Analyzing Mayring Interviews: Qualitative Content Analysis Step-by-Step

Interviews provide nuances, justifications, and contradictions. To turn them into more than just a collection of interesting quotes, you need a transparent and logical process. Analyzing Mayring interviews means unlocking interview material using predefined rules, developing categories based on sound reasoning, and consistently aligning your interpretation with your research question.

This guide takes you from the cleaned-up interview transcript to the documented presentation of your results. You will learn when this method is appropriate, how to combine deductive and inductive category formation, and where AI can support the preparation process without replacing scientific decision-making.

If you want to structure recurring text analysis or document processes, a conversation about a custom-configured AI agent might be beneficial.

What does qualitative content analysis according to Mayring mean for interviews?

Qualitative content analysis according to Philipp Mayring is a rule-based method of qualitative research. It organizes communication material in a way that allows a research question to be answered systematically. When working with interviews, you don't simply work through the text section by section based on personal impressions. Instead, you define in advance which material will be examined, what you are looking for, and the rules for assigning statements to a category.

The procedure is suitable for semi-structured and open-ended interviews, as well as open-ended survey responses, documents, or other fixed communication. The decisive factor is not the medium itself, but a specific interest in gaining knowledge. For example, if you want to understand the hurdles employees describe regarding a new process, you can systematically extract recurring themes, differences, and justifications.

The research question guides every subsequent decision. It influences the selection of material, the direction of the analysis, the form of content analysis, and the depth of the categories. Therefore, formulate it precisely before you process your first interview. A helpful introduction to the basic forms and units is provided by the Method Center at Ruhr University Bochum.

When is Mayring's method suitable for interview analysis?

Suitable research questions and types of material

Analyzing Mayring interviews is particularly appropriate when you want to examine extensive text material in a structured way. It is well-suited for theses, evaluations, or internal studies involving multiple interviewees. The method helps to organize relevant statements in a comparable way without reducing the respondents' perspectives to mere metrics.

A deductive start is useful when theory, interview guides, or hypotheses already suggest clear themes. In an evaluation, "usage," "obstacles," and "suggestions for improvement" could serve as initial main categories. If the field is still relatively unexplored, you can develop categories from meaningful statements as you work through the material. In many projects, a combination is most effective: an initial framework provides orientation, while new aspects are allowed to emerge from the material.

Limitations: When other qualitative methods are a better fit

This method does not reliably answer purely quantitative questions. If you want to prove representative frequencies or statistical correlations, you need an appropriate quantitative design. Other approaches may also be more suitable for very in-depth case reconstructions or the analysis of linguistic nuances.

The strength of Mayring’s method lies in its transparent, content-focused structure. This makes it well-suited for comparing numerous statements against a clear research question. The limits of interpretation remain visible: a frequently coded category is not automatically the most important finding. Context, material selection, and theoretical framing remain crucial.

Analyzing Mayring interviews: The 6-step process

1. Define the research question, material, and unit of analysis

First, determine what you want to find out, which interviews are included, and the order in which you will process them. The unit of analysis describes which material is examined sequentially, such as all eight interviews conducted for an evaluation. Document your selection criteria, the data collection period, language, and any exclusions. This ensures that your findings remain traceable later on.

Practical example: In a survey about a new learning platform, you could analyze all interviews with active users while marking conversations from a pilot phase as separate material. This decision must be made before coding, not after.

2. Transcribe interviews and prepare the material

For Mayring interviews you need a reliable transcript. Determine a level of transcription that fits your research question. If you are focusing on themes and assessments, a readable transcript is often sufficient; if you are analyzing pauses, emphasis, or conversational dynamics, a more detailed transcription may be necessary. Anonymize personal information, secure consent forms, and store the material securely.

Clean up obvious technical errors without altering the meaning. Assign unique identifiers for each interview, page, or line. These will make it easier for you to find anchor examples, cross-reference, and provide evidence when presenting your results. You can also find tips on preparation under transcribing interviews (https://www.scribbr.de/methodik/qualitative-inhaltsanalyse/).

3. Determine the direction of analysis, coding unit, and context unit

Now, decide from which perspective you will read the material. Does your analysis examine the text content, the respondents' views, the target group, or the social background? This direction of analysis limits what counts as a relevant statement.

Also, define your coding units. The analysis and coding unit determines the smallest segment that can be coded, such as a sentence or a meaningful statement. The context unit determines the largest scope you will use for interpretation, such as the entire response to a guiding question. These rules prevent individual words from being taken out of context.

Practical example: If a person says, "The introduction was difficult at first, but helpful later on," the meaningful unit would not just be "difficult." The contrast within the response is part of the interpretation.

4. Develop a category system: deductive, inductive, or combined

In deductive category development, you derive categories from theory, your research question, or your interview guide. Each category requires a definition: what belongs in it and what does not? Add an anchor example and coding rules for borderline cases. This creates a coding guide that is more than just a list of headings—it makes your decisions verifiable.

In inductive category development, you work closely with the material. You reduce relevant statements, group similar content together, and gradually formulate categories from them. New categories may emerge, but they must be justified and documented. A clear presentation of definitions, anchor examples, and differentiation rules can be found at the University of Paderborn.

For Mayring interviews a combined approach is often practical. Create theoretically expected main categories and maintain a residual category for relevant statements that do not yet fit. After an initial pass through the material, check whether new subcategories emerge. Revise the coding guide in a traceable manner and re-code the affected passages.

Element in the coding guidePurposeExampleCategoryNames the aspect being examined“Hurdles to usage”DefinitionDefines the content of the categoryStatements about difficulties during applicationAnchor exampleShows a typical case“I didn't know where to start at first.”Coding ruleClarifies boundariesDo not code technical malfunctions as training hurdles

5. Coding interviews, revising categories, and synthesizing results

When coding, assign relevant text passages to your category system. A passage can be assigned multiple categories if it actually addresses several aspects. Stick to your rules instead of spontaneously reinterpreting every phrasing. If you are unsure, write a memo note: What is unclear, which rule is missing, and what decision was made?

A trial run with a portion of the material is valuable. It reveals overlaps, categories that are too broad, or missing boundaries. If you revise categories, document the time, reason, and consequences. If multiple people are coding, a joint comparison helps uncover different interpretations. Intercoder reliability is not an end in itself, but an opportunity to refine rules.

Next, synthesize the findings for each category. Look not only for majorities but also for deviant cases and conditions. For Mayring interviews a table containing the category, central statement, supporting evidence, and interpretation can help make the path to the result transparent.

6. Interpreting results and documenting transparently

Coding is not yet the answer to your research question. Only in the interpretation phase do you relate categories, contexts, and theoretical expectations to one another. Explain what the statements mean, which differences become visible, and what alternative interpretations are conceivable. Support important conclusions with appropriate, anonymized quotes or references to the source material.

Document your material selection, transcription rules, analytical direction, category system, adjustments, and limitations. The summarizing, explicating, and structuring content analysis each have different focuses: in summarizing, you reduce the material to the essentials; in explicating, you draw on context material for clarification; and in structuring, you filter based on predefined criteria.

What forms of qualitative content analysis according to Mayring are there?

Summarizing content analysis

This form reduces extensive material without losing the core content. You paraphrase relevant statements, generalize them to an appropriate level of abstraction, and bundle similar items. It is suitable when you want to gain a manageable overview of recurring content.

Explicating content analysis

Here, you clarify incomprehensible, ambiguous, or context-poor passages using additional material. This could be the further course of the conversation, a document, or professional background information. Use supplementary material in a controlled manner so that it explains the statement rather than replacing it unnoticed.

Structuring content analysis

In structuring content analysis, you examine the material against a predefined framework. It is particularly suitable when a guide, a theoretical model, or concrete evaluation criteria are available. A precise coding guide is indispensable here.

Quality criteria: How to make interview analysis transparent?

Transparency and intersubjective verifiability

Quality does not come from every interpretation being perfectly objective. It comes from others being able to follow how you arrived at your conclusions. Therefore, document your access to the material, the formation of categories, coding rules, any changes made, and your interpretation steps. Intersubjective verifiability means that your decisions regarding the material and the rules applied are open to discussion.

Reliability, validation, and a reflective approach to interpretation

Check whether your procedure is applied consistently and whether your categories truly capture the intended subject matter. Second coding, peer discussions, or feedback from participants can strengthen this verification. Triangulation—combining different data sources or methods—can also provide additional perspectives.

Reflexivity is also essential: identify your own preconceptions, the quality and scope of the material, and any open questions of interpretation. Especially with Mayring interviews , a clean coding guide is more important than a seemingly definitive interpretation.

Using AI to prepare for interview analysis

A transparent workflow for structured preliminary work

AI can perform structured preliminary work when dealing with large volumes of approved transcripts. However, it cannot establish scientific validity or decide which interpretation is academically sound. A sensible workflow would look like this:

1. You provide only approved, prepared transcripts and the research question.
2. You define the unit of analysis, the coding unit, and the context unit.
3. You provide an initial deductive coding guide with definitions and rules.
4. The agent generates transparent assignment suggestions for text passages.
5. Unclear or contradictory cases are flagged and escalated to researchers.
6. You review the suggestions, maintain the categories, and test changes to the routine.

A platform for AI agents like SCALAN can support this process when recurring preliminary work needs to be carried out in a structured manner. A user describes the desired workflow, provides the relevant knowledge, and defines permissible actions, allowing the AI agent to automatically handle the preliminary work and coding.

What AI does not decide

AI does not replace the choice of method or the interpretation of borderline cases. You remain responsible for data protection checks, rights to the material, consent, quality criteria, and the professional quality of the category system. Furthermore, check every suggestion in the context of the entire interview. A plausible-sounding assignment is not yet a methodologically sound result.

In this scenario, SCALAN is not an analysis method, but an operating platform for individual AI agents. It can take a lot of work off your hands by making it easy to deploy AI agents for tasks like these. Whether and how you use them depends on your material, data protection requirements, and your research design.

Frequently asked questions about analyzing Mayring interviews

How many interviews are needed for a qualitative content analysis according to Mayring?

There is no fixed minimum number. The decisive factors are the research question, the heterogeneity of the field, the depth of the material, and the justification for your selection. Describe transparently why your sample fits the question and what limitations result from it.

Can I adjust categories during the analysis?

Yes, especially with inductive or combined approaches. However, changes should not be made silently. Document what was adjusted, why it was necessary, and whether material that had already been processed was reviewed again.

What is the difference between inductive and deductive category formation?

Deductive categories are primarily derived from theory, hypotheses, or the data collection instrument. Inductive categories emerge step-by-step from the material. Both approaches can be combined if the rules and transitions are clearly documented.

Which software or tools are suitable for coding?

For small amounts of data, spreadsheets, word processing, or paper material may suffice. For more extensive projects, programs like MAXQDA are helpful. However, the software does not make methodological decisions. It supports your work on the category system.

Mayring interviews are analyzed convincingly when the research question, material, categories, and interpretation align. If you want to organize the preparation of recurring text analyses with clear rules, tests, and human oversight, test SCALAN and build your own AI agent or schedule a consultation.