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ANALYSIS AND INTERPRETATION OF QUALITATIVE

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Introduction Transcribing data Types of analysis General strategies Coding Examples

Chapter Objectives
By the end of this lecture, students should be able

 become familiar with the process of data

 Understand the different coding techniques
 learn about coding qualitative data
 explore grounded theory as a strategy of

qualitative data analysis

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Introduction
• All types of qualitative research yield mountains of

data that need to be analyzed.
• “Trust the process”

 Researchers use this phrase to remind
themselves to have faith that there are important
themes in their data and that they will have the
insight and skill to find these themes.

• Data analysis often involves routine, time-
consuming activities, but the process is also open-
ended and creative, with high points of excitement
that accompany unexpected insights.

• Data analysis requires qualitative researchers to be
open to new insights, able to make connections to
theoretical frameworks, and able to think
reflexively about their position within the research

• Reflexivity is the process through which
qualitative researchers examine and explain how
they have influenced a research project through
their social status, situation (gender, age, etc.),
and the experiences they bring to the project.

Qualitative Research Goals

• Meaning: how people see the world
• Context: the world in which people act
• Process: what actions and activities people do
• Reasoning: why people act and behave the way they do

Remember the

qualitative
research are to
understand:

• The quality of the data collector
• listening skills, interpersonal skills, observational skills

• The quality of the data analyzer
• interpretation, inference

• The quality of the presenter / writer
• if the person is able to effectively communicate the

phenomenon

Getting good
qualitative
data results
depends on: Cha

Transcribing the data

Qualitative researchers usually
transcribe their data; that is,

they type the text (from
interviews, observational notes,

memos, etc.) into word
processing or excel documents.

It is these transcriptions that are
later analyzed, typically using

one of the qualitative data
analysis computer programs –

qualitative data analysis
software (QDAS) or as CAQDAS
e.g. Atlas.ti, MAXqda, Nvivo etc

Challenge – “working with data,
organizing it, breaking it into
manageable units, synthesizing
it, searching for patterns,
discovering what is important
and what is to be learned, and
deciding what you will tell
others” (Bogdan & Biklen,
•to place the raw data into logical,
meaningful categories;

•to examine them in a holistic fashion;
•to communicate this interpretation to

Begin with Memo
• Analysis starts as soon as you begin to collect

your data.
• For this reason, you should regularly jot down

memos throughout the research process.
• Your memos should include anything that might

help you understand your data later on:
 your own preliminary ideas
 connections you make while in the field
 references to articles or books that may

contain useful information
• Your memos, field notes, and/or transcripts will

assist you when you begin the process of
coding. Le

Coding interview transcripts
& fieldnotes

Analyzing qualitative data is
labour intensive, and it requires

time for you to think and let
ideas percolate.

There are no real shortcuts while
interpreting data.

As you analyze your data, you
will be refining your research

questions.

Remember that because
analysis occurs throughout the
research process, your research
questions will change across the

life of your study.

Coding means systematically
going through data, finding
terms or phrases to categorize
chunks of data, and organizing
the data into a form the
researcher can work with.
•Codes are simply names for the topics,
activities, events, and people that come
up in your transcripts or field notes.

Analyzing Qualitative Data

Categories Step 3

Themes Step 4

Data Analysis : Coding
This is the next major stage of qualitative data
oThis is where you carefully read your transcribed
data, line by line, and divide the data into
meaningful analytical units (i.e., segmenting the

oWhen you locate meaningful segments, you code

oCoding is defined as marking the segments of data
with symbols, descriptive words, or category

oTypes of coding:
Open Coding
Focused/Selective Coding
Axial Coding
Structured/Affinity Diagramming

Open Coding
Open coding is also known as line-by-line coding and
provides a good starting point to identify initial
phenomena while producing a list of themes of
importance to the participants
O Treat data as answers to open-ended questions

O ask data specific questions
O assign codes for answers
O record theoretical notes

O We can code for patterns in large dataset (e.g.
interview excerpts) by looking at (Saldana, 2009):
O similarity (things that happen the same way)
O difference (they happen in predictably different

O frequency (they happen often or seldom)
O sequence (they happen in a certain order)
O correspondence (they happen in relation to

other activities or events)
O causation (one appears to cause another)

Focused and Selective Coding
Focused coding starts with selecting the main category, and then
systematically relating it to the other categories

Categories are created ahead of

from existing literature
from previous open coding

Code the data just like open coding

During this process, the categories and their relationships are
combined to form a ‘storyline’ that describes ‘what happens’ in
the phenomenon being studied.

Axial Coding
Axial coding consists of identifying relationships among the open codes.

Axial coding in Grounded Theory is the process of relating codes (categories and
concepts) to each other. For example:

Category: Physical Oppression
• Code: PUSHING
• Code: SCRATCHING
• Code: FIGHTING
• Subcode: SCRATCHING
• Subcode: PUSHING
• Subcode: PUNCHING

Category: Verbal Oppression
• Code: NAME-CALLING
• Code: THREATENING
• Code: LAUGHING AT

Structured and Affinity Diagramming
• A tool for organizing field data and

consolidating insights from collected data.
• Common technique to find recurring

patterns/themes
• Arranges the notes from interpretation sessions

into a hierarchy that reveals common issues an
themes across all users.

• Can be used for many purposes (including
analysis) e.g., – brainstorming about design
ideas – comments from users – problems
observed/reported by users

• Goal: what are the main themes?
 Write ideas on sticky notes or cards
 Place notes on a large wall / surface
 Look for related ideas
 Group notes hierarchically to see main themes

Analysis Procedures: A Concluding
• Researcher impressions during
• Patterns are being formed when

listening to interviewees
• Can be interesting
• Sometimes unreliable
• Confirmation bias

• Post-interview
• Examine patterns closely
• Look for grounded findings
• Often most interesting results
• Final code categories = expected,

discovered results
• Follow the 3C’s of data Analysis

General Strategies – Analytic

This is the next major stage of qualitative data analysis.
oThis is where you carefully read your transcribed data, line by line, and divide
the data into meaningful analytical units (i.e., segmenting the data).
oWhen you locate meaningful segments, you code them.

• Qualitative analysis is an iterative process
 Analysis starts after some data has been collected
 Further data is gathered on the basis of that analysis

• Analytic induction
 A general research question is devised
 Some data are gathered
 An hypothesis is proposed

 If a case is inconsistent with the hypothesis, the hypothesis is redefined to exclude the case, or the
hypothesis is dropped or fundamentally revised

 The researcher continues to gather data until no contradictory cases are found
• Difficulties with analytic induction

 Because all cases must be explained, the hypotheses generated may be too broad to be useful
 There are usually no guidelines on how many cases must be reviewed before the validity of the

hypothesis is accepted

General Strategies – Grounded Theory
•“Theory that was derived from data, systematically
gathered and analyzed through the research process”
(Strauss & Corbin, 1998, p. 12).
The most widely used framework for analyzing qualitative data

•Also an inductive, iterative process
Systematically gather data
Analysis throughout the research process

Features of Grounded Theory
Grounded theory includes processes:
•Constant comparison (of data and concepts)
•Theoretical saturation

A point in time when nothing new is being learned

Outcomes of Grounded Theory
•Categories (encompass two or more concepts)
•Properties (attributes of a category)
•Hypotheses
•Theory (substantive or formal)

Substantive Theory
 Observed patterns are related to each other and a
theory is developed to explain the connections in that

Formal Theory
 Theory formulated at a higher level
 Requires data collection in different settings
 Applicable to a variety of settings

Step 1: The researcher begins with a general research question

Step 2: Relevant people and/or incidents are theoretically sampled

Step 3: Relevant data are collected

Step 4: Data are coded, which may, at the level of open coding, generate concepts (step 4a)

Step 5: Through constant comparison of indicators and concepts categories are generated (step 5a).
It’s crucial to ensure a fit between indicators and concepts
Step 6: Categories become saturated in the course of the coding process

Step 7: Relationships between categories are explored in such a way that hypotheses about
connections between categories emerge (step 7a)
Steps 8 and 9: Further data are collected via theoretical sampling

Steps 10 and 11: The collection of data is likely to be governed by the theoretical saturation
principle and the testing of the emerging hypotheses (step 11), which leads to specification of
substantive theory (step 11a)Step 12: The substantive theory may eventually be explored using grounded theory processes in a
different setting from the one in which it was generated

Steps in Analysis There is a constant movement backward and forward among the first four steps, so that
early coding suggests a need
for new data, which leads to
theoretical sampling, and so

Initial Categories

• Maintenance
• Physical attraction
• Intimacy
• Tensions/barriers
• Problems
• Issues with

boyfriend/girlfriend
• Meaning of friendship
• Issues of homosexuality

Analysis Example II – Calendar

Families were interviewed
about their calendar

routinesWhat calendars they had
Where they kept their calendars

What types of events they recorded

Written notes Audio recordings

Step 1: translate field notes (optional)

Calendar Routine Contd.

Step 3: Go through data and ask

Step 2: list questions /
focal points

Where do families keep their
calendars?
What uses do they have for their
calendars?
Who adds to the calendars?
When do people check the
calendars?

(you may end up adding
to this list as you go
through your data)

Analysis Example – Calendar Routine

• The result:
 list of codes
 frequency of each code
 a sense of the importance of each

 frequency != importance

Pictures were taken of family calendars

Analysis Calendar Entries

Step 3: Go through data and ask

Step 2: list
questions / focal

What type of
events are on the

Who are the
events for?

What other
markings are
made on the

Continue for the remaining questions….

anniversary

Reporting Results
• Find the main themes
• Use quotes / scenarios to represent them
• Include counts for codes (optional)

• INTRODUCTION TO QUALITATIVE DATA
ANALYSIS SOFTWARE –

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Chapter Objectives
Introduction
Qualitative Research Goals
Transcribing the data
Begin with Memo
Coding interview transcripts & fieldnotes
Analyzing Qualitative Data
Data Analysis : Coding
Open Coding
Focused and Selective Coding
Axial Coding
Structured and Affinity Diagramming
Analysis Procedures: A Concluding Remark
General Strategies – Analytic Induction
General Strategies – Grounded Theory
Outcomes of Grounded Theory
Steps in Analysis
Coding examples
Analysis Example
Analysis Example II – Calendar Routines
Calendar Routine Contd.
Analysis Example – Calendar Routine Contd.
Analysis Calendar Entries
Reporting Results

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