ADMN 502A UNIT 9
Lecture Notes
November 2, 2010
Caitlin Morrison
Introductory Remarks
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Professor Brady:
o Wiki’s this week: Some issues in terms of comprehensiveness. The material is
dense, but that makes it even more important to be understandable. Imagine that
your reader is trying to fill the gaps in their 598 critique research. An outside
reader would have had trouble understanding this week’s wiki. It should be a
stand-alone document. Try to put yourself in the place of an outside reader when
you write the material.
o Question: what is the balance between not enough and too much detail?
o Answer: it is not about length and quantity, but clarity of material (sentence
structure, etc.).
o Also think about typos. Typos make it seem like you do not care.
QUANTITATIVE DATA ANALYSIS
Overview
Aim of Lecture
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Introduce you to the basic steps involved in four common approaches to qualitative data
analysis
o We will mainly work on two approaches: content analysis and thematic analysis.
Provide you with an overview of the key skills involved in qualitative data analysis
Class outline
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Review
Content Analysis
o Exercise
Thematic Analysis
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Grounded Theory
Post-Structural Discourse Analysis
Summary
Review of data collection
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The way you collect data influences the way you analyze it.
Interviews
Documents
Observations
Surveys
Content Analysis
Features
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The use of objective and systematic counting and classification procedures to produce a
quantitative description of the symbolic content in a text
o Not used a lot in 598s but useful because it gives you an idea of the most
structured and positivistic approach.
o Doing content analysis is a good grounding for quantitative data comprehension.
o Content = words, meanings, pictures, symbols, ideas, themes, or any message that
can be communicated
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Text = anything written, visual, or spoken that serves as a medium for
communication
Usually used within a positivistic or post-positivistic epistemological framework.
Concerned with:
o Systematic and objective coding;
o Creating a coding system that can be replicated by others;
 Other positivistic research designs are also concerned with replicability (as
noted in earlier weeks).
 Anyone should be able to reproduce the work.
Most commonly used with a large volume of data
o Intended to be generalizable, therefore uses large samples.
o Ex. Twitter’s trend analysis. Same idea, but lacks the sophistication.
Steps of conducting content analysis
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Choose your unit of analysis
o Is our unit the whole newspaper article, a page, or a paragraph.
o Your unit of analysis is important to show the validity of coding.
Create a code book with:
o A coding frame: a list of items (variables you want to count) and how you want to
count them (values)
 Example of variables from first page of this week’s workbook: Anchor,
reporter, experts, male, female, hard news, soft news.
 It is important to be clear about defining variables (eg.
What do hard and soft news entail?)
 values must be mutually exclusive and exhaustive
o Coding notes: rules for coding
 A clear and comprehensive set of coding notes will help ensure
consistency among multiple coders.
Exercise
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We watched national newscasts from two different stations and recorded results in the
workbook. We were instructed to fill out the paper as we felt we should. We were not
given clear instructions about the meaning of variables.
National newscasts: code top stories based on the hand-out
o CBC The National http://www.cbc.ca/thenational/watch/
o CTV National News http://www.ctv.ca/ctvnews/
Results: Where were the differences?
o Experts: Some counted every non-anchor and non-reported. Some only counted
names that came up on screen. Others decided for themselves who was an expert.
o Hard and soft: Only two groups had agreement.
o The most discreet variables are the most likely to be agreed on.
o Did everyone count multiples of experts or just the first person who appeared?
Everyone did multiples.
Point of the mixed results
o Even concepts that seem straightforward must be clearly defined. This is time
consuming and difficult to do, But it is important to get consistent results.
o Ex. Knowing the definitions of hard and soft news is especially important in this
case because the concepts were the focus of the research question.
Strengths and Weaknesses
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Strengths
o It can ensure that the analysis is:
 rigorous (categories are precisely defined)
 reliable (measures are applied consistently)
 ex. Despite the fact that we were given very little
information about how to fill out the worksheet, we came
up with similar results especially in the cases of anchors
and reporters.
 replicable (the project can be replicated by another researcher).
o Works well with multiple coders
 Even with little training, there is a reasonable amount of consensus that
can be reached.
Weaknesses
o Measurement is tricky; how you count counts
 Can you count what’s not there?
 Dealing with frequency, replication, intensity
o Concerns about the validity of the texts
 Have the texts been chosen correctly? Is this most representative of what
we want to analyze (ex. Are CBC and CTV representative of all news in
Canada?
 Purpose and intended audience for the communication
o Not everything that counts can be counted
 Context; words and phrases have different meanings in different contexts
 From exercise: things like time on screen are not part of analysis, and they
may make a difference.
 Content analysis can elide overall meanings and messages
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Can be reductive
Thematic Analysis
Features
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Less structured that content analysis.
Fits with positivist, post-positivist and hermeneutic epistemology
Richie & Spencer take a post-positivist approach to thematic analysis
Their approach involves structured steps in which:
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o Data is reviewed
o A thematic framework is identified
o An index is created and applied to the data
o Summaries of data are entered into a chart
o These summaries are charted and brought together
More interpretive than content analysis.
Exercise Two
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In order to get an idea of the process of thematic analysis, we did an exercise in which we
examined five interviews. The aim of the research was to determine how people in social
service professions felt about the government’s proposal about working single parents.
We began by reading over interviews and taking notes on common themes.
We then attempted to create an index and apply it to one or two of the interviews (figure
12.2, seen below, was the example provided to assist us in creating our indexes).
Next, we charted the material according to our indexed categories. The charts were
written in table format. Each column contained a broad theme and the rows contained
summaries of the material from each interviewee that matched the themes.
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The next step would be to create a diagram based on common themes and opinions.
Unfortunately, due to time constraints, most students did not get to this part of the
exercise.
Lessons from the Exercise
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As you move through the process, themes begin to emerge and the research question is
gradually made answerable.
Strengths and Weaknesses
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Strengths
o Data analysis process is clearly defined and can be explained to funders and other
interested parties.
o Question becomes clearer as the process goes on.
o May help researcher realize whether or not particular concepts are important to
answering the question.
o Process is quite:
 reliable (measures are applied consistently)
 replicable (the project can be replicated by another researcher)
Weaknesses
o Analysis may be reductive
 Some context may be lost by breaking down the information.
 No consideration for the reasons answers are given the way they are
(issues of power, for example).
o Can be time consuming
 Exercise example: took a great deal of time to reach agreement within
groups.
o Themes are considered in isolation from broader social context
o Issues of power not explicitly considered
Grounded Theory
Features
Exercise in workbook should be tried if you are interested in grounded theory. We do not have
time to go over it in class today
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Developed by Glaser & Strauss: The Discovery of Grounded Theory.
Used extensively within applied disciplines (nursing, teaching, physical education, public
administration).
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Aim – produce theory grounded in rigorously collected and analyzed data on people’s
“on the ground” experiences.
o Moreso than thematic analysis, the aim is to collect data that are based on
people’s experiences.
Epistemology – hermeneutic / post positivist.
o Aim to understand the worldview of subjects.
o More structured than most hermeneutic research.
Coding Steps
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Open coding
o Open up, break down and fragment the data
 Similar to the step of indexing in thematic analysis.
o Create categories to code the data
o Creating memos
Axial coding
o Establishing relationships among the categories created.
o Record comments & questions about relationships in memos.
Selective coding
o Like the interpretation phase of thematic analysis.
o Establish your ‘story line’
 do this in writing or use diagrams
 and /or review and sort memos and make summaries of them
 and/or review existing literature and use as a basis of comparison
Strengths and Weaknesses
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Strengths
o May be less reductive than thematic analysis
 Not focusing so much on breaking down and categorizing the data.
o Data analysis steps are flexible yet reasonably clear.
Weaknesses
o Aim is to come to data without theoretical lens – not clear if this is possible
 Can you really come in a blank slate?
o Themes are considered in isolation from broader social context (and texts)
o Issues of power not explicitly considered
 Trying to understand the world view of the group being researched, not
why the views are the way they are.
Post-Structuralist Discourse Analysis
What is Discourse?
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“System of statements which constructs an object” (Parker)
o Idea that language creates the objects.
Language and statements that allow us to make sense of and see things.
Discourses are “practices that systematically form the objects of which they speak”
(Foucault)
Discourses involve power and knowledge
o There is a relationship between the two
What is Post-Structuralism? (Review)
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Rejection of simplistic causality
o There may be causal relationships, but they are really complicated if they do exist.
Denial of inevitable and linear historic progress
Subjectivities are de-centered, plural, never completely fixed.
Concerned with relations between power, knowledge and language
o Ex. Bullying in the workplace reading from a previous week.
Example
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European Employment Strategy and related practices (Triantafillou)
o Texts: EES documents, EU documents, EES guidelines, transcripts of EES peer
feedback sessions.
o Argued that EESs create a problematic that policy makers can act on.
 How is the definition of unemployment linked to particular solutions?
o This problematic creates certain subjectivities.
 Meaning the way we think about ourselves.
How might you apply this approach to the Job Network interviews?
o Start placing what the interviewees say within the concept of a broader network of
documents.
 What do the documents say? What language is used?
o What subjectivities come up?
o There is no exact proper way to do this. No systematic steps. Must just go on the
previous work of others.
Strengths and Weaknesses
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Strengths
o Themes are not considered in isolation from broader social context (and texts)
o Issues of power (what we can say and cannot) explicitly considered
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 How does official policy change what interviewees say.
o Least reductive approach
 Most comprehensive approach
Weaknesses
o Data analysis steps not necessarily clear
o Can be hard to communicate this approach to funders or to others on team
o Not suitable for positivistic research questions and designs
 Causal relationships cannot be explained
Summary
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A range of approaches to qualitative data analysis
Approaches range from positivist to post-structuralist, and from highly structured to loose
sets of guidelines
Best approach depends on research question, aims of project, and timelines.
Do the grounded theory exercise if you are interested and contact Prof Brady with any
questions.