1. CONTENT ANALYSIS Name/Surname :- SAVAGE ABDUL-RAZAQ.O. Student Number :- 145624 Course Code/Name :- TEXT MINING ITEC 547

2. 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? 1 st 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

3. 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

4. - 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

5. 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

6. 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

7. - manifest content (surface structure): perceptible, clear, comprehensible message - latent content (deep structure): implied, unstated message MANIFEST vs. LATENT CONTENT ANALYSIS

8. UNITS AND CATEGORIES Units = Codes ‘Code’ the elements into ‘Inductive Categories’ ex. Words, items, themes…

9.  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

10. 3 major procedures: 1. Common classes 2. Special classes 3. Theoretical classes Classes and Categories

11. 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

12. THEORETICAL CLASSES: -- EMERGE IN THE PROCESS OF ANALYZING THE DATA -- FUNCTION: GROUNDED IN THE DATA GET A THEORY CLASSES AND CATEGORIES

13.  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

14. 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

15. 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

16. 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

17. 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.

18. 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

19. 3. Frequently interrupt the coding to write a theoretical note. -- comments ideas 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

20. 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

21. 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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