Content analysis is a study, categorization, and classification of texts and other forms of communication, including sound, images, and video. Researchers use content analysis to find words, phrases, or concepts within different communication artifacts such as:
- Web content
- Newspapers
- Video and pictures
- Social media
- Speeches, discussions
- Advertising
- Interviews
- Audio recordings
- Informal conversation
- Books, film, and theater
Once identification is complete for these concepts, the researcher then quantifies, classifies, and makes inferences about the data in the texts. Completion takes into account the audience’s culture and the time/date the communication recording took place.
Certain practices of content analysis tend to differ with each academic discipline. However, the bottom line is always the same: to study all manner of recorded communication to find hidden concepts and information deemed useful based on the prevailing hypothesis.
What Is The Goal Of Content Analysis?
Content analysis is one of many methods that researchers use to find information and make sense of substantive queries. When doing this, the researcher must be able to answer several guiding principles, which should help them acquire useful, objective information from their research.
Understanding these six steps is vital.
- The type of data you want to be analyzed
- What methods to use to analyze the data
- Which population the data derives from
- The type of context you consider relevant
- The scope and limits (if any) of your analysis
Researchers use content analysis to figure out the effects and purposes of communication artifacts. They gather information into groupings called units that help them surmise the approach and impact the publication of content has had on its audience. Content analysis can also reveal the best methods to communicate with an audience, whether for educational or promotional purposes.
Content Analysis: A Brief History
Content analysis has been around since the 1930s and took off in the 50s when mass communication studies increased. Back then, the process was done manually or sometimes using mainframe machines to scan and decode punch cards. A single study would last long, employing thousands of punch cards and dozens of researchers to go through piles of documents.
Results were inaccurate in some studies, and findings indicated that the cost of human error was high. In its early days, the content analysis focused entirely on written texts, intending to find repeating patterns and frequency of certain words or phrases.
Later, researchers needed to glean more information from the study. A more sophisticated research method developed that didn’t just look at word frequency, but also the concepts and relationships between words and phrases. It was when qualitative research became the common goal in content analysis.
Since then, content analysis has focused on exploring mental attitudes, with research going into social, cognitive, cultural, and linguistics aspects.
What Are The Uses Of Content Analysis?
Content analysis on practically all known recorded communications is possible; it has applied to scores of fields, industries, and niches. Some of the areas that use content analysis more aggressively include advertising, media, social science, literature, political science, artificial intelligence, gender studies, and psychology. This old list of responsibilities is still relevant today. It can help describe the uses of content analysis as it relates to the world today:
- Highlight differences in communicating content
- Identify wrong or misleading information in published content
- Establish communication trends and intentions of groups, individuals, or institutions
- Show behavioral patterns, attitudes, and audience response to content
- Reveal the emotional and psychological states of audiences
How To Do Content Analysis In 13 Steps:
ONE – Start By Asking A Research Question
Ultimately, you want to solve an existing problem using content analysis. The best starting point is a clear understanding of the main goal of the study. Here’s an example:
How can I use content marketing to understand my online audience and communicate more effectively in the future? Or,
What steps can I take to create more powerful online posts for my audience?
If, for instance, your goal is to increase the level of engagement for your audience, one good place to start is by drawing on relevant theory and research. You test the effects of different types of content relevant to your audience.
TWO – Select The Type Of Content To Analyze
Your initial hypothesis will guide you when making decisions, such as the methodology and type of data needed to test the hypothesis. Having figured out the best way to answer your questions, it’s now time to gather different content for you to analyze. Are you dealing with websites, social media, and opinion posts; or advertising material?
Don’t collect large volumes of texts for analysis. If the material is too large, you will need to work with a sample to analyze yourself or with a small team.
THREE – Draw A Sample
When doing content analysis, you will deal with quantitative and qualitative generalizations in varying degrees. In this case, you need to study a sample containing the ideal version of your content. With it, infer accurate assessments about the population from which you drew the sample. The best way to get accurate samples is to take it randomly from a non-stratified population. To make the sample genuinely random, you, the researcher, must eliminate all favoritism, and ensure that the probability of picking any particular unit in the population is the same.
The way to be sure of this is to learn as much as you can about all relevant pieces of content published during a set period. Likely, you won’t know all that has published, but for the sake of the sample, make your source of data as vast and varied as possible.
FOUR – Create A Coding System
You have two main ways to create a coding system for your content. In one method, the deductive approach, the researcher forms an initial coding scheme based on predetermined hypotheses and existing literature on the topic. In the second method, the researcher develops a coding scheme based entirely on the data presented in the content.
There’s an option for a hybrid method, such as criterion validity. It is formed by analyzing the correspondence between the code and the content analyzed. There is also construct validity, which generally describes how much one measure connects to another measure in ways that prove or disprove a theoretical hypothesis.
To make a coding scheme work, one must use multiple categories in which every aspect of the construct accurately represents itself to test various hypotheses, set clear guidelines and principles for the coding scheme, and use examples.
Likely, you won’t develop your coding scheme for content analysis, so instead, use codes developed by other researchers (e.g., Stansbury, 2002, Maloney-Krichmar, 2005). However, if the coding is modified, make sure it is re-adjusted to the existing units to keep from drawing the wrong conclusions from your data. Then, gradually add new concepts as you read the transcripts.
FIVE – Define And Classify The Data
Content classification is usually into units or themes. It makes it easier to comb through the material to find meanings and units of value. Create different categories and follow a theme or idea to classify the content that you add to that theme. Any data related to that theme should then post in the right category.
As mentioned before, different types of content in the qualitative analysis are employed. However, before proceeding, the researcher should be clear about the following:
- The exact data to be collected and used in the transcription.
- Will observations and verbalizations be noted down?
- Figure out the criteria for inclusion – for instance, web content that broadcast to your audience, or an online platform selling your type of merchandise.
A rigorous and methodological analysis of content is what will make the results reliable. Understanding the objective of the study will answer questions such as these.
SIX – Set Rules When Coding Data
You need to have a set of rules that will help organize the units into well-defined categories. Rules are especially important when coding as a team. When doing conceptual analysis, this will determine what should and shouldn’t find usefulness in the text.
Use your primary data or content, existing theories, and empirical studies. As a rule, develop the categories and sets of units based on whether you’re going to take a deductive or inductive approach for the study. If you take a deductive approach, find a way to link your theories with data interpretation. And when taking the inductive approach, expand your source of information before making inferences to come to new theories.
SEVEN – Use An Objective Coding Scheme
Qualitative analysis requires a more thoughtful approach than in quantitative analysis. You will be required to answer the initial research question. However, other aspects of the research must be allowed to affect the final inference, such as the transformation that may occur once the initial questions go through the coding or if new questions pose themselves as relevant. Usually, what comes out of qualitative analysis is a broad picture of what is studied. This composite may influence the context, just as much as the other units like population and theories about the audience.
EIGHT – Use Computer Software
Unless you’re working with very few documents, content analysis is very tedious work. Researchers can reduce much of this work by using software to manage different aspects of the research. Here are some of the benefits:
- Reduces the work of marking up data, selecting different categories, separating the work into blocks for analysis, and setting up the work for coding and global editing.
- When coding, the software does a faster job of extracting and matching data against well-known dictionaries used for coding.
- Storing an electronic copy of the coded data and updating every step of the analysis also allows for multiple duplications of the copy.
- Faster counting of quantitative analysis, such as word frequency.
NINE – Have Specific Research Questions In Mind
Analyze all the text and enter the useful data into the categories. At this point, you have to draw inferences based on a clear interpretation of the data you have in all the categories. Remember, the qualitative analysis yields a lot of testable hypotheses. However, it can just as easily lead the researcher to identify new concepts that were not part of the research question but which are relevant nonetheless. If that happens, the researcher may have to alter or modify the parameters of the study.
TEN – Identify Useless Data
As mentioned previously, there are different ways in which the researcher can collect data for use in qualitative analysis. This data needs to transform into useable categories and units before proceeding with the analysis. Have clear reasons for selecting the data you pick for use in the study and know which data serves no purpose. Everything rides on your ability to select the right samples or pieces of content to use in the research. Therefore, knowing what not to use is just as important. Refer to the study parameters in terms of the range of date, location, and other relevant data.
ELEVEN – Validity and reliability of concepts
Issues of reliability and validity are similar to those of any other research method, but in this case issues such as reliability, which describes the tendency of researchers to code data in the same way over some time, or a classification of text that holds up to a particular standard, cannot be wholly overcome. The validity of categories involves using different classifiers to determine specific definitions of the category.
Qualitative analysis faces challenges as to the nature of conclusions. The main question is whether the conclusions followed available data, or if other phenomenon influenced the procedure. Coding errors are not unavoidable, so seek to attain about 80% margin for reliability.
TWELVE – Analyze and present your findings
Following all steps properly to prepare the data for analysis, the researcher can obtain a clearer picture and make accurate inferences. The researcher must be able to identify a particular unit of coding, whether it’s a word, paragraph, picture, or symbol. This last part involves establishing relationships between different codes by comparing them to specific themes or categories.
Results should present under specific themes, with the researcher’s conclusions supported by additional data and quotes from the categories. Results may present in a conceptual framework or statistical analysis. The reader should be able to understand the interpretations and draw inferences about the content, the content creators, and the audience.
THIRTEEN – Procedure: Analyze Both Qualitative And Quantitative Aspects Of Your Data
The simplest method of content analysis uses the content’s essential characteristics, such as how often a particular word appears on a piece of text, the duration of content such as audio or video interview, and other forms of analysis that are non-subjective. It may be an easy and accurate way to get objective data from a piece of content. However, it fails to address the underlying contexts of words and phrases and the ambiguities formed by synonyms. The problem with this approach is that it oversimplifies complex content to make the report easy to understand.
An essential distinction in content analysis is the difference between qualitative and quantitative analysis. The qualitative analysis looks at much more than the frequency of words, focusing instead on the latent meaning of words and phrases. While qualitative analysis allows for subjective interpretation of data, quantitative analysis mainly focuses on word frequency.
What Are The Main Features Of Content Analysis?
- It helps in identifying trends and intentions of groups or individuals.
- Content analysis provides insight into human language.
- It provides the qualitative and quantitative analysis of information.
What is coding in the content analysis?
Coding is when the researcher places qualitative data into groups. Coding is not unlike categorizing answers in a survey, which may include grouping responses, and knowing how to write up the number and value of responses.
What Are The Advantages Of Content Analysis?
- It’s a discreet method of analyzing interactions
- It can provide insight into human thought and the sophisticated use of language.
- By studying direct communication via text or other forms, content analysis helps us understand social interaction patterns.
- Content analysis can reveal useful cultural and historical insights.
What Are The Disadvantages Of Content Analysis?
- It is tedious.
- May contain errors, both in qualitative and quantitative analysis.
- It is often difficult to automate.
- It is often reductive, particularly when analyzing complex texts.
- May fail to grasp the context in specific texts.
Why Is Content Analysis Useful?
Content analysis has provided researchers with answers to many research questions in mass communication, management, anthropology, sociology, and many more. There are many nuanced descriptions of content analysis that exist. However, it is essential to know as reflecting its gradual historical development.
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