This document provides an overview of content analysis. It discusses qualitative data sources that can be analyzed, including open-ended questions, interviews, observations, and documents. It then explains that content analysis is a method used to systematically analyze communication to infer meanings, trends, and patterns. The document outlines the steps of content analysis, including determining objectives, developing categories, ensuring reliability and validity, and analyzing the coded data. It discusses advantages such as being unobtrusive and useful for analyzing various data sources, and disadvantages like limited data and potential for researcher bias.

2. CONTENTS
0 QUALITATIVE DATA
0 CONTENT ANALYSIS
0 APPLICATIONS
0 CATEGORIZATION
0 STEPS
0 USING THE COMPUTER
0 ADVANTAGES & DISADVANTAGES

3. QUALITATIVE DATA
Open-ended
questions
Testimonials
Individual
interviews
Discussion
grps/focus grp
interviews
Logs, journals
and diaries
Observations
Documents, re
ports, news
articles
Stories Case studies

4. What is CONTENT ANALYSIS?
0 Method used to collect and analyse data.
0 A technique that enables researchers to study human
behaviour through analysis of their communication.
0 A methodology that is often used in conjunction with
other methods; historical and ethnographic research.

5. APPLICATIONS
Describe trends
Understand organizational patterns
Infer attitudes, values, cultural patterns
Compare
Give researchers insights into problems/hypotheses

6. CATEGORIZATION
• Determines categories before
analysis begins1 – Preset
categories
• Become familiar with the
descriptive information
collected
• And allows the categories to
emerge
2 Emergent
categories

7. STEPS IN CONTENT
ANALYSIS
Determine
objectives
Define
Terms
Specify the
Unit of
Analysis
Locate
Relevant
Data
Develop a
Rationale
Develop a
Sampling
Plan
Formulate
Coding
Categories
Check
Reliability
& Validity
Analyse
Data

8. 1. Determine Objectives
0 Some reasons to do content analysis:
0 To obtain descriptive information about a topic
0 To formulate themes
0 To check other research findings
0 To obtain information useful in dealing with
educational problems
0 To test hypotheses

9. 2. Define Terms
0 To avoid frustrations, terms need to be clearly defined
beforehand or as the research progresses.
3. Specify Unit of Analysis
0 Be sure of the unit that is being analysed – what is
analysed; words? Sentences? Phrases?

10. 4. Locate Relevant Data
0 Locate the relevant data that will be analysed which
are relevant to the OBJECTIVES.
0 The relationship between the content to be analysed
and the objectives should be clear.
0 HOW TO ENSURE THIS?
HAVE A SPECIFIC RESEARCH QUESTION (AND
POSSIBLY A HYPOTHESIS)

11. 5. Develop a Rationale
0 A conceptual link is required to explain how the data
are related to the objectives.
0 Sometimes, the link between question and content
can be obviously seen. E. g. A study on bias in
advertisement  study the contents of newspaper &
magazine advertisements.

12. 0 However, it may not always be the case. E. g. A study on
changes in perception towards police officers  study the
way they are portrayed in short stories in magazine
published at different times.
0 Content analysis – use available materials.
0 To generate own data – use OPEN-ENDED QUESTIONS.
0 Then the data will be analysed.

13. 6. Develop a Sampling Plan
0 Any form of communication may be sampled at any
conceptual level that is appropriate.
0 Example: Television programmes may be sampled by
type, channel, sponsor, producer, or time of day
shown.

14. Examples of Sampling
Designs
Purposive Sampling
Design
Select only the
samples which
researcher find have
specific purpose for
the research
This relies on the
researcher’s
previous knowledge
Random
Sampling
Each & every
member of the
population has an
equal and
independent chance
of being selected

15. 0 Cluster Sampling
• The researcher divides the entire
subjects into different subgroups.
• And then randomly selects the final
subjects proportionally from different
strata. ( http://sociology.about.com )
Stratified
Sampling
• The entire population is divided into groups,
or clusters and a random sample, of these
clusters are selected.
• All observations in the selected clusters are
included in this sample. (
http://www.stats.gla.ac.uk/glossary/?q=nod
e/65)
Cluster
Sampling

16. 7. Formulate Coding
Categories
0 Formulate categories that are relevant to the
investigation.
0 Categories should be EXPLICIT that other researcher
could use them.
0 Categories developed often emerge from the date.
0 It is a complex process.

18. Manifest Content (MC) vs. Latent
Content (LC)
0 MC content of a communication refers to the obvious,
surface content;
0 They are directly accessible.
0 No inferences or underlying meaning are necessary.
0 ADVANTAGE  Easier coding and reliability
0 DISADVANTAGE  Validity
WORDS, PICTURE
S, IMAGES ETC

19. 0 LC refers to the meaning underlying what is said or shown.
0 Example; a researcher might read through the entire
outline or sample of pages.
0 From there the researcher will make an overall assessment
of the degree of a certain aspect they’ve analysed.
0 ADVANTAGE  Getting the underlying meaning
0 DISADVANTAGE  Reliability
CLASSROOM ACTIVITIES,
HOMEWORK ASSIGNMENTS

21. Which one to use?
BOTH–
Whenever
possible

22. 8. Check Reliability &Validity
0 How to check RELIABILITY?
Selecting second sample of materials or
dividing the original sample in half.

23. 0 How to check VALIDITY?
Check MC against LC and compare
either or both with results from
different instruments.

24. 9. Analyse Data
0 The end product of the coding process MUST BE NUMBERS.
0 Common ways of doing this are by recording:
0 the frequencies of a particular incident or item and
0 the proportion of particular occurrences to total
occurrences
0 Developing themes based on the particular coding from the
study.
0 Using computer analysis to interpret coded data, or to
develop the initial categories used in the analysis.
0 http://prezi.com/0mw0ozuuh1vy/ch-20-content-analysis/

25. Column 1 – List of newspapers by numbers
Column 2 – List of location
Column 3 – List of circulation
Column 4 – Number of editorials coded for each paper
Column 5 – Subjective assessment by the researcher of each newspaper’s
editorial policy
Column 6 & 7 – Record the number of certain types of editorials

26. Illustration of Content
Analysis
An analysis of all the research studies
published in Theory and Research in Social
Education (TRSE), 1979 – 1986.

27. The Analysing Process
0 Read the studies that was published during the period
that fell in ONE of these categories.
0 To test their agreement  They read the same studies
& categorized them. After that, they compared.
0 They had almost similar categories & added some
subcategories and new categories.

28. FINAL SET OF
CATEGORIES (pg.
487)
• They reread the
initial six &
remaining 40 using
the new categories.
• Sat down &
compared their
assessments.
• Although there
were some
disagreements,
they finally came to
an agreement with
the assessments.

29. Using Computer Analysis
0 Word processing programmes; using the ‘find’ command.
0 Examples of qualitative computer programmes; ATLAS.ti,
QSR, NUD*IST, Nvivo and HyperResearch.
0 They identify words, phrases, sentences, tabulate their
occurrences, print and tabulate graphs and sort and
regroup words, phrases, sentences according to how they fit
in a particular set of categories.

30. Example of Qualitative Data
Analysis
0

31. An overview

32. The Method

33. Categories of Content
0 Violent behavior was further divided into one of
several subcategories:

34. The Results
0 Overview of findings

35. Advantages & Disadvantages
ADVANTAGES DIASADVANTAGES
 It is UNOBTRUSIVE  Limited to recorded information
 Useful in analysing INTERVIEW
& OBSERVATIONAL DATA
 Establishing validity
 Can delve into records and
documents
 Researchers have the tendency to
say that interpretations gleaned
from a particular content
analysis indicate the causes of a
phenomenon
 Simple and economical logistics
 Data are readily available & can
be returned to if necessary 
replication of study