InstructionsConsider the data collection methods presented in the Week 2 assignment and select one method for this assignment.After review of the Week 3 Resources, consider two potential data analysis methods to propose for your qualitative research design that aligns with the traditions of the particular approach you proposed in Week 1.Discuss the advantages and potential challenges of the proposed method as well as how and why it aligns with your chosen qualitative research tradition.Length: 4 paragraphs (3/4 – 1 page) and References pageReferences: Cite a minimum of 3 scholarly resources used to support your consideration and rationale for the two data analysis methods.Your rationale should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University’s Academic Integrity Policy.
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Types of Data and Their Analysis
In: The SAGE Handbook of Qualitative Data Analysis
Edited by: Uwe Flick
Pub. Date: 2013
Access Date: December 6, 2019
Publishing Company: SAGE Publications Ltd
City: London
Print ISBN: 9781446208984
Online ISBN: 9781446282243
Print pages: 295-296
© 2014 SAGE Publications Ltd All Rights Reserved.
This PDF has been generated from SAGE Research Methods. Please note that the pagination of the
online version will vary from the pagination of the print book.
SAGE Research Methods
2014 SAGE Publications, Ltd. All Rights Reserved.
Types of Data and Their Analysis
After approaches to the data and their backgrounds were the focus in the earlier parts of the handbook, in
Part IV the approach is taken from the other side. In the 12 chapters, specific types of data are the starting
point for outlining the specific challenges they produce for qualitative data analysis. Data coming from the
application of specific methods of data collection such as interviews (see Roulston, Chapter 20), focus groups
(see Barbour, Chapter 21) and observation (see Marvasti, Chapter 24) will be discussed, as well those coming
from documenting specific practices such as conversations (see Toerien, Chapter 22) or discourses (see
Willig, Chapter 23).
Various kinds of documents (see Coffey, Chapter 25), and media such as news media (see Hodgetts
and Chamberlain, Chapter 26) or sounds (see Maeder, Chapter 29), are discussed for their challenges to
qualitative analysis. A number of chapters are devoted to visual data such as images (see Banks, Chapter
27), films (see Mikos, Chapter 28) and video data (see Knoblauch et al., Chapter 30), complemented by a
chapter on virtual data (see Marotzki et al., Chapter 31).
Guideline questions as an orientation for writing chapters were the following: How did these kinds of data
become an issue for qualitative data analysis? What are the theoretical and epistemological backgrounds of
working with these data? What are specific challenges of working with these data? How can these data be
prepared and elaborated for analysis? How does one proceed (maybe step by step?) in analysing these kinds
of data? What is a recent example of using these types of data in a qualitative study? What are the limits of
using these kinds of data? What are the new developments and perspectives in this context?
Reading the chapters in Part IV should help to answer questions like the following ones for a study and its
method(s): What are the specific characteristics of these types of qualitative data? How can data analysis in
qualitative research be planned for these specific types of data? How can these data be prepared for analysis
– specific needs in transcribing or elaborating the data? What are the steps in applying the selected method
for analysing these types of data? What characterizes good (and bad) example(s) of analysing these types
of data? What are the main stumbling blocks in analysing these types of data? What are the criteria of good
practice in analysing these types of qualitative data? What are the specific ethical issues in analysing these
forms of data?
In answering questions like the ones just mentioned, the chapters in this part are meant to contribute to
developing data-sensitive ways of analysing empirical material in qualitative studies and thus to further
develop the methodological toolkit for qualitative data analysis.
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Types of Data and Their Analysis
Qualitative Data Analysis
In: The SAGE Encyclopedia of Educational Research,
Measurement, and Evaluation
By: Joseph A. Maxwell
Edited by: Bruce B. Frey
Book Title: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation
Chapter Title: “Qualitative Data Analysis”
Pub. Date: 2018
Access Date: December 6, 2019
Publishing Company: SAGE Publications, Inc.
City: Thousand Oaks,
Print ISBN: 9781506326153
Online ISBN: 9781506326139
Print pages: 1335-1339
© 2018 SAGE Publications, Inc. All Rights Reserved.
This PDF has been generated from SAGE Research Methods. Please note that the pagination of the
online version will vary from the pagination of the print book.
SAGE Research Methods
2018 SAGE Publications, Ltd. All Rights Reserved.
Data analysis in qualitative research is quite different from that in quantitative research due not only to
differences in the data themselves but also to substantial differences in the goals, assumptions, research
questions, and data collection methods of the two styles of research. Because qualitative approaches
and methods are an important part of educational research, both researchers and practitioners need to
understand these differences, the strengths and limitations of the two approaches, and how they can be
productively integrated. Data analysis may be the least understood aspect of qualitative research, partly
because the term qualitative analysis has several different meanings. This entry reviews the aspects of
qualitative research that are most important for data analysis, describes the history of its development, and
surveys the current diversity of approaches to analysis in qualitative research.
Data Analysis
The phrase qualitative analysis in the physical sciences, and in some quantitative research in the social
sciences, refers to categorical rather than numerical analysis. For example, qualitative analysis in chemistry
simply determines what elements are present in a solution, while quantitative analysis also measures the
amount of each element. Some quantitative researchers have assumed that this distinction also applies to the
social sciences—that qualitative analysis deals with data that are simply categorized, rather than measured
numerically, and that the basic principles of quantitative research can be applied to both. This represents a
profound misunderstanding of qualitative research and analysis, which rests on quite different premises from
quantitative research, and uses distinct strategies for analyzing data.
These strategies are grounded in the primarily inductive, rather than hypothesis testing, nature of qualitative
research. This, in turn, is shaped by the nature of qualitative data. Such data are primarily descriptions of
what people did or said in particular contexts—either observations of actual settings and events or transcripts
of interviews. Instead of converting these descriptions to variables and measuring or correlating these, as
quantitative researchers do, qualitative researchers retain the data in their original, descriptive form and
analyze these in ways that, at least to a greater extent than in quantitative research, retain their narrative,
contextualized character. Qualitative research reports tend to contain many verbatim quotes and descriptions,
and the analysis process is to a substantial extent devoted to selecting these as well as to aggregating,
comparing, and summarizing them. The use of numbers, to make more precise statements of how often
something happened or how many participants reported a particular experience or event, is legitimate and
common in qualitative research, but such uses are supplementary to the primary descriptive and interpretive
goals of analysis.
Because of the inductive character of qualitative research, and its particularistic focus, data analysis is not
a “stage” that occurs in a sequential order with theorizing, research design, data collection, and writing up
results. Data analysis should begin as soon as any data are collected and should be continued as long as any
significant questions remain about the meaning and implications of the data. Although the relative emphasis
on the different aspects of the research process varies over time, they are not chronologically separated
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Qualitative Data Analysis
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components of a linear series.
Although the term qualitative research is a more recent development, its actual practice has a long history,
extending back at least to the 19th-century work of anthropologists and the study of social problems by
Charles Booth, Jane Addams, and others and to later community studies such as Middletown and Yankee
City. Despite this, the analysis of qualitative data has, until relatively recently, received little theoretical
attention. This is in striking contrast to quantitative research, which has a well-developed theory, statistics,
which informs quantitative analysis.
The first widely recognized, named, and systemically developed method for analyzing qualitative data
was analytic induction (AI). It was created by the sociologist Florian Znaniecki in the 1930s, during his
research with W. I. Thomas for their classic work The Polish Peasant in Europe and America, and was
further developed by Alfred Lindesmith in his research on opiate addiction in the 1940s. In contrast to
quantitative research, which typically collects and analyzes data in order to test previously developed
theories, AI proceeds inductively to generate categories, concepts, and theories from the data. These
inductively developed theories specify the necessary preconditions for a type of case (e.g., of people who
embezzle money from a firm to deal with unexpected personal financial problems); the theory is tested by
seeking negative instances, and revising the theory, or limiting its scope, until no negative cases are found.
The goal of AI was to develop explanatory theories about the phenomena studied. This was done by
iteratively examining cases to see whether the theorized conditions were present; any case that lacked one of
these preconditions required revision of the theory. However, the view that any exception to the preconditions
necessitated revision of the theory is now seen by most researchers as too stringent. However, the inductive
development of categories for sorting and classifying (coding) data has been a feature of most subsequent
strategies for qualitative analysis.
The most influential and widely used strategy for qualitative analysis, grounded theory, was presented by
Barney Glaser and Anselm Strauss in their 1967 book The Discovery of Grounded Theory. Their work was in
part a response to the growing prestige of quantitative research in sociology and some other social sciences,
as sophisticated statistical analyses of survey data became dominant in academic influence and funding.
The book challenged the growing separation of theory development from research, in which broad abstract
theories, often generated without reference to actual data, were then tested by researchers, using quantitative
data to establish correlations between variables derived from the theories. As with AI, grounded theory
emphasized the inductive development of theory but established a more systematic and flexible way of doing
this. The phrase grounded theory was intended to emphasize the generation of theory that was “grounded”
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in, and developed in interaction with, the collection and analysis of data.
A key concept for grounded theory was the constant comparative method, a strategy that Glaser and Strauss
distinguished from both the quantification of data in order to test existing theory and the simple examination
of data to generate theory. Constant comparison integrates the coding of data with the development of theory
and hypothesis generation in an iterative process. This strategy, a radical departure from standard practice
(at least as theorized) when it was first presented, is now a fairly typical part of most qualitative research.
A second innovation that Glaser and Strauss introduced was the use of memos (written reflections on
methods, data, or other aspects of the research) as an explicit data analysis strategy. Although memos
were used informally in earlier research, Glaser and Strauss recognized these as a distinct strategy for
qualitative analysis. The Discovery of Grounded Theory treated memos very briefly, in only a few paragraphs,
but Strauss’s later work (Qualitative Analysis for Social Scientists, 1987; Strauss and Corbin, Basics of
Qualitative Research, 1990), as well as that of Matthew Miles and A. Michael Huberman, provided a much
more extensive discussion of the uses of memos for data analysis and theory development.
In his later work, Strauss also developed additional strategies for analysis, including what he called axial
and selective coding. The terminology he used for these is potentially confusing, because neither involves
“coding” in the usual sense of creating categories and sorting data by category; Strauss used “coding”
to mean broadly “the process of analyzing data.” In axial coding, the researcher connects a categorized
phenomenon to the conditions that gave rise to it, its context, the strategies by which it is handled, and the
consequences of these; selective coding involves relating a category to the core categories of the emerging
theory. These are both ways of connecting a category to other categories; such strategies are discussed in
more detail later in this entry.
There are now at least three different versions of grounded theory in use: Glaser’s development of traditional
grounded theory, Strauss’s and Juliet Corbin’s subsequent elaboration of this approach (which Glaser
rejected), and constructivist grounded theory, as developed by Kathy Charmaz. The latter combines the
grounded theory approach with social constructivism, the epistemological position that people construct the
realities in which their lives are embedded. The latter view, a reaction to the positivism that has dominated
quantitative research, has become widespread (though by no means universal) in qualitative research. It
emphasizes research relationships, participants’ subjectivity, and the social context of the research.
Another major contribution to the development of qualitative analysis was Miles and Huberman’s Qualitative
Data Analysis: A Sourcebook of New Strategies (1984). This work, although it covered most traditional forms
of analysis, emphasized what they called displays—visual ways of presenting and analyzing data. Most of
these strategies were qualitative adaptations of two forms of data analysis and presentation that had been
used in quantitative research: matrices (tables) and networks (concept maps or flowcharts). In contrast to
quantitative displays such as numerical tables or structural equation models, Miles and Huberman presented
numerous examples of genuinely qualitative displays. Matrices are formed by crossing lists of categories
(including individuals, groups, or times) to create cells; but rather than numbers, the cells contain qualitative
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data, either verbatim quotes or field note excerpts, or summaries of these. Networks, on the other hand, can
display relationships among categories (similar to what are called concept maps) or the sequence of actual
events. Networks can be used to display both sequences of specific events or properties of a particular group
or institution (what they called an event-state network) and hypothesized relationships (usually causal) among
categories. Both types of displays can be used either within particular cases or in cross-case analysis.
Charles Ragin’s qualitative comparative analysis, a method originally developed in political science and
sociology but more recently used in other fields as well, is a way of analyzing a collection of cases
(traditionally done using qualitative case study methods) in a more systematically comparative way to identify
cross-case patterns. It is actually a combination of qualitative and quantitative strategies for identifying
different combinations of causal conditions (variables) that can generate the same outcome. It is most useful
when the number of cases is larger than qualitative researchers can easily handle but too small for rigorous
statistical analysis. Ragin’s 2014 presentation of this approach dropped the term qualitative, titling the book
The Comparative Method: Moving Beyond Qualitative and Quantitative Strategies.
All these approaches are based on some form of coding and categorization of data. However, there are other
ways of doing qualitative data analysis that draw more from the humanities than the social sciences. The most
widespread of these is narrative analysis, but this is really a loose collection of rather different approaches to
analyzing narrative forms of data. Some of these approaches involve coding and thematic analysis and are
thus similar to the types discussed previously. Others focus on the structure of narratives, using strategies
drawn from literature or linguistics. However, all of these tend to be more holistic in their approach than are
approaches that primarily involve coding, which intrinsically segment or “fracture” the data and re-sort the
segments into categories; they focus more on identifying connections within the data and retaining these
connections in the analysis.
The more holistic types of narrative research result in rather different forms of presentation of the results of
the analysis, and the creation of these forms of presentation may largely constitute the analysis. For example,
Irving Seidman, in his book Interviewing as Qualitative Research, described two types of presentation of life
history interviews, which he called vignettes and profiles. These are created by rearranging and condensing
the interview transcripts, to generate a clearer flow to the narrative, while retaining the interviewee’s own
words. Similarly, what Frederick Erickson called ethnographic microanalysis of interaction involves taking
observations (usually videotaped and transcribed) of some event, analytically decomposing these, and then
reconnecting them to create a holistic portrayal of social interaction. This sort of analytic segmentation and
rearrangement of data is common in qualitative case studies, as well as in much narrative research, but has
rarely been discussed as a type of analysis.
Other researchers have used poetry as a way to communicate the meaning of interviews, but the analytic
strategies that are involved in this are rarely explicit. An exception, which Carolyn Mears called the gateway
approach for analyzing and displaying interview material, is presented in her book Interviewing for Education
and Social Science Research. Drawing on humanity-based practices, including oral history interviewing,
poetic forms of transcription and display, and Elliot Eisner’s educational connoisseurship, Mears created
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2018 SAGE Publications, Ltd. All Rights Reserved.
poetic renditions of her interviews, retaining the interviewee’s own language, but editing and rearranging this
to better convey the experience and emotion that may be obscured or missed in a verbatim transcription.
It is also possible to combine categorizing and connecting strategies in analysis—not simply by connecting
the results of a prior categorizing analysis, as Strauss did with axial and selective coding, but by integrating
connecting strategies from the beginning of the analysis. An example is the listening guide approach to
analysis, developed by Carol Gilligan and her associates, for analyzing interviews. This approach, which they
describe as a voice-centered relational method, involves a series of “listenings” that attempt to identify the
“plot” (the story that is being told), the stance of the speaker (identifying the “I” statements and creating a
separate document from these, an “I poem”), and different � …
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