2. CONTENT ANALYSIS AS A TECHNIQUE
- for making theory
- by analyzing, examining, and selecting data
- systematically & objectively
CRITERIA OF SELECTION
- clearly & fully expressed rules
- set up before analysis
- explain various data completely
- applied strictly
3. CATEGORIES = MAJOR POINTS = PROS & CONS
SHOULD BE
- connected with what is being discussed
in the messages
- exact wording used in the statement
SHOULD NOT BE
- based on personal opinions
- irrelevant to the messages
4. QUANTITATIVE 量化 vs. QUALITATIVE 質性
- Quantitative : objective, systematic,
procedures of analysis
arbitrary limitation, relevant categories
- Qualitative : definitions, symbols, detailed
explanations, etc
no absolute truth, but context-bound
6. COMMUNICATION COMPONENTS
1. message
↓
2. Sender (participants)
↓
3. Audience (interviews)
- in vivo codes: wording that participants use
in interview
- constructed codes: coded data from in
vivo codes, created by researcher,
academic terms
7. LEVELS & UNITS OF ANALYSIS
words, phrases, sentences, paragraphs,
sections, chapters, books, ideological
stance, subject topic, elements relevant to
the context
8. I. Random Sampling
1. Simple Random Sampling 簡單隨機抽樣
to draw subjects from an identified
population ( 母群體 )
2. Systematic Sampling 系統抽樣
(Interval Random Sampling 間隔隨機抽樣 )
select nth name from the population
Population 母群體總數
Sampling interval = Numbers of persons desired
抽樣間隔 取樣數目
* Random Numbers Table 亂數表
SAMPLING STRATEGIES
9. 3. Stratified Sampling 分層抽樣
- divide population into stratum
- ensure : dissimilarity between stratum ↑
similarity inside of each strata ↑
∴ produce a representative sample
II. Non-random Sampling
Purposive Sampling 立意抽樣
researcher select subjects according to
his/her research purpose and
understanding of the population
- researcher: with sufficient knowledge or
expertise
- subjects: represent the population
10. GROUNDED THEORY 紮根理論
a process of constructing:
various data →induction/deduction→theory
* explain the phenomena
- development of theory
- collect, analyze, & compare data
systematically
- theory is grounded on data
11. 7 MAJOR ELEMENTS IN WRITTEN MESSAGES
1. Words – the smallest unit, frequently used
2. Themes: simple sentences, string of words
with S + predicate (e.g. You are beautiful)
3. Characters: persons
4. Paragraphs: difficult to classify ∵ various
things are stated & implied in a single
paragraph, infrequently used
5. Items: books, letter, diary, etc
6. Concepts: an idea, more latent
e.g. crime
7. Semantics: how affected the words may be
12. Combinations of Elements
Interview #1 Ah…I do not think I improve grammar and
word dictions because my teacher did not correct my
grammar and word dictions. Actually, I know I am not
good at writing, and I really want to improve my
writing ability. Hmm……However, I also wrote articles
which were asked from professors as homework
while I wrote dialogue journal writing. Well, for the
first time, I can accept that I had so many writing
mistakes, and I know I still have room to improve it
after teacher’s correction. Unfortunately, after many
times corrections, the articles which were corrected
by professors still appeared many grammar problems
and sometimes had word dictions problems. This is
why I do not think dialogue journal writing can
improve our writing ability. (Shake head)
13. Units and Categories
Units = Codes
‘Code’ the elements into ‘Inductive
Categories’
ex. Words, items, themes…
14. Classes and Categories
3 major procedures:
1. Common classes
2. Special classes
3. Theoretical classes
15. Classes and Categories
Common Classes:
-- a culture in general
People in society to tell apart persons,
things, and events
Ex. Age, gender, mother…
16. Classes and Categories
Special Classes:
-- the labels used by members of certain areas
to tell apart persons, things, and events within
their limited province
out-group – people in society
in-group – people in the specific group
17. Classes and Categories
Theoretical Classes:
-- emerge in the process of analyzing the data
-- Function:
1. grounded in the data
2. Get a theory
18.
19. Open Coding
1. Major Problems:
-- can not read between the line
-- do not get the real motivation
2. Can get the points Coding can
continue.
20. Open Coding
4 basic guidelines:
1. Ask the data a specific and consistent set
of questions.
-- What study are these data suitable?
-- What category does this incident indicate?
Benefits:
-- sometimes find unexpected results
21. Open Coding
2. Analyze the data minutely.
categories, incidents, interactions, and the
like
be coded
<during open
coding>
extensive theoretical coverage
<be thoroughly grounded>
systematic coding
22. Open Coding
3. Frequently interrupt the coding to write a
theoretical note.
-- comments ideas <take notes>
4. Never assume the analytic relevance of any
traditional variable until the data show it to be
relevant.
-- any traditional variable
ex. Age, sex, social class…
-- earn their way into the grounded theory
24. Coding Frames
Axial Coding:
1. Different ideas organize and
construction
2. New ideas
25. Coding Frames
Data
MJ 1 MJ 2 MJ 3
Open coding
Axial Coding
MJ 1 MJ 3
Axial
*MJ=Major Point
26. A Few More Words on Analytic Induction
Involve several refinements.
Glaser and Strauss suggest:
- Combine 2 data analysis.
1. Analysis of data after coding.
2. Analysis of data while integrating.
Incorporate all appropriate modes of inquiry:
Induction, deduction, and verification
27. Interrogative Hypothesis Testing
4 steps of negative case testing:
1. Make a rough hypothesis.
2. Conduct a thorough search.
3. Discard or reformulate hypothesis.
4. Examine all relevant cases.
28. 4 Safeguards against the potential flaws
1. Examples should be lifted at random.
2. Assertion should be more than 3 examples.
3. Analytic interpretations should be examined
by independent reader.
4. Check no invalidated overall patterns.
Use safeguards can avoids “exampling”
29. STRENGTHS AND WEAKNESSES OF THE
CONTENT ANALYSIS PROCESS
Advantages:
1. It can be virtually unobtrusive.
2. It is cost effective.
3. It provides a means of study a process.
Weaknesses:
1. Limited to examining already recorded
messages.
2. Ineffective for testing causal relationships
between variables.
3. Not appropriate in every research situation.
30. COMPUTERS AND QUALITATIVE ANALYSIS
Using qualitative research programs:
1. Help qualitative sorting and data management.
2. Takes times to learn.
3. Researchers still need to think.
4. Offer clear directions for novice.
Quantitative research programs help researchers to
deal with the vast number of statistical data.
