This document discusses content analysis as a qualitative data analysis technique. It begins by defining content analysis as a method to systematically reduce and categorize textual data to identify patterns and relationships. The document then outlines the coding process, describing codes as labels assigned to segments of text that are then grouped into categories. It provides examples of different types of codes and discusses hierarchical coding structures. Steps in the content analysis process are also outlined, from defining research questions to data analysis and interpretation. Issues of reliability in content analysis are raised at the end.

1. CODING AND CONTENT
ANALYSIS
© LOUIS COHEN, LAWRENCE
MANION & KEITH MORRISON

2. STRUCTURE OF THE CHAPTER
• Coding
• What is content analysis?
• How does content analysis work?
• A worked example of content analysis
• Reliability in content analysis

3. CONTENT ANALYSIS
• Data reduction is a major issue in qualitative data
analysis.
• Content analysis reduces text to fewer categories.
• Categories may be pre-ordinate (decided in
advance) or responsive (emerging from the data
themselves).

4. CODING
• A code is a name or label that the researcher gives to
a piece of text that contains an idea or a piece of
information.
• Coding is the translation of question responses and
respondent information to specific categories for the
purpose of analysis.
• Coding is the ascription of a category label to a piece
of data, the process of breaking down segments of
text data into smaller units (based on whatever criteria
are relevant), and then examining, comparing,
conceptualizing and categorizing the data.
• The same piece of text may have more than one code
ascribed to it, depending on the richness and contents
of that piece of text.

5. CODING
• Coding enables the researcher to identify
similar information.
• Coding enables the researcher to search and
retrieve the data in terms of those items that
bear the same code.
• Codes can be at different levels of specificity
and generality when defining content and
concepts.
• Some codes which subsume others, thereby
creating a hierarchy of subordination and
superordination, creating a tree diagram of
codes.

6. CODING
• A code is a word or abbreviation sufficiently close
to that which it is describing for the researcher to
see at a glance what it means.
• Codes are frequently abbreviations.
• Codes should be kept as discrete as possible.
• Coding should start earlier rather than later.
• Coding involves iteration and reiteration to ensure
comprehensiveness and consistency of coding.
• The researcher goes through the data
systematically, typically line by line, and writes a
descriptive code by the side of each piece of
relevant datum.

7. TYPES OF CODE
• Open code
– A new label that the researcher attaches to a
piece of text to describe and categorize that
piece of text, line-by-line, phrase-by-phrase,
sentence-by-sentence, paragraph-by-
paragraph, or unit-of text-by-unit-of-text.
• Analytic code
– Interpretive and explanatory

8. TYPES OF CODE
• Axial code
– A category label ascribed to a group of open
codes whose referents (the phenomena being
described) are similar in meaning.
– Connects related codes and subcategories into
a larger category of common meaning

9. TYPES OF CODE
• Selective code
– Similar to an axial code, but at a greater level of
abstraction than an axial code.
– Identifies the core category/ies of text data,
integrating them to form a theory.
– A core category is that central category or
phenomenon around which all the other
categories identified and created are integrated,
and to which other categories are systematically
related and by which it is validated.

10. WORKING WITH CODES
• Once codes have been assigned, ordered, and
grouped, they can be structured into hierarchies of
subsumption.
• Lower order codes (e.g. descriptive codes) are
subsumed under analytic and axial codes, which,
in turn are subsumed under a selective code.
• Keep hierarchies ‘shallow’ (not too many levels).
• Take care with coding, as there is a risk of losing
temporality, context and sequence in the coding
and retrieval of text (the researcher may prefer to
write a narrative account).

11. WHAT IS CONTENT ANALYSIS?
• The process of summarizing and reporting written
data – the main contents of data and their
messages.
• Content analysis defines a rule-governed, strict and
systematic set of procedures for the rigorous
analysis, examination and verification of the
contents of written data.
• Content analysis reduces and interrogates text into
summary form through the use of both pre-existing
categories and emergent themes in order to
generate or test a theory.
• Content analysis can yield frequencies (quantitizing
text).

12. NARRATIVE AND BIOGRAPHICAL
APPROACHES TO DATA ANALYSIS
• Narratives and biographies are selective, based on:
– Key decision points in the story or narrative
– Key, critical (or meaningful to the participants)
events
– Themes
– Behaviours
– Actions
– People
– Key experiences
– Key places

13. SYSTEMATIC APPROACHES TO
DATA ANALYSIS
• Comparing different groups simultaneously
and over time
• Matching the responses given in interviews to
observed behaviour
• Analyzing deviant and negative cases
• Calculating frequencies of occurrences and
responses
• Assembling and providing sufficient data that
keeps separate raw data from analysis

14. HOW DOES CONTENT ANALYSIS
WORK?
• Numerical content analysis:
– Define the units of analysis (e.g. words,
sentences) and the categories to be used for
analysis.
– Code the texts and place them into
categories.
– Count and log the occurrences of words,
codes and categories.
– Apply statistical analysis and quantitative
methods and interpret the results.

15. HOW DOES CONTENT ANALYSIS
WORK?
• Non-numerical content analysis
– Code and categorize data
– Compare categories and make links
between them
– Conclude – draw theoretical conclusions
from the text.

16. STEPS IN CONTENT ANALYSIS
Step One: Define the research questions to be
addressed by the content analysis.
Step Two: Define the population from which
units of text are to be sampled.
Step Three: Define the sample to be included.
Step Four: Define the context of the generation
of the document.
Step Five: Define the units of analysis.
Step Six: Decide the codes to be used in the
analysis.

17. STEPS IN CONTENT ANALYSIS
Step Seven: Construct the categories for analysis.
Step Eight: Conduct the coding and categorizing
of the data.
Step Nine: Conduct the data analysis.
Step Ten: Summarize.
Step Eleven: Make speculative inferences.

18. RELIABILITY IN CONTENT ANALYSIS
• Witting evidence (intended to be imparted) and
unwitting evidence (what is inferred and
unintended);
• The text may have been written for a very
different purpose from that of the research; the
researcher will need to know or be able to infer
the intentions of the text;
• The documents may be limited, selective, partial,
biased, non-neutral and incomplete because they
were intended for a different purpose other than
that of research;
• It may be difficult to infer the direction of causality
in the texts;

19. RELIABILITY IN CONTENT ANALYSIS
• Texts may not be corroborated or able to be
corroborated;
• Classification of text may be inconsistent;
• Words are inherently ambiguous and polyvalent;
• Coding and categorizing may lose the nuanced
richness of specific words and their connotations;
• Category definitions and themes may be
ambiguous, as they are inferential;
• Some words may be included in the same
category but may have more/less significance in
that category;

20. RELIABILITY IN CONTENT ANALYSIS
• Words in a category may have different
connotations and their usage may be more
nuanced than the categories recognize;
• Categories may reflect the researcher’s agenda
and imposition of meaning more than the text may
sustain or the producers of the text may have
intended;
• Aggregation may compromise reliability;
• A document may deliberately exclude something
for mention, overstate an issue or understate an
issue.