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Coding
Data Resource Kit
CODING YOUR DATA
Making sense of data involves some intuition
and some method of being systematic. The intuitive part is about seeing
categories, themes, patterns, making guesses, making arguments, and asking
further questions about the data. In this respect you may find that being
familiar with your discipline and course will help you determine important
themes. Being systematic about tracking particular behaviors, words, and
attitudes will help focus your efforts on the most salient aspects of
your research.
As patterns, arguments and questions arise
the data begins to be transformed from a pile of notes, transcripts, documents
and other recordings into 'evidence' of some phenomena.
Beginning Coding
Organizing
Creating Categories
and Themes
Building Theories
Beginning
Coding
There are at least three ways to begin coding. The first method requires
clear definitions of the activities and behaviors you want to track. In
this case, you will need to know what it is you seek and what it looks like.
The second two methods rely on keen insight, as you read your data and keep
track of emerging categories and codes. In the second and third situations,
one must keep in mind the larger question at hand, in order to recognize
that which is relevant.
Method A
1. Begin with a rough draft of categories you know
will be important. Examples may include:
- Levels of understanding content (surface—deep).
- Levels of skill development (novice—expert).
- Mark sections, paragraphs, words, or phrases of student work that
relate to or illustrate these levels.
- Refine your definitions as you proceed.
- Ex. As a professor of Philosophy, you realize that expert use of
the falsification principle is demonstrated when one applies
it to certain examples and that novices tend to replicate the classroom
definition. Develop a set of codes through which you can mark focus
group discussions of this term—marking “E” when
a student uses it as an expert and “N” when a student
shows novice behavior.
Method B
2. Pull out key words that recur or illustrate
some level of understanding.
- Ex. Many students refer to the falsification
principle during a review session of a 1st semester philosophy
seminar. Highlight this concept in the transcription. That the idea
recurs may signify general understanding, confusion, or importance.
Method C
3. Write categories that make sense of what you
see, a student says, or a group of students describes.
- Ex. Following the same example, you find that students
use the term, falsification principle, in various ways. Some
describe it as a rule that can prove statements true or false. Others
argue that the concept instead relates to the meaningfulness of a
statement and has little to do with truth. One student provides examples
of ways the principle has been used to dismantle religious doctrine.
Another student disagrees and shows it should be used to refute weather
conditions.
- As you read this exchange, you notice that some students talk about
the theoretical definition of the principle while others apply what
they know about the principle. Some responses are correct, others
are not. Categories of definition and application,
misunderstanding and understanding emerge.
- Later in the exchange, all students come to agreement. A category
noting convergence emerges.
| Pre Determined Categories |
a. Develop codes based on research question. |
| Emergent Categories |
b. Track and extract recurring or alarming aspects.
c. Write descriptions of what these mean or what you see. |
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Organizing
One way to organize your coding scheme is to keep
your notes and codes in separate columns. For example, keep your transcribed
data in one column and your codes in another. As you read the transcription,
jot down categories or themes that relate to particular sentences
in the column next to the text. Coding software, like Nud/ist, would
allow you to highlight and make notes in various colors. At the bottom
of the page, in a separate notebook, or with post-it notes, make your
personal and theoretical notes.
The memo section (or post-it notes) at the bottom
of each page is particularly helpful. In effect, a memo is a note
to yourself about some hypothesis you have about a category or property,
and particularly about relationships between categories. This step
will help develop your theory or argument later. See figure
No. 1 at left. |
Creating Categories
& Themes
Categories:
As you code, you will create categories.
Label these as Theoretical notes and include any initial explanations
for what you see.
- Example: Students came to the correct
understanding because they were forced by more dominant members of
the class. (Your initial ideas may be very imprecise—write them
anyway.)
Core categories:
After a time, one category (occasionally more) will
be found to emerge with high frequency of mention and to be connected
to many of the other categories. This is your "core category."
- Example: You frequently find convergence
after most prompts in the discussion. It is somehow linked to most
of the other categories. This may be your core category and concept
that emerges from your data. In some ways, working backwards, you
can develop a theory for how and why convergence happened and why
it happened again and again or maybe why it failed to happen in certain
instances.
Themes:
As you read your data and begin coding it for initial
categories, you will become aware of themes which
begin to emerge. “Themes” implies a slightly larger grouping
of categories. As you find several instances of the same categories,
they become themes that you will pay special attention to. You may
find yourself progressively focusing upon them while, of course, not
ignoring other matters.
Building Theories
As you go further you might see relationships among
themes. You will have to be careful that the relationships you see are
not merely through your own eyes and biases. A second round of reading
may be needed to ascertain that you are not misrepresenting your data.
During the course of your study, the relationships which cement your themes
together will become your theories.
The theories you begin developing may emerge from
1. Nascent theses (Initial explanations for why
it happened),
- Example: Students come to converge on some
meaning of a principle due to dominant students
2. Sketchy concept maps (possibly a diagram illustrating
cause and effect).
3. Elaborate narrations (what happened)
- Example: "During an on-line discussion of the
falsification principle, students A, B, C, and D all converged
upon C's working definition of the principle. Although all students
posted to the board, C responded to all students' posts...
And Ask:
1. How or how not does this respond to my research
questions?
2. What more do I need to know to understand what
happened?
3. What do I do with this information?
Test the categories and explanations you have culled
from your data against the variety of cases you have recorded. Are
there alternative explanations for what you think
you have seen so far? What can you learn from looking at the data
from a variety of perspectives?
Example: Is “convergence” the correct
category or is it dominance?
Try triangulating among the various forms of data
you have gathered. If a point or an explanation holds across several
sources you have gathered - if, for example, it can be supported by
interviews, think alouds, and/or other students’ work - then
you can be more sure that you have found something integral to understanding
your course.
Example: Did this same thing happen during the
focus group discussion or is this a unique feature of on-line discussions?
Once you have arrived at some conclusions regarding the data gathered,
your may want to consider the question of how to re-focus
on the guiding question which drove the research. Can that
question be answered from what you learned? Is another question more
appropriate? What other questions has the research provoked?
Example: My initial question involved the use
of asynchronous discussions in a Philosophy course. This first attempt
at trying to see what students learn leaves me wondering…
Purposes
and Uses of this Kit
Coding
Your Data
Collaboration
Resources
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