Colin Lankshear / Michele Knobel)
  • The Aim
  • The Underlying Research Ideal
  • A New Literacies Research Focus
  • Finding the Authors
  • This Book’s Approach
  • Organization and Content
  • References
  • Chapter Two: Critical Junctures in the Design and Conduct of Affinity Space Research (Jayne C. Lammers)
  • Studying the Sims Writers’ Hangout
  • Critical Junctures
  • (a) Starting an Affinity Space Study
  • (b) Crafting Research Questions
  • (c) Navigating Review Board Approval
  • (d) Generating Initial Understandings
  • (e) Gaining Insiders’ Perspectives
  • Lessons Learned About Affinity Space Research
  • Lesson 1: Read the Research
  • Lesson 2: Talk to Others
  • Lesson 3: Document Everything
  • Lesson 4: Refer to Theory
  • References
  • Chapter Three: Conversation Analysis, Transcription, and Local Productions of Order (Aaron Chia Yuan Hung)
  • Introduction
  • Rationale
  • Basics of CA
  • Transcription notations
  • Turn-taking
  • Adjacency pairs
  • Repair
  • Data Collection
  • Findings
  • Opening sequences in party chats
  • Fairness
  • Discussion
  • Conclusion
  • References
  • Chapter Four: Theorizing context: A design-based analysis of an online affinity space (Alecia Marie Magnifico)
  • Introduction
  • Theoretical Framework: The Study of Affinity Spaces
  • Background: figment.com and The Origins of This Study
  • Research Questions
  • Methods
  • Results
  • Looking In: Close Reading and Design Analysis
  • Looking Out: Content Analysis of Official Site Content
  • Discussion
  • References
  • Chapter Five: Understanding Twitter as a Networked Field Site: Implications for Research on Teacher Professional Learning (Jen Scott Curwood / Carly Biddolph)
  • Introduction
  • Conceptualizing Professional Learning: New Literacies, New Practices
  • Pushing the Boundaries: Twitter as A Networked Field Site
  • Methodology
  • Twitter as a Research Context
  • Designing Our Study
  • Participants
  • Data Collection
  • Data Analysis
  • Tracing the Analytical Process: Decisions, Examples, and Conclusions
  • Developing Codes for Online Thematic Analysis
  • Interpreting Emergent Findings, Drawing on Theoretical Perspectives
  • Moving Forward
  • References
  • Chapter Six: The messiness of actor-network theory in an online gaming ethnography: The inside story of Leet Noobs (Mark Chen)
  • Introduction
  • Purpose of Study and How it Affected Data Collection
  • Analyses and Alignment to Theory
  • Pass 1
  • Pass 2
  • Pass 3
  • General World of Warcraft Life
  • The Last Chapter
  • Reporting
  • Final Thoughts
  • Note
  • References
  • Chapter Seven: Classroom digital literacies as interactional accomplishments (Ibrar Bhatt)
  • Introduction
  • Research Context
  • Theoretical Background
  • (a) The New Literacy Studies
  • (b) Digital and new literacies
  • (c) A sociomaterial understanding
  • (d) Videographic methodologies
  • Data Collection and Analysis
  • (a) General approach
  • (b) Overall procedure
  • (c) Videography
  • Conclusions
  • Notes
  • References
  • Chapter Eight: Discourse analytic approaches to understanding new literacies in online fan fiction writing communities (Rebecca W. Black)
  • Issues and Questions Addressed
  • Theoretical Grounding: New Literacy Studies and Studies of New Literacies
  • Collecting Data in Online Environments
  • Discourse Analytic Approaches to Analysis
  • Worked Example
  • The Role of Theory in Interpreting Findings
  • Explaining Why Anyone Should Care About This Work
  • Acknowledgments
  • References
  • Chapter Nine: Games, films, and media literacy: Frameworks for multimodal analysis (Andrew Burn)
  • Film: The Kineikonic Mode
  • Analytical Approach 1: Spatial and Temporal Syntagms
  • Step 1: The “eikonic syntagm”
  • Step 2: The “kinetic syntagm.”
  • Analytical Approach 2: The Metamodal Kineikonic
  • Step 1: Modal decomposition
  • Step 2: Intermodal functions
  • Multimodality in Videogames
  • Analytical Approach 1: Play, Avatars, and Person
  • Analytical Approach 2: Modality in Games
  • Analytical Approach 3: Coherence and Cohesion in Videogames
  • Analytical Approach 4: Code as Mode
  • Step 1: Identifying the code
  • Step 2: Identifying the orchestrating functions of the code
  • Step 3: Relating the orchestrating and contributory modes
  • Machinima
  • Step 1: Identifying the laminates
  • Step 2: Drilling through the layers
  • Conclusion: From Text to Context
  • References
  • Chapter Ten: Connecting content coding and Discourse analysis to investigate online affinity spaces (Sean C. Duncan)
  • Investigating Informal Scientific Reasoning
  • Investigating Design
  • Content Coding
  • Discourse Analysis
  • Connecting Methods
  • Conclusions and Future Directions
  • References
  • Chapter Eleven: “There’s a Relationship”: Negotiating Cell Phone Use in the High School Classroom (Anita S. Charles)
  • Critical Sociocultural Theory
  • Methodology
  • Participants
  • Rationale for Sample Population
  • Rationale for an Ethnographic Approach
  • Gee’s “Building Tasks”
  • Data Management
  • Data Analysis
  • Intersection of New and Old Literacies
  • What Belongs in a Classroom?
  • Discourse and discourse: Understanding the Difference
  • Findings
  • A New Mindset: “It’s Just Part of What We Do”
  • Teacher Use of Social Digital Media
  • Social and Academic Discourses
  • Rule-Setting, Rule-Breaking, and Relationships
  • Keep Discourses Straight: “You Just Don’t Cross Them”
  • Conclusion
  • References
  • Contributors
  • Names Index
  • Subject Index
  • Series index

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We want to acknowledge the generous efforts of the chapter authors who have made this volume possible. They are exemplary colleagues, and we have greatly enjoyed working with them.

Our appreciation is also owed to the many anonymous people who participated in the various research projects reported in the chapters that follow. Such participation requires much goodwill, effort and trust, without which researchers simply could not explore our social world.

As always, we are indebted to Bernadette Shade for her care and oversight during the production of this book, as well as for her work on numerous other volumes in the New Literacies series.

Thanks are also extended to the many permissions holders who have allowed the authors to reproduce material necessary for the integrity of their chapters.

We acknowledge the institutions with which we and the chapter authors are affiliated, for supporting the research activity reported in this volume.

Finally, as reflected in our dedication, we are truly grateful for the contribution Brian Street has made over the past 35 years to the study of literacies as social practices. And we are especially appreciative of the support he gave us, individually and together, as newbies to the field, and thereafter: initially through written comments on typewritten hard copy received and sent by post; subsequently in face to face communication; and, since the early 1990s, via email and whenever we get the chance to meet up. Brian’s career has exemplified the very best of academic collegiality, and we count ourselves among the many fortunate beneficiaries of his generosity and his passion for literacy research and scholarship.

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Researching New Literacies

Addressing the Challenges of Initial Research Training



This book aims to provide guidance and support for students and academic and professional researchers involved in initial (or novice) research training in the area of empirically based qualitative studies of new literacies. Our observations during the past 20 years of involvement, in varying roles and capacities, with graduate research training and mentoring, have convinced us of three things.

1. Students who enroll in graduate programs offering opportunities to learn how to conduct empirical qualitative research in the area of literacy generally, and literacy practices more specifically, typically arrive with strictly limited prior knowledge and understanding of academic research as a distinctive kind of engagement. A high proportion have come through teacher education programs that provide little direct exposure to the academic disciplines and their constitutive theories and research literatures (Bridges, 2006; Krishnan, 2009). Typically, so far as becoming academic researchers from a broadly social science standpoint is concerned, these students are very much “starting from scratch.”

2. Academics responsible for overseeing the initial research training of students undertaking empirical qualitative studies of literacy practices often struggle with the demands of their roles. Graduate research student numbers have grown exponentially since the 1980s, without corresponding growth in staffing. Academics find themselves with increased numbers ← 1 | 2 → of students to advise or supervise. Furthermore, they are often obliged to supervise or advise outside their areas of strength or expertise. In many universities, increased research student numbers have necessitated pressing into research training roles academics whose own research experience is limited or otherwise fragile.

3. The quality of the final outcomes of initial research training, in the form of dissertations and theses, is often disappointing, even allowing for the fact that this has been initial research training (Boote & Beile, 2005).

In light of these observations, we conceived a book comprising chapters written by new literacies researchers whose work we regard as exemplary in terms of our view of what counts as elegant, robust, and cogent qualitative research that is empirically based (see Lankshear & Knobel, 2004). Furthermore, we deliberately selected a high proportion of early career researchers, and asked them to base their chapters directly on their graduate research studies and/or research that grew out of their graduate work and followed immediately upon it. This was done to ensure that the discussion in their chapters pertains as closely as possible to the kinds of situations, issues, challenges, and decisions to be made that commonly arise within initial research training and experiences.


Empirically based qualitative studies of social phenomena involve looking in depth at a typically small number of research subjects in order to obtain rich and detailed data about such things as what they are doing, how and why they are doing it (in those ways), what happens in the course of doing it, and how we (as researchers) make sense of certain aspects of what has gone on. To do this well a researcher has to aim for the most powerful and economical coherence they can get between (i) a set of research goals or purposes (aims, objectives, research questions); (ii) relevant theory; (iii) data collection approach and methods; (iv) data analysis approach and methods; and (v) interpretive techniques to be applied to the results of data analysis. In other words, they must ensure that there is an optimal degree of “fit” between what they aim to describe, understand, and explain, and the way they design their study.

So, for example, they have to be able to show that their data collection approach and the methods and techniques they develop and use are consistent with and grow out of their research purposes and their informing theory and key theoretical concepts. Researchers have to be able to describe and demonstrate how they have “translated” their research purposes and theory and concepts into a data collection strategy and a set of data collection components (instruments) that will ← 2 | 3 → help them address their research question. Readers have to be able to see and understand (and accept) that there is a rational connection between, say, an interview question or a questionnaire item or types of artifacts to be collected, and the research purposes and theoretical and conceptual framing of the study. Researchers can’t hope to do everything; it is better to do something (that is worth doing) well than to try to do too much and end up achieving little. Therefore, at each point in the overall research process, such as developing a data collection approach and generating data collection instruments or guides and procedures, researchers have to learn to make hard decisions about what to leave in and what to set aside. The guiding principle here is to get as much as one can that is relevant and valid from as little complication as possible, and to make the case for how it is done.

In the case of graduate students in the process of learning to become researchers, the point here is as much about learning how to take those decisions, and how to pursue and demonstrate coherence and elegance, as it is about what they actually end up doing and producing. It’s as much about learning to play the game as it is about the result of the game. The temptation is often to try and “do too much”—certainly, to do more than is necessary to get a convincing finite result—and, in the process, fail to learn the need to achieve and demonstrate coherence and elegance, and to see how and why less is often more.

The same applies to data analysis. The types of data analysis tools and techniques developed and employed in a study must fit coherently, and as economically or elegantly as possible, with the research purposes, theoretical and conceptual framings, and the data to be collected. Here again, less is often more, and the main point during research training is to learn how to make good decisions, how to achieve depth, and how and when to let go in the interest of learning one’s craft well. Trying to juggle multiple data analysis approaches and methods can, and often does, get in the way of becoming a good data analyst of one kind or another. There is no need to try to master multiple data analysis approaches at the outset. Similarly, learning to apply one data analysis approach—and knowing what data to use it with—expertly is time well spent in learning to become a researcher. There is ample time later to diversify if one wants to try on a different researcher identity. The researcher one becomes during initial training may or may not be the same researcher one ends up as. One thing, however, is sure. It is much more likely that one will become a good and competent Type Y researcher later if one has already become a good Type X researcher in one’s initial research formation.

To elaborate further on the point at issue, a key goal of graduate research (training) is to acquire a strong and clear sense of research as a structure of knowing. It is about seeing how research purposes, theory and concepts, data collection approaches and techniques, data analysis approaches and techniques, and interpretive procedures hang together as a system. To get control of this system and to be able to operate it, and make adaptations within and between the different ← 3 | 4 → components in order to get a more efficient and tighter “operation” is, precisely, to develop a certain kind of mind. It is to “get” research logic and to be able to apply it as an extended argument. Doing research is a sophisticated form of argumentation. Once one has that kind of mind in the first place it is easy to make variations upon it, just as one can make variations within any kind of argument structure by, say, modifying one or more premises. Within research training, what committees and advisers/supervisors and examiners should be looking for in the first instance is the demonstration of the presence of that kind of mind through the dissertation and defense.

Each key step in the process, from conceptualizing the research design and methodology to interpreting the analytic results, needs to be demonstrated and justified as being at the very least a defensible option and, at best, an optimal one. This involves a lot of argumentation. Getting to the decision about why data analysis method A is appropriate and is better than B (which may also be appropriate), and reporting the process of getting to that decision, may involve as much or more “work” and demonstration than is involved in actually applying the method to data and reporting the application. To have grounds for believing that a novice researcher has acquired a “researcherly” mind we need to see that a sound case has been made for decisions about design and methodology, as well as being able to see that the study components have been implemented competently. This is why less is often more. There is less to have to provide detailed justification for, and justice can be done to providing the underlying rationale for what is actually conducted in the study.

In the course of providing the rationale aspect of a study, it may (and often will) become apparent that something needs to be pruned or modified somewhere else within the overall study design. Seeing this and doing it is as much a learning achievement as is wielding a data collection or data analysis method. When researchers talk about a study being robust and cogent they mean, among other things, that the logic of the inquiry has been laid bare—made transparent—and that it has been implemented competently (LeCompte & Schensul, 2010; Maxwell, 2012; Miles, Huberman, & Saldaña, 2014).



X, 254
ISBN (Hardcover)
ISBN (Softcover)
Publication date
2018 (May)
New York, Bern, Berlin, Bruxelles, Frankfurt am Main, Oxford, Wien, 2017. X, 254 pp.

Biographical notes

Michele Knobel (Volume editor) Colin Lankshear (Volume editor)

Michele Knobel: PhD from Queensland University of Technology (Australia). Professor of Education, Montclair State University (USA). Co-author of Literacies: Social, Cultural and Historical Perspectives (2011) and co-editor of DIY Media: Creating, Sharing and Learning with New Technologies(2010), both with Colin Lankshear. Colin Lankshear: PhD from Canterbury University (New Zealand); Adjunct Professor James Cook University (Australia) and Mount St Vincent University (Canada). Co-editor of A New Literacies Reader (2013) and co-author of New Literacies: Everyday Practices and Social Learning (2011), both with Michele Knobel.


Title: Researching New Literacies