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rights in Africa: A comment on conceptual linkages. Sisule Fredrick Musungu*. Consultant, Programme on International Trade and Development, South Centre.

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The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings. It is a reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers. It allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure. As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising.

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Mayring: Qualitative content analysis: PsychOpen

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Psychology Reference library. The procedure for a content analysis is shown below:. Strengths of content analysis It is a reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers It is an easy technique to use and is not too time consuming It allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure Weaknesses of content analysis Causality cannot be established as it merely describes the data As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis.

Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question. Decide how many concepts to code for: develop pre-defined or interactive set of categories or concepts.

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Decide either: A. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

Conducting a Content Analysis

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared. When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text. Should text be coded exactly as they appear or coded as the same when they appear in different forms?

The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes. What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept?

Develop rules for coding your texts. After decisions of steps are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules.

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In content analysis, obeying by the translation rules is equivalent to validity. Decide what to do with irrelevant information: should this be ignored e. Code the text: This can be done by hand or by using software.


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By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize error far more easily e. If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs.

Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information.

Typically, general trends and patterns can be identified. Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts.

Mayring: Qualitative Content Analysis

Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts. To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized.

Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results. There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text. Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Other experts write about the difference between conceptual analysis and relational analysis.

Conceptual analysis determines how often a text uses certain words or phrases, while relational analysis determines how those words and phrases relate to certain broader concepts. Conceptual analysis is the more traditionally used form of content analysis.


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  • By examining word use in context, researchers can draw broader conclusions!

Typically, researchers start by identifying questions they would like to answer through content analysis. For example, they might want to consider how women are portrayed in advertising. If so, the researchers would choose a data set of advertising—perhaps the scripts for a series of television commercials—to analyze. They then would look at the use of certain words and images.

To continue the example, the researchers might study the television ads for stereotypical gender roles, for language implying that women in the commercials were less knowledgeable than the men, and for sexual objectification of either gender. Content analysis can be used to provide insights into particularly complex subjects like gender relations.