semester 2: lecture 9 analyzing qualitative data: content analysis prepared by: dr. lloyd waller ©

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Semester 2: Lecture 9 nalyzing Qualitative Data: ontent Analysis Prepared by: Dr. Lloyd Waller

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Page 1: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Semester 2: Lecture 9

Analyzing Qualitative Data: Content Analysis

Prepared by: Dr. Lloyd Waller ©

Page 2: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

CONTENT ANALYSIS STUDIES:

• Conversations• Recorded narratives• Transcriptions of events• Case record narratives• Journals• Documents• Digital media

Page 3: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Content Analysis

• Content analysis is a procedure for the categorization of verbal or behavioral data, for purposes of analyzing the communication of people and organizations

• Allows researchers to make inferences by identifying specific characteristics of messages

Neuendorf, K. A. (2002). The Content Analysis Guidebook. Thousand Oaks, CA: Sage Publications.

Page 4: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Basic Principles of Content Analysis• A number of messages can be classified

into a set of categories– Elements classified together have similar

meanings

• Categories produce frequency counts to allow for comparisons– Researcher addresses the relevance of

frequencies to the theoretical propositions supporting the study

Page 5: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

What Content Can Be Analyzed?• Any message or aspect of a message that

can be captured– Sources or senders of messages– Reasons for sending messages– Channels messages are sent through– Content of messages– Message effects– Recipients of messages

Page 6: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Content Analysis

• Advantages– useful tool for gathering data on a variety of topics– Unobtrusive– Data is in permanent form and so can be subject to be re-

analysis, allowing reliability checks and replication studies.

– Provides a low cost form of longitudinal analysis when a series of documents of a particular type is available. 

Page 7: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Content Analysis

• Disadvantages – Coding can be time consuming– Very Subjective– Secondary data can be limited (No probing)

– It is very difficult to assess casual relationships. Are documents causes of social phenomena or reflections? 

Page 8: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Content Analysis

• Two Levels of Analysis- Manifest / Conceptual Analysis : Basic Level

(Surface Structure)- Descriptive Account of Data - (those elements that are physically present and countable)

- Latent/Relational analysis : Advanced/Higher Level (Interpretive (Deep Structure) - what was meant by the response, what was inferred or implied - interpretive reading of the symbolism underlying the physically presented data / exploring the relationships between the concepts identified

Page 9: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Content AnalysisHow to carry out content analysis

• Start with a research question – Ds there political bias on a specific radio show?

• Decide on sampling strategy - Deciding what documents to look for and where to find them

• Define the recording unit – What words words/sentences be used to decide bias?

• Construct categories for analysis – Anti PNP– Pro PNP– count presence or absence of a category– place each piece into one of many categories (forced choice)Test the coding on samples of text and reliability – Member Checks for verification

• Carry out the analysis – Manifest– Latent

Page 10: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Latent vs Manifest Analysis

• counting--little judgment, less discovery

• rating--much judgment, more discovery

• usually have a combination

Page 11: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Things to look for

• Look for repeated patterns within content.• Look for alternative confirmation from

different sources.• Compare with existing theories.• Compare with studies of same content.• Provide multiple supporting examples.• Note disconfirming examples.

Page 12: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

– explicit themes,

– emphasis on various topics,

– amount of space or time devoted to topics,

– etc.

Page 13: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

count angry words in text or rate anger in sentences or paragraphs• “count” angry words, what is an

angry word???

• end up using some judgment unless it’s totally spelled out

• and the more it’s spelled out, the more you lose discovery

Page 14: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

e.g. Anger• count these words:

– fed up IIII II– irritated III– disgusted II– etc. IIII III

• or rate these sentences:– I find the idea to be distasteful.

very warm very angry

0 1 2 3 4 5

Page 15: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

examples of elements to count: • items, • words• sentences• paragraphs, • characters, • semantics• concepts, • themes,

Page 16: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Items

• represents the whole unit of the sender's message

• may be an entire book, a letter, speech, diary, newspaper, or even an in-depth interview.

Page 17: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Words.

• smallest element

• least judgment

• generally results in frequency distributions

Page 18: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Sentences

• definitely more judgment than words but less than paragraphs, etc.

Page 19: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Paragraphs

• difficulties result in attempting to code and classify

• poor consistency among writer on how to write a paragraph

Page 20: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Semantics

• meanings

–of overall sentence, paragraph, etc.

• requires a lot of judgment

Page 21: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Characters (persons)

• count the number of times a specific person is mentioned

Page 22: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Concepts• involve words grouped together into conceptual

clusters (ideas) • e.g. a conceptual cluster may form around the

idea of deviance. – Words such as crime, delinquency, money

laundering, and fraud might cluster around the conceptual idea of deviance

• leads toward more latent than manifest content, more rating, judgment, etc. although word clusters could be simply counted

Page 23: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Themes

• broader than a concept (almost like a mood)

• can be made up of many concepts

• must further specify the unit --theme of each sentence, each paragraph, the whole book????

Page 24: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

example--Interview #60: • ORTHODOX

– Well, I guess, Orthodox keep kosher in [the] home and away from home. Observe the Sabbath, and, you know..., actually if somebody did [those] and considered themselves an Orthodox Jew, to me that would be enough. I would say that they were Orthodox.

• CONSERVATIVE– Conservative, I guess, is the fellow who doesn't want to say he's

Reform, because it's objectionable to him. But he's a long way from being Orthodox.

• REFORM– Reform is just somebody that, say they are Jewish because they

don't want to lose their identity. But actually want to be considered a Reform, 'cause I say I'm Jewish, but I wouldn't want to be associated as a Jew if I didn't actually observe any of the laws.

Page 25: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Three major approaches to categorizing in a coding system:

• common classes,

• special classes, and

• theoretical classes

Page 26: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

1. Common classes.

• used by virtually anyone in society

–(for example, age, gender, mother, father, teacher, boss, lover, etc.)

• essential in assessing whether certain demographic characteristics are related to patterns that arise from other coding

Page 27: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Interpreting Coding Results

• Analysis must be relevant to hypothesis or research question– Frequencies– Differences– Trends– Patterns– Standards

Page 28: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Grounded Theory: Background

• Qualitative methodology• Developed by Glaser & Strauss (1967)

during investigations of institutional care of terminally ill patients

• Represented ‘philosophical shift’ from existing ‘grand theories’ (abstract) to more local understandings/everyday contexts

• The research focus is in on ‘emergence’ and NOT hypothesis testing

Page 29: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Grounded Theory

• Involves identification and integration of categories of meaning from data (Willig, 2001)

• GT begins with a research situation, researcher’s task is to understand what is going on in situation

• Theory is emergent and grounded in the data

Page 30: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Steps in the Grounded Theory Process

• Step 1: Select a topic of Interest (Formulate your research question)– Researchers require an initial question

upon which to focus their attention in the area/phenomenon they select

– Question should identify but not make assumptions about the phenomenon of interest (a difficult task-difficult to ask q’s without making Initial question can change radically during the GT process

Page 31: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Steps in the Grounded Theory Process

• Step 2: Determine the Purpose of the Research– To generate theory– To verify existing theory– To evaluate accuracy of earlier evidence– To make generalizations based on

experience– To identify a unit of measurement for a one

case study

Page 32: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Steps in the Grounded Theory Process

• Step 3: Select a group to be studied– The process of picking the sources that can

provide the most information about the research topic.

– The aim of Theoretical Sampling is to “minimize opportunities to compare events, incidents, or happenings to determine how a category varies in terms of its properties and dimensions (Strauss and Corbin, 1998: 2002)

Page 33: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Steps in the Grounded Theory Process

• Step 4: Collect Data– GT suitable for use with most forms of

qualitative data– semi structured interviewing– participant observation– focus groups– Diaries– Tape recorded data– Verbatim transcription of words spoken, not

timing, pauses etc– NB after each round of data collection

engage in ‘note taking’ (noting key issues)

Page 34: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

– Semi-structured interviews (data). Analysis conducted alongside data collection, thus interview schedule changes throughout process

– As GT is the systematic generation of theory from data

– theories empirically grounded in data from which they arise

– Therefore theory informs data collection

Page 35: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Steps in the Grounded Theory Process• Step 5: Data Analysis

– Aim behind GT is to generate theory to explain what is central in data

– 3 stages of analysis: – find conceptual categories in data – find relationships between categories – core categories to explain/account for

relationships

Page 36: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Grounded Theory: Beginning Analysis

• Begin by (re)reading textual database and label categories & their interrelationships (coding)

• Categories are instances (i.e. of events; processes) that share common features with each other

• Categories can be grouped at low level of abstraction & function as ‘descriptive labels’, i.e. references to ‘anxiety’, ‘anger’, ‘pity’ can be grouped under ‘Emotions’

• Grouping can also occur at higher level of abstraction, categories are analytic that is they interpret rather than simply describe

Page 37: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Coding

• Three kinds of coding used in GT:

• 1. Open coding to find categories

• 2. Axial coding to find links

• 3. Selective coding to find core category

• A Code book is often useful-maintaining an inventory of codes with their descriptions

Page 38: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Grounded Theory: Coding

• Your task is to identify categories (equivalent to ‘themes’) & their properties (ie their sub categories)

• GT category labels are in vivo -they make use of words/phrases utilised by P’s

• Resultant strength is that it…• Places strong emphasis on P’s own accounts of

social/psychological events and enables research to avoid imposing existing theory into analysis

Page 39: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Open Coding

Split transcript into parts

• tag sections of text with codes

• look for naturally appearing groupings of ideas in the data– Create categories/groupings across data

Show example

Page 40: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Axial Coding

The process of assigning categories into more inclusive groups

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Page 41: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Key Analytic assumptions: Constant Comparison

• A process which ensures coding momentum by identifying similarities and differences between emerging categories

• i.e. categories can be broken down i.e. anxiety, anger, pity, joy jealousy, hate give rise to category ‘emotion’

• But…by comparing instances of emotion (joy, pity, etc) we can construct subcategories of emotion (i.e. those requiring an object-hate, jealousy & those which don’t i.e. joy, anxiety)

Page 42: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Key Analytic assumptions: Constant Comparison• Constant comparison ensures ‘2 way’

process of building up categories, and deconstructing them into smaller units of meaning

• ‘Homogenizing impulse is counteracted’ (Willig, 2001)

• Main objective of CC to connect categories so that emerging theory captures wide variation/complexity of data corpus

Page 43: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Key Analytic assumptions: Theoretical Sampling• Collecting more data as a product of

previously emergent categories/sampling new cases as analysis proceeds

• Process of data collection & analysis continues until point of theoretical saturation (i.e. no new categories can be identified & instances of variation cease to emerge).

• Now, a set of categories and subcategories represent the data

Page 44: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Selective Coding

• Process of choosing one category as the core category and relating all other categories to that one.

• Essential idea is to develop a single story line and to locate everything else around it

Page 45: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

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Page 47: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

THEORY OF GENDEREDPOLITICAL IDEOLOGY

There is a strong positive relationship

between gender and political ideology

Page 48: Semester 2: Lecture 9 Analyzing Qualitative Data: Content Analysis Prepared by: Dr. Lloyd Waller ©

Analytic Process: Summary

• Theory of phenomenon developed from data which can integrate all instances

• To qualify as a grounded theory:– The theory must closely fit the topic and

disciplinary area studied– The theory must be understandable and useful to

the actors in the studied situation– The theory must be complex enough to account for

a large portion if not most of the variation in the area studied