What is Conceptual Content Analysis in Qualitative Research? Step-by-Step Guide
In conceptual content analysis, you choose concepts to study and then quantify and count their presence in textual data through specific words. The concept is used to analyze the consistency or regularity of explicit notions or ideas that are represented within the text.
While implicit concepts may be included in conceptual analysis, this approach is more complicated as coding implicit concepts requires a coding dictionary or translational rules. As conceptual studies tend to analyze exceptionally large amounts of text, implicit coding is generally resigned to other methods of content analysis.
Instead, the goal of conceptual analysis is to explore the occurrence—or frequency—of the concept through a manifest analysis. Using a deductive (top-down) approach, researchers are most concerned with explicit concepts that are easily identified “on the surface” of the text. Concepts are taken at face value and their meaning is apparent to readers without inference.
By reducing the text to concepts consisting of a word, set of words, or phrases through manifest analysis, researchers only code for the concepts that help answer their research questions.
When to use conceptual content analysis?
Similar to other deductive methods of content analysis—such as directed content analysis or manifest content analysis—a conceptual content analysis is used when you want to:
Study a topic within an existing theory or framework.
Understand the usage of specific words or content in textual data.
Build upon preexisting studies or theories.
Explore copious amounts of textual data to perform a study.
Navigate the different meanings specific words can have.
Gauge sentiment on a topic across vast amounts of text.
Analyze sensitive, emotionally-charged topics at a distance.
Source materials for conceptual content analysis
Conceptual content analysis can be used to analyze any occurrence of communicative language that is presented in text form. Data sources include but are not limited to:
News articles
Textbooks
Journals
Transcribed documentaries
Transcribed news stories
Poems
Essays
Research field notes
Social media
Exemplifying Conceptual Content Analysis
In our theoretical study example, a team of researchers examines a series of Joe Biden's speeches about the economy during his 2020 election run.
The source materials are 30 transcribed speeches, which were coded for the existence of specific words. The goal was to analyze the sentiment of concepts (positive vs. negative) that now-President Biden used to describe his own economic plans as opposed to the sentiment used to describe the economy of the United States at the time the speeches were delivered.
For instance, “strong”, “growth”, and “stability” are concepts that suggest positive sentiment. Conversely, concepts like “weak”, “turmoil”, or “uncertainty” invoke negative sentiments.
Why did researchers use conceptual content analysis?
Researchers decided to use conceptual content analysis for three main reasons.
First, the study examined 30 speeches that lasted up to two hours in some cases. To keep the workload manageable, they were only interested in quantifying specific words—not in how they are related—as they would in latent content analysis or conventional content analysis.
Second, the team wanted to apply sentiment coding to identify the presence of positive versus negative words used in relation to proposed versus current economic plans in the US.
Third, the current political landscape in the US is an emotionally-charged minefield. Frequency counts inherently require quantitative, objective research elements. This allowed the team to offer surface-level analysis without evoking emotional responses in readers (or themselves).
The step-by-step approach to qualitative conceptual content analysis
Tl;dr version
First, you identify the research question and choose content samples for analysis. Next, you code the text into logical content concepts and categories. The entire process relies on a “kind of selective reduction”[1]. By filtering the text through these concepts, you streamline the coding process by only coding the specific words or patterns that inform the research question. Tabulate the quantitative data and explain how they help answer the research question.
The unabridged steps to qualitative conceptual content analysis
Continuing with our hypothetical study, here is the step-by-step process to conduct conceptual content analysis.
1. Determine the depth of analysis
First, the researchers determine the level of analysis to apply. They decide whether key concepts are a single word, phrase, or entire sentence. For instance, whether to code for single words, such as “weak”, or sets of words or phrases, like “economic downturn.”
In our speech example, the researchers decide to code for single words.
To clarify, the “level of analysis” determines the words, set of words, or phrases that represent a concept. According to Carley (1993), 100-500 concepts are generally used when coding for a specific topic but the number varies on a study-by-study basis.
2. Determine the number of concepts
Next, the researchers establish how many total concepts to code for. They decide whether the concepts are pre-defined or dynamic sets that can be updated during analysis. Basically, whether the sets of concepts to code for are flexible or inflexible.
Picking a pre-defined, inflexible number of concepts helps examine very specific things and keeps researchers focused. On the other hand, allowing a level of coding flexibility allows new material to be introduced that could have a notable impact on the results.
Additionally, the team in our hypothetical study choose whether to code for every single positive or negative word in the speeches, or only certain words that they collectively determine are most relevant to the topic of the economy.
They choose single words that denote positive or negative economic meaning. For instance, “strong”, “weak”, “uncertainty”, and “stability”. They may also have chosen phrases such as “equal pay”, “job creation” and “jobs overseas” or complete sentences.
3. Decide whether to code for existence or frequency of a concept.
Now, researchers decide whether to code for the presence or the frequency of the key concepts. This decision dictates the subsequent coding process.
If they decide to code for existence, they only count a concept like “threats” one time per speech—regardless if it appears once or 15 times. Coding for existence is a very basic coding process and provides a constrained perspective of texts.
When coding for frequency, you quantify the total number of times a concept appears in all of the source materials. Frequency provides deeper insight into the text by indicating the intensity of specific concepts.
4. Determine how to distinguish between concepts.
Researchers decide if they want the text coded exactly as it appears or if they wish to have it coded the same way even when it appears in a different form. For instance, “recession” and “recessionary”. The goal here is to create coding rules so that these word segments are transparently categorized and clearly understood by the reader.
At this point, researchers also have to choose the level of implication to allow in their code. Do you only want to code explicit use of the word or include concepts that imply the same meaning? For instance, “threatening” and “the plan is dangerous.” The more implication allowed, the more time-consuming the research process becomes.
Deeper, latent analytical implications are usually not the goal of conceptual analysis due to the sheer amount of textual data that is typically studied using this method.
5. Establish the coding rules.
After deciding how to approach the generalization of their concepts, researchers create clear coding rules. The coding rules act as a guardrail to keep the coding scheme organized and the researchers consistent throughout the coding process.
For instance, if one researcher coded "economically challenging" as a separate concept from "threats" in one speech, then coded it under the umbrella of "threats" when it occurred in the next, the data would be invalid—as well as any of the interpretations pulled from that data. Coding rules help avoid this from happening.
6. Code the text.
Next, researchers decide to code their data either by hand or with CAQDAS software such as Delve. The team chose Delve’s intuitive software as code frequency counts are instantaneous and easily searchable. The team decided this was the most efficient and organized tool to approach the copious amounts of text considered in their study.
7. Analyze the results.
Once the coding is done, the researchers examine the data and attempt to extract trends or patterns that may indicate larger ideas and themes.
During the examination, the team organizes concepts by frequency using code frequency tables and concept code matrices. These organizational tools make it easier to spot trends and connections between speeches. Tables clearly quantify the frequency counts for examining the intensity of specific concepts. The matrices show how frequently codes overlap—and how codes correlate to descriptors or attributes.
Returning to the example from step three, let’s say the concept "threats" appears 50 times in the frequency table, compared to 15 occurrences of "strong." With that information, the team logically extrapolates that there appears to be greater emphasis on the current weakness of the economy in 2020, as opposed to future prospects if Biden is elected.
While the largely quantitative nature of conceptual analysis does limit its focus, this method is very effective at extracting trends in this way, especially when analyzing large swaths of documents and text
The advantage of conceptual content analysis
Researchers used conceptual analysis to identify trends within the vast amounts of textual samples. Even though the team employed only a surface-level conceptual analysis, trends such as frequency indicated larger ideas within the texts. Tabulating the data and explaining it in the write-up makes it easy for the reader to understand the methodology and validity of the analysis.
The best CAQDAS software
The best software for content analysis is the Delve qualitative analysis tool.
How Delve works
After identifying your coding framework of choice, you can prepare your data and translate that framework into a Delve codebook. You can then easily import text files into Delve, and use intuitive coding features to apply your codebook to the text. The software is fully cloud-based and the convenient auto-save feature ensures your work—and time—is never lost.
Advanced coding options for frequency counts
Code frequency is a cornerstone element of conceptual content analysis. With Software such as Delve, not only are frequency counts instantaneous but the software also provides advanced code frequency through code co-occurrence matrices. As mentioned, the matrices visualize how frequently codes overlap, and how your codes correlate to descriptors or attributes.
Delve is cloud-based, collaborative, and easy to learn. It includes free tutorial videos, responsive customer support, and flexible payment options. It’s the #1 ranked Qualitative Data Analysis software. Start a free trial today.
As researchers ourselves, we understand the confusing complexity of most CAQDAS software. See why researchers like Lisa are switching to our easy-to-use, drag-and-drop coding software.
“Delve helped me to organize and code qualitative research for my doctoral dissertation. It was very easy to use and intuitive in its use.”
References
Content Analysis. (n.d.). https://www.publichealth.columbia.edu/research/population-health-methods/content-analysis
Carley, Kathleen. (1993). Coding Choices for Textual Analysis: A Comparison of Content Analysis and Map Analysis. Sociological Methodology. 23. 10.2307/271007.
Cite this blog post:
Delve, Ho, L., & Limpaecher, A. (2023c, January 25). What is Conceptual Content Analysis in Qualitative Research? Step-by-Step Guide. https://delvetool.com/blog/what-is-conceptual-qualitative-content-analysis-step-by-step-guide