Manifest Content Analysis vs. Latent Content Analysis in Qualitative Research
Qualitative content analysis is a widely practiced research methodology that highlights specific words or phrases from textual data in order to infer meaning about your research topic.
Researchers can use qualitative content analysis when they want to put these words and phrases—the frequency or prevalence is also considered—into context by analyzing how they are used within a text.[1] This allows you to generate a construct or concept within the text for substantiation or create a more organized structure for the text you are describing in your study (Kleinkeksel, Winston, Tawfik, Wyatt, 2020).
Rather than being a single method, two prototypical and subcategorical methods of qualitative content analysis are manifest content analysis and latent content analysis.
While there are several sub-categorical approaches to qualitative content analysis found in the formative literature—directed content analysis being one—this article keeps the focus on the manifest and latent approaches.
As you may have noticed an overlap in the initial stages of both qualitative content analysis and quantitative content analysis, we will first address the difference between these two methods before exploring manifest and latent qualitative content analysis.
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Qualitative Content Analysis vs. Quantitative Content Analysis
To understand the difference between latent and manifest content analysis, it is helpful to first understand the difference between qualitative and quantitative content analysis.
The clearest distinction between these two approaches is the intention behind your analysis (Kleinkeksel, et.al, 2020). That is to say that distinguishing between the two often comes down to the results you intend to report at the conclusion of your study.
For instance, a quantitative approach analyzes numerical data and intends to provide objective, numerical results—usually in the form of statistics, graphs, or charts.
A qualitative approach analyzes language with the intent to write a narrative summary of the content. The summary includes phenomenological descriptions—depicting lived experiences in order to gain deeper insights into how people understand those experiences—and allows you to organize large amounts of text into categories that reflect a shared meaning.[2]
To clarify these distinctions…
As an example, if you counted the frequency of the word “anxiety” within a news article about PTSD, quantitative content analysis may find "80% of people with PTSD state that their anxiety increases during the holidays."
In contrast, qualitative content analysis requires your interpretation and explanation of the prevalence (frequency) of anxiety in the text or by how this word is used within the context of the article—depending on whether you use a manifest or a latent content analysis approach.
What’s the difference between manifest content analysis and latent content analysis?
The first physical step in qualitative content analysis is data collection. However, even before data collection begins, you must decide whether your qualitative analysis will apply one of two theoretical approaches: a manifest content analysis or a latent content analysis.
In manifest content analysis, context is derived from the visible and literal meaning of the words—taken at face value. In latent content analysis, you apply a deeper, interpretive analysis that seeks to infer underlying meaning from the words or phrases you choose to analyze.
Continuing, both manifest and latent qualitative content analysis produce phenomenological descriptions that seek to explain people's lived experiences and the way they explain those experiences. But the approach to generating these descriptions differs.
Manifest content comes through a deductive, top-down research approach where there are usually preexisting studies or theories available on your research topic.
On the other hand, latent content typically results from an inductive, bottom-up analysis where you begin your study with no preconceived categories or theories. Generally, this is because preexisting theory or literature on the topic is sparse.
Keep in mind that what distinguishes both qualitative methods from their quantitative counterparts is that they go beyond word counts to infer meaning and generate interpretations of people’s lived experiences.
Manifest Content Analysis
In manifest content analysis, the researcher describes what the informants actually say, uses words themselves, and describes what’s on the surface of the text. In the PTSD example article, this would be the literal number of times “anxiety” appears within the text.
To expand, let’s say a source in the article states, “The holidays really trigger my anxiety.”
A manifest approach takes this statement literally and tallies one “unit of measurement” each time it appears. Similar to quantitative content analysis, the study considers the prevalence of anxiety as a unit of measurement.
What differs is that the intent of a quantitative study would be to provide a numerical, objective result as in the example in the previous section.
Conversely, qualitative manifest content analysis requires interpreting your frequency counts and organizing that data into categories that reflect a pattern or shared meaning.
For example, say that you drop your textual data into Delve for analysis, and find “anxiety” is mentioned 25 times. In most cases, social expectations are mentioned in the same instances. You can then infer meaning from this prevalence, such as: “For those with PTSD, anxiety levels are impacted by the increased social expectations of the holidays.”
Frequency counts like this are used in understanding a phenomenon to reflect face-level analysis. They also assume there is objective truth in the data that can be revealed with very little interpretation. This truth is observable both to readers and researchers without the need to discern intent or identify deeper meaning (Kleinkeksel, et.al., 2020).
Latent Content Analysis
In comparison, latent content analysis extends the manifest analysis to an interpretive level and unearths implicit meaning that is implied rather than stated literally. This is where you delve under the surface of the text and flesh out what the text—and people quoted within the text—are saying (Berg, 2001; Catanzaro, 1988; Downe-Walbolt, 1992).
Using a latent approach, you could explore why social expectations of the holidays trigger an increase in anxiety for those with PTSD, i.e. the implied meaning. For instance, your study may find a pattern in several texts that link the holidays to the hallmark symptoms of agoraphobia—a phobia when people don’t feel safe in public places, especially where crowds gather.
Upon further analysis, this pattern suggests that people with PTSD may often have undiagnosed agoraphobia. This group continually expresses fear of large gatherings or extended time away from home but never directly mentions this phobia. Your analysis would explore this phenomenon so that you can write a final narrative to explain the subject.
To summarize, manifest analyses are typically conducted in a way that you maintain distance from the objects of the study to report data at face value that are easily discerned. Latent analyses require you to co-create meaning with the text and objects of the study through deeper analysis.[3]
The Best Tool for Qualitative Content Analysis
Code frequency is elemental to your reporting and results in quantitative content analysis. Content analysis done with pen and paper or a word document requires manually counting frequencies from large amounts of text. This can often lead to coder fatigue—and coding errors.
With Delve’s qualitative content analysis tool, code counts are instantaneous and organized through the simple drag-and-drop feature. You can easily track how prevalent codes and categories are in your data and even create memos and notes to refer back to later.
By streamlining the most time-consuming part of the research process, you are able to commit more time to your analysis, reduce coder bias, and provide deeper insights for your study.
Advanced code frequency & co-occurrence matrices—with Delve
Delve also provides advanced code frequency through code co-occurrence matrices. These matrices show how frequently codes overlap, and how codes correlate to descriptors or attributes that you identify. With this information, you can pinpoint the overarching themes of your study.
Delve is cloud-based, collaborative, cost-effective, and easy to learn. It includes free tutorial videos, responsive customer support, and flexible payment options. Start your free trial today.
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References
Bengtsson, Mariette. (2016). How to plan and perform a qualitative study using content analysis. NursingPlus Open.
Lincoln YS, Guba EG. (1985). Naturalistic Inquiry. Beverly Hills, CA: SAGE.
Kleinheksel, A. J., Rockich-Winston, N., Tawfik, H., & Wyatt, T. R. (2020). Demystifying Content Analysis. American Journal of Pharmaceutical Education, 84(1).
Berg, B.L. (2001) Qualitative Research, Message for the Social Sciences. 4th Edition, Allin and Bacon, Boston.
M. Catanzaro. (1988). Using qualitative analytical techniques. N.F. Woods, M. Catanzaro (Eds.), Nursing: research theory and practice, The CV Mosby Company, St.Louis.
B. Downe-Wambolt. (1992). Content analysis: method, applications and issues. Health Care for Women International.
Cite this blog post:
Delve, Ho, L., & Limpaecher, A. (2022c, December 15). Qualitative Content Analysis: Manifest Content Analysis vs. Latent Content Analysis. https://delvetool.com/blog/manifest-content-analysis-latent-content-analysis