Afrodita Fuentes
SED 600
03/14/07
Data Coding - Review #2
When conducting an action research, a great volume of information in the form of written
language from interviews, surveys, and observations is collected. This data is qualitative
because of the written words for responses to surveys; explanations of techniques used in the
classroom; descriptions, opinions, and observations of teaching and learning. Such data can be
wordy and lengthy, and can also include a variety of ideas and elements which can be
categorized thematically (Metler, 2006 & Jonhson, 2008). Such voluminous information in
written language needs to be put in numerical language, which can then be represented in tables
and graphs (Jonhson, 2008). In other terms, the language of words needs to be translated into the
language of numbers for efficient communication of findings. Qualitative data needs to be
translated into a quantitative type. According to our discussion with Dr. Foley, data coding is a
systematic approach for analyzing and translating qualitative data into quantitative data. The
mechanism that facilitates such monumental task is inductive analysis. It seems that data coding
is in fact inductive analysis, or perhaps a part of inductive analysis. In his book, Metler (2006)
explains that inductive analysis is a three-step process: organization, description, and
interpretation.
1. Organization in inductive reasoning involves the reduction of the amount of information
(leaving the meaning of information intact) collected through interviews, surveys,
observations, etc. This is accomplished by looking at the data in many ways and many
times to categorize the information in patterns and themes. To do this, one needs to look
for words or phases that are constantly repeated throughout the data. This will lead to
names of categories in which to place each response to a survey question or each
description of a lesson, for example. Lots of time and patience is required for this step.
One needs to read, reread, and reread to find patterns in the narrative information. In class
with Dr. Foley, we practiced this very step with responses to survey questions from
undergrad education students. I enjoyed organizing student responses into categories. I see
now that organization in inductive analysis is data coding. It was a bit difficult figuring out
the categories, I usually work slowly, especially when working with quicker and more
vocal individuals. This is where I can use my talent as an arranger, according to the results
of the talent survey last semester. At the end of this step, I should have a clear and concise
scheme (names of categories) and data strategies coded (survey responses coded, field
notes coded, etc.).
A data strategy that I am planning to use is a survey that examines students’ attitudes about
science in general. I will conduct the survey at the beginning of the semester and then at
the end of the semester. Some categories and coding that I predict emerging from
responses to the survey are: negative experience with science (-ES), positive experience
with science (+ES), unaware of everyday science (-AS), aware of everyday science (+AS),
and interested in science related careers (+IS), and not interested in science related careers
(-IS).
I will also use checklist or rubrics for presentations to assess students in areas such as,
knows facts about the topic (+ KT), comprehends the topic (+CT), able to apply the topic
by giving examples (+AT), able to organized material (+OT), uses academic language
(+AL), communicates clearly - voice and eye contact (+CL), engages the audience (+EA),
and shows confidence (+C)
2. Description of inductive analysis is explaining the main features or characteristics of the
categories resulting from coding of the data. This is the place where the researcher begins
to make connections between the data and the original, or emerging, research questions. In
this step, I should see how my findings help me understand my research topic. I should
also see how the data helps me answers or not answers my research question.
3. Interpretation in inductive analysis is examining all the data that has been simplified and
organized. In this step the researcher looks for events, behaviors, or observations
represented in the categories for relationships, similarities, contradictions, etc. This final
examination should clearly point to evidence that supports or challenges my question, or
even modifies and changes my question for further research.
Some of the data collection strategies that I am planning to use include surveys, test scores,
checklists/rubrics, journals, videotapes, and field notes. These strategies need to be well planned
and I feel that I lack the knowledge or the tools to prepare these materials. I am afraid the
research I did last semester will help me little or not at all in my action research. Two of the
subtopics in my action research will be on students teaching other students and strategies in how
to properly access and use students’ prior knowledge, for which I have no research. In addition,
I need to prepare a few documents such as surveys and checklist before carrying out my action
research in the classroom. I hope we get to work on this in class or get some guidance for this. I
am afraid if I do not have the surveys and checklists in place, my data collection will be a failure
and data coding (organization) will be meaningless.
In summary, I understand that in my action research I will collect mostly qualitative data.
To make sense and meaning of this, I need to translate it into quantitative data. To do this, I will
use the three steps of inductive analysis after collecting all data. The first step, organization, I
understand completely. I am bit unclear on how to do the description and interpretation of my
data without having solid strategies of data collection for my action research. I see a need to
discuss the specific types of techniques and strategies of data collection.
References
Johnson, A. (2008). A Short Guide to Action research (3rd Edition). Boston: Pearson.
Mertler, C. A., (2006). Action research: Teachers as researchers in the classroom. Thousand
Oaks, CA: Sage.