1. CODING AND CONTENT ANALYSIS
© LOUIS COHEN, LAWRENCE MANION AND KEITH MORRISON
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

2. STRUCTURE OF THE CHAPTER
Coding
Concerns about coding
What is content analysis?
How does content analysis work?
A worked example of content analysis
Reliability in content analysis
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

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).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

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 subsume others, thereby creating a hierarchy of subordination and superordination, creating a tree diagram of codes.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

7. TYPES OF CODE Open code Analytic code Axial 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.
Open code
Interpretive and explanatory
Analytic 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.
Axial code
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

8. 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.
Selective code
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

9. TYPES OF CODE Theoretical code
Researchers see how codes and categories are integrated and fit together to create a theory or hypothesis.
Theoretical codes are the underlying logic that comes from pre-existing or emergent theories.
Theoretical code
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

10. GLASER’S SIX FAMILIES OF THEORETICAL CODES
The six C’s
Causes, contexts, contingencies, consequences, co-variances and conditions
Processes
Phases, progressions, passages, transitions, careers, trajectories, sequences, cycles
Type
Styles, classes, genre
Identity
Self-image, self-concept, self-worth, self-evaluation, identity, transformations of self
Degrees
Range, gradations, levels, limits
Culture
Social values, beliefs and norms
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

11. 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).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

12. CONCERNS ABOUT CODING
Risks losing temporality, context and sequence in the coding and retrieval of text.
Risks stripping out important contexts from the study.
Risks fragmenting holistic data into small segments, thereby losing the whole picture and having only a series of decontextualized codes.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

13. CONCERNS ABOUT CODING
Temptation to ascribe the same code to an observed behaviour regardless of the setting, time, prevalent conditions, states of mind, actors involved, intervening events, when, in fact, the meaning and significance of the behaviour is not the same in different contexts or points in time.
All data are swept up and treated as equally important.
Coding too easily feeds the propensity of humans to look for patterns where none exist.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

14. 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).
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

15. 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
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

16. SYSTEMATIC APPROACHES TO DATA ANALYSIS
Comparing different groups simultaneously and over time.
Matching the responses given in interviews to observed behaviour.
Analysing deviant and negative cases.
Calculating frequencies of occurrences and responses.
Assembling and providing sufficient data that keeps separate raw data from analysis.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

17. HOW DOES CONTENT ANALYSIS WORK?
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.
Numerical content analysis
Code and categorize data.
Compare categories and make links between them
Conclude: draw theoretical conclusions from the text.
Non-numerical content analysis
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

18. STEPS IN CONTENT ANALYSIS
Define the research questions to be addressed by the content analysis.
Step 2
Define the population from which units of text are to be sampled.
Step 3
Define the sample to be included.
Step 4
Define the context of the generation of the document.
Step 5
Define the units of analysis.
Step 6
Decide the codes to be used in the analysis.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

19. STEPS IN CONTENT ANALYSIS
Construct the categories for analysis.
Step 8
Conduct the coding and categorizing of the data.
Step 9
Conduct the data analysis.
Step 10
Summarize.
Step 11
Make speculative inferences.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

20. 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 than that of research.
It may be difficult to infer the direction of causality in the texts.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

21. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors

22. 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.
© 2018 Louis Cohen, Lawrence Manion and Keith Morrison; individual chapters, the contributors