Deze samenvatting bevat alle aantekeningen van de lectures, de lecture clips en de nuttige artikelen. Artikelen in deze samenvatting:
- Tracy (2013)
- Kelle, U. (1997)
- Pratt (2009)
- Welch (2002)
Week 4 – Module D + Article Tracy (2013)
Data analysis
Look for patterns in your data
Iterative analysis: combines inductive and
deductive research. Cycle between finding
patterns in data and connecting to existing
theory/literature.
The primary cycle is inductive, and the
secondary cycle is deductive.
Steps in iterative analysis:
1. Prepare the raw data
a. Assign pseudonyms to names of specific people in your data
b. Organize the data in the order you plan to analyze
i. Chronologically
ii. By type (interview vs observation)
iii. By source (employee vs manager)
2. Primary cycle
a. Read your interview transcript very carefully and find out what stands out
b. Start open coding
i. Identifying data as belonging to or representing some type of
phenomenon
ii. Codes are words and short phrases that captures that salient attribute of
your data
iii. Descriptive and from the data itself
iv. Example: not shiny small green apple, not fruit, use green apple
v. Manually: print out your transcripts and notes and hand write the codes
in the margins
vi. Word comments: highlight the section that you are going to code and in
the comment you put your code
vii. Unit of coding: sentence-paragraph
viii. Creating code: stick closely to what the participants of your data said
ix. Constant comparative method: compare new uncoded data with your
old codes. Try to apply the same code to see if it fits to the new data.
x. Simply describe, do not yet interpret!
3. Create a codebook
a. Your codebook is your map to help you navigate through your data
b. Use excel or atlas.ti
c. Start by creating a list of all your codes
d. Combine codes that are the exact the same
e. After cleaning up for duplicate codes, count how many times each code appears
in your data set.
i. If a code appears only once or very infrequently, you have to decide
whether it is unimportant or that you have to collect more data on it
f. Assess your codes against your initials research question
i. Does it look like you have answered your RQ?
ii. If not: collect more data or revise your RQ
1
, g. Create definitions for all remaining codes
i. Should explain exactly what the code means in the context of your data
h. Choose an exemplar quote in the data that best represents each code
4. Secondary cycle
a. Organize, synthesize, and categorize your open codes into interpretive concepts
b. You create hierarchical categories – an umbrella under which you are going to
cover several codes
i. Go through your codebook and read through all of your code definitions
ii. Arrange codes that seem to be related next to each other in codebook
c. First level open codes are generated by the data, second level category’s are
generated by the researcher
d. It a form of data reduction
e. It is the first step towards noticing broader patterns in your data
f. It should begin to answer your RQ
g. May include prospective conjecture, in which researchers consider novel
theoretical juxtapositions and borrow from other fields, models, and
assumptions (Tracy, 2013)
5. Revisit scholarly literature on your topic to focus rest of analysis
6. Create relationships between category’s
Tips for open coding:
- Start with your best interview/observation so far
- Do not code data looking only for answers to your subquestions
- You can change/refine your codes as you go along
- Open codes may range from 20 to 300
Article Kelle, U. (1997)
Computer supported qualitative data analysis
Computers do not create the theory, the researchers do.
Computers can help with indexing and cross-referencing.
Indexing: an electronic index is usually constructed by storing index words together with the
‘addresses’ of text pages. Such an address may contain the beginning and the end, in terms of line
numbers, of a certain text passage to which the index word refers. Software programs which are
based on the principles have been called ‘code-and-retrieve’ programs.
Cross-referencing: electronic cross references can be constructed with the help of ‘hyperlinks’. By
pressing a button the user of a textual database can jump between the text passages which are
linked together.
Atlas.ti -> the query function in the desktop version lets you retrieve sections of the text that are
associated with codes you have made. The desktop version comes with a set of standard hyperlink
relations, but the researcher can create new ones as needed.
The role of qualitative data analysis software:
1. The structuring of the material with the help of common-sense concepts or abstract
theoretical concepts. Thereby, the code scheme can be developed before the coding takes
place or while the material is being coded.
2. First level coding is the necessary prerequisite for step two: a systematic comparison of text
passages: test segments are retrieved and analyzed in order to discover ‘dimensions’ which
can be used as a basis for comparing different cases.
3. It is this comparison which becomes the basis of step three: the construction of concepts,
types and categories that form the building blocks of an emerging theory.
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