research seminar lecture_10_analysing_qualitative_data

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Research Seminar for

Educational Sciences

Prof. Dr. Chang Zhu

Department of Educational Sciences

� Analysing qualitative data

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Qualitative data

• Non-numeric

• Texts (descriptive-narrative), transcripts,

documents

• Visual data, video

• Verbal data, audio

(Flick, 2007)

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Qualitative data collection

• Qualitative interviewing

• Questionnaire: open ended questions

• Focus groups

• Observations

• Documents, reports, records, journals,

field notes…4

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Distinction between data collection

methods and the type of data

collected

• Quantitative methods can be used to

collect either quantitative or qualitative

data.

• Qualitative methods can be used to

collect either quantitative or qualitative

data.5

Interview questions

• Open ended questions (preferable)

• Obtain participants’ views from their own

words….

• Inquire opinions, experiences of individuals

or groups

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Qualitative data analysis

� Coding and categorizing

� Search for relevant data

� Comparing them with other data

� Naming and classifying them

� Building a hierarchy between categories

� Identify a structure in the data

� Develop an understanding of the issue and the

data7


� The aim of qualitative analysis is often to

develop a theory or to identify patterns and

structure

� The categories for coding are often developed

from the material/data, rather than from

existing theories

� But it is possible and usual as well to start

from existing theories, and modify and

add/complement…

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� Comparison & Thematic coding

� Within a category: Can we find in different

interviewees the same/relevant category?

� With a case/ an interviewee: is the respondent

consistent across several categories?

� Between cases/interviewees: how different or

similar are the responses of the interviewees

on a topic or a category?

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� Identifying common characteristics and

differences

�Identify common

statements/opinion/meaning across

different cases

� Identify differences

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� Triangulation

�Combine qualitative and quantitative

standardized data

�Refer to different sorts and sources of

qualitative data

� Seek for respondent validation (integrate

participants perspectives on the data) (Gibbs,

2007)

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� Quality

�Researchers be reflective (assessing their own

role as well as the data)

� Checking the transcripts and the codes by

different researchers � reliability

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Iterative steps: Step 1

� Reading

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Step 2

� Memoing

�Underlining important issues

�Writing memos to yourself as you develop

the coding scheme

�These notes help the researcher recall ideas

for coding and developing concepts

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Step 3

� Describing

� Comprehensive descriptions of the setting, participants, etc.

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� Keep your research questions in mind when

conducting the data analysis

Can I find answers to the research questions?

What results can be found corresponding to

the research questions?

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� Coding

�Give a label or a code to the same or similar

text/meaning

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• Open coding

• The researcher begins with “open coding,” the

process of creating many codes as one takes an

initial look at the data.

• Focus primarily on the text to define concepts

and categories

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• Axial coding (themes/categories/coding

families)

• Open coding is followed by “axial coding,” or

the process of selecting the key codes and

concepts of interest. Axial coding involves a

regrouping of the data into the main coding

scheme.

• Connections are made amongst the categories

and the subcategories.

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� Categorizing/Classifying

� Breaking data into analytic units

� Categories

� Grouping into themes (common themes that emerge, repeating themselves)

� You can also work with predetermined categories

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• Coding of qualitative data can create either

qualitative or quantitative categories.

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Which results are important?

� Discovering Patterns

• Frequencies: How often?

• Magnitudes: To what extent?

• Structures: What relationship?

• Processes: In what order?

• Causes: Why?

• Consequences: With what outcomes

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Once a coding scheme is finalized, to the

extent that any coding scheme is “final,”

the researcher will try to assign instances

(such as quotations) to the existing

coding scheme.

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Step 7

� Data interpretation

�Finding meaning

� What is important in the data? Why?

�What can be learned from it?

�Can we form some theories?

�Can the findings compared to existing

theories and/or previous findings?

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Step 8

� Ensuring credibility & reliability

�Did you conduct the interview/observation

yourself?

� In what circumstances did the

interview/observation take place?

�How reliable are those providing the data?

�What motivations might have influenced a

participant’s report/responses?

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Two types of data analysis

• Interpretational analysis (identify

constructs, themes, and patterns, use

categories and coding)

• Reflective analysis (depends more on the

personal judgment of the researcher)

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• Do I read the lines, or read ‘behind the

lines’?

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Units of analysis

• Coding units: concepts are coded as the

units of analysis.

• a single sentence

• several sentences

• a paragraph

• a meaningful unit

• a unit of analysis might be coded with

several codes simultaneously28

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�Using software for qualitative data

analysis

e.g. Atlas.ti, Nvivo

Help you to organize your data

Develop the scheme, theme, category,

coding, etc.

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Coding Qualitative Data

• https://www.youtube.com/watch?v=GZKZKU

ycqFU

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Software for qualitative data

analysis

• Atlas.ti

• Nvivo

• MAXQDA

• The Ethnograph

• HyperQual

• HyperResearch

• HyperSoft

• Qualrus

• QUALOG

• Textbase Alpha3

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• It is important to note that qualitative

analysis and quantitative analysis are

neither competing nor incompatible.

• Learn to conduct both types of analysis

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