Analysis of results is a crucial step in any research or study. Marketing, human resources, consumer behaviour or even basic human psychology are just some of the examples of research areas that require analysis. In all fields of research, results are either qualitative or quantitative.
Qualitative results are focused on qualities – data that can’t be measured. Some examples of qualitative data include the eye colour of the study participants, their age, gender. This is data about the characteristics of the subject being studied.
On the other hand, quantitative results are information that can be counted or measured. An example being the number of participants in a study, how much time they spend doing a certain activity or maybe how many of them are smokers. This type of data can be easily calculated.
Sometimes the research requires qualitative data to be measured – so what do we do when the data received from research or study is all qualitative? This is where content analysis comes into play.
What is Content Analysis?
Content analysis is a widely used research process. In simple terms, it is a method of research that changes qualitative data into quantitative figures. This is done by making accurate understandings through reading and coding the qualitative data.
Qualitative data may include documents, texts or even oral communications. Texts are assigned labels to show if there are any important patterns within.
Analysing content helps in the study of many challenging topics of interest to researchers. This may include organisational behaviour strategy, managerial reasoning, human resources, technology and innovation management, as well as international management.
Content analysis methods can help in many aspects of business problems. It can close the gap between researches with large samples and ones with smaller samples. That is why each sample size has its own benefits and drawbacks.
Small sample research can help in collecting primary data as well as in-depth analyses, however, it may suffer from external credibility problems. On the other hand, large sample research may have internal validity issues.
Therefore, content analysis will help in improving the quality of the research by considering the benefits of both the small and the large sample research.
Moreover, content analysis helps researchers to see the amount of patterns in the data as well as understanding the links between patterns. Programs that analyse the qualitative documents offer efficient work-flow and controlling tools for coding.
Content Analysis Approaches
Content analysis has three main approaches: conventional, directed, and summative. All methods are similar in their aim – they are used to understand the meaning of a text from its content.
Conventional Content Analysis
Conventional content analysis is commonly used where a study’s main intention is to appoint a certain incident. It is usually applied when research on the topic is limited.
When using conventional analysis, researchers don’t use fixed categories. Instead, they let the categories and their labels flow from the literature. Meanwhile, they engage themselves in the data to allow new observations to develop.
The process is as follows: first, data is analysed generally by reading all information together. This is done in order to promote engagement and gain a sense of the whole idea.
Next, data is read again, but this time it is prepared word by word. This step involves first highlighting specific words from the information that seem to display crucial concepts.
Then, the researcher makes notes of any patterns, thoughts, and the initial understandings – this is done with the aim of developing codes. As this process lasts, labels for codes are made that reflect the main thoughts of the data.
After that, codes are organised into groups and the grouping system is done based on how codes relate to each other. The grouping’s main aim is to group codes into meaningful clusters.
Directed Content Analysis
The researcher would choose a directed approach to content analysis when previous incomplete research about the incident is available. The main objective of using a directed approach is to help the researcher create further developments – prior research would help.
Prior research would also support the development of the research question and provide predictions about the key variables and the links between them. As a result, this can help in finding the first coding scheme.
The process of using a directed approach is more organised than using a conventional approach. It starts by identifying the main variables as initial coding categories, then operational definitions for each category are determined. Based on the research question, data and the researcher’s goals, labelling/coding can follow two main strategies.
If the research objective is to classify and sort all cases of the incident examined, then reading and highlighting all data that seems to represent the needed reactions is considered the best solution.
The step that follows is to label all highlighted information by the initial codes. Any data that could not be coded from the coding scheme would be given a new code.
The second strategy that can be used in directed content analysis starts with immediate labelling using the predetermined codes. In this case, the researcher is confident that initial coding will not bias the action of picking out relevant text.
Through the process, information that could not be given code is recognised and analysed later to decide if they represent a new category in the coding scheme.
Summative Content Analysis
A study using a summative approach begins with organising and counting specific words in the data. The main objective of this step is to investigate the usage of specific words, instead of trying to understand the meaning of the content.
This type of analysis is also known as manifest content analysis. If the process ends at this step, the analysis would be considered as quantitative analysis. However, a summative method’s main goal is to discover underlying data meaning by quantifying words.
As mentioned earlier, the process of a summative approach starts with counting the occurrences of specific words. This can be done either by hand or by computer. Quantifying words in data can help in identifying patterns as well as inspect the codes. It also helps in understanding the text in terms of the specific words used.
A summative approach has various advantages. It is a subtle method to explore the concerned aspect and perhaps the most straightforward. It can also deliver simple insights into the usage of certain words for better understanding.
On the other hand, this approach has some drawbacks; the results using this method are limited to the extensive meanings within the data. Besides, this approach generally depends on reliability, so in order to verify reliability, the results have to be compatible with the interpretation.
Differences Between the Approaches
There are many major differences between the three approaches to qualitative content analysis, particularly in terms of coding systems, origins of codes, as well as threats to credibility.
The conventional content analysis develops the labelling system directly from the text documents. However, during the analysis of a directed method, the process starts with relevant research results instead, as guidance for initial codes.
Regarding a summative content analysis, it implicates counting and comparisons of keywords or content, followed by the interpretation of the underlying context.
Nonetheless, understanding each approach in full detail is a significant matter to researchers. This is mainly to identify which method will work best in their study of interest.
In short, to answer the question: what is content analysis – it is a method used for describing written, verbal or graphic communications.
It basically develops a quantitative description that comes from a qualitative one. This helps researchers in studying, developing and furthering knowledge in other areas.
There are three types of content analysis that could be used in a study; conventional, directed, and summative. Different research designs and analysis are used depending on the objective and the research available.
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