Written by Dr. Hannes Nel

What, do you think, is the biggest challenge for somebody who embarks on doctoral or master’s degree studies?

Well, the answer to this question will probably be different for different people, depending on their circumstances, perceptions, value systems and culture.

If we were to combine all the possible challenges, we will probably arrive at “to understand”.

In my opinion that is the biggest challenge facing any post-graduate student.

Not only do you need to understand endless concepts, phenomena, theories and principles, you also must explain them in your thesis or dissertation.

And on doctoral level you will be required to define and explain new concepts, phenomena, theories and principles.

Data analysis is necessary for such elucidation.

I discuss the following data analysis methods in this article:

1. Schema analysis.
2. Situational analysis.
3. Textual analysis.
4. Thematic analysis.

Schema analysis

Schema analysis requires that you simplify cognitive processes to understand complex concepts and narrative information more readily. In this manner a narrative that might otherwise be difficult to understand because of the level of language used, cultural differences or any other reason, is made easier to understand for those who might find the language challenging or the cultural context alien.

Schema analysis might require additional explanation, interpretation and reconstruction of the message. An individual who grew up in the city might not know how to milk a cow and a farmer might not know how to obtain food from a street vending machine. 

Today schema analysis is also used in computer programming, where a schema is the organisation or structure for a database. A schema is developed by modelling data.  The purpose remains the same as when you would have done schema analysis manually – it is a process of rendering data more user-friendly.

Situational analysis

As opposed to comparative analysis, situational analysis focuses more on non-human elements. It implies the analysis of the broad context or environment in which an event takes place. It can include an analysis of the state and condition of people and the ecosystem, including the identification of trends; the identification of major issues related to people and ecosystems that require attention and an analysis of key stakeholders.

Textual analysis

Textual analysis, also called ‘content analysis’, is a data collection technique as well as a data analysis technique. It helps us to understand information on symbolic phenomena. It is used to investigate symbolic content such as words that appear in, for example, newspaper articles, comments on a blog, political speeches, etc. It is a qualitative technique in which the researcher attempts to describe the denotative meaning of content in an objective way.  

There are two levels of meaning, namely denotative and connotative meaning. The denotative meaning of a word refers to the literal meaning that you will find in a dictionary. This meaning is free from any form of interpretation. The connotative meaning of a word refers to the connotation that we ascribe to a particular word, based on the feeling or idea that the word invokes in us, which is often based on our prior experiences.

For example, the denotative meaning of the word ‘host’ is ‘one who lodges or entertains a stranger or guest at his or her house’. However, a woman who was abused by a host in whose guest house she stayed in her youth might conjure up in her mind a host as being a dangerous and sly human being who takes advantage of vulnerable people. The connotative meaning of ‘host’ is, therefore, largely the opposite of what the word is supposed to mean. In textual analysis we only work with the denotative meaning of words to make valid and reliable assumptions of the data within context.

You can only work with what was reported when doing qualitative research and you should not make any assumptions about the originator’s intended meaning. The context in which the information was used, however, also needs to be taken into consideration.

Textual analysis can be subjective because its interpretation is done by fallible people. It can include the analysis of freshly collected data as well as transcribed data. You should transcribe all the raw data that you collected from the written and verbal responses of participants during conversations, interviews, focus groups, meetings, etc. Electronically recorded interviews will need to be retyped word for word to facilitate textual analysis.

Thematic analysis

Also known as concept analysis or conceptual analysis, it is actually a coding regime, according to which data is reduced by means of identifying certain themes. Thematic analysis uses deductive coding by grouping concepts under one of a prepared list of themes.

In thematic analysis you first need to familiarise yourself with the data before you can even select themes. You should list the themes that you would like to cover in your research when you do your literature review. After having listed themes, the next step would be to generate codes. Codes serve as an important foundation for the structuring and arrangement of data by means of qualitative computer software. Even though one might not call it coding, capturing information on cards is also a form of coding, even though rather simple and limited in usability.

You can also search for themes now if you did not do so as a first step already. This is done by collating the codes that you identified into potential themes. Themes are actually “headings” under which related or linked codes are grouped, or clustered. Most qualitative research computer software allows you to review and edit your codes and themes when necessary, which will inevitably happen as you progress with your research.

Summary

Schema analysis:

1. Requires that you simplify cognitive processes.
2. Might require additional explanation, interpretation and reconstruction of selected data.
3. Is also used in computer programming.

Situational analysis:

1. Focuses on non-human elements.
2. Analysis the broad context or environment for the research.
3. Can include an analysis of the state and condition of people and the ecosystem.

Textual analysis

1. Combines data collection and analysis.
2. Helps to understand information on symbolic phenomena.
3. Attempts to objectively describe the denotative meaning of content.
4. Takes the context in which information was used into consideration.
5. Can be subjective.
6. Can include the analysis of freshly collected as well as transcribed data.

Thematic analysis

1. Is a coding regime.
2. Reduces data in terms of certain themes.
3. Requires the identification of themes before coding can be done.

Close

That concludes my articles on data analysis and all the other concepts and theories behind doctoral and master’s degree studies.

In the remaining 14 articles I will focus more on the structure and layout of a thesis or dissertation.

Enjoy your studies.

Thank you.