CONTENT ANALYSIS

Name/Surname :- SAVAGE ABDUL-RAZAQ .O.

Student Number :- 145624

Course Code/Name :- TEXT MINING ITEC 547

Content analysis is a method of coding qualitative and/or imitative narrative data to identify the prevalence of key themes and issues in relation to a particular context.

CONTENT ANALYSIS

What is Content Analysis?

1st Definition

is a research technique (or method of inquiry) for systematic and replicable analysis of the content of communication, and for making inferences from that data to their context

When is content analysis useful?When . . .•your research question is best analysed by organising the data in a thematic way•your research requires you to gather narrative data from interviews, focus groups or field notes

When . . .•the codes for analysing your data can be derived before the data is collected•it is important to identify the context within which certain words and terms are used•the results do not need to be generalizable to the wider population

- for making theory- by analyzing, examining, and selecting data- systematically & objectively

CONTENT ANALYSIS AS A TECHNIQUE

- clearly & fully expressed rules- set up before analysis - explain various data completely- applied strictly

CRITERIA OF SELECTION

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

CATEGORIES / MAJOR POINTS

QUANTITATIVE VS. QUALITATIVE

- Quantitative : objective, systematic,

procedures of analysis

arbitrary limitation, relevant categories

- Qualitative : definitions, symbols, detailed

explanations, etc

no absolute truth, but context-bound

- manifest content (surface structure): perceptible, clear, comprehensible message

- latent content (deep structure): implied, unstated message

MANIFEST vs. LATENT CONTENT ANALYSIS

UNITS AND CATEGORIES

Units = Codes

‘Code’ the elements into ‘Inductive Categories’

ex. Words, items, themes…

Want to understand emotion in student discussions Might choose turn-at-talk Want to study argument in decision-making discussion Might choose thought-unit (because more than one

argument can occur in larger units) Want to study conflict in online discussion Might choose whole discussion, or partnered turns-at-talk

EXAMPLES OF UNITS

3 major procedures:

1. Common classes

2. Special classes

3. Theoretical classes

Classes and Categories

CLASSES AND CATEGORIES

Common Classes:

-- a culture in general

People in society to tell apart persons, things, and events

Ex. Age, gender, mother…

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

THEORETICAL CLASSES:-- EMERGE IN THE PROCESS OF ANALYZING THE DATA

-- FUNCTION:GROUNDED IN THE DATA

GET A THEORY

CLASSES AND CATEGORIES

Units of analysis may differ from units of observation

Observe story content to analyze newspaper differences

Sample selection depends largely on unit of analysis Example, if studying differences between authors, the unit of

observation may be books, pages, paragraphs, or sentences

Need to be clear about unit of analysis before planning sampling strategy to avoid problems later

Since you can rarely observe all content, must sample from available content for coding pool

SAMPLING IN CONTENT ANALYSIS

I. Random Sampling 1. Simple Random Sampling to draw subjects from an

identified population

2. Systematic Sampling (Interval Random Sampling ) select nth name from the population

Population Sampling interval = Numbers of persons desired

* Random Numbers Table

3. Stratified Sampling - divide population into stratum - ensure : dissimilarity between stratum ↑ similarity inside of each strata ↑

∴ produce a representative sample

II. Non-random Sampling Purposive Sampling researcher select subjects according to his/her research purpose and understanding of the population

- researcher: with sufficient knowledge or expertise - subjects: represent the population

CODING IN CONTENT ANALYSIS

Coding is the heart of content analysis Process of converting raw data into a standardized form Classify content in relation to a conceptual framework

Ex. Emotionality, Partisan Bias, Source Attribution, etc. Must carefully conceptualize coding categories

Relevant concepts and relevant categories within concepts Manifest (visible surface) / Latent (underlying meaning)

How big a leap between observation and inference The more manifest, the more reliable - ex. counting words The more latent, the more interesting - ex. assessing meaning

CONTENT CODING EXAMPLE #1

Bush's only argument was that Kerry was "wish-washy" with

his stances. He was forceful in attacking his opponent and

relied heavily on why Kerry wouldn’t be an appropriate

president versus - why he would be best to continue in office.

Kerry attempted to take a stance on many issues in his

debates. He had strong ideas in the domestic debates. He

also took notes through the entirety of the debates in order

to prepare to attack Bush's stance. Overall Kerry was a more

composed, direct speaker.

1. Major Problems:

-- can not read between the line-- do not get the real motivation

2. Can get the points where Coding can continue.

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

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

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.

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