1. INSPIRING CREATIVE AND INNOVATIVE MINDS CONTENT ANALYSIS A careful, detailed, systematic examination and interpretation of a particular body of material in an effort to identify patters, themes, biases and meanings (Berg & Latin, 2008)

2. -Should be related to the literature and broader concerns and to the original research questions -The analysis provides the researcher a means by which the subjects or the authors view their social worlds and how it fit into the larger frame -“It is a passport to listening to the words of the text and understanding better the perspectives of the producer” (Berg, 2008)

3. Manifest Versus Latent Content Analysis -Manifest content – elements that are physically present and countable -Latent content – deep structural meaning conveyed by the message

4. Category Development -Inductive – the researcher “immerse” in the documents in order to identify the dimensions or themes -Deductive – the researcher use some categorical scheme suggested by a theoretical perspective

5. What to count -Words – frequency distribution of specified words or terms -Themes – a string of words (simple sentence) -Characters – the number of times a specific person or persons mentioned -Paragraphs – combination of various and often numerous thoughts

6. -Items – the whole unit of the sender’s message (letter, speech, etc.) -Concepts – conceptual clusters (ideas), e.g., concept of deviance (toward more latent than manifest content) -Semantics – how strong a word to the overall sentiment of the sentence

7. Combinations of Elements -In certain situation content analysis requires the use of combinations of the content analytic elements -Eg., in understanding certain concepts, the researcher might wants to use a combination of item and paragraph, and so on

8. Open Coding -The researcher need to hold their interpretations, questions, or even possible answers in order to allow the coding process is completed -Strauss (1987) – “believe everything and believe nothing”

9. Strauss (1987) suggested 4 guidelines in conducting open coding: 1. Ask the data a specific and consistent set of questions 2. Analyse the data minutely 3. Frequently interrupt the coding to write a theoretical note 4. Never assume the analytic relevance of any traditional variable (gender, age, and so on) until the data show it to be relevant

10. Asking Specific and Consistent Questions -The researcher must keep the general question in mind -The original of the study may not be accomplished but rather an alternative or unanticipated goal may be identified in the data

11. Analyse the Data Minutely -The researcher is required to analyse the data minutely -The researcher should consider how detail the data can be coded to ensure every part and information of the data is analysed -The process will eventually saturate the document

12. Frequently Interrupt the Coding -A comment in the document triggers ideas – jot down -Fail to do so, the idea is very much likely to be forgotten

13. Never Assume Any Traditional Variable -The variables should be grounded in the data -The researcher should look into the data whether the variables are relevant to the assumption of the study or not

14. Negative Case Testing -The researcher intentionally seeking negative or unique cases until the data are saturated and built into an emerging pattern -Null hypothesis trick (Becker, 1998) – the assumption the researcher have on the data to reveal no patterns of relationships

15. Thank you very much for your attention…