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Imagine you read a research paper with a huge chunk of text stating how and what the participants reported in an experiment. It wouldn't be easy to understand this and establish if the findings support the study's hypothesis. Thus, researchers use content analysis to make this clear.
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Sign up for freeWhat can inferential tests show?
What stage is the data transformed from qualitative to quantitative in content analysis?
What is done in the final two stages of content analysis?
What type of data does content analysis analyse?
What type of data does content analysis generate?
How many stages of content analysis are there?
How many stages of content analysis are there?
Researchers should identify categories before tallying frequency, true or false.
There is a standard procedure that all researchers must adhere to when conducting content analysis, true or false.
Which type of analysis will be used when carrying out a case study?
What can inferential tests show?
What stage is the data transformed from qualitative to quantitative in content analysis?
What is done in the final two stages of content analysis?
What type of data does content analysis analyse?
What type of data does content analysis generate?
How many stages of content analysis are there?
How many stages of content analysis are there?
Researchers should identify categories before tallying frequency, true or false.
There is a standard procedure that all researchers must adhere to when conducting content analysis, true or false.
Which type of analysis will be used when carrying out a case study?
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Team Content Analysis Teachers
Content analysis is an observational method used to analyse qualitative data, such as identifying words, themes and concepts and converting them into quantitative data. Once done, inferential tests are carried out to learn more about patterns and trends in the data.
Fig. 1 - Content analysis is a method used to analyse qualitative data to identify themes and convert them into quantitative data.
Researchers typically look for specific characteristics or traits to help answer a question or test their hypothesis when analysing data. Certain aspects of the data must reflect their tests; this is where content analysis comes in.
The method follows a similar protocol as thematic analysis.
Researchers can use content analysis for various data collection methods as long as it generates qualitative data. Some examples of data collection methods include:
Interviews.
Observations.
Speeches.
Diaries.
Letters.
Remember, the purpose of content analysis is to change data from qualitative to quantitative so that later statistical tests can be used.
There are several steps researchers must follow when using content analysis as a data analysis method. We describe these below.
Stage 1 – Researchers must prepare the data, i.e. transcribe or format it in some way to analyse it.
Stage 2 – Determining how to measure data, i.e., the units of measurement. These may be words, phrases, or topics highlighted each time they appear in the text.
Stage 3 – Determining codes and the coding system. The researcher identifies common themes in the phenomenon and decides what to include in their analysis. These are predefined words or themes highlighted each time they appear in the text.
The themes should all be a unit of measurement for the variables relevant to the hypothesis. The coding system essentially 'counts' each time a selected theme or word appears (the transformation to quantitative data).
The researcher can define these based on the data, previous researchers, and established theories. They will then find a way to code the text.
Stage 4 – Testing the coding sample on an excerpt of the text. It is similar to a pilot study and allows the researcher to determine if the coding system is a valid measure of the phenomenon and if adjustments are needed.
Stage 5 – Coding the text. Researchers convert the data from qualitative to quantitative. I.e., by tallying how frequent the theme is observed.
Stage 6 – Checking the reliability of the coding system and the data. Researchers need to ensure that if the same data is coded again, similar results will be reported. It is good to have more than one person do the coding and compare their results to see if they are similar, which indicates high reliability.
Stage 7 – Using the coded data for inferential statistics and concluding whether the data support or negate the proposed hypothesis.
Stage 8 – The final stage is to report the results and draw conclusions.
The following example is based on a research scenario that uses semi-structured interviews to investigate children's levels of aggression and loneliness six months after being adopted.
Stage 1 – The data must be prepared.
The first step is to transcribe the interview, i.e., to record every word or sentence and every sound and action made.
Stage 2 – Determining how to measure data.
In this case, the researchers decide to code for words and behaviours that indicate loneliness or aggression.
Stage 3 – Determining codes and the coding system.
The researcher must then determine in advance which words and behaviours they will highlight that indicate loneliness or anger. For example, participants raise their voices, curse, cry, or say phrases such as 'I feel lonely'. The coding system tallies how frequently the 'units' of the variables occur.
These are called units because they are a way to measure the variables.
Fig. 2 - In content analysis, themes are tallied to quantify qualitative data.
Stage 4 – Testing the coding sample on an excerpt of the text.
For instance, the researchers use the answer to the first question as a pilot extract to determine if the coding system is a valid and reliable measure of the variables.
Stage 5 – Coding the text.
After adjusting the system and proving its reliability and validity, the entire data can be coded.
Stage 6 – Checking the reliability of the coding system and the data.
Then another researcher codes the transcript without looking at the other researcher's work. Once this is done, the coded data is checked to see if both researchers have reached similar conclusions.
Stages 7 and 8 – Using the coded data for inferential statistics, reporting the results and drawing conclusions.
In the final stages, the researchers transform the data to use it for inferential statistics. In this case, they analyse the participants' data to obtain an overall score for aggressiveness and loneliness.
They conducted an independent t-test to compare the scores of adopted versus non-adopted children. Finally, the researchers must report the results and the conclusions drawn.
These are the basic steps of conducting a content analysis. However, they may vary from research to research as there is no standard procedure when conducting content analysis.
The use of content analysis is widespread in psychological research. There are many advantages to using this data analysis method. However, there are also disadvantages to consider when using this method. Researchers must keep these in mind to determine if the data analysis method is appropriate.If researchers determine that the method is inappropriate for their research, using the wrong method may invalidate or omit important information in their results. For example, if their research is better suited to another qualitative data analysis method, such as thematic analysis.
The strengths of content analysis are:
However, the weaknesses of content analysis are:
Content analysis is an analysis method for identifying words, themes, and concepts in qualitative data and converting them into quantitative data.
The two types of analysis differ: content analysis quantifies qualitative data (converts it from qualitative to quantitative), while thematic analysis produces qualitative data.
The analysis type the researcher uses depends on the data they are looking for.
For example, if the researcher is conducting a case study, they would use thematic analysis to obtain enriched data that will help them learn more about the patterns or trends of the phenomenon.
On the other hand, they may use content analysis to determine the relationship between specific themes/behaviours and a phenomenon, for example.
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What is content analysis?
The content analysis definition is an observational analysis method used to identify words, themes, and concepts in qualitative data and convert them into quantitative data.
How to do the content analysis?
There are eight steps to conducting content analysis:
Is content analysis qualitative or quantitative?
Content analysis is carried out on qualitative data. However, its procedure involves transforming the qualitative data into quantitative.
How do you write content analysis methodology?
Content analysis methodology needs to be written in thorough detail to replicate the research. In addition, the researcher needs to justify why they chose to do what they did to identify any potential biases.
What is the difference between content analysis and thematic analysis?
The two types of analysis differ in that content analysis quantifies qualitative data (transforms it from qualitative to quantitative), whereas thematic analysis analyses and produces qualitative data.
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Lily Hulatt is a Digital Content Specialist with over three years of experience in content strategy and curriculum design. She gained her PhD in English Literature from Durham University in 2022, taught in Durham University’s English Studies Department, and has contributed to a number of publications. Lily specialises in English Literature, English Language, History, and Philosophy.
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Gabriel Freitas is an AI Engineer with a solid experience in software development, machine learning algorithms, and generative AI, including large language models’ (LLMs) applications. Graduated in Electrical Engineering at the University of São Paulo, he is currently pursuing an MSc in Computer Engineering at the University of Campinas, specializing in machine learning topics. Gabriel has a strong background in software engineering and has worked on projects involving computer vision, embedded AI, and LLM applications.
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