In this video, we are going to unpack the oftentimes misunderstood topic of qualitative
content analysis. We will explain what it is discuss its strengths and weaknesses and
look at when to use this analysis method. By the end of this video, you will have a solid,
big-picture view of content analysis so that you can make a well-informed decision for your
project. By the way, if you are currently working on a dissertation, thesis or research project be
sure to grab our free dissertation templates to help fast-track your write-up. These tried and
tested templates provide a detailed roadmap to guide you through each chapter section by
section. If that sounds helpful you can find the link in the description below.
So what exactly is content analysis? Well at the simplest level content analysis is a qualitative
analysis method that focuses on analysing recorded communication taken from artefacts. For example,
extracts from books, newspaper articles, interviews, audio and video recordings or even
blogs. Importantly content analysis can be used on both primary and secondary data. In other words,
data you collected yourself or data that was already in existence. This makes it quite a
flexible qualitative analysis method as you have more choices in terms of data sources. By the way,
if you are new to qualitative analysis be sure to check out our primer video up here.
Now let us go a little deeper. At the highest level, there are two types of content analysis
or rather two ways to perform content analysis. These are conceptual content analysis and
relational content analysis. These two approaches are quite different in terms of how they work so
let us take a look at each of them. First up is conceptual content analysis. In this case,
the analysis is focused on the explicit data. You look at the actual appearance of particular words
and phrases without offering interpretation of them. So the main concern here is the frequency
of words and phrases. In other words the number of times they appear in the selected data set.
For example, you might be researching changes in attitudes to women's rights issues. In this case,
you could look at the frequency of words like equal pay, gender equality and patriarchy in
popular culture over a certain period of time. So in short conceptual content analysis is explicit,
non-interpretational and surface-level focused. It is also somewhat quantitative in nature in other
words, it considers some numbers even though it is still a qualitative analysis method at its core.
Next up is relational content analysis. Here the focus is on the meaning and the
use of words and phrases. This meaning is derived from looking at the relationships
between the various words and phrases to those around them. Contrary to conceptual analysis
the one we just looked at the focus here is on the implicit data or the information
interpreted from looking at how certain words are used in relation to others. For example,
if a research project is aimed to analyse polling data around a particular political candidate to
understand general sentiment relational content analysis could be used to look at the words used
around mentions of that candidate, for example, ethical, trustworthy, dubious
etcetera. To determine patterns and themes of meaning that could indicate popularity
and sentiment. So in short relational content analysis is implicit, interpretational and is
focused on meaning. It is also worth mentioning that there are multiple approaches to relational
analysis but we will not dig into them in this introductory video. If you are interested you
can check out our detailed blog post covering all of that. The link is in the description. To
recap then content analysis can be undertaken using either a conceptual approach where you
are interested in the frequency of concepts or a relational approach where you are interested in
the meaning of language based on the connections or relationships between words and phrases.
Now that we have a clearer picture of what content analysis is it is important to discuss
the strengths and weaknesses so that you can make the right choice in terms of analysis methods for
your research project. One of the main strengths of content analysis is its flexibility as it can
be used on a wide range of data types including written records, interview recordings and speech
transcripts as well as non-text-based data. This means you have more choices in terms of the data
sources you can draw on allowing you to develop a rich data set. Additionally, content analysis
tends to be very unobtrusive since quite often the analysis can be performed on data that already
exists. This means that there are fewer ethical issues to consider and it is easier to access the
data you need. All that said as with any analysis method content analysis has its drawbacks. First,
there is the problem of reliability after all drawing conclusions from the frequency of
words and phrases or their relationship to each other can be a subjective process and not quite
scientifically rigorous enough. This is especially true if more than one researcher is working on the
data set. Content analysis can also sometimes be considered as rather reductive. In other words,
the focus on particular words and phrases can, of course, result in you missing context,
nuance and culture-specific meanings. Lastly, the results from a content analysis cannot usually be
easily generalised. Since content analysis is often time intensive it can be difficult
to analyse a data set large enough to draw broad conclusions about the research topic. Of course,
this can be said for many qualitative methods but it is worth keeping in mind if you are considering
using content analysis. Hey if you are enjoying this video so far please help us out by hitting
that like button. You can also subscribe for loads of plain language actionable advice. If you are
new to research check out our free dissertation writing course which covers everything you need
to get started on your research project. As always links in the description. Now that we have got a
clearer picture of what content analysis is the logical next question is when should you use it?
As a qualitative method focused on recorded communication content analysis is often most
appropriate for research topics focused on changes and patterns in communication around social,
economic or political issues. For example, a research project that involves analysing
government policy regarding healthcare in the UK might look at the use of phrases like healthcare,
the NHS and hospitals in political commentary. An analysis could then be done on the frequency of
these phrases and or their relationship to other associated words and phrases. On the other hand,
research that focused on the use of language in context might not be the best fit for content
analysis. In other words, if your research is about the particular impact of language in
specific social contexts then content analysis could potentially be too narrowly focused. For
example, if you are wanting to assess how political speech is used in impoverished
environments to impact beliefs and opinions a more context-oriented analysis method such as
discourse analysis could be more appropriate. Simply put make sure that you always consider
the nature of your research aims when you are deciding on an analysis method.
Okay, that was a lot so let us do a quick recap. Content analysis is a qualitative analysis method
that draws findings from analysis of recorded communication which can include both primary and
secondary data. As we discussed content analysis can be approached in two ways. Conceptual analysis
where the focus is on the frequency of concepts and relational analysis where the focus is on
the meaning of and relationship between concepts. As with any analysis method content analysis has
its own set of strengths and weaknesses. As a result content analysis is generally most
appropriate for research focused on changes and patterns in recorded communication.
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