In this article, Focal Points will define what media analysis is, as well as explore the differences between quantitative and qualitative analysis. This will be done by combining academic research and industry insights, which will be used to analyse the relative pros and cons of both types of analyses.
Media Analysis as a Subset of Content Analysis
Media content analysis, also referred to as media analysis, is a subset of content analysis. There are many definitions of content analysis, but the one offered by Jim Macnamara, professor of Public Communication at the University of Technology Sydney in Australia, (who borrows from Lasswell, Lerner and Pool) is very insightful. He describes content analysis as “a technique which aims at describing, with optimum objectivity, precision, and generality, what is said on a given subject in a given place at a given time”.1
In other words, if it is done accurately and without bias, content analysis tells you who said what about what in which way – and when and where it was said. This is incredibly valuable, as it can help you work out why certain things were said, what the likely impact will be, and how to respond.
Media analysis focuses on one specific area: the media landscape. This landscape consists of editorial media – online aggregation sites, news articles, investigative features, opinion columns, letters to the editor, radio broadcasts, etc. – as well as social media, such as Twitter, Instagram, Facebook, and YouTube. Even though media analysis extends far beyond editorial data (and can even include advertorial media data), this paper will concentrate solely on editorial media and the commercial aspects of media analysis.
There are two methods of media analysis: quantitative and qualitative. The former is based on statistics, while the latter draws on narratives. The strengths and weaknesses of each type of analysis are discussed below.
Quantitative Analysis: Pros and Cons
Quantitative analysis involves the collection of media ‘mentions’ (a process known as media monitoring), and the provision of statistics on these mentions. These figures can include the ‘clip count’ (total number of mentions for an entity or topic), circulation (the number of copies distributed for each publication), media segmentation (a breakdown of media sources), ‘share of voice’ (how often an entity or topic is mentioned relative to its competitors), along with many others.
Quantitative analysis is popular because statistics are relatively easy to obtain, quick to compare, and they show figures for key performance metrics. If you use favourability or automated sentiment analysis, which tells you whether your coverage was positive, negative or neutral, then it is very easy to see whether media sentiment has improved or regressed by reviewing the statistics. Many experts also believe that the metrics mentioned above are powerful proxies for measuring audience impact and brand awareness.2
However, quantitative analysis also comes with some drawbacks, as statistics on their own cannot account for omissions, misstatements, or nuances, among other things. Thus, statistics cannot give you the ‘full picture’. That is to say, numbers and graphs cannot always provide you with meaningful context (or meaning in a context), nor can they fully capture the impact on audiences. This is because there is not always a straightforward, clearcut link between data and social perception. Being mentioned 1 000 times per week might mean your messages are gaining media traction, but you won’t necessarily know how those messages are being portrayed or whether those messages are actually influencing the public.
In order to answer these questions, many scholars and industry leaders believe that text needs to be parsed – studied in detail – to be truly understood.3 This is where qualitative analysis becomes valuable.
Qualitative Analysis: Pros and Cons
Qualitative analysis involves the examination of the relationship between a text or speech and its likely audience. Peter Larsen, professor of Media Studies at the University of Bergen in Norway, explains that text should be understood “as an indeterminate field of meaning in which intentions and possible effects intersect”.4 The role of the analyst is to interpret the text.
Because qualitative analysis is concerned with the interpretation of text, it pays special attention to target audiences, media sources, and other contextual factors. This helps to determine the most likely meaning, on top of being able to provide a more insightful idea of social impact than simple statistics are able to give. All this is laid out in narrative form.
Almost immediately, however, you might wonder: how do you know whether an interpretation or finding is correct? Can an analyst, whose role it is to interpret the text, ever be truly objective? The solution is ‘intersubjectivity’. Essentially, what this means is that there is a consensus among various analysts on a given topic or interpretation. If several people agree on a particular way of understanding certain media content, then that understanding has much greater credibility. Although not foolproof, with the right checks and balances in place, this system of intersubjectivity can avoid the vast majority of errors and misinterpretations.
However, not everyone is satisfied by this response. Macnamara reports that many people consider the qualitative method of analysis “unscientific” because of its reliance on small sample sizes (it is difficult to write narratives on a huge dataset) and the potential unreliability of replicating findings.5 It is undoubtedly true that identifying the intended meaning or likely impact of a piece of content is a tricky endeavour, especially without the benefit of statistics.
Quantitative versus Qualitative? Or Both?
So, which is better: statistics or stories? The answer will likely depend on your specific needs. However, an ideal approach will include both quantitative and qualitative analysis. This is because a multi-prong approach can combine the advantages of each method while negating or minimising their respective disadvantages.
For example, if you wanted to know how your brand stood in relation to your competitors, then it would be helpful to receive statistics on how often you were mentioned in top industry media sources in comparison with your competitors. This shows the importance of quantitative analysis.
However, mere figures will not be enough to give you an accurate reflection of the public perception of your brand. It is also important to know whether those mentions consisted of in-depth feature articles or if they were brief mentions at the end of an article or broadcast. To extract value from these different mentions, it is necessary to have qualitative analysis.
Conclusion
Media analysis comes in two general flavours. Quantitative analysis gives you valuable data on your messages and your competitors. On the other hand, qualitative analysis gives you in-depth insights into the way your brand is being perceived in the media. Both approaches bring their own strengths. In fact, the best approach is a combination of both quantitative and qualitative analysis. Without the one, some aspect of the ‘big picture’ will be missing. Together, they offer a more complete image of what you are looking for in your media data.