emotion coding qualitative research

The Coding Manual for Qualitative Researchers

Student resources.

Welcome to the companion website for The Coding Manual for Qualitative Research , third edition, by Johnny Saldaña.  This website offers a wealth of additional resources to support students and lecturers including:

CAQDAS links giving guidance and links to a variety of qualitative data analysis software.

Code lists including data extracted from the author’s study, “Lifelong Learning Impact: Adult Perceptions of Their High School Speech and/or Theatre Participation” (McCammon, Saldaña, Hines, & Omasta, 2012), which you can download and make your own practice manipulations to the data.

Coding examples from SAGE journals providing actual examples of coding at work, giving you insight into coding procedures.

Three sample interview transcripts that allow you to test your coding skills.

Group exercises for small and large groups encourage you to get to grips with basic principles of coding, partner development, categorization and qualitative data analysis

Flashcard glossary of terms enables you to test your knowledge of the terminology commonly used in qualitative research and coding.

About the book

Johnny Saldaña’s unique and invaluable manual demystifies the qualitative coding process with a comprehensive assessment of different coding types, examples and exercises. The ideal reference for students, teachers, and practitioners of qualitative inquiry, it is essential reading across the social sciences and neatly guides you through the multiple approaches available for coding qualitative data.

Its wide array of strategies, from the more straightforward to the more complex, is skilfully explained and carefully exemplified, providing a complete toolkit of codes and skills that can be applied to any research project. For each code Saldaña provides information about the method's origin, gives a detailed description of the method, demonstrates its practical applications, and sets out a clearly illustrated example with analytic follow up. 

This international bestseller is an extremely usable, robust manual and is a must-have resource for qualitative researchers at all levels.

This website may contain links to both internal and external websites. All links included were active at the time the website was launched. SAGE does not operate these external websites and does not necessarily endorse the views expressed within them. SAGE cannot take responsibility for the changing content or nature of linked sites, as these sites are outside of our control and subject to change without our knowledge. If you do find an inactive link to an external website, please try to locate that website by using a search engine. SAGE will endeavour to update inactive or broken links when possible. 

Chapter 19. Advanced Codes and Coding

Introduction: forest and trees.

Chapter 17 introduced you to content analysis, a particular way of analyzing historical artifacts, media, and other such “content” for its communicative aspects. Chapter 18 introduced you to the more general process of data analysis for qualitative research, how you would go about beginning to organize, simplify, and code interview transcripts and fieldnotes. This chapter takes you a bit deeper into the specifics of codes and how to use them, particularly the later stages of coding, in which our codes are refined, simplified, combined, and organized for the purpose of identifying what it all means , theoretically. These later rounds of coding are essential to getting the most out of the data we’ve collected. By the end of the chapter, you should understand how “findings” are actually found.

I am going to use a particular analogy throughout this chapter, that of the relationship between the forest and trees. You know the saying “You can’t see the forest for the trees”? Think about what this actually means. One is so focused on individual trees that one neglects to notice the overall system of which the trees are a part. This is something beginning researchers do all the time, and the laborious process of coding can make this tendency worse. You focus on the details of your codes but forget that they are merely the first step in the analysis process, that after you have tagged your trees, you need to step back and look at the big picture that is the entire forest. Keep this metaphor in mind. We will come back to it a few times.

Let’s imagine you have interviewed fifty college students about their experiences during the pandemic, both as students and as workers. Each of these interviews has been transcribed and runs to about 35 pages, double-spaced. That is 1,750 pages of data you will need to code before you can properly begin to make sense of it all. Taking a sample of the interviews for a first round of coding (see chapter 17), you are likely to first note things that are common to the interviews. A general feeling of fear, anxiety, or frustration may jump out at you. There is something about the human brain that is primed to look for “the one common story” at the outset. Often, we are wrong about this. The process of coding and recoding and memoing will often show us that our initial takes on “what the data say” are seriously misleading for a couple of reasons: first, because voices or stories that counter the predominant theme are often ignored in the first round, and, second, because what startles us or surprises us can drive away the more mundane findings that actually are at the heart of what the data are saying. If we have experienced the pandemic with little anxiety, seeing anxiety in the interviews will surprise us and make us overstate its importance in general. If we expect to find something and we see something very different, we tend to overnotice that difference. This is basic psychology, I am sure.

This is where coding comes in to help you verify, amplify, complicate, or delimit your initial first impressions. Coding is a rigorous process because it helps us move away from preconceptions and other judgment errors and pin down what is actually present in the data. It helps you identify the trees, which is actually important before we can properly see the forest. We start with “It’s a forest” (not really that helpful), then move to “These are specific trees, with particular roots and branches,” and finally move back to a better understanding of the forest (“It’s a boreal forest that works like this…”). Coding is the rigorous connecting process between the first (often wrong or incomplete) impression and the final interpretation, the “results” of the study (figure 19.1). If you remember that this is the point of coding, you will be less likely to get lost in the woods. Coding is not about tagging every possible root and branch of every tree to create some kind of master compendium of forest particulars. Coding is about learning how to identify what is important about that forest overall. [1] When you are new to the forest, you won’t know which root or branch is of importance, but as you walk through it again and again, you will learn to appreciate its rhythms and know what to pick up as important and what to discard as irrelevant.

There is no single correct way to go about coding your data. When I first began teaching qualitative research methods, I resolutely refused to “teach” coding, as I thought it was a little like trying to teach people to write fiction. It’s very personal and best developed through practice. But I have come to see the value of providing some guidelines—maps through the forest, if you will. I have drawn heavily here from Johnny Saldaña’s extensive and beautiful “coding manual,” but the particular suggestions here are what have worked best for me. We are going to walk through the forest many times, first in an open exploratory way and then in a more focused way once we have found our stride. Finally, we will sit down with all of our maps and materials and see what it is we can discover about the world by looking at our data.

First Walks in the Woods: Open Coding

Saldaña ( 2014 ) provides dozens of types of codes and coding processes, but we are going to confine our discussion two five. These are the five kinds of codes that I think work best for beginning researchers in your first walks through the woods. Used together, they have the potential to get at the heart of what is important in social science research. They are descriptive , i n vivo , process , values , and emotions . Select a sample of your data in the first round of coding. If you tried to tag everything in these initial rounds, you will never get out of the woods. Your sample should be broad enough to capture essential aspects of your data corpus but small enough to allow you free rein to pick up as many branches as you think interesting. Set aside a significant amount of time for this. And then double or triple that time allotment. You’ll need it.

Descriptive codes are codes used to tag specific activities, places, and things that seem to be important in particular passages. They are identifying tags (“This is a branch from an elm tree”; “This is an acorn”). Be careful here because you can really end up trying to identify everything—every word, every line, every passage. Don’t do that! It’s helpful to remind yourself what your research is about—what is your research question or focus? Some twigs can stay on the forest floor. Saldaña’s ( 2014 ) use of the term is narrower. Descriptive codes are meant to summarize the basic topic of a passage in a single word or short phrase, what is also called “topic coding” or “index coding.” These descriptive codes will allow you to easily search for and return to passages about a particular topic or feature of the forest; this will allow you to make better comparisons in later rounds of analysis. The actual word or phrase you come up with will be rather personal to you and dependent on the focus of your research. Here is an exemplary passage from a fictitious interview with a working-class college student: “I had no idea what scholarships were available! No one in my family had ever gone to college before, so there was no one I could ask. And my high school counselor was always too busy. What a joke! Plus, I was a little embarrassed, to be honest. So, yeah, I owe a lot of money. It’s really not that fair.”

What descriptive codes can be developed here? How would you define the topic or topics of this passage? On the one hand, the subject appears to be scholarships or how this student paid for college. “How Pay” might be a good descriptive code for the entire passage. But there are a lot of other interesting things going on here too. If your focus is on how peer groups work or social networks, you might focus on those aspects of the passage. Perhaps “No Assistance” could work as a descriptive code in this first round of coding. Descriptive codes are pretty straightforward, so they are easy for beginning researchers to use, but “they may not enable more complex and theoretical analyses as the study progresses, particularly with interview transcript data” ( 137 ).

In vivo codes are codes that use the actual words people have used to tag an important point or message. In the above passage, “no one I could ask” might be such a code. These indigenous terms or phrases are particularly useful when seeking to “honor or prioritize” the voice of the participants ( Saldaña 2014:138 ). They don’t require you to impose your own sense on a passage. They are also rather enjoyable to generate, as they encourage you to step into the shoes of those you have interviewed or observed. The terms or phrases should jump out at you as something salient to your research question or focus (or simply jump out at you in surprising ways that you hadn’t expected, given your research question).

Process codes are codes that label conceptual actions. This is another way to describe the data, but rather than focus on the topic, we organize it around key actions and activities. For example, we could tag the passage above with “asking for help.” By convention, process codes are gerunds , those strange verb forms that end in -ing and operate a bit like nouns. Process codes are particularly helpful for studies that focus on change and development over time, as the use of tagged gerunds can really highlight stages, if such exist. Grounded theorists often employ process codes for this reason. I find it useful, as it reminds me to focus not only on what participants say and how they say it but on the activities that they are engaged in.

Values codes are codes that reflect the attitudes, beliefs, or values held by a participant. Values codes capture things such as principles, moral codes and situational norms (“values”), the way we think about ourselves and others (“attitudes”), and all of our personal knowledge, experience, opinions, assumptions, biases, prejudices, morals, and other interpretive perceptions of the world (“beliefs”). They are extremely powerful tags and absolutely essential for phenomenological researchers. We might attach the values code “unfair” to the passage above or even note the “What a joke!” passage as disbelief or disgust.

Values codes are a particular subset of affective coding , where codes are developed to “investigate subjective qualities of human experience (e.g., emotions, values, conflicts, judgments) by directly acknowledging and naming those experiences” ( Saldaña 2014:159 ). The fifth suggested code is also another form of affective coding, emotions codes , labels of feelings shared by the participants. “Embarrassment” is an obvious emotion code in the above passage. In the kinds of research I mostly do, phenomenological and interview based, often about sensitive subjects around discrimination, power, and marginalization, coding emotions is incredibly helpful and productive: “Emotion coding is appropriate for virtually all qualitative studies, but particularly for those that explore intrapersonal or interpersonal participant experiences and actions, especially in matters of identity, social relationships, reasoning, decision-making, judgment, and risk-taking” ( 160 ).

A Final Purposeful Hike through the Forest: Closed Coding

After initial rounds of coding (several walks through the woods), you should begin to see important themes emerge from your data and have a general idea of what is important enough to look at more closely. Between first-cycle coding and your last hike through the forest, you will have created a list of codes or even a codebook that records these emergent categories and themes (see chapter 18). It is quite possible your research question(s) or focus has shifted based on what you have seen in the first rounds of coding. [2] If you need more data collection based on these shifts, collect more data. Once you feel comfortable that you have reached saturation and know what it is you are looking at and for, you are ready for one final purposeful hike through your forest to tag (code) all your data using a pared-down set of codes.

Building Meaning, Identifying Patterns, Comparing Trees, and Seeing Forests

The final cycle of coding is also the time to generate analyses of your data. As with so much qualitative research, this is not a linear process (finish stage A and move to stage B followed by stage C). To some extent, analysis is happening all the time, even when you are in the field. Journaling, reflecting, and writing analytical memos are important in all stages of coding. But it is in the final stages of coding that you truly start to put everything together—that’s when you start understanding the nature of the forest you have been walking through. That, after all, is the point. What do all these codes of various people’s actions (fieldnotes) or people’s words (interviews) tell you about the larger phenomenon of interest? This will require mapping your codes across your data set, comparing and contrasting themes and patterns often relative to demographic factors, and overall trying to “see” the forest instead of the trees.

Different researchers employ various tools and methods to do this. Some draw pictures or concept maps, seeking to understand the connections between the themes that have emerged. Others spend time counting code frequencies or drawing elaborate outlines of codes and reworking these in search of general patterns and structure. Some even use in vivo codes to generate found poems that might provide insight into the deeper meanings and connections of the data. Mapping word clouds is a similar process. As a sociologist who is interested in issues of identity, my go-to method is to look for interactions between the codes, noting demographic elements of comparison. For example, in the very first study I conducted ( Hurst 2010a ), I used emotion codes. Specifically, I found numerous examples of sadness, anger, shame, embarrassment, pride, resentment, and fear. With the exception of pride, these are not very positive emotions. I could have stopped there, with the finding of overwhelming instances of negative emotions in the stories told by working-class college students. But I played around with these categories, clustering them by incidence and frequency and then comparing these across demographic categories (age, race, gender). I found no race or gender differences and only a hint of a difference between traditional-age college students and older students. What I did find, however, was that the emotions sorted themselves out in clusters relative to other codes. Embarrassment, shame, resentment, and fear were often found together in the same interview, along with a pattern of using “they” to refer to working-class people like the interviewees’ families. Conversely, anger, sadness, and pride were often found together, along with a pattern of using “we” to refer to working-class people. This led me to develop a theory about how working-class students manage their class identities in college, with some desirous of becoming middle class (“Renegades”) and others wanting very strongly to remain identified as working class (“Loyalists”; Hurst 2010a ).

Saldaña ( 2014 ) summarizes many of these techniques. He draws a distinction between "code mapping" and “ code landscaping .” Code mapping is a systematic and rigorous reordering of all codes into an increasingly simplified hierarchical organization. One can move from fifty or so specific stand-alone codes of various types (e.g., sadness, “I was so alone,” socializing, financial aid) and attempt to impose some meaningful order on them by clustering like phenomena with like phenomena. Perhaps sadness (an emotion code), “I was so alone” (an in vivo code), and socializing (an action code) are understood as belonging together, perhaps under a category of SOCIAL CONNECTIONS or, depending on what has emerged from your data, EXCLUSION. Code mapping is an iterative process, meaning that you can do a second or a third take of simplification and reordering. In the end, you might be left with one or two big conceptual themes or patterns.

Code landscaping “integrates textual and visual methods to see both the forest and trees” ( Saldaña 2014:285 ). Using computer-assisted word cloud mapping (WordItOut.com, wordclouds.com, wordle.net) is one way of doing this, or at least a way to jump-start the process. Word clouds quickly allow you to see what stands out in the interview or fieldnotes and can suggest relationships of importance between codes. Manually, one can also diagram the codes in terms of relationship, stressing the processual elements (what leads to what: “I felt so alone” >> sadness).

Another helpful suggestion is to chart the incidence of codes across your data set. This is particularly helpful with interview data. What (simplified) codes emerge in each interview transcript? Is there a pattern here? The two categories of Loyalist and Renegade would not have emerged had I not made these kinds of code comparisons by person interviewed. You might create a master document or spreadsheet that places each interview subject on its own row, with a brief description of that person’s story (what emerges as the focus of the interview or who they are in terms of social location, character, etc.) in a separate column and then a third column listing the key codes found in the interview. This is a good way to “see” the forest in a snapshot.

Whatever method or technique is employed, the general direction is to move from simple tags (codes) to categories to themes/concepts (figure 19.2). Eventually, those identified themes/concepts will help you build a new theory or at a minimum produce relevant theoretically informed findings, as in the second example at the end of this chapter.

Grounded Theory has its own vocabulary when it comes to coding and data analysis, so if you are trying to do a “proper” Grounded Theory study, you might want to read up on this in more detail ( Charmaz 2014 ; Strauss 1987 ; Strauss and Corbin 2015 ). A quick summary of the approach follows. First-cycle coding employs the following kinds of codes: in vivo , process, and initial. Second-cycle coding employs focused , axial , and theoretical codes. The names of these second-cycle codes are meant to evoke the Grounded Theory approach itself: in the second cycle, the grounded theorists focus the study on axes of importance to generate theories. Focused coding pulls out the most frequent or significant codes from the first round. Axial coding reassembles data around a category, or axis. These categories or axes are meant to be concept generating: “Categories should not be so abstract as to lose their sensitizing aspect, but yet must be abstract enough to make [the emerging] theory a general guide” ( Glaser and Strauss 1967:242 ). Theoretical codes “function like umbrellas that cover and account for all other codes and categories” ( Saldaña 2014:314 ). Key words or key phrases (e.g., “Exclusion” or “Always Crying”) capture the emergent theory in the theoretical code.

Describing and Explaining the Forest: Findings and Theories

It is only now, after the laborious process of coding is complete, that you can actually move on to generate and present findings about your data. Many beginning researchers attempt to skip the middle work and get straight to writing, only to find that what they say about the data is pretty thin. The quality of qualitative research comes from the entire analytical process: open and closed coding, writing analytical memos, identifying patterns, making comparisons, and searching for order in the voluminous transcripts and fieldnotes.

But let’s say that you have followed all the steps so far. You have done multiple rounds of coding—refining, simplifying, and ordering your codes. You’ve looked for patterns. You think you have seen some master concepts emerge, and you have a good idea of what the important themes and stories are in your data. How do you begin to explain and describe those themes and stories and theories to an audience? Chapter 20 will go into further detail on how to present your work (e.g., formats, length, audience, etc.), but before we get to that, we need to talk about the stage after coding but before writing. You will want to be clear in your mind that you have the story right, that you have not missed anything of importance, and that you have searched for disconfirming evidence and not found it (if you have, you have to go back to the data and start again on a new track).

Begin with your research question(s), either as originally asked or as reformulated. What is your answer to these questions? How have your underlying goals (see chapter 4) been addressed or achieved by these answers? In other words, what is the outcome of your study? Is it about describing a culture, raising awareness of a problem, finding solutions, or delineating strategies employed by participants? Perhaps you have taken a critical approach, and your outcome is all about “giving voice” to those whose voices are often unheard. In that case, your findings will be participant driven, and your challenge will be to present passages (direct quotes) that exemplify the most salient themes found in your data. On the other hand, if you have engaged in an ethnographic study, your findings may be thick, theoretically informed descriptions of the culture under study. Your challenge there will be writing evocatively. Or to take a final example, perhaps you undertook a mixed methods study to find the best way to improve a program or policy. Your findings should be such that suggest particular recommendations. Note that in none of these cases are you presenting your codes as your findings! The coding process merely helps you find what is important to say about the case based on your research questions and underlying aims and goals.

The gold star of qualitative research presentation is the formulation of theory. Even for those not following the Grounded Theory tradition, finding something to say that goes beyond the particulars of your case is an important part of doing social science research. Remember, social science is generally not idiographic. A “theory” need not be earth shattering, as in the case of Freud’s theory of Ego, Id, and Superego. A theory is simply an explanation of something general. [3] It is a story we tell about how the world works. Theories are provisional. They can never be proven (although they can be disproven). My description of Loyalists and Renegades is a theory about how college students from the working class manage the problem of class identity when their class backgrounds no longer match their class destinations. While qualitative research is not statistically generalizable , it is and should be theoretically generalizable in this way. Loyalists and Renegades are strategies that I believe occur generally among those who are experiencing upward social mobility; they are not confined solely to the twenty-one students I interviewed in 2005 in a college in the Pacific Northwest.

What is the story your research results are telling about the world? That is the ultimate question to ask yourself as you conclude your data analysis and begin to think about writing up your results.

Further Readings

Note: Please see chapter 18 for further reading on coding generally.

Charmaz, Kathy 2014. Constructing Grounded Theory . 2nd ed. Thousand Oaks, CA: SAGE. Although this is a general textbook on conducting all stages of Grounded Theory research, a significant portion is directed at the coding process.

Strauss, Anselm. 1987. Qualitative Analysis for Social Scientists . Cambridge: Cambridge University Press. An essential reading on coding Grounded Theory for advanced students, written by one of the originators of the Grounded Theory approach. Not an easy read.

Strauss, Anselm, and Juliet Corbin. 2015. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory . 4th ed. Thousand Oaks, CA: SAGE. A good basic textbook for those exploring Grounded Theory. Accessible to undergraduates and graduate students

• A small aside here on social science in general and sociology in particular: It is often believed that sociologists are concerned about “people” and what people do and believe. Actually, people are our trees. We are really interested in the forest, or society. We try to understand society by listening to and observing the people who compose it. Behavioral science, in contrast, does take the individual as the object of study. ↵
• It might be helpful to read the first example of writings about qualitative data analysis in the "Further Readings" section. ↵
• Saldaña ( 2014 ) lists five essential characteristics of a social science theory: “(1) expresses a patterned relationship between two or more concepts; (2) predicts and controls action through if-then logic; (3) accounts for parameters of or variation in the empirical observations; (4) explains how and/or why something happens by stating its cause(s); and (5) provides insights and guidance for improving social life” ( 349 ). ↵

A form of first-cycle coding in which codes are developed to “investigate subjective qualities of human experience (e.g., emotions, values, conflicts, judgments) by directly acknowledging and naming those experiences” (Saldaña 2021:159).  See also emotions coding and values coding .

A technique of second-cycle coding in which codes developed in the first rounds of coding are restructured into an increasingly simplified hierarchical organization, thereby allowing the general patterns and underlying structure of the field data to emerge more clearly.

A technique of second-cycle coding that “integrates textual and visual methods to see both the forest and trees" (Saldaña 2021:285).

A first-cycle coding process in which terms or phrases used by the participants become the code applied to a particular passage.  It is also known as “verbatim coding,” “indigenous coding,” “natural coding,” “emic coding,” and “inductive coding,” depending on the tradition of inquiry of the researcher.  It is common in Grounded Theory approaches and has even given its name to one of the primary CAQDAS programs (“NVivo”).

A later stage coding process used in Grounded Theory that pulls out the most frequent or significant codes from initial coding .

A later stage coding process used in Grounded Theory in which data is reassembled around a category, or axis.

A later stage-coding process used in Grounded Theory in which key words or key phrases capture the emergent theory.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Research, Digital, UX and a PhD.

A Guide to Coding Qualitative Data

Published September 18, 2014 by Salma Patel

Coding qualitative data can be a daunting task, especially for the first timer. Below are my notes, which is  a useful summary on coding qualitative data (please note, most of the text has been taken directly from The Coding Manual for Qualitative Researchers  by Johnny Saldana ).

Background to Coding

A coding pattern can be characterised by:

• similarity (things happen the same way)
• difference (they happen in predictably different ways)
• frequency (they happen often or seldom)
• sequence (they happen in a certain order)
• correspondence (they happen in relation to other activities or events)
• causation (one appears to cause another)

A theme is an outcome of coding

Questions to consider when you are coding:

• what are people doing? What are they trying to accomplish?
• How, exactly, do they do this? What specific means and/or strategies do they use?
• How do members talk about, characterise, and understand what is going on?
• what assumptions are they making?
• what do I see going on here?
• what did I learn from these notes?
• why did I include them?
• what surprised me? (To track your assumptions)
• what intrigued me? (To track your positionality)
• what disturbed me? (To track the tensions within your value, attitude, and belief systems)

Writing Analytic Memos

Gordon-Finlayson (2010) emphasises that “coding is simply a structure on which reflection (via memo writing) happens. It is memo-writing that is the engine go grounded theory, not coding”. Glazer and Holton (2004) further clarify that “Memos present hypotheses about connections between categories and/or their properties and begin to integrate these connections with clusters of other categories to generate the theory”.

The coding cycles

Depending on the qualitative coding method(s) you employ, the choice may have numerical conversion and transformation possibilities for basic descriptive statistics for mixed method studies.

First Cycle Coding

 1. Grammatical Methods include

• attribute coding (essential information about the data and demographic characteristics of the participants for future management and reference)
• magnitude coding (applies alphanumeric or symbolic codes to data, to describe their variable characteristics such as intensity or frequency, example, Strongly (STR) Moderately (MOD) No opinions (NO). They can be qualitative, quantitative and/or nominal indicators to enhance description, and it’s a way of quantitizing and qualitizing data
• sub coding and simultaneous coding.

2. Elemental methods are primary approaches to data analysis. They include:

• structural coding is a question-based code that acts as a labelling and index device, allowing researchers to quickly access data likely to be relevant to a particular analysis from a larger data set. It’s used as a categorisation technique for further qualitative data analysis.
• descriptive coding summarises in a word or noun the basic topic of a passage of qualitative data.
• In Vivo Coding refers to coding with a word or short phrase from the actual language found in the qualitative data record.
• Process coding uses gerunds (“-ing” words) exclusively to connote action in the data.
• Initial Coding is breaking down qualitative data into discrete parts, closely examining them, and comparing them for similarities and differences.

3. Affective methods investigate subjective qualities of human experience (eg emotions, values, conflicts, judgements) by directly acknowledging and naming those experiences. They include:

• Emotion coding labels the emotion recalled or experienced
• Values coding assess a participant’s integrated value, attitude, and belief systems. (side note: Questionnaires and surveys such as Likert scales and semantic differentials, are designed to collect and measure a participant’s values, attitudes, and beliefs about selected subjects).
• Versus Coding acknowledges that humans are frequently in conflict, and the codes identify which individuals, groups, or systems are struggling for power.
• Evaluation Coding focuses on how we can analyse data that judge the merit and worth of programs and policies.

4. Literary and Language Methods are a contemporary approach to the analysis of Oral communication. They include Dramaturgical Coding, Motif Coding, Narrative coding and Verbal Exchange Coding, and all explore underlying sociological, psychological and cultural constructs.

5. Exploratory Methods are preliminary assignment of codes to the data, after which the researcher might proceed to more specific First Cycle or Second Cycle coding methods.

• Holistic Coding applies a single code to each large unit of data in the corpus to capture a sense of the overall contents and the possible categories that may develop.
• Provisional Coding begins with a “start list” of researcher- generated codes based on what preparatory investigation suggest might appear in the data before they are analysed.
• Hypothesis Coding applies researcher-developed “hunches” of what might occur in the data before or after they have been initially analysed.

6. Procedural Methods consist of pre- established systems or very specific ways of analysing qualitative data. They include:

• Protocol Coding is coding data according to a pre-established, recommended, standardised or prescribed system.
• OCM (Outline of Cultural Materials) Coding is a systematic coding system for ethnographic studies.
• Domain and Taxonomic Coding is an ethnographic method for discovering the cultural knowledge people use to organise their behaviours and interpret their experiences.
• Causation coding is to locate, extract, and/or infer causal beliefs from qualitative data.

Code Mapping and Landscaping

Code Mapping is categorising and organising the codes, and code landscaping is presenting these codes in a visual manner, for example by using a Wordle graphic.

Operational Model Diagramming can be used to map or diagram the emergent sequences or networks of your codes and categories related to your study in a sophisticated way.

Second Cycle Coding

Second cycle coding is reorganising and condensing the vast array of initial analytic details into a “main dish”. They include:

1. Pattern coding is a way of grouping summaries into a smaller number of sets, themes, or constructs.

2. Focused coding searches for the most frequent or significant codes. It categorises coded data based on thematic or conceptual similarity

3. Axial coding describes a category’s properties and dimensions and explores how the categories and subcategories relate to each other.

4. Theoretical coding progresses towards discovering the central or core category that identifies the primary theme of the research

5. Elaborative coding builds on a previous study’s codes, categories, and themes while a current and related study is underway. This method employs additional qualitative data to support or modify the researcher’s observations developed in an earlier project.

6. Longitudinal coding is the attribution of selected change processes to qualitative data collected and compared across time.

After Second Cycle Coding

Code weaving  is the actual integration of key code words and phrases into narrative form to see how the puzzle pieces for together. Codeweave the primary codes, categories, themes, and/or concepts of your analysis into as few sentences as possible. Try writing several variations to investigate how the items might interrelate, suggest causation, indicate a process, or work holistically to create a broader theme. Search for evidence in the data that supports your summary statements, and/or disconfirming evidence that suggests revision of your statements.

From Coding to Theorising

A social science theory has three main characteristics: it predicts and controls action through an if-then logic; explains how and/or why something happens by stating it’s cause(s); and provides insights and guidance for improving social life.

The stage at which I seem to find a theory emerging in my mind is when I create categories of categories.

Use categories and analytic memos as sources of theory.

If I cannot develop a theory, then I will be satisfied with my construction of a key assertion, a summative and data supported statement about the particulars of a research study, rather than generalisable and transferable meanings of my findings to other settings and contexts.

Findings at a glance can be presented as follows:

The coding journey should be noted in the analytical memos and discussed in your dissertation.

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Published in Headline Qualitative Research Research Methods

• coding data
• coding qualitative data
• qualitative research
• research design

10 Comments

Very helpful article. Can you please list the references you mentioned in the article? which book are you refering to explain the coding types?

Thanks. Which specific reference would you like? I can look it up in the book.

Same question. You show a number of books in the various images, none of which look familiar. Could you list/cite those resources? That would be most helpful

The reference is mentioned at the top of the article. It links to the book these images are all from. See here: https://www.amazon.co.uk/gp/product/1446247376 (Sadana, 2012)

Best wishes, Salma

I also got a a question regarding the second cycle coding. Is it possible to use multiple coding forms? For example, can I code my interviews by using pattern coding, focused coding and axial coding? Or would I have to decide on one?

Yes Lara, you can use multiple coding forms. Just keep a note of it for your write up.

Thank you so much, Salma Patel, for taking the time to lay this critical and complex process out with all the illustrations. It’s so helpful to me. May your work and life continue to flourish.

thank you so much Ms. Patel

great article. thanks a lot

Great article. Very helpful! Can you point us to any examples of how researchers have coded data using some of these techniques? It would be helpful to see this in action, especially in helping understand how these different aspects of coding sit side by side in the analytical process. Thanks again!

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What is Emotional Coding in Qualitative Research?

Table of Contents

Emotional coding in qualitative research: an essential guide.

Emotional coding in qualitative research is the process of identifying and categorizing emotions expressed in the data to better understand participants’ experiences. This method emphasizes the significance of emotions in shaping responses and interactions, which can provide deeper insights into the research subject. By focusing on the emotional content of the data, researchers can uncover patterns and meanings that might be overlooked with traditional coding methods.

Qualitative research relies heavily on the interpretation of text data, such as interview transcripts or open-ended survey responses. Emotional coding enhances this interpretation by revealing the underlying emotional landscape, which can be crucial for fields that study human behavior and interactions. For instance, emotional coding can help in understanding how participants feel about certain topics, which can highlight essential themes relevant to the study.

When done effectively, emotional coding adds a valuable layer of depth to qualitative analysis. Researchers can create a more nuanced and comprehensive understanding of their data, leading to findings that accurately reflect the complexity of human emotions and experiences. Engaging with these emotional aspects can ultimately lead to more impactful and empathetic research outcomes.

Foundations of Emotional Coding

Emotional coding is a key method in qualitative research that helps researchers understand human emotions in data. This section explores its definition and historical development.

Definition of Emotional Coding

Emotional coding is a method used to identify and categorize emotions expressed by participants in qualitative research. This process involves labeling segments of data—such as interview transcripts or field notes—with terms that describe emotional experiences. The method is versatile and can be integrated with various qualitative approaches including grounded theory and ethnography.

According to Saldaña, emotional coding is useful for studying interpersonal experiences and actions. Emotions like joy, anger, sadness, and fear are commonly coded. This practice allows researchers to uncover deeper insights into participants’ emotional states and reactions, providing a richer understanding of the data.

History and Evolution

The concept of using emotions as data is not new. It has roots in early psychological studies where researchers like Ekman asserted that emotion is a universal human feature. Over the years, the methodology has evolved to include more systematic approaches.

Lustick (2021) developed a framework called “emotion coding” to formalize this method. This framework helps in systematically analyzing emotional content within qualitative data. Researchers have found emotional coding particularly beneficial in studies focusing on sensitive topics like discrimination and marginalization, as it allows for a nuanced exploration of participant experiences.

For more about how emotional coding has adapted to contemporary research needs, visit this comprehensive discussion on emotion coding .

Methodology in Emotional Coding

Emotional coding in qualitative research focuses on identifying, categorizing, and coding emotions to better understand the data. It requires careful attention to detail and a consistent approach to capture the depth of emotional experiences present in the analyzed materials.

Identifying Emotions

To start, researchers need to pinpoint the emotions expressed in the data. This involves reading through transcripts, field notes, or any qualitative text and noting where emotions are displayed. They may look for words or phrases that indicate feelings such as “happy,” “angry,” or “frustrated.” Non-verbal cues in interviews, such as tone of voice or body language, can also be critical indicators. Researchers aim to capture both obvious and subtle emotional expressions to ensure a comprehensive analysis.

Categorizing Emotions

Once identified, emotions should be grouped into categories. This step reduces complexity by organizing similar emotions together. For example, “joy,” “satisfaction,” and “happiness” might be listed under a “positive emotions” category, while “anger,” “fear,” and “sadness” could fall under “negative emotions.” This categorization helps in analyzing patterns and trends within the data. It is important to create clear definitions for each category to maintain consistency throughout the research.

Coding Process

The final step involves coding, where each defined emotion category is applied to relevant sections of the data. Researchers use code labels or tags to mark text segments that correspond to specific emotions. This can be done manually or with software like QDA Miner. The coding process allows for the efficient retrieval and examination of emotional patterns. It is crucial to review and refine codes regularly to enhance accuracy and reliability. The goal is to systematically track how emotions influence and shape the findings, ensuring a nuanced understanding of the research topic.

Applications of Emotional Coding

Emotional coding is used in various settings to uncover deeper insights. It is particularly useful in case studies and has specific applications in multiple industries.

Case Studies

Emotional coding sheds light on participants’ feelings and experiences in qualitative case studies. By identifying and analyzing emotional expressions, researchers can gain a deeper understanding of personal narratives and social contexts.

For example, in studies about discrimination, emotional coding can reveal how participants feel about their experiences of unfair treatment. This helps to understand the intensity and type of emotions involved, such as anger, sadness, or frustration. By using this technique, researchers can create more detailed and nuanced case studies that capture the complexity of human emotions.

In educational settings, emotional coding helps to understand students’ feelings towards learning environments. It allows researchers to see how emotions like anxiety or motivation influence learning outcomes. This approach provides a richer context for interpreting qualitative data, making it possible to devise more effective educational strategies.

Industry-Specific Uses

In healthcare, emotional coding helps in understanding patient experiences and their emotional responses to treatments. It can identify emotional triggers and stressors, which aids in improving patient care and communication.

In business, emotional coding is used to analyze customer feedback and employee satisfaction. By assessing emotions in feedback, companies can better understand customer needs and improve service quality. Employees’ emotional coding can highlight workplace issues, leading to better management practices and a healthier work environment.

In social work, this method helps to explore the emotional well-being of clients. By examining clients’ emotional responses, social workers can tailor their support strategies to address the specific needs of individuals. This approach is vital in dealing with sensitive issues such as trauma and abuse, where understanding emotions is key to providing effective support.

Society can benefit greatly from the insights provided by emotional coding across these diverse fields.

Challenges in Emotional Coding

Emotional coding offers valuable insights, but it comes with significant challenges. These include dealing with subjectivity and navigating ethical considerations.

Subjectivity Issues

One major challenge in emotional coding is subjectivity. Researchers must interpret emotions from data, which can vary greatly between individuals. Different coders might assign different meanings to the same piece of data, leading to inconsistent results.

This subjectivity can also influence the overall findings, making it difficult to ensure objectivity. Personal biases may skew the interpretation, affecting the reliability of the research.

To mitigate this, employing multiple coders and using standardized coding frameworks can help, but it’s not a foolproof solution. Training coders and encouraging regular discussions can also partially address these inconsistencies.

Ethical Considerations

Ethical considerations play a crucial role in emotional coding. Researchers must handle sensitive data with care. Emotionally charged information can be deeply personal, and misinterpreting or mishandling it can cause harm.

Informed consent is essential. Participants must understand how their emotional data will be used. Researchers must ensure confidentiality to protect participants’ privacy. Balancing the need for detailed emotional insights with respecting participants’ boundaries is critical.

Additionally, the potential impact on researchers themselves should be acknowledged. Dealing with intense emotional data can be taxing, requiring mental health support and self-care strategies to prevent emotional burnout. Researchers must craft ethical guidelines that consider all these factors.

Advancements and Future Directions

Emotional coding in qualitative research continues to innovate through the integration of technology and the development of predictive analytic tools. These advancements aim to enhance accuracy and provide deeper insights into emotional patterns.

Technological Integration

With the rise of artificial intelligence (AI) and machine learning, emotional coding has seen significant enhancements. Tools powered by AI can now automate parts of the coding process. For example, text analysis software can quickly identify and categorize emotions in large datasets, making it faster and more efficient.

Additionally, advancements in natural language processing (NLP) improve the detection of nuanced emotional expressions. This means AI can understand subtleties in language, such as sarcasm or mixed emotions, which can be difficult for human coders to catch consistently.

Visual and audio analysis tools also contribute to this field. Software can analyze facial expressions and vocal tones during interviews, providing a richer context for understanding participants’ emotions beyond just their spoken words.

Predictive Analysis and Trends

Another promising area is the use of predictive analytics to identify emotional trends over time. Researchers can analyze large volumes of qualitative data to forecast how emotions might evolve in certain contexts. For instance, trends in emotional responses to social issues can help policymakers develop better strategies.

By applying machine learning models, researchers can predict outcomes based on emotional patterns. This can be particularly useful in fields like marketing, where understanding future customer emotions can inform campaign strategies.

Moreover, the ability to track changes in sentiment over time allows for more dynamic and responsive research designs. This ensures that studies remain relevant and accurately reflect participants’ evolving emotional landscapes.

Frequently Asked Questions

Emotional coding in qualitative research helps identify and classify emotional expressions in data. This section covers important questions about its application, role, and differences from other types of coding.

How can emotional coding be applied in qualitative data analysis?

Emotional coding can be used to analyze interviews, focus groups, and other qualitative data. Researchers tag keywords and non-verbal cues to identify emotions. This method helps transform raw data into meaningful insights by highlighting emotional responses. More details on this can be found here .

What is the role of emotional coding within the broader spectrum of qualitative coding techniques?

Emotional coding is one method among many in qualitative research. It focuses on identifying feelings expressed in the data, adding depth to the analysis. Unlike thematic coding, which looks for patterns and themes, emotional coding zeroes in on the emotional tone and context of the responses.

How does emotional coding differ from other types of coding in qualitative research?

Emotional coding specifically focuses on emotions communicated through words, tone, and non-verbal cues. Other types, like thematic and pattern coding, concentrate on themes, patterns, or repeating ideas within the data. This unique focus allows a nuanced understanding of the emotional landscape in the research.

Can you provide a clear example of how emotional coding is used in a research study?

In a study on patient experiences in healthcare, researchers might use emotional coding to tag words like “scared” or “relieved.” Non-verbal cues like sighs or laughter might also be noted. This data helps build a picture of emotional responses to treatment, which is crucial for improving patient care.

What are the advantages of using emotional coding in analyzing qualitative data?

Emotional coding provides a deeper understanding of participants’ feelings and experiences. It can uncover hidden emotions that thematic coding might miss. This approach adds richness to the data, offering insights into how emotions influence behaviors and decisions.

How does one differentiate between emotional coding and pattern coding in qualitative research?

Emotional coding identifies specific emotions expressed in the data. Pattern coding, on the other hand, looks for recurring themes or patterns across the dataset. Emotional coding is more about the “feel” of the data, while pattern coding is about organizing it into meaningful categories.

For more examples and detailed explanations, consider reading the article on advanced coding techniques .

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28 Coding and Analysis Strategies

Johnny Saldaña, School of Theatre and Film, Arizona State University

• Published: 04 August 2014
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This chapter provides an overview of selected qualitative data analytic strategies with a particular focus on codes and coding. Preparatory strategies for a qualitative research study and data management are first outlined. Six coding methods are then profiled using comparable interview data: process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Strategies for constructing themes and assertions from the data follow. Analytic memo writing is woven throughout the preceding as a method for generating additional analytic insight. Next, display and arts-based strategies are provided, followed by recommended qualitative data analytic software programs and a discussion on verifying the researcher’s analytic findings.

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Qualitative Assessment of Emotion Regulation Strategies for Prevention of Health Risk Behaviors in Early Adolescents

Amy hughes lansing.

Bradley/Hasbro Children’s Research Center, & Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA

Kate M. Guthrie

Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University & The Miriam Hospital, Providence, RI, USA

Wendy Hadley

Angela stewart.

Bradley Hospital, Providence, RI, USA

April Peters

Bradley/Hasbro Children’s Research Center, Providence, RI, USA

Christopher D. Houck

Author Notes: A.H. Lansing is now at the University of Nevada, Reno.

The ability to regulate emotions has been linked to a variety of adolescent health risk behaviors, including sexual risk behaviors, especially for adolescents who are experiencing mental health symptoms. However, there is limited information available on intuitive emotion regulation strategies for early adolescents with mental health symptoms to facilitate the adaptation of emotion regulation interventions for psychopathology to health risk behavior prevention. For example, interventions to prevent sexual risk behaviors in early adolescence have yet to specifically target emotion regulation. This paper describes the use of focus groups to identify emotion regulation strategies that were understood by and acceptable to early adolescents with mental health symptoms who are also more likely to engage in risky health behaviors. Qualitative data were collected through focus groups (k=5 groups) with 15 early adolescents with mental health symptoms. The most commonly generated emotion regulation strategies were leaving the situation, distraction, physical release, expressing oneself to someone, positive thinking, and considering other options. Translation of these findings for use in preventive health-risk behavior interventions (including for sexual risk) is discussed.

Early adolescents experiencing mental health symptoms are a particularly important group for which we need to better understand emotion regulation skills, i.e., the ability to modulate both positive and negative emotions in the service of goal directed action ( Gross, 2014 ). Problems with emotion regulation are a key mechanism that might place early adolescents with mental health symptoms at greater risk for poor outcomes across adolescence and emerging adulthood ( Bell & McBride, 2010 ). For example, adolescents with mental health symptoms are more likely to engage in sexual risk behaviors ( DiClemente et al., 2001 ; Lucenko et al., 2003 ;). Problems with emotion regulation have been identified as a mechanism contributing to greater engagement in risky sexual behaviors ( Brown et al., 2013 ). Early adolescents with mental health symptoms might benefit greatly from developmentally appropriate preventive interventions targeting emotion regulation. However, among early adolescents with mental health symptoms, research has yet to identify what emotion regulation strategies young people find to be intuitive, comfortable, and acceptable. This lack of information regarding early adolescents’ experiences with emotion regulation strategies has impeded the development of developmentally appropriate emotion regulation preventive interventions.

Motivated by this need in the context of sexual risk behavior prevention, this research team conducted a two-part research program: (1) conducting focus groups with early adolescents with mental health symptoms to elicit their experiences managing emotions in a variety of challenging contexts and to capture the range of emotion regulation strategies that they find acceptable and feasible; and (2) testing a novel sexual risk behavior prevention program grounded in the work of the focus groups on emotion regulation. This study describes the outcomes of the first part of the research program, a qualitative examination of emotion regulation strategies in early adolescents with mental health symptoms as well as a discussion of how those findings were translated into a developmentally appropriate framework for integrating emotion regulation into a prevention program for sexual risk behaviors. Results from the second part of the research program, the intervention testing, are already available elsewhere and discussed herein. However, there remains substantial benefits that might be drawn from the qualitative information gathered on emotion regulation strategies in early adolescents with mental health symptoms for future emotion regulation programs. First, we will describe the sexual risk behavior and emotion regulation context that motivated this research and then we will introduce the qualitative research program.

Sexual Risk Behaviors in Adolescence

Adolescents with mental health symptoms are more likely to engage in sexual risk behaviors earlier in, and across, adolescence ( Brooks, Harris, Thrall & Woods, 2002 ; Brown, Houck, Hadley & Lescano, 2005 ; DiClemente et al., 2001 ; Lucenko et al., 2003 ; Noll, Haralson, Butler & Shenk, 2011 ). Youth Risk Behavior Surveillance data indicate that before age 13, 4% of adolescents report having had sexual intercourse, but by tenth grade, 36% of adolescents report having done so ( Kann et al., 2016 ). In contrast to rates in the general population, 42% of adolescents with emotional disorders have had sex before the age of 13 ( Valois, Bryant, Rivard & Hinkle, 1997 ), and among adolescents in outpatient mental health treatment, 54% had been sexually active, 29% did not use a condom during last sexual activity, and 14% had tested positive for a sexually transmitted infection ( Brown et al., 2011 ). These data highlight the importance of intervening to reduce and prevent risky sexual behaviors earlier in adolescence, especially for adolescents with elevated mental health symptoms.

Emotion Regulation and Sexual Risk Behaviors

Across adolescence, problems with emotion regulation are associated with increased engagement in risky health behaviors, including sexual risk behaviors ( Hessler & Katz, 2010 ; Raffaelli & Crockett, 2003 ; Brown et al., 2013 ). Adolescents taking risks rarely pause to engage in a thoughtful decision making process ( Steinberg, 2010 ). Instead, emotions occurring in the moment are thought to drive adolescent risk taking behavior ( Bell & McBride, 2010 ; Dahl, 2001 ; Steinberg, 2010 ; Wills et al., 2006 ). With regards to risky sexual behaviors, adolescent sexual activity is often preceded by increasing positive affect ( Shrier, Koren, Aneja & de Moor, 2010 ; Shrier et al., 2012 ). Further, momentary increases in negative affect have been associated with greater likelihood that adolescents who are depressed would have sex with a non-main partner ( Blood & Shrier, 2013 ). Adolescents’ abilities to modulate both positive and negative emotions in the moment, rather than coping or longer term mood regulation ( Gross, 2014 ), are likely critical to adolescent decision-making in the context of emotionally arousing features of risk situations. For example, it may be more challenging for adolescents to make safe sexual decisions when feeling anxious about maintaining a relationship, embarrassed for being sexually inexperienced, or curious about sexual activity. Adolescents who can momentarily down regulate these positive and negative emotions may be more likely to engage in a decision-making process that is influenced by healthier knowledge, attitudes and behaviors.

Unfortunately, the few prior interventions that targeted emotion regulation in sexual risk situations ( Brown et al., 2011 , Brown et al., 2013 ) were developed for older adolescents who were already experiencing severe psychopathology. These intervention models for older adolescents with psychopathology often emphasize developmentally advanced cognitive restructuring and distress tolerance skills which are theorized to be challenging for early adolescents to implement ( Steinberg, 2010 ). At the same time, there is limited data about the emotion regulation skills that early adolescents with mental health symptoms identify as applicable to and acceptable for in-the-moment regulation of positive and negative emotions in challenging emotion regulation situations (e.g., avoiding a fight, passing an important test at school, or avoiding overwhelm at a funeral). Qualitative examination of early adolescents with mental health symptoms’ experiences managing emotions in a variety of challenging contexts could help to capture the range of emotion regulation strategies that they find acceptable and feasible. This data can inform developmentally appropriate interventions targeting emotion regulation to prevent risky sexual behaviors. In addition, such information about what strategies are comfortable for and understood by early adolescents with mental health symptoms in the service of in-the-moment regulation can be applied to emotion regulation interventions for other target behaviors (e.g., substance use, school performance, social relationships).

The Current Study

The goal of this study was to identify emotion regulation strategies that were understood by and acceptable to early adolescents with mental health symptoms in situations that elicited challenging emotions. This study conducted focus groups with early adolescents with mental health symptoms. The focus group approach allowed early adolescents to both describe preferred emotion regulation strategies and comment on their experiences with common strategies found in existing emotion regulation interventions for adolescents. Data from these focus groups were then analyzed via thematic and conceptual analyses. The results present the common emotion regulation strategies and rationales identified. The discussion explores those strategies in the context of emotion regulation theory and extends the findings to considering how these strategies might be applied in a developmentally appropriate emotion regulation intervention (both in the context of sexual risk behavior prevention and other intervention targets).

Participants

Early adolescents with mental health symptoms were recruited from three urban middle schools in New England. Five gender-stratified groups consisting of three participants each were enrolled. Nine boys and six girls participated; 11 participants were 13 years old, and 4 participants were 14 years old. The racial distribution was 5 White, 5 African-American, 1 Native American, 2 Multiracial, and 2 No Response. Latino ethnicity was endorsed by 5.

All study procedures were approved by the applicable Institutional Review Board (IRB). Participants were referred to the study by school counselors, who identified students with suspected mental health problems using a referral form listing common teen mental health symptoms. To be included, school counselors identified at least two symptoms of a disorder of mood (e.g. depression, withdrawal, nervousness, mood swings) or conduct (e.g., hyperactivity, defiance, frequent suspensions) and one symptom of impairment (e.g., declining grades, frequent absenteeism, declining involvement in activities). Counselors then contacted parents to explain the study and request permission to allow study staff to contact them regarding participation and informed consent. Counselors were not informed which students ultimately participated in the focus groups. Prior to enrolling in the study, written informed consent was obtained from all parents and assent was obtained from each participant.

Focus Group Moderator Guide

Focus group guides were developed to provide a consistent framework from which moderators facilitated focus groups. This included an “intent statement” for each section, which outlined in detail what data were being sought and included definitions so all moderators collected the same types of information. The guides used vignettes and open-ended questions, followed by additional probes if needed. Vignettes have been successfully used in qualitative research with adults to study risky behaviors ( Hughes, 1998 ) and health behavior decision making ( Gourlay et al., 2014 ). The intent was to capture the target population’s use of emotion regulation strategies in a variety of challenging situations to elicit the range of and comfort with these strategies. The current study was motivated by a need for a developmentally appropriate emotion regulation intervention to prevent risky sexual behaviors in early adolescents with mental health symptoms. However, the aim of this study was to identify emotion regulation strategies that early adolescents felt would be applicable across a variety of contexts and situations that might elicit challenging emotions in early adolescence. With this aim, and the knowledge that the majority of early adolescents with mental health symptoms have not yet had sex, only one vignette referred to a sexual situation. Other emotion-arousing vignettes (described below) were used to increase the range of emotions to be regulated and possible strategies generated. In this way, an understanding of the most intuitive emotion regulation strategies across several types of situations and emotions that youth might experience could be determined.

The focus group guides had two primary sections. The first aimed to have youth independently generate emotion regulation strategies, while the second provided them with strategies sometimes taught in emotion regulation interventions for older populations and asked for their feedback to examine comprehension of those strategies. This ensured youths’ thoughts regarding a range of emotion regulation strategies were queried.

Development

Before developing the focus group guides, exploratory interviews were conducted with adolescents in the target age range to explore the thought processes of the study population related to emotion regulation and to facilitate the design and framing of the focus group queries. Prior to conducting these interviews, investigators reviewed literature on emotion regulation intervention to develop initial concepts and interview questions. This centered on identifying possible emotion regulation strategies to be queried via open-ended vignettes. The vignettes were written to be situations that with which early adolescents may be familiar. Each vignette identified a situation, emotions evoked by the situation, and a specific emotion regulation goal so that youths would be prompted to discuss how they would regulate the identified emotions to achieve that goal in context rather than discuss broader mood regulation. From the scenarios/vignettes piloted in the interviews, two were used in the focus groups (being teased and test anxiety). Another two vignettes were developed based on the interviews. In particular, pressure to have sex was added as a vignette as this context was consistent with the motivation behind the current study, and a vignette about attending a funeral of a loved one was added to create context for the second section of the focus groups where youths’ comprehension of common emotion regulation strategies was explored.

The exploratory interviews also informed the decision to conduct small focus groups, with three individuals in each group, as (1) our interpretation was that youth might be more comfortable in a group rather than a one-on-one conversation with an adult about affective and sexual risk topics, (2) youth seemed to feel more comfortable and able to engage without disruption when numbers were smaller, and (3) smaller groups allowed more time to encourage depth in explanations and provide more nuanced understanding with respect to emotion regulation.

Focus Group Part 1: Generating Strategies.

Focus groups began with the discussion of three emotion-arousing situations, using a matched-gender person as the main character (see Table 1). For example, “John is alone in a bedroom with his girlfriend at a party. They are making out and his girlfriend says she wants to have sex for the first time. John is curious about it, but isn’t sure he wants to do it right now. But his girlfriend is pressuring him and he knows that everybody at the party is going to ask what they did in the bedroom. He is horny, but confused and pressured too.” The other two situations included managing anxiety while taking a test and avoiding a fight when being teased in front of peers. These situations were scripted to provide early adolescents with an emotion regulation goal and require them to respond with immediate emotion regulation strategies (vs. long term coping strategies), as is the case with situations involving risky sexual behaviors. Each vignette presentation was followed by the prompt, “What should John (Kate) do to keep his (her) feelings from taking over?” to elicit strategies for managing the identified feelings. Each participant-generated strategy was followed with the query, “How could that help feelings from taking over?” to assess adolescents’ perceptions of how that strategy would influence emotion.

Focus Group Part 2: Comprehending Strategies.

After participants were given the opportunity to generate emotion regulation strategies for each situation, a fourth situation involving a teen feeling overwhelmed at a grandparent’s funeral was presented (with a clear goal of managing strong emotions at the funeral, not mood across time after the death). This situation was followed by the description of a series of seven emotion regulation strategies drawn from the literature and other interventions for improving emotion regulation ( Brown et al., 2011 ; Linehan, 1993 ): distraction, leaving a situation, expressing oneself to someone, physical release, affect tolerance, positive thinking, and normalizing. Each strategy was briefly defined, then participants were asked, “How could that help feelings from taking over?” For example, with regards to affect tolerance participants were asked, “How about ‘waiting it out,’ where a person just feels the feeling and waits for it to get weaker so that they can make a decision? How could that help feelings from taking over?”.

Prior to study initiation, all focus group moderators were trained on leading focus groups and the agenda content. Then during the study, moderators met regularly to debrief, share facilitation strategies, and consider whether participant data was generating redundancy in themes.

Data Collection and Management.

Single-gender focus groups were conducted to increase the probability that these young adolescents would speak openly about the sensitive topics of emotion regulation including sexual decision-making. Each focus group was conducted by two same-gender facilitators and audio recorded. Recordings were then transcribed verbatim and cleaned (e.g., personal identifiers were removed and transcription errors corrected). Focus groups lasted approximately 90 minutes. Debriefing summaries were completed after each group and used to assess the repetition of themes and ensure saturation was reached (i.e., as data collection progressed no new conceptualizations of primary study constructs were obtained) despite the small sample size.

A formal coding scheme was developed iteratively. Initial codes were derived from a priori research questions and elements of the agenda. A single transcript was then reviewed by the entire coding team and initial codes were used to identify thematic elements being sought using a deductive approach grounded in emotion regulation intervention literature. As transcripts were reviewed, an inductive approach was also used, whereby codes were added representing emergent themes and refined to accommodate the data. Consistent with project goals, transcripts were coded to indicate whether groups independently generated each strategy and whether they demonstrated understanding of how implementing that strategy might function to manage one’s affect. Using this data coding strategy, each subsequent transcript was coded by at least two independent coders. Discrepancies between coders were discussed with the full, four-person team. Once consensus was reached (i.e., master coding), these final, master coded documents were entered into an NVivo database. With this approach, reliability of coding between raters was not assessed quantitatively as agreement was required for the master coding. Transcripts were analyzed for thematic content using the principles of thematic and content analysis ( Miles & Huberman, 1994 ; Strauss, 1987 ; Weber, 1990 ).

Participants independently generated five of the seven a priori identified emotion regulation strategies of interest: leaving the situation, distraction, physical release, expressing oneself to someone, and positive thinking. Some groups generated an unanticipated emotion regulation strategy, “considering other options,” which was added to the coding scheme. Furthermore, all groups provided comments on their understanding of the seven strategies presented by the investigators.

Leaving the Situation

All five groups independently generated the idea that leaving the situation, by getting away from triggers causing strong feelings, is a useful strategy for managing emotions. Teens suggested leaving the situation by going outside, to their room, or to the bathroom. For example, teens said, “Walk away from whatever is bothering you,” and, “Just leave.”

Four of the five groups provided a logical rationale for how leaving a situation would help to manage strong emotions. Some teens recognized that removing themselves from the situation might keep them safe by decreasing exposure to emotional cues. Teens suggested that leaving a situation prevented them from being overwhelmed by triggers for emotions, such as when one boy said, “…gonna see people crying and then you’re gonna start feeling sad and you’re not if you just go outside...” Finally, some teens believed that leaving a situation would help to suppress all thoughts and emotions related to the situation. One teen suggested, “Could’ve just walked away and pretend like it never happened” and another stated, “They’d forget about it for a while.”

Distraction

All five groups identified distraction, in which a person thinks about or does something in response to an emotionally provoking situation to take his mind off of his feeling(s) by refocusing his attention on something else, as a useful emotion regulation strategy. Teens suggested a variety of distraction strategies, such as “count backwards,” “watch TV,” and “go play basketball.” One girl suggested leaving the situation by removing herself and distracting herself, stating, “Like if she walked away and started having a conversation with her friend.”

All five focus groups understood that by focusing on something else the strong feelings associated with the trigger (e.g., bullying peer) would either decrease in intensity or be replaced by a different feeling. Teens stated that distraction would be useful to help you “forget about it [the triggering situation]… It won’t get him so mad.” One teen suggested watching TV as a strategy and stated that it would work by replacing the feeling, “it would probably make her happy if she’s watching a funny movie or something like that.”

Physical Release

Four of the five groups independently generated the idea that physical actions, such as deep breathing or exercising, could help manage strong emotions. Teens suggested taking deep breaths, washing your face with cold water, and punching a pillow, a punching bag, or a wall. This was a commonly named strategy for managing anger for both boys and girls.

Three of the five groups expressed that physical actions can release emotions and physically calm the body to regulate emotions. For example, one teen said that physical actions help get “your anger out by, like, using force.” A male described a cathartic process: “Like it’s in front of you, like clench your fist, think that his face is right there, the person who shoved you, you’re angry at him, you punch him, there’s a hole in the wall, so there’s a hole in his face. The rage is gone.” A female also described a cathartic feeling from punching a pillow, suggesting that it feels better to let strong feelings out, but at a safe distance:

Participant: “…just imagine that you are screaming at that person, but you’re just screaming at a plain pillow.”‘ Moderator: “Okay, and how does that help the feeling?” Participant: “Cuz you’re letting it all out.”

Finally, taking deep breaths was described as a physical action that could be useful, because, as one girl stated, “You are calming your feeling, calming your nerves down, it’s relaxing your body” and because, as another boy stated, “…when you’re mad, like you start breathing heavy and stuff like that. You start and your adrenaline pumps and stuff like that, so you just calm down … and you’ll stop and it will help you breathe slower, stuff like that.”

Expressing Oneself

All of the five teen groups generated and comprehended the idea of expressing feelings verbally as a way of managing feelings. Talking with someone other than the person causing the strong feeling (e.g., friend, teacher) was the most commonly endorsed method of expressing oneself, and teens often described it as useful because it allowed strong feelings to escape from the body. One boy stated “…after you talk about it, you’ll just, after having it out of your system, you won’t have to worry about it.” A girl noted the benefits of expressing herself as preventing an emotional explosion, “…her feelings wouldn’t stay in and it wouldn’t, like, become worse and worse until it all came out one day and it would have came out negative instead of positive.” In suggesting talking to a teacher in response to a vignette about being bullied, another girl noted “she’ll express her feelings to the teacher and she can let, like, all the stress out that she have against that bully instead of getting into a fight, just by talking about it.” Written expression of emotions was not spontaneously generated by any of the focus groups. While this strategy was not generated, prompts during the focus group to discuss this strategy revealed that written expression of emotions was still acceptable to early adolescents.

Other teens identified expressing feelings as useful for managing feelings through other mechanisms. One young man, in explaining singing as a way to express feelings, emphasized the distraction aspects of the activity over the expression aspects, “You’d probably be more focused on that [singing] than other feelings they had.” When facilitators asked about artistic expression as a way of managing feelings, participants in two groups noted that by drawing or expressing oneself verbally they improved their self-image, which in turn helped them manage their emotions, “…you really grab the pencil and just like you make a really cool picture and you feel better about yourself. You’re like ‘Look what I just did.’ It makes you feel better.”

Teens spontaneously noted that talking about feelings elicited social support. At times this social support was described as an emotion management strategy, as when one girl commented, “It makes her feel good that he understands her.” At other times, however, it was clear that the talking was not about affect regulation, but about problem solving, such as “It’s not just you now, you’ve got an adult to take care of it.”

Positive Thinking

Positive thinking was defined as focusing on something positive or thinking about someone who cares about you. All five groups spontaneously generated the idea that focusing on positive aspects of a situation is a helpful strategy for managing emotions. Notably, this strategy was generated primarily in response to two scenarios: taking a test and the death of a loved one.

When asked what a teen might do to manage feelings of being nervous before taking a test, teens suggested focusing on positive outcomes (e.g., “He should think about what you want, what your reward is, what you get when you do this”), avoiding negative thoughts (e.g., “He’s gotta think positive, not negative”), and giving oneself encouragement (e.g., “Telling herself that she can do it” and “… remember that he, like, studied for a long time”).

Positive thinking was also generated in response to the situation in which a teen felt sad after the death of a grandparent. Teens suggested thinking positively by focusing on good thoughts and memories. For example, one boy stated, “Think about all the happy times together,” while one girl said, “She could think about positive stuff her grandma did when she was a little girl or whatever and maybe she won’t feel all that sad.”

When asked how thinking positively would help manage strong emotions, four of the five groups demonstrated comprehension, suggesting that positive thinking would generate positive feelings, such as confidence, happiness, pride, and courage, which would decrease the strong negative emotion. Positive thinking was also believed to promote laughter (e.g., “reverses sad to happy,”) and decrease negative feelings (e.g., “It can make the feeling go away… like if you think you are nervous about something just think positive and your nerves will go away. You probably won’t think about that no more”).

Normalizing

Normalizing was defined as reminding oneself that others have been in this situation or experienced these feelings before in an effort to help decrease the intensity of the emotions and to increase the sense of manageability or control of the emotion. None of the groups independently generated using this strategy. However, when presented with this option, all five groups understood how normalizing would help to manage strong feelings. In general, these examples centered on knowing that peers had gone through similar situations, such as “After you think about that [that your friend has gone through a similar situation] and you think about ‘Yeah, I can do it, I can do it!’ you get actually happy. You’re happy because you can do anything your friend can do.” Another teen reported that it was useful to recognize that they were not targeted “…if you always thought, ‘Wow everything always happens to me,’ you always get mad about it because you think that’s the only person it happens to, you think that it happens to other people too so it just makes you feel better”.

Affect Tolerance

Tolerating affect was described as “waiting it out” in which a person allows themselves to feel a particular feeling and wait for it to get weaker until they can decide how to act. None of the groups independently generated this strategy nor could any of the groups identify how this alone would help one manage strong feelings (“I don’t think it would work”). Two groups indicated that adolescents would feel compelled to act in some way in order to manage feelings, that passive acceptance did not seem feasible (“I would do something about it”). One group suggested that teens were developmentally unable to wait out feelings, stating “I don’t think we’re patient enough.” Another thought this could make feelings stronger, possibly leading to trouble later. Instead they favored a strategy of expression.

Participant 1: I don’t think that helps. Moderator: That wouldn’t work? Participant 2: It would make it worse. Moderator: It would make it worse? How come? Participant 1: Because over time emotions build up.

Considering Options

Considering options was characterized as a cognitive emotion regulation strategy in which an individual considers other ways to think about the triggering situation that brought on strong feelings. While not presented to the focus groups as part of the protocol, several groups identified reflecting on alternative approaches or outcomes as a strategy. For example, considering the possibility of getting a new pet when a pet dies would be an example of considering options. Of the five focus groups, three groups independently generated this cognitive strategy as a way to manage strong feelings. In response to the test situation, a participant stated that the student could “think of one question at a time instead of all of them.” In contrast, others suggested focusing on possible consequences such as “if he fights he’ll get kicked out of school” and “think about what would happen if I did something bad” as a way of reducing affect by focusing on feelings associated with those consequences.

Only one of the three groups that generated considering options was able to describe how it would regulate emotions. The adolescent stated that thinking about one question at a time rather than the entire test would be useful because “they [test questions] won’t overwhelm her….It would calm her down and then it would, like, make her think easier.”

This study explored emotion regulation strategies that were understood by and acceptable to early adolescents with mental health symptoms in diverse situations that elicited challenging emotions. Results indicated that these early adolescents with mental health symptoms commonly and spontaneously generated several strategies that they perceived to be beneficial and feasible. Specifically, leaving the situation, distraction, self-expression, and positive thinking were suggested by all five of the focus groups as methods for preventing one’s feelings from “taking over.” Physical strategies aimed at altering or calming the physical sensations associated with strong feelings were also generated by most of the groups, as were cognitive strategies. Many of the suggested approaches involved simple concrete actions (e.g., walking away, punching a pillow) that early adolescents could execute when emotionally aroused, as opposed to a more passive approach such as affect tolerance (i.e., waiting for negative emotions to pass). The repeated nature of these themes and their general level of comprehension suggest that these strategies are somewhat intuitive for early adolescents with mental health symptoms and may form the foundation of emotion regulation in this developmental stage for this population.

Other techniques were not often generated by early adolescents with mental health symptoms. No groups proposed affect tolerance as a feasible method, suggesting this may not be a natural fit for early adolescents. Though successfully used in some treatments with older adolescents and adults (e.g., dialectical behavior therapy; Linehan, 1993 ), tolerating affect and waiting for it to pass was not understood as potentially helpful and viewed as possibly harmful by early adolescents. This is consistent with research suggesting that early adolescents may lack access to the effective use of developmentally advanced cognitive restructuring and distress tolerance skills ( Steinberg, 2010 ). On the contrary, while no groups suggested normalizing their experience as a way of managing strong emotions, all of the groups were able to identify how this might be helpful, suggesting that this may also be a developmentally feasible strategy for this age group.

The data from these focus groups represented the views of early adolescents with mental health symptoms on applicable and effective emotion regulation strategies. At the same time, the acknowledged motivation behind this current study was to translate these findings into developmentally appropriate emotion regulation strategies for integration in preventive interventions for sexual risk behaviors in early adolescents with mental health symptoms. Accordingly, it is also important and relevant to consider and discuss the data with regards to developmental appropriateness, consistency with the evidence base on emotion regulation, and parsimony—with the simplest approach to organizing (and recalling) the strategies described by early adolescents prioritized.

First, strategies may be best considered as developmentally appropriate if 1) they were self-generated by early adolescents with mental health symptoms as potential responses to situations presented and 2) early adolescents with mental health symptoms understood how using the strategy might help one manage feelings to avoid actions based primarily on emotions. Based on the data analyses from the focus groups, the strategies of Leaving the Situation, Distraction, Physical Release, Expressing Oneself, Positive Thinking, Normalizing, and Considering Options might be considered as developmentally appropriate, while Affect Tolerance would be excluded.

Second, some of these emotion regulation strategies identified by early adolescents with mental health symptoms also need to be reformed into a skill that is consistent with the evidence base on emotion regulation. It is important to capitalize on naturalistic urges that early adolescents with mental health symptoms have to use particular emotion regulation strategies and then recast those strategies in ways that promote healthy emotion regulation. For example, at-risk early adolescents commonly reported leaving the situation as a helpful emotion regulation strategy. This strategy could easily be construed as avoidance or suppression, and in fact, some adolescents in the focus group even expressed that emotional suppression would be a goal of leaving a situation. Yet, in the moment, leaving the situation could be helpful in facilitating healthy decision-making. Specifically, physically leaving the situation could be a helpful strategy if the adolescent could respectfully leave the arousing situation and then return to the situation after allowing their emotions to calm and initiating a thoughtful decision-making process. In addition, brief distraction was identified as a similar strategy that could achieve the same effect if an adolescent could not respectfully and/or physically leave a situation. In both cases, after calming their emotions and returning to the situation, physically or mentally, adolescents would then be better able to engage in a healthy decision-making process. This may be similar to long-term coping strategies, such as cognitive-behavioral reappraisal. However, emotion regulation places greater emphasis on the immediate goal of modifying one’s appraisal of a situation to change the significance attributed to it and facilitate specific goal-directed action.

Third, to simplify the findings in a parsimonious way for use in developing a emotion regulation prevention for sexual risk behaviors, it was also beneficial to consider how these emotion regulation strategies might be grouped into broad categories based on conceptual links identified from the focus group data. For example, participants sometimes reported leaving the situation and distracting oneself at the same time, and their explanations for how the strategies were helpful were similar. Therefore, we also considered clusters of emotion regulation strategies, with each cluster consisting of related strategies with similar mechanisms of action, and identified three possible groupings of emotion regulation strategies that were developmentally appropriate and consistent with the evidence based on emotion regulation. In an effort to use developmentally appropriate language consistent with that used by participants, we’ll discuss those emotion regulation strategies as “Get Out,” “Let It Out,” and “Think It Out”.

Early adolescents frequently endorsed strategies that distracted them from the emotional cues. Getting away from emotional triggers was a common thread between adolescent explanations of leaving the situation (physically getting away from emotional triggers) and distraction (cognitively getting away from these triggers). As such, these were combined into a broader category, “Get Out,” in which adolescents were encouraged to momentarily get away from whatever is causing their intense feeling as a strategy for decreasing the intensity of that feeling in the moment and allowing healthier decision making. For example, when feeling pressured to drink by peers, separating oneself from the others by going to the bathroom for a moment can provide an opportunity to self-regulate away from the cues that are causing the strong feelings (e.g., peers, beer). Decreasing this affective arousal may increase the likelihood of decision-making based on knowledge or personal values and decrease decisions based on embarrassment or curiosity.

In addition, early adolescents often commented on the cathartic qualities and physical changes associated with having the feeling “out of your system” via self-expression and physical release strategies. These techniques were thus combined to form “Let It Out,” in which teens are encouraged to do something to keep feelings from staying “bottled up,” either something physical or something to express oneself. For example, if a teen felt hurt, confused, and angry because her boyfriend breaks up with her, calling a friend to talk about it, journaling, or exercising to release pent up emotions may decrease intense feelings that could lead to unhealthy responses, such as picking a fight with this boyfriend or self-cutting.

Finally, cognitive strategies, such as positive thinking (for negative emotions) and thinking about consequences (for positive emotions), were labeled “Think It Out,” in which early adolescents are encouraged to look for other ways to think about emotional triggers. Broadening the strategy from “positive thinking” was viewed as important, since positive cognitions may not always be realistic or helpful in reducing risk behaviors. For example, if an early adolescent is feeling curious about having sex while making out with a partner, positive thinking may increase those feelings, leading to risky behaviors. Taking a moment to consider the consequences (e.g., having a baby or getting a disease) may decrease feelings of arousal or curiosity, facilitate more balanced decision-making, and reduce the likelihood of having risky sex.

Get Out, Let It Out, Think It Out describes and organizes the adolescent generated emotion regulation skills that early adolescents with mental health symptoms are developmentally likely to understand as well as learn to use in healthy ways.

Integration with Emotion Regulation Theory

It is noteworthy that these early adolescent-generated strategies for emotion regulation largely correspond with Gross’ (2014) process model of emotion regulation, even though Gross’ model was not used to guide the focus groups or the organization of the three emotion regulation categories. Specifically, Gross’ model includes two processes that overlap with “Get Out” strategies, situation modification and attentional deployment, which are consistent with youth-identified strategies of leaving the situation and distraction. Gross’ response modulation process overlaps with “Let It Out” strategies including physical release and expressing oneself. Last, Gross’ cognitive change process overlaps with the “Think It Out” strategies including positive thinking, normalizing, and considering the options. The only emotion regulation process in Gross’ model not identified during the focus groups was situation selection, which refers to planning that increases or decreases the likelihood of being in an emotionally arousing situation. This is not surprising given that this process was not as applicable to the focus group prompts emphasizing one’s presence in an emotionally evocative situation. Further, from our view, it is not developmentally appropriate or feasible to expect early adolescents to completely abstain from contexts in which emotions related to sex or other behaviors that can harm a person may be triggered. Overall, the correspondence of the focus group findings with Gross’ model provides further construct validity for the early adolescent-generated emotion regulation strategies.

Sexual Risk Behavior Prevention

A noted motivation for this study was to facilitate a developmentally appropriate emotion regulation-focused sexual risk behavior prevention program for early adolescents with mental health symptoms. The described “Get Out, Let It Out, Think It Out” organization has since been incorporated into a manualized prevention program for early adolescents with mental health symptoms (Project TRAC: Talking about Risk and Adolescent Choices). The TRAC program begins by raising awareness of emotions, as well as teaching identification and labeling of feelings. Teens then explore how emotions can affect behavior and how managing emotions can improve decision making “in the moment.” Get Out, Let It Out, and Think It Out are taught and practiced with non-risk behavior and then applied to risk behavior situations (e.g., sexual risk scenarios). The program integrated sexual health information throughout twelve, one-hour, small-group, after-school sessions. When examined through a randomized controlled trial, this intervention was found to be efficacious compared to a time-and attention-matched health promotion control group in use of emotion regulation strategies and in reducing engagement in sexual and other risk behaviors, with effects persisting even 30 months after intervention completion (Blinded for Review; Blinded for Review; Blinded for Review). The success of this program is consistent with research linking emotion regulation with adolescent sexual risk ( Brown et al., 2013 ; Hessler & Katx, 2010 ; Raffaelli & Crockett, 2003 ) and indicates that emotion regulation skills are a feasible target for minimizing risk behaviors that can affect health and that prevention is a successful model for targeting these behaviors.

Further, the success of these intervention studies provides support for the early adolescent generated emotion regulation strategies in the current qualitative study, and suggests that “Get Out, Let It Out, and Think It Out” might also be applicable to other health targets beyond sexual risk behaviors. A benefit of the qualitative approach of the current study is that it did not focus only on sexual risk behaviors, increasing the generalizability of the findings to novel contexts in early adolescence. For example, research has identified emotion regulation as critical to self-regulating behaviors (e.g., school performance, substance use, eating) and chronic illness management in early adolescence ( Garnefski, Koopman, Kraaij, & ten Cate, 2009 ; Lansing & Berg, 2014 ). In particular, the strategies identified in this study may be highly applicable to addressing early adolescent behaviors that need to be regulated from moment-to-moment and in the context of changes in emotion throughout the day, such as school performance, healthy eating, medication adherence, and type 1 diabetes or cystic fibrosis management.

Limitations

While the focus groups yielded a variety of perspectives on emotion regulation from early adolescents with mental health symptoms, data were from a relatively small sample of students in one geographic area and therefore may not generalize to other regions. Also, these perspectives may differ from those of youths without mental health symptoms. Similarly, because this study did not follow students as they got older, these data do not provide insights into the development of emotion regulation; rather, they are snapshots of the early adolescent developmental period. Also, these data reflect hypothetical situations. Participants did not personally experience these situations, nor were they emotionally aroused when generating responses. These discussions reflect what they know and might suggest to a peer but may not reflect what they actually do when they are emotionally dysregulated. Last, three of the four vignettes used in this study involved more negative affect than positive affect. Therefore, less information was gathered on how teens manage positive feelings, which are often associated with risk behaviors such as sexual risk taking and substance use.

Future Directions

Further research on emotion regulation and early adolescent risk behaviors to confirm these findings is necessary. This might include further testing of the strategies through surveys and focus groups with additional early adolescents. Such research should also involve momentary sampling methods for capturing momentary emotions and attempts at regulating those emotions, which would be preferable to only retrospective self-reports which may be biased by longer term mood regulation (Shrier et al., 2005). This research could help to identify the particular situations in which early adolescents with mental health symptoms are most likely to experience positive and negative emotions that disrupt healthy decision making and how in-the-moment emotion regulation attempts are linked with behaviors. Further studies could also be conducted to further assess the role of context in strategy selection. This work should also aim to explore the generalizability of the identified emotion regulation strategies to the broader early adolescent population.

Finally, given the paucity of research on early adolescent emotion regulation relative to research on longer-term mood regulation and coping skills, we feel that qualitative data were integral to understanding the developmental level and the acceptability of emotion regulation skills for early adolescents with mental health symptoms. Focus groups, even with a small number of youth, can be an effective tool to use to explore new developmental levels or new populations. We encourage researchers and clinicians to consider, or continue, using qualitative methods more frequently in their theory and program development processes to capitalize on these valuable perspectives.

Acknowledgments:

The authors would like to thank Timothy Walker, M.S., for his assistance with the project.

Funding Sources: Research supported by the National Institutes of Health (R34 MH 078750 and R01 NR 011906 to Dr. Christopher Houck) and the Lifespan/Tufts/Brown Center for AIDS Research. ClinicalTrials.gov ID: {"type":"clinical-trial","attrs":{"text":"NCT00741975","term_id":"NCT00741975"}} NCT00741975

Conflict of Interest: The authors declare that they have no conflict of interest.

Contributor Information

Amy Hughes Lansing, Bradley/Hasbro Children’s Research Center, & Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.

Kate M. Guthrie, Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University & The Miriam Hospital, Providence, RI, USA.

Wendy Hadley, Bradley/Hasbro Children’s Research Center, & Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.

Angela Stewart, Bradley Hospital, Providence, RI, USA.

April Peters, Bradley/Hasbro Children’s Research Center, Providence, RI, USA.

Christopher D. Houck, Bradley/Hasbro Children’s Research Center, & Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA.

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Emotional Measurement in Qualitative Research: Key Considerations

Market Research , Qualitative Research

Prefrontal Cortex, Limbic System, System 1, Behavioral Economics, Emotional Quotient – all phrases that didn’t matter in the market research industry until recently (the definition of recently being up for debate). The point is, market researchers have learned that emotions play a critical role in consumer behavior and satisfaction. 

Knowing that emotions play a role and knowing which emotions play what role are two very different things. While no one has all the answers to this problem (and all the answers may not even exist), here are some issues to consider when you take on the task of understanding emotions in a business context.

• Segmentation: It’s not news that people are different from one another. But particularly with emotional measurement, the emotions that people feel about a category, product, or brand are often related to their degree of engagement. For example, heavy users of Apple have a different and deeper emotional connection to the products and brand than moderate users of Apple. In addition to engagement, the emotional nature of the individual person can play a role in the connection to the brand. There are several ‘personality traits’ type models that can help structure the understanding that comes through qualitative research.
• Context: When delving into the emotions elicited by a category, product, or brand – context can be incredibly important depending on the nature of the product and the situations in which the product is used. Some products are personal by nature in that there is not much social interaction related to the product. Cleaning supplies are a reasonable example in that they are generally used in a private setting (Saturday morning around the house) and not part of a large social effort. In contrast, birthday cakes are almost always used in a social setting – be it large or small. The emotions elicited by either can be generally viewed as the emotions in any situation where these products are used. But let’s take the example of coffee. Coffee is sometimes used in personal settings and sometimes in social settings. The emotions elicited in each can be very different from each other with the exact same product. 

• Conscious: Many of the System 1 approaches would have you believe that all emotions are nonconscious or the decisions made from these emotions are all nonconscious. While this may be true for some people, most can articulate real emotions with a modest degree of depth and accuracy. Happy vs. Sad, Scared vs. Comforted, etc. In situations where nuance is not warranted, in depth interviews (and a good tool kit from a moderator) may be sufficient to uncover the emotions and emotional drivers for a project.
• Nonconscious – Implicit vs. Biometric Measures: In those cases where nuance is required or the respondent may be either unwilling or unable to understand or articulate, implicit association and biometric feedback are the two categories of tools that offer the most insight. A distinction is made here because definitions of these two words are wavering – Implicit is any tool that delves into nonconscious emotions but does not measure some component of the body in doing so. For (at least our definition of) implicit, the tool kit is generally an implicit association test or metaphor elicitation. There are several good tools and techniques that fall in or near these definitions. Biometric feedback has made important advance in quality and cost over the past few years and is therefore being used by more and more clients. The most common of these tools are eye-tracking and facial coding. Others that are reasonably available for qualitative research include Galvanic Skin Response, EEG, fMRI, and heart rate monitoring. While these are still early in their evolution and there is much to learn, these tools can help researchers understand emotions and emotional triggers.

Emotional measurement is difficult. However, with guidance and tools, it is easier than it ever has been, and these tools make a useful addition to the qualitative researcher’s toolkit to help deliver deeper insights that deliver greater business value.

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RL2024-4 Putting Feelings Where They’re Useful: Using Emotions as Data in Qualitative Analysis

Recorded On: 07/02/2024

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RL2024-4 Putting Feelings Where They’re Useful: Using Emotions as Data in Qualitative Analysis Recorded: Tuesday, July 2, 2024   INSTRUCTORS Hilary Lustick, University of Massachusetts – Lowell

Abeer Hakouz, University of Massachusetts – Lowell

Xiaoye Yang, University of Massachusetts – Lowell

The Emotion Coding Technique (Lustick, 2021) is a systematic method of qualitative analysis that captures emotions as they arise and helps us process them as information about us, our participants, and our research objectives. In this course, we will review the emotion coding technique, which applies a set of reflexive questions to a chunk of data (Lustick, 2021). We will then talk about some of the complexities of naming and reflecting on emotions during data analysis. We will share our own best practices and hear some additional strategies from the instructor, including an emotion wheel to choose from. Lastly, we will shift into independent work time to practice and reflect on the technique. The course is open to all qualitative and mixed methods researchers, with graduate students and early career researchers in mind. Please have basic qualitative and mixed methods training, including an understanding of positionality and reflexivity. You are advised, though not required, to have available original qualitative data, such as an interview transcript, with which to practice the technique.

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Scientific models for qualitative research: a textual thematic analysis coding system - part 2

Affiliations.

• 1 Forensic Mental Health Research Unit, Middelfart, Faculty of Health Science, Department of Regional Health Research, University of Southern Denmark, Denmark.
• 2 University of Newcastle School of Nursing and Midwifery, Callaghan, NSW, Australia.
• PMID: 37440301
• DOI: 10.7748/nr.2023.e1893

Background: Models are central to the acquisition and organisation of scientific knowledge. They can be viewed as tools for interpretive description as well as cognitive representations of an empirical phenomenon. However, discussions about how to develop models in qualitative research - particularly in the literature on thematic analysis - are sparse.

Aim: To discuss an approach to scientific qualitative modelling that uses the new technique described in the first part of this article ( Gildberg and Wilson 2023 ): the Empirical Test for Thematic Analysis (ETTA).

Discussion: The authors discuss scientific models and their inherent limitations and strengths, so that others may assess models and their potential.

Conclusion: A limitation of ETTA is the risk that excessive rigour and systematisation could reduce creativity in the construction of models. However, on balance there is a scientific need for qualitative researchers to improve their capability to refine and describe the techniques they use to construct models, adequately explain the reliable generation of models, and improve transparency regarding the epistemological and methodological basis for the construction of models.

Implications for practice: By using ETTA on qualitative data obtained from clinical practice it becomes possible to illuminate the interconnections among themes within the data. This approach not only assists in illustrating these connections, it also enables clinicians and researchers to gain a comprehensive understanding of specific clinical phenomena through the use of models. The process of developing and using these models enables the simulation and strategic intervention development based on data that addresses the specific problem being investigated.

Keywords: data analysis; methodology; qualitative research; research.

© 2023 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

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None declared

Erratum for

• Scientific models for qualitative research: a textual thematic analysis coding system - Part 1. Alkier Gildberg F, Wilson R. Alkier Gildberg F, et al. Nurse Res. 2023 Sep 7;31(3):36-42. doi: 10.7748/nr.2023.e1893. Epub 2023 May 31. Nurse Res. 2023. PMID: 37254707

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