quantitative research coding

• Data Management Expert Guide

Quantitative coding

• 2. Organise & Document
• Sources and further reading
• 6. Archive & Publish
• 7. Discover
• 8. Contributors

Table of Contents

Quantitative coding is the process of categorising the collected non-numerical information into groups and assigning the numerical codes to these groups. Numeric coding is shared by all statistical software and among others, it facilitates data conversion and measurement comparisons.

Closed-ended questions

For closed-ended questions in survey questionnaires, the coding scheme is often incorporated directly into the questionnaire and data is entered numerically. This process is automated in computer-assisted interviewing (CAPI, CATI, etc.), where an answer and its code are saved immediately into a computer in the course of data collection. Answers can also be coded on paper questionnaires when coders record codes in a designed spot of the questionnaire before they are digitalised. If the numberical codes are not incorporated in your questionnaire, set up a detailed procedure of how to code the different alternatives.

Open-ended questions and other textual information

More complex coding exercises, e.g. for textual answers in survey questionnaires, require an independent coding process with a clearly defined design: a coding structure and a procedure and schedule of exercises if there are several coders.

Documentation

The meaning of codes must be documented. Specialized analytic software (SPSS, SAS, STATA, etc.) lets the user assign labels directly to the codes. For the principles of the construction of labels, please, see the sub-section ' Organisation of variables '. If the software does not allow you to assign code labels directly to data, you have to document the codes in a separate document as part of the metadata.

Coding recommendations

In the accordion below you find coding recommendations which are inspired by ICPSR (2012).

Include identification variables

All identification variables should be included at the beginning of your data file. Identification variables usually include a unique identification of your study/data file, unique ID numbers of cases in your data file (e.g. ID of the respondent, ID of his/her household, etc.) as well as the identification of other characteristics essential for analysis (e.g. identification of different methods of data collection or sources, identification of the over-sample, etc.).

Make code categories exclusive and coherent throughout the database

Code categories should be mutually exclusive, exhaustive, and precisely defined. Ambiguity will cause coding difficulties and problems with the interpretation of the data. You should be able to assign each response of the respondent into one and only one category.

Preserve original information

Recording original data, such as age and income, is more useful than collapsing or bracketing the information. With original or detailed data, secondary analysts can determine other meaningful brackets on their own rather than being restricted to those chosen by others.

Document the coding schemes

Responses to closed-ended questions should retain the original coding scheme to avoid errors and confusion. For open-ended questions, investigators can either use a predetermined coding scheme or construct a coding scheme based on major categories that emerge in survey responses. Any coding scheme and its derivation should be reported in study documentation.

Check verbatim text data for data disclosure risk

Responses recorded as full verbatim (word for word) must be reviewed for disclosure risk and if necessary treated in accordance with applicable personal data protection regulations.

Check coding

It is advisable to verify the coding of selected cases by repeating the process with an independent coder. This provides means for verification of both the coder’s work and the functionality of your coding scheme.

Distinguishing between major and lower level categories

If a series of responses require more than one field or if the response is very complex (for example a detailed description of one´s occupation), it is advisable to apply a coding scheme distinguishing between major, secondary and any possible lower level categories. The first digit of the code identifies a major category, the second digit can distinguish specific responses within the major categories, etc.

The International Standard Classification of Occupations (ISCO) (International Labour Organisation, 2016) is an example of such a hierarchical category scheme. An example of its use is given below.

Consider the following ...

• I. Standardised coding schemes

The use of standardised classifications and coding schemes brings many advantages, e.g.:

• Comparability with data from other studies using the same concept;
• Comprehensibility for researchers who work with these concepts.

A disadvantage lies in the necessity to adapt your research intentions in line with the concept of the coding scheme.

Several standardised classification and coding schemes exist that you can use. For coding occupations it is the International Standard Classification of Occupations (ISCO) (International Labour Organisation, 2016), for coding education it is the International Standard Classification of Education (ISCED) (Unesco, 2011), for geographic territories it is the Nomenclature of territorial units for statistics (NUTS) (Eurostat, 2013), for economic activities it is the Statistical classification of economic activities (NACE) (Eurostat, 2008), for languages it is ISO 639.2 (Library of Congress, n.d.), for disease it is the International Classification of Diseases (ICD) (World Health Organisation, 2016), etc.

Occupational classifications such as such as the International Standard Classification of Occupations (ISCO) (International Labour Organization, 2010) are examples of widespread standard coding schemes. ISCO is an example of a hierarchical category scheme.

Occupational information has several dimensions and in questionnaire surveys, these need to be collected in detail. This is, as a rule, done by means of one or more open-ended questions.

The current ISCO-2008 uses four-digit codes. In the table below you see some examples.

Source: International Labour Organization (2016). For an example of a recommended methodology of collection of information on occupations see Ganzeboom (2010) .

• II. Coding missing values

Not all the questions in a questionnaire are answered by all respondents, which results in missing values on a variable level in the data file (so-called item non-response). It is crucial for data integrity to distinguish at least the situations when values are missing, because the variable is not applicable to the particular respondents.

Furthermore, it is often useful for analyses to identify whether the value is missing because the respondent did not know the answer, refused to answer or simply did not answer or consider other reasons for missing values (see the example below). The information on missing values is always an important part of your documentation and promotes transparency of your research work. However, bear in mind that possibilities to differentiate between many different types of the missing values in analysis can be limited by the abilities of your software.

It is advisable to establish a uniform system for coding missing values for the entire database. Typically, negative values or values like 7, 8, 9 or 97, 98, 99 or 997, 998, 999, etc. (where the number of digits corresponds to the variable’s format and the number of valid values) are used for numeric coding of missing values. The coding scheme for missing values should prevent overlapping codes for valid and missing values. For instance, whenever the digit zero is used for missing values, we should bear in mind that zero may represent a valid value for many variables such as personal income.

Respondents in surveys sometimes do not answer all questions in a questionnaire. It is advisable to distinguish between various reasons that data went missing (ICPSR, 2012). The following situations are distinguished in survey research (frequently used acronyms are bracketed):

• No answer ( NA ): The respondent did not answer a question when he/she should have;
• Refusal: The respondent explicitly refused to answer;
• Don’t Know ( DK ): The respondent did not answer a question because he/she had no opinion or did not know the information required for answering. As a result, the respondent chose ‘don’t know’, ‘no opinion’ etc. as the answer;
• Processing Error: The respondent provided an answer but, for some reason (interviewer error, illegible record, incorrect coding etc.), it was not recorded in the database.
• Not Applicable/Inapplicable ( NAP/INAP ): A question did not apply to the respondent. For example, a question was skipped following a filter question (e.g. respondents without a partner did not answer partner-related questions) or some sets of questions were only asked of random subsamples.
• No Match: In this case, data are drawn from different sources, and information from one source cannot be matched with a corresponding value from another source.
• No Data Available: The question should have been asked, but the answer is missing for a reason other than those above or for an unknown reason.

Training coders to prevent coder variance

Coders may vary in the way they assign codes to variable values, i.e. each of them uses the same coding scheme in a slightly different way. This results in so-called “coder variance”. Coder variance is a specific source of non-sampling error (i.e., error additional to the statistical “sampling” error) and may cause systematic deviations of the sample.

Coding of textual information is a complicated cognitive process and the coder may pose a significant influence on the information that appears in the database, as well as become a source of systematic error. That is why the implementation of complicated coding schemes often requires the construction of a theoretically and technically well-founded design and requires specific coder’s competencies and training.

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Chapter 18. Data Analysis and Coding

Introduction.

Piled before you lie hundreds of pages of fieldnotes you have taken, observations you’ve made while volunteering at city hall. You also have transcripts of interviews you have conducted with the mayor and city council members. What do you do with all this data? How can you use it to answer your original research question (e.g., “How do political polarization and party membership affect local politics?”)? Before you can make sense of your data, you will have to organize and simplify it in a way that allows you to access it more deeply and thoroughly. We call this process coding . [1] Coding is the iterative process of assigning meaning to the data you have collected in order to both simplify and identify patterns. This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

To those who have not yet conducted a qualitative study, the sheer amount of collected data will be a surprise. Qualitative data can be absolutely overwhelming—it may mean hundreds if not thousands of pages of interview transcripts, or fieldnotes, or retrieved documents. How do you make sense of it? Students often want very clear guidelines here, and although I try to accommodate them as much as possible, in the end, analyzing qualitative data is a bit more of an art than a science: “The process of bringing order, structure, and interpretation to a mass of collected data is messy, ambiguous, time-consuming, creative, and fascinating. It does not proceed in a linear fashion: it is not neat. At times, the researcher may feel like an eccentric and tormented artist; not to worry, this is normal” ( Marshall and Rossman 2016:214 ).

To complicate matters further, each approach (e.g., Grounded Theory, deep ethnography, phenomenology) has its own language and bag of tricks (techniques) when it comes to analysis. Grounded Theory, for example, uses in vivo coding to generate new theoretical insights that emerge from a rigorous but open approach to data analysis. Ethnographers, in contrast, are more focused on creating a rich description of the practices, behaviors, and beliefs that operate in a particular field. They are less interested in generating theory and more interested in getting the picture right, valuing verisimilitude in the presentation. And then there are some researchers who seek to account for the qualitative data using almost quantitative methods of analysis, perhaps counting and comparing the uses of certain narrative frames in media accounts of a phenomenon. Qualitative content analysis (QCA) often includes elements of counting (see chapter 17). For these researchers, having very clear hypotheses and clearly defined “variables” before beginning analysis is standard practice, whereas the same would be expressly forbidden by those researchers, like grounded theorists, taking a more emergent approach.

All that said, there are some helpful techniques to get you started, and these will be presented in this and the following chapter. As you become more of an expert yourself, you may want to read more deeply about the tradition that speaks to your research. But know that there are many excellent qualitative researchers that use what works for any given study, who take what they can from each tradition. Most of us find this permissible (but watch out for the methodological purists that exist among us).

Qualitative Data Analysis as a Long Process!

Although most of this and the following chapter will focus on coding, it is important to understand that coding is just one (very important) aspect of the long data-analysis process. We can consider seven phases of data analysis, each of which is important for moving your voluminous data into “findings” that can be reported to others. The first phase involves data organization. This might mean creating a special password-protected Dropbox folder for storing your digital files. It might mean acquiring computer-assisted qualitative data-analysis software ( CAQDAS ) and uploading all transcripts, fieldnotes, and digital files to its storage repository for eventual coding and analysis. Finding a helpful way to store your material can take a lot of time, and you need to be smart about this from the very beginning. Losing data because of poor filing systems or mislabeling is something you want to avoid. You will also want to ensure that you have procedures in place to protect the confidentiality of your interviewees and informants. Filing signed consent forms (with names) separately from transcripts and linking them through an ID number or other code that only you have access to (and store safely) are important.

Once you have all of your material safely and conveniently stored, you will need to immerse yourself in the data. The second phase consists of reading and rereading or viewing and reviewing all of your data. As you do this, you can begin to identify themes or patterns in the data, perhaps writing short memos to yourself about what you are seeing. You are not committing to anything in this third phase but rather keeping your eyes and mind open to what you see. In an actual study, you may very well still be “in the field” or collecting interviews as you do this, and what you see might push you toward either concluding your data collection or expanding so that you can follow a particular group or factor that is emerging as important. For example, you may have interviewed twelve international college students about how they are adjusting to life in the US but realized as you read your transcripts that important gender differences may exist and you have only interviewed two women (and ten men). So you go back out and make sure you have enough female respondents to check your impression that gender matters here. The seven phases do not proceed entirely linearly! It is best to think of them as recursive; conceptually, there is a path to follow, but it meanders and flows.

Coding is the activity of the fourth phase . The second part of this chapter and all of chapter 19 will focus on coding in greater detail. For now, know that coding is the primary tool for analyzing qualitative data and that its purpose is to both simplify and highlight the important elements buried in mounds of data. Coding is a rigorous and systematic process of identifying meaning, patterns, and relationships. It is a more formal extension of what you, as a conscious human being, are trained to do every day when confronting new material and experiences. The “trick” or skill is to learn how to take what you do naturally and semiconsciously in your mind and put it down on paper so it can be documented and verified and tested and refined.

At the conclusion of the coding phase, your material will be searchable, intelligible, and ready for deeper analysis. You can begin to offer interpretations based on all the work you have done so far. This fifth phase might require you to write analytic memos, beginning with short (perhaps a paragraph or two) interpretations of various aspects of the data. You might then attempt stitching together both reflective and analytical memos into longer (up to five pages) general interpretations or theories about the relationships, activities, patterns you have noted as salient.

As you do this, you may be rereading the data, or parts of the data, and reviewing your codes. It’s possible you get to this phase and decide you need to go back to the beginning. Maybe your entire research question or focus has shifted based on what you are now thinking is important. Again, the process is recursive , not linear. The sixth phase requires you to check the interpretations you have generated. Are you really seeing this relationship, or are you ignoring something important you forgot to code? As we don’t have statistical tests to check the validity of our findings as quantitative researchers do, we need to incorporate self-checks on our interpretations. Ask yourself what evidence would exist to counter your interpretation and then actively look for that evidence. Later on, if someone asks you how you know you are correct in believing your interpretation, you will be able to explain what you did to verify this. Guard yourself against accusations of “ cherry-picking ,” selecting only the data that supports your preexisting notion or expectation about what you will find. [2]

The seventh and final phase involves writing up the results of the study. Qualitative results can be written in a variety of ways for various audiences (see chapter 20). Due to the particularities of qualitative research, findings do not exist independently of their being written down. This is different for quantitative research or experimental research, where completed analyses can somewhat speak for themselves. A box of collected qualitative data remains a box of collected qualitative data without its written interpretation. Qualitative research is often evaluated on the strength of its presentation. Some traditions of qualitative inquiry, such as deep ethnography, depend on written thick descriptions, without which the research is wholly incomplete, even nonexistent. All of that practice journaling and writing memos (reflective and analytical) help develop writing skills integral to the presentation of the findings.

Remember that these are seven conceptual phases that operate in roughly this order but with a lot of meandering and recursivity throughout the process. This is very different from quantitative data analysis, which is conducted fairly linearly and processually (first you state a falsifiable research question with hypotheses, then you collect your data or acquire your data set, then you analyze the data, etc.). Things are a bit messier when conducting qualitative research. Embrace the chaos and confusion, and sort your way through the maze. Budget a lot of time for this process. Your research question might change in the middle of data collection. Don’t worry about that. The key to being nimble and flexible in qualitative research is to start thinking and continue thinking about your data, even as it is being collected. All seven phases can be started before all the data has been gathered. Data collection does not always precede data analysis. In some ways, “qualitative data collection is qualitative data analysis.… By integrating data collection and data analysis, instead of breaking them up into two distinct steps, we both enrich our insights and stave off anxiety. We all know the anxiety that builds when we put something off—the longer we put it off, the more anxious we get. If we treat data collection as this mass of work we must do before we can get started on the even bigger mass of work that is analysis, we set ourselves up for massive anxiety” ( Rubin 2021:182–183 ; emphasis added).

The Coding Stage

A code is “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data” ( Saldaña 2014:5 ). Codes can be applied to particular sections of or entire transcripts, documents, or even videos. For example, one might code a video taken of a preschooler trying to solve a puzzle as “puzzle,” or one could take the transcript of that video and highlight particular sections or portions as “arranging puzzle pieces” (a descriptive code) or “frustration” (a summative emotion-based code). If the preschooler happily shouts out, “I see it!” you can denote the code “I see it!” (this is an example of an in vivo, participant-created code). As one can see from even this short example, there are many different kinds of codes and many different strategies and techniques for coding, more of which will be discussed in detail in chapter 19. The point to remember is that coding is a rigorous systematic process—to some extent, you are always coding whenever you look at a person or try to make sense of a situation or event, but you rarely do this consciously. Coding is the process of naming what you are seeing and how you are simplifying the data so that you can make sense of it in a way that is consistent with your study and in a way that others can understand and follow and replicate. Another way of saying this is that a code is “a researcher-generated interpretation that symbolizes or translates data” ( Vogt et al. 2014:13 ).

As with qualitative data analysis generally, coding is often done recursively, meaning that you do not merely take one pass through the data to create your codes. Saldaña ( 2014 ) differentiates first-cycle coding from second-cycle coding. The goal of first-cycle coding is to “tag” or identify what emerges as important codes. Note that I said emerges—you don’t always know from the beginning what will be an important aspect of the study or not, so the coding process is really the place for you to begin making the kinds of notes necessary for future analyses. In second-cycle coding, you will want to be much more focused—no longer gathering wholly new codes but synthesizing what you have into metacodes.

You might also conceive of the coding process in four parts (figure 18.1). First, identify a representative or diverse sample set of interview transcripts (or fieldnotes or other documents). This is the group you are going to use to get a sense of what might be emerging. In my own study of career obstacles to success among first-generation and working-class persons in sociology, I might select one interview from each career stage: a graduate student, a junior faculty member, a senior faculty member.

Second, code everything (“ open coding ”). See what emerges, and don’t limit yourself in any way. You will end up with a ton of codes, many more than you will end up with, but this is an excellent way to not foreclose an interesting finding too early in the analysis. Note the importance of starting with a sample of your collected data, because otherwise, open coding all your data is, frankly, impossible and counterproductive. You will just get stuck in the weeds.

Third, pare down your coding list. Where you may have begun with fifty (or more!) codes, you probably want no more than twenty remaining. Go back through the weeds and pull out everything that does not have the potential to bloom into a nicely shaped garden. Note that you should do this before tackling all of your data . Sometimes, however, you might need to rethink the sample you chose. Let’s say that the graduate student interview brought up some interesting gender issues that were pertinent to female-identifying sociologists, but both the junior and the senior faculty members identified as male. In that case, I might read through and open code at least one other interview transcript, perhaps a female-identifying senior faculty member, before paring down my list of codes.

This is also the time to create a codebook if you are using one, a master guide to the codes you are using, including examples (see Sample Codebooks 1 and 2 ). A codebook is simply a document that lists and describes the codes you are using. It is easy to forget what you meant the first time you penciled a coded notation next to a passage, so the codebook allows you to be clear and consistent with the use of your codes. There is not one correct way to create a codebook, but generally speaking, the codebook should include (1) the code (either name or identification number or both), (2) a description of what the code signifies and when and where it should be applied, and (3) an example of the code to help clarify (2). Listing all the codes down somewhere also allows you to organize and reorganize them, which can be part of the analytical process. It is possible that your twenty remaining codes can be neatly organized into five to seven master “themes.” Codebooks can and should develop as you recursively read through and code your collected material. [3]

Fourth, using the pared-down list of codes (or codebook), read through and code all the data. I know many qualitative researchers who work without a codebook, but it is still a good practice, especially for beginners. At the very least, read through your list of codes before you begin this “ closed coding ” step so that you can minimize the chance of missing a passage or section that needs to be coded. The final step is…to do it all again. Or, at least, do closed coding (step four) again. All of this takes a great deal of time, and you should plan accordingly.

Researcher Note

People often say that qualitative research takes a lot of time. Some say this because qualitative researchers often collect their own data. This part can be time consuming, but to me, it’s the analytical process that takes the most time. I usually read every transcript twice before starting to code, then it usually takes me six rounds of coding until I’m satisfied I’ve thoroughly coded everything. Even after the coding, it usually takes me a year to figure out how to put the analysis together into a coherent argument and to figure out what language to use. Just deciding what name to use for a particular group or idea can take months. Understanding this going in can be helpful so that you know to be patient with yourself.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Note that there is no magic in any of this, nor is there any single “right” way to code or any “correct” codes. What you see in the data will be prompted by your position as a researcher and your scholarly interests. Where the above codes on a preschooler solving a puzzle emerged from my own interest in puzzle solving, another researcher might focus on something wholly different. A scholar of linguistics, for example, may focus instead on the verbalizations made by the child during the discovery process, perhaps even noting particular vocalizations (incidence of grrrs and gritting of the teeth, for example). Your recording of the codes you used is the important part, as it allows other researchers to assess the reliability and validity of your analyses based on those codes. Chapter 19 will provide more details about the kinds of codes you might develop.

Saldaña ( 2014 ) lists seven “necessary personal attributes” for successful coding. To paraphrase, they are the following:

• Having (or practicing) good organizational skills
• Perseverance
• The ability and willingness to deal with ambiguity
• Flexibility
• Creativity, broadly understood, which includes “the ability to think visually, to think symbolically, to think in metaphors, and to think of as many ways as possible to approach a problem” (20)
• Commitment to being rigorously ethical
• Having an extensive vocabulary [4]

Writing Analytic Memos during/after Coding

Coding the data you have collected is only one aspect of analyzing it. Too many beginners have coded their data and then wondered what to do next. Coding is meant to help organize your data so that you can see it more clearly, but it is not itself an analysis. Thinking about the data, reviewing the coded data, and bringing in the previous literature (here is where you use your literature review and theory) to help make sense of what you have collected are all important aspects of data analysis. Analytic memos are notes you write to yourself about the data. They can be short (a single page or even a paragraph) or long (several pages). These memos can themselves be the subject of subsequent analytic memoing as part of the recursive process that is qualitative data analysis.

Short analytic memos are written about impressions you have about the data, what is emerging, and what might be of interest later on. You can write a short memo about a particular code, for example, and why this code seems important and where it might connect to previous literature. For example, I might write a paragraph about a “cultural capital” code that I use whenever a working-class sociologist says anything about “not fitting in” with their peers (e.g., not having the right accent or hairstyle or private school background). I could then write a little bit about Bourdieu, who originated the notion of cultural capital, and try to make some connections between his definition and how I am applying it here. I can also use the memo to raise questions or doubts I have about what I am seeing (e.g., Maybe the type of school belongs somewhere else? Is this really the right code?). Later on, I can incorporate some of this writing into the theory section of my final paper or article. Here are some types of things that might form the basis of a short memo: something you want to remember, something you noticed that was new or different, a reaction you had, a suspicion or hunch that you are developing, a pattern you are noticing, any inferences you are starting to draw. Rubin ( 2021 ) advises, “Always include some quotation or excerpt from your dataset…that set you off on this idea. It’s happened to me so many times—I’ll have a really strong reaction to a piece of data, write down some insight without the original quotation or context, and then [later] have no idea what I was talking about and have no way of recreating my insight because I can’t remember what piece of data made me think this way” ( 203 ).

All CAQDAS programs include spaces for writing, generating, and storing memos. You can link a memo to a particular transcript, for example. But you can just as easily keep a notebook at hand in which you write notes to yourself, if you prefer the more tactile approach. Drawing pictures that illustrate themes and patterns you are beginning to see also works. The point is to write early and write often, as these memos are the building blocks of your eventual final product (chapter 20).

In the next chapter (chapter 19), we will go a little deeper into codes and how to use them to identify patterns and themes in your data. This chapter has given you an idea of the process of data analysis, but there is much yet to learn about the elements of that process!

Qualitative Data-Analysis Samples

The following three passages are examples of how qualitative researchers describe their data-analysis practices. The first, by Harvey, is a useful example of how data analysis can shift the original research questions. The second example, by Thai, shows multiple stages of coding and how these stages build upward to conceptual themes and theorization. The third example, by Lamont, shows a masterful use of a variety of techniques to generate theory.

Example 1: “Look Someone in the Eye” by Peter Francis Harvey ( 2022 )

I entered the field intending to study gender socialization. However, through the iterative process of writing fieldnotes, rereading them, conducting further research, and writing extensive analytic memos, my focus shifted. Abductive analysis encourages the search for unexpected findings in light of existing literature. In my early data collection, fieldnotes, and memoing, classed comportment was unmistakably prominent in both schools. I was surprised by how pervasive this bodily socialization proved to be and further surprised by the discrepancies between the two schools.…I returned to the literature to compare my empirical findings.…To further clarify patterns within my data and to aid the search for disconfirming evidence, I constructed data matrices (Miles, Huberman, and Saldaña 2013). While rereading my fieldnotes, I used ATLAS.ti to code and recode key sections (Miles et al. 2013), punctuating this process with additional analytic memos. ( 2022:1420 )

Example 2:” Policing and Symbolic Control” by Mai Thai ( 2022 )

Conventional to qualitative research, my analyses iterated between theory development and testing. Analytical memos were written throughout the data collection, and my analyses using MAXQDA software helped me develop, confirm, and challenge specific themes.…My early coding scheme which included descriptive codes (e.g., uniform inspection, college trips) and verbatim codes of the common terms used by field site participants (e.g., “never quit,” “ghetto”) led me to conceptualize valorization. Later analyses developed into thematic codes (e.g., good citizens, criminality) and process codes (e.g., valorization, criminalization), which helped refine my arguments. ( 2022:1191–1192 )

Example 3: The Dignity of Working Men by Michèle Lamont ( 2000 )

To analyze the interviews, I summarized them in a 13-page document including socio-demographic information as well as information on the boundary work of the interviewees. To facilitate comparisons, I noted some of the respondents’ answers on grids and summarized these on matrix displays using techniques suggested by Miles and Huberman for standardizing and processing qualitative data. Interviews were also analyzed one by one, with a focus on the criteria that each respondent mobilized for the evaluation of status. Moreover, I located each interviewee on several five-point scales pertaining to the most significant dimensions they used to evaluate status. I also compared individual interviewees with respondents who were similar to and different from them, both within and across samples. Finally, I classified all the transcripts thematically to perform a systematic analysis of all the important themes that appear in the interviews, approaching the latter as data against which theoretical questions can be explored. ( 2000:256–257 )

Sample Codebook 1

This is an abridged version of the codebook used to analyze qualitative responses to a question about how class affects careers in sociology. Note the use of numbers to organize the flow, supplemented by highlighting techniques (e.g., bolding) and subcoding numbers.

01. CAPS: Any reference to “capitals” in the response, even if the specific words are not used

01.1: cultural capital 01.2: social capital 01.3: economic capital

(can be mixed: “0.12”= both cultural and asocial capital; “0.23”= both social and economic)

01. CAPS: a reference to “capitals” in which the specific words are used [ bold : thus, 01.23 means that both social capital and economic capital were mentioned specifically

02. DEBT: discussion of debt

02.1: mentions personal issues around debt 02.2: discusses debt but in the abstract only (e.g., “people with debt have to worry”)

03. FirstP: how the response is positioned

03.1: neutral or abstract response 03.2: discusses self (“I”) 03.3: discusses others (“they”)

Sample Coded Passage:

* Question: What other codes jump out to you here? Shouldn’t there be a code for feelings of loneliness or alienation? What about an emotions code ?

Sample Codebook 2

This is an example that uses "word" categories only, with descriptions and examples for each code

Further Readings

Elliott, Victoria. 2018. “Thinking about the Coding Process in Qualitative Analysis.” Qualitative Report 23(11):2850–2861. Address common questions those new to coding ask, including the use of “counting” and how to shore up reliability.

Friese, Susanne. 2019. Qualitative Data Analysis with ATLAS.ti. 3rd ed. A good guide to ATLAS.ti, arguably the most used CAQDAS program. Organized around a series of “skills training” to get you up to speed.

Jackson, Kristi, and Pat Bazeley. 2019. Qualitative Data Analysis with NVIVO . 3rd ed. Thousand Oaks, CA: SAGE. If you want to use the CAQDAS program NVivo, this is a good affordable guide to doing so. Includes copious examples, figures, and graphic displays.

LeCompte, Margaret D. 2000. “Analyzing Qualitative Data.” Theory into Practice 39(3):146–154. A very practical and readable guide to the entire coding process, with particular applicability to educational program evaluation/policy analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook . 2nd ed. Thousand Oaks, CA: SAGE. A classic reference on coding. May now be superseded by Miles, Huberman, and Saldaña (2019).

Miles, Matthew B., A. Michael Huberman, and Johnny Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook . 4th ed. Thousand Oaks, CA; SAGE. A practical methods sourcebook for all qualitative researchers at all levels using visual displays and examples. Highly recommended.

Saldaña, Johnny. 2014. The Coding Manual for Qualitative Researchers . 2nd ed. Thousand Oaks, CA: SAGE. The most complete and comprehensive compendium of coding techniques out there. Essential reference.

Silver, Christina. 2014. Using Software in Qualitative Research: A Step-by-Step Guide. 2nd ed. Thousand Oaks, CA; SAGE. If you are unsure which CAQDAS program you are interested in using or want to compare the features and usages of each, this guidebook is quite helpful.

Vogt, W. Paul, Elaine R. Vogt, Diane C. Gardner, and Lynne M. Haeffele2014. Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods . New York: The Guilford Press. User-friendly reference guide to all forms of analysis; may be particularly helpful for those engaged in mixed-methods research.

• When you have collected content (historical, media, archival) that interests you because of its communicative aspect, content analysis (chapter 17) is appropriate. Whereas content analysis is both a research method and a tool of analysis, coding is a tool of analysis that can be used for all kinds of data to address any number of questions. Content analysis itself includes coding. ↵
• Scientific research, whether quantitative or qualitative, demands we keep an open mind as we conduct our research, that we are “neutral” regarding what is actually there to find. Students who are trained in non-research-based disciplines such as the arts or philosophy or who are (admirably) focused on pursuing social justice can too easily fall into the trap of thinking their job is to “demonstrate” something through the data. That is not the job of a researcher. The job of a researcher is to present (and interpret) findings—things “out there” (even if inside other people’s hearts and minds). One helpful suggestion: when formulating your research question, if you already know the answer (or think you do), scrap that research. Ask a question to which you do not yet know the answer. ↵
• Codebooks are particularly useful for collaborative research so that codes are applied and interpreted similarly. If you are working with a team of researchers, you will want to take extra care that your codebooks remain in synch and that any refinements or developments are shared with fellow coders. You will also want to conduct an “intercoder reliability” check, testing whether the codes you have developed are clearly identifiable so that multiple coders are using them similarly. Messy, unclear codes that can be interpreted differently by different coders will make it much more difficult to identify patterns across the data. ↵
• Note that this is important for creating/denoting new codes. The vocabulary does not need to be in English or any particular language. You can use whatever words or phrases capture what it is you are seeing in the data. ↵

A first-cycle coding process in which gerunds are used to identify conceptual actions, often for the purpose of tracing change and development over time.  Widely used in the Grounded Theory approach.

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”).

Computer-assisted qualitative data-analysis software.  These are software packages that can serve as a repository for qualitative data and that enable coding, memoing, and other tools of data analysis.  See chapter 17 for particular recommendations.

The purposeful selection of some data to prove a preexisting expectation or desired point of the researcher where other data exists that would contradict the interpretation offered.  Note that it is not cherry picking to select a quote that typifies the main finding of a study, although it would be cherry picking to select a quote that is atypical of a body of interviews and then present it as if it is typical.

A preliminary stage of coding in which the researcher notes particular aspects of interest in the data set and begins creating codes.  Later stages of coding refine these preliminary codes.  Note: in Grounded Theory , open coding has a more specific meaning and is often called initial coding : data are broken down into substantive codes in a line-by-line manner, and incidents are compared with one another for similarities and differences until the core category is found.  See also closed coding .

A set of codes, definitions, and examples used as a guide to help analyze interview data.  Codebooks are particularly helpful and necessary when research analysis is shared among members of a research team, as codebooks allow for standardization of shared meanings and code attributions.

The final stages of coding after the refinement of codes has created a complete list or codebook in which all the data is coded using this refined list or codebook.  Compare to open coding .

A first-cycle coding process in which emotions and emotionally salient passages are tagged.

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.

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Coding in Research: An Overview and Explanation in Sociology

In the field of sociology, coding in research plays a crucial role in analyzing and interpreting data. Coding is a systematic process of categorizing and organizing qualitative or quantitative data, allowing researchers to identify patterns, themes, and relationships within their data sets. This article aims to outline and explain the concept of coding in research, its importance, and its various methods used in sociological studies.

What is Coding in Research?

Coding in research refers to the process of assigning labels or categories to data in order to make sense of it. It involves breaking down raw data into smaller, manageable units and assigning meaning to these units. By coding data, researchers can identify patterns, themes, and connections that may not be immediately apparent.

The Importance of Coding in Research

Coding is essential in research for several reasons:

• Data Organization: Coding allows researchers to organize large amounts of data into meaningful categories, making it easier to analyze and interpret.
• Data Analysis: By coding data, researchers can identify patterns, themes, and relationships within the data set, leading to deeper insights and understanding.
• Comparability: Coding provides a standardized framework for analyzing data, enabling researchers to compare findings across different studies or datasets.
• Reliability and Validity: Coding helps ensure the reliability and validity of research findings by providing a systematic and transparent process for analyzing data.

Methods of Coding in Research

There are various methods of coding used in sociological research. Some common methods include:

1. Open Coding

Open coding involves the initial exploration and identification of concepts, categories, or themes within the data. Researchers analyze the data without any preconceived notions, allowing new ideas and patterns to emerge. This method is often used in the early stages of research when the data is unstructured or when exploring a new topic.

2. Axial Coding

Axial coding involves the identification and categorization of relationships between concepts or categories. Researchers look for connections and linkages between different codes, creating a more comprehensive understanding of the data. This method helps in developing theories and models that explain the relationships between various sociological phenomena.

3. Selective Coding

Selective coding involves the refinement and integration of codes into a coherent framework or theory. Researchers focus on the most significant codes and develop a narrative or explanation that connects them. This method helps in creating a comprehensive and coherent analysis of the data, leading to meaningful conclusions.

4. Thematic Coding

Thematic coding involves the identification and categorization of themes or patterns within the data. Researchers look for recurring ideas, concepts, or issues and assign them specific codes. This method helps in understanding the main themes or topics that emerge from the data, providing a structured and organized analysis.

Challenges in Coding

While coding in research offers numerous benefits, it also presents some challenges:

• Subjectivity: Coding involves interpretation, and different researchers may assign different codes to the same data. This subjectivity can impact the reliability and validity of the findings.
• Time and Effort: Coding can be a time-consuming and labor-intensive process, especially when dealing with large datasets.
• Inter-coder Reliability: When multiple researchers are involved in coding, ensuring inter-coder reliability becomes crucial. Consistency in coding decisions among different researchers is essential to maintain the integrity of the analysis.

Coding in research is a vital component of sociological studies. It enables researchers to organize, analyze, and interpret data, leading to meaningful insights and conclusions. By using various coding methods, researchers can identify patterns, themes, and relationships within their data sets. While coding presents challenges, careful consideration and transparency can help mitigate these issues. Overall, coding in research enhances the rigor and validity of sociological studies, contributing to the advancement of knowledge in the field.

Mr Edwards has a PhD in sociology and 10 years of experience in sociological knowledge

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Data Coding in Research Methodology

Research methodology is the foundation of any scientific study. It gives us a structured way to gather, sort, and make sense of data so we can draw meaningful conclusions. One critical step in research is called “data coding.”

Data Coding

Data coding is the act of converting unstructured data into a more manageable and structured format so that researchers may find themes, patterns, and relationships in their data.

Types of Data Coding

1. nominal coding.

This is like giving data labels or categories. For example, if we asked people about their marital status, we could code “Single” as 1, “Married” as 2, “Divorced” as 3, and “Widowed” as 4.

2. Ordinal coding

3. dichotomous coding, 4. numeric coding.

This is when we use numbers for data. For example, if we’re asking about age groups, we might code “18-24 years old” as 1, “25-34 years old” as 2, “35-44 years old” as 3, and so on.

5. Derived variables

6. truncation, what are the implications of data coding.

There are several reasons why data coding is crucial to the  research process .

Examples of Data Coding in Research

Now, let’s look at some examples to see how data coding is used in different kinds of research:

Qualitative Research

Historical research.

Even historical research benefits from data coding. Themes, key events, and time periods can all be used by historians to assign codes to historical texts. This allows them to better examine historical data for underlying patterns and trends.

Survey Research

Content analysis.

For example, in a study about news articles on climate change, you could code articles as “supportive of climate action,” “neutral,” or “skeptical of climate change.” This helps you see how different ideas are presented in the media.

Medical Research

It’s like sorting and organizing data, helping you discover hidden patterns, draw conclusions backed by evidence, and add to what we know in your field.

Other articles

Related posts, inductive vs deductive approach: which is more effective, 8 types of validity in research | examples, nominal, ordinal, interval, and ratio scales | measurement of scale, how to build a pls-sem model using amos, statistical analysis | 5 steps & examples, principal components analysis (pca) using spss, content analysis | methods & examples, quantitative data analysis | examples, what is multivariate analysis, using different statistical tools: a guide to choosing the right tool for data analysis.

Data Analysis Through Coding

• First Online: 11 November 2020

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When the data collection process reaches a stage where enough evidence is collected in terms of the quantity and scope as well as quality and contextual depth, the focus of research should be directed toward the most challenging aspect of research, that is, making sense of the gathered data. This is done in a process traditionally known as data ‘analysis’. Perhaps the most widely used data analysis procedures in qualitative inquiry in areas of social sciences and humanities are based on a general approach known as ‘grounded theory’. This broad theoretical approach is practically translated into a series of ‘coding’ procedures that are used to extract meanings embedded in the landscape of qualitative data. The application of grounded theory and coding procedures for data analysis in qualitative language education research shape the main discussions of this chapter.

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The term “coding” has different meanings in empirical research. Generally speaking, coding becomes relevant whenever data at hand are unstructured, and coding then provides a structure for a systematic analysis of these data. In quantitative research using standardized instruments, coding is the process of tagging data about a given unit of analysis, in order to assign these units to a category. Usually, these categories correspond to numbers that allow the information to be processed by statistical software. Coding gains particular importance in quantitative content analysis, where it represents the main task of researchers. Likewise, the term is used in qualitative research to describe how data gathered with nonstandardized methods is broken down into components relevant to the research question under study (Bryman 2004, 537). Within statistical data analysis, the term “recoding” is technically used for the procedure of regrouping the categories of a variable; this meaning of the term is not elaborated on in greater detail here.

Coding In Quantitative Research

Data tagging.

For the purposes of computerized data analysis, researchers need empirical information to be tagged with numeric codes. This is true whenever processing is based on structured data collection commonly labeled as “quantitative.” The coding of items can be carried out either by the data source (usually the interviewee) or by the researcher (Sullivan 2001; Babbie 2002).

In the first case, coding by the data source, the researcher prepares a precoded instrument, e.g., a questionnaire with closed-ended questions. This means that the interviewee is exposed to a fixed selection of categories from which he or she can choose as appropriate with regard to the stimulus (for the most part, a question or an object to be rated). Accordingly, this is a two-step process that first requires the researcher to generate relevant and meaningful categories. These categories should be distinct, exhaustive, and adequate with respect to the related construct. In the second step, the interviewee gives the required assessment based on the given alternatives.

The advantages of this procedure are obvious: with the completion of the data entry, descriptive results of the research are quickly available. But this procedure also raises at least two problems in terms of validity. On the one hand, the results of the data analysis depend heavily on the thoroughness of category construction exercised by the researcher. Second, the difficult task of transforming empirical reality into numeric codes is handed over to the interviewee. Thus, data quality reflects the way the interviewee understands the different answering options and the mental effort he or she puts into the responses. Variability in this respect is a substantial source of error, which is why careful category construction focused on minimal ambiguity is important.

The second case mentioned above, coding by the researcher, refers primarily to the use of so-called open-ended questions in a questionnaire. Here the steps are performed in reverse order. First, the interviewees are given a question without precoded categories and are encouraged to express their thoughts in their own individual style. In a selfadministered questionnaire, respondents are required to write down all relevant aspects. During an interview, their answer is either recorded literally or in note form, or the interviewer translates the answer immediately into precoded categories usually not available to the interviewee. When data collection includes nonstandardized entries, the process of coding is postponed until the data editing phase, when categories are defined in a coding frame and codes are assigned to the answers.

Pros and cons are distributed exactly the other way around in this case. After collecting the data, further coding efforts are necessary, which postpones the results. Furthermore, people’s answers to open-ended questions may vary substantially across a sample – a phenomenon that becomes increasingly important when the reference point for answers is not clear. As no categories are administered, the researcher does not obtain comparable results from all units of analysis. This might impose severe restrictions on data analysis. Still, open-ended questions are an important tool since they produce “unbiased” data in the sense that interviewees can reproduce their thoughts without needing to perform any kind of mental transfer beforehand. Furthermore, if the subject matter lacks evidence to establish precoded categories in the run-up to the survey, open-ended questions provide an opportunity to collect a broad range of relevant aspects. To this effect, it resembles coding in qualitative research (see below).

While coding by the researcher has so far been applied to the example of survey research, it is relevant to other methods of empirical data collection as well. For instance, in the case of structured or systematic observation research, the schedule for the recording of observations such as interpersonal communication or group communication may include precoded categories as well as space for individual notes to be coded afterwards (for a classic example of coding communication behavior in small groups, see Bales 1950).

Content Analysis

One major methodological approach in communication research is the standardized content analysis of messages in general and of media coverage in particular. For this purpose, particular features of these messages need to be identified and assigned according to a set of relevant categories previously defined by the researcher. Thus, coding is the main procedure when applying this method, which explains why the term is prominent in almost every stage of a content analysis research process (Krippendorff 2004, 125 –149). It is used to address the primary preoccupation of the research assistants we call coders, and, moreover, it serves as a general label for the fieldwork in content analysis as a whole.

Procedures in standardized content analysis are quite similar to data tagging in survey research, as mentioned above. Researchers will determine the concepts to be measured and operationalize these concepts by defining categories accordingly. These categories are summarized in a coding manual, together with further instructions and examples. Coders are provided with a coding scheme that is prepared to hold the codes attributed to each category by the coder. Data entry is based on these coding schemes or, if computer-aided coding applies, the coder enters each code into a preformatted data file (Neuendorf 2002).

With regard to the mental processes involved in coding, the term refers to the capability of each coder to identify the relevant characteristics of each unit of analysis, and to assign these coding units correctly to the categories and their respective codes. Thus, the difficult task of translating the content of messages to numerical data is left to the coder. Therefore, his or her performance is considered the main source of error in standardized content analysis. Different techniques are applied to ensure correspondence of coding acts, beginning with intense coder training and ending with checks of coding agreement between and within coders (Riffe et al. 1998, 104 –134).

Besides this standardized type of content analysis, there is a longstanding tradition of so-called qualitative content analysis. This method basically refers to techniques of document analysis. Messages are extracted from these documents by using a more holistic approach since texts (or pictures, films, or voice recordings) are considered complex structures of meaning. Condensation of issues and arguments often follows a more implicit process. Quotes from the materials are presented in the text report, and are important to illustrate the reconstructions of the researcher.

Coding In Qualitative Research

Although qualitative research depends on standardization to a substantially lesser degree, coding still remains an issue. It applies, for example, to grounded theory as a general strategy of qualitative data analysis where coding is considered to be a key process. “It entails reviewing transcripts and/or field notes and giving labels (names) to component parts that seem to be of potential theoretical significance and/or that appear to be particularly salient within the social worlds of those being studied” (Bryman 2004, 402).

The meaning of “coding” in qualitative research (which some authors also refer to as “indexing”) differs from the meaning prevalent in quantitative research, where coding is a technique for transferring information to numeric data, classification, and data handling. In this case, important decisions about what a single code actually means are made in advance, during the procedure of operationalization. In contrast to this rather instrumental sense, coding in qualitative research is a first step toward creating a theory. It represents an ongoing state of potential revision and fluidity, as the resulting codes are not treated as fixed “data.” In contrast, qualitative research merely treats codes as potential indicators of concepts, with these indicators being permanently under revision. This is because, while research proceeds, they are compared with previous codes and modified with regard to the best fit of concepts.

Following Strauss and Corbin (1998), three types of coding practice can be distinguished, which are often not exclusive but used sequentially in the elaboration of categories. First, open coding implies the least regulated step, when data are examined, compared, and broken down into codes (or “concepts”), which are later grouped and turned into categories. Second, axial coding usually follows open coding and aims at connecting the categories obtained by relinking the codes to contexts such as causes, consequences, or patterns of interaction. Finally, selective coding identifies the core category, which is the one central aspect that serves as the main concept for interpretation. All other categories are organized around the core category and thus integrated into a larger frame that is called the “storyline.”

These steps are not undisputed in research practice. Researchers who fear terminating the exploratory step too early in the process suggest refraining from axial coding and performing only an initial and a focused coding. There are several different approaches to conceptualizing the coding process in qualitative research, but most of them share the general distinction between one phase that stays closer to the original data, and a second phase where codes are transferred to a more abstract level in order to construct meaning about the phenomena under study.

Research practice has developed indications and guidelines for developing codes. Considerations that may lead to relevant codes include the topic of an item of data, its underlying type of event, the persons involved, statements and intentions of these persons, causal attributions, and descriptions of opinions, emotions, and behaviors. In the process of coding it is suggested to code promptly after data collection, to keep in mind that each item of data can be coded in more than one way, and, above all, to constantly move forward and backward between the different steps of coding. Only from this permanent rereading and reviewing may the relevant connections between concepts and categories emerge. Finally, coding should not be equated to analysis in general: although coding represents an important step within the analysis (particularly because it reduces the amount of data available), interpretation is still the step in which codes are assigned to meaning, the significance of the coded material is assessed, and the findings are reflected in the light of theoretical considerations and earlier research.

References:

• Babbie, E. (2002). The basics of social research, 2nd edn. Belmont, CA: Wadsworth Thompson Learning.
• Bales, R. F. (1950). Interaction process analysis: A method for the study of small group interaction. Cambridge, MA: Addison Wesley.
• Bryman, A. (2004). Social research methods, 2nd edn. Oxford: Oxford University Press.
• Krippendorff, K. (2004). Content analysis: An introduction to its methodology, 2nd edn. Thousand Oaks, CA: Sage.
• Neuendorf, K. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage.
• Riffe, D., Lacy, S., & Fico, F. G. (1998). Analyzing media messages: Using quantitative content analysis in research. Mahwah, NJ: Lawrence Erlbaum.
• Strauss, A., & Corbin, J. M. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory, 2nd edn. Thousand Oaks, CA: Sage.
• Sullivan, T. J. (2001). Methods of social research. Fort Worth, TX: Harcourt.

Coding Qualitative Data: A Beginner’s How-To + Examples

When gathering feedback, whether it’s from surveys , online reviews, or social mentions , the most valuable insights usually come from free-form or open-ended responses.

Though these types of responses allow for more detailed feedback, they are also difficult to measure and analyse on a large scale. Coding qualitative data allows you to transform these unique responses into quantitative metrics that can be compared to the rest of your data set.

Read on to learn about this process.

What is Qualitative Data Coding?

Qualitative data coding is the process of assigning quantitative tags to the pieces of data. This is necessary for any type of large-scale analysis because you 1) need to have a consistent way to compare and contrast each piece of qualitative data, and 2) will be able to use tools like Excel and Google Sheets to manipulate quantitative data.

For example, if a customer writes a Yelp review stating “The atmosphere was great for a Friday night, but the food was a bit overpriced,” you can assign quantitative tags based on a scale or sentiment. We’ll get into how exactly to assign these tags in the next section.

Inductive Coding vs Deductive Coding

When deciding how you will scale and code your data, you’ll first have to choose between the inductive or deductive methods. We cover the pros and cons of each method below.

Inductive Coding

Inductive coding is when you don’t already have a set scale or measurement with which to tag the data. If you’re analysing a large amount of qualitative data for the first time, such as the first round of a customer feedback survey, then you will likely need to start with inductive coding since you don’t know exactly what you will be measuring yet.

Inductive coding can be a lengthy process, as you’ll need to comb through your data manually. Luckily, things get easier the second time around when you’re able to use deductive coding.

Deductive Coding

Deductive coding is when you already have a predetermined scale or set of tags that you want to use on your data. This is usually if you’ve already analysed a set of qualitative data with inductive reasoning and want to use the same metrics.

To continue from the example above, say you noticed in the first round that a lot of Yelp reviews mentioned the price of food, and, using inductive coding, you were able to create a scale of 1-5 to measure appetisers, entrees, and desserts.

When analysing new Yelp reviews six months later, you’ll be able to keep the same scale and tag the new responses based on deductive coding, and therefore compare the data to the first round of analysis.

3 Steps for Coding Qualitative Data From the Top-Down

For this section, we will assume that we’re using inductive coding.

1. Start with Broad Categories

The first thing you will want to do is sort your data into broad categories. Think of each of these categories as specific aspects you want to know more about.

To continue with the restaurant example, your categories could include food quality, food price, atmosphere, location, service, etc.

Or for a business in the B2B space, your categories could look something like product quality, product price, customer service, chatbot quality, etc.

2. Assign Emotions or Sentiments

The next step is to then go through each category and assign a sentiment or emotion to each piece of data. In the broadest terms, you can start with just positive emotion and negative emotion.

Remember that when using inductive coding, you’re figuring out your scale and measurements as you go, so you can always start with broad analysis and drill down deeper as you become more familiar with your data.

3. Combine Categories and Sentiments to Draw Conclusions

Once you’ve sorted your data into categories and assigned sentiments, you can start comparing the numbers and drawing conclusions.

For example, perhaps you see that out of the 500 Yelp reviews you’ve analysed, 300 fall into the food price/negative sentiment section of your data. That’s a pretty clear indication that customers think your food is too expensive, and you may see an improvement in customer retention by dropping prices.

The three steps outlined above cover just the very basics of coding qualitative data, so you can understand the theory behind the analysis.

In order to gain more detailed conclusions, you’ll likely need to dig deeper into the data by assigning more complex sentiment tags and breaking down the categories further. We cover some useful tips and a coding qualitative data example below.

4 Tips to Keep in Mind for Accurate Qualitative Data Coding

Here are some helpful reminders to keep on hand when going through the three steps outlined above.

1. Start with a Small Sample of the Data

You’ll want to start with a small sample of your data to make sure the tags you’re using will be applicable to the rest of the set. You don’t want to waste time by going through and manually tagging each piece of data, only to realise at the end that the tags you’ve been using actually aren’t accurate.

Once you’ve broken up your qualitative data into the different categories, choose 10-20% of responses in each category to tag using inductive coding.

Then, continue onto the analysis phase using just that 10-20%.

If you’re able to find takeaways and easily compare the data with that small sample size , then you can continue coding the rest of the data in that same way, adding additional tags where needed.

2. Use Numerical Scales for Deeper Analysis

Instead of just assigning positive and negative sentiments to your data points, you can break this down even further by utilising numerical scales.

Exactly how negative or how positive was the piece of feedback? In the Yelp review example from the beginning of this article, the reviewer stated that the food was “a bit overpriced.” If you’re using a scale of 1-5 to tag the category “food price,” you could tag this as a ⅗ rating.

You’ll likely need to adjust your scales as you work through your initial sample and get a clearer picture of the review landscape.

Having access to more nuanced data like this is important for making accurate decisions about your business.

If you decided to stick with just positive and negative tags, your “food price” category might end up being 50% negative, indicating that a massive change to your pricing structure is needed immediately.

But if it turns out that most of those negative reviews are actually ⅗’s and not ⅕’s, then the situation isn’t as dire as it might have appeared at first glance.

3. Remember That Each Data Point Can Contain Multiple Pieces of Information

Remember that qualitative data can have multiple sentiments and multiple categories (such as the Yelp review example mentioning both atmosphere and price), so you may need to double or even triple-sort some pieces of data.

That’s the beauty of and the struggle with handling open-ended or free-form responses.

However, these responses allow for more accurate insights into your business vs narrow multiple-choice questions.

4. Be Mindful of Having Too Many Tags

Remember, you’re able to draw conclusions from your qualitative data by combining category tags and sentiment tags.

An easy mistake for data analysis newcomers to make is to end up with so many tags that comparing them becomes impossible. This usually stems from an overabundance of caution that you’re tagging responses accurately.

For example, say you’re tagging a review that’s discussing a restaurant host’s behavior. You put it in the category “host/hostess behavior” and tag it as a ⅗ for the sentiment.

Then, you come across another review discussing a server’s behaviour that’s slightly more positive, so you tag this as “server behaviour” for the category and 3.75/5 for the sentiment.

By getting this granular, you’re going to end up with very few data points in the same category and sentiment, which defeats the purpose of coding qualitative data.

In this example, unless you’re very specifically looking at the behaviour of individual restaurant positions, you’re better off tagging both responses as “customer service” for the category and ⅗ for the sentiment for consistency’s sake.

Coding Qualitative Data Example

Below we’ll walk through an example of coding qualitative data, utilising the steps and tips detailed above.

Step 1: Read through your data and define your categories. For this example, we’ll use “customer service,” “product quality,” and “price.”

Step 2: Sort a sample of the data into the above categories. Remember that each data point can be included in multiple categories.

• “This software is amazing, does exactly what I need it to [Product Quality]. However, I do wish they’d stop raising prices every year as it’s starting to get a little out of my budget [Price].”
• “Love the product [Product Quality], but honestly I can’t deal with the terrible customer service anymore [Customer Service]. I’ll be shopping around for a new solution.”
• “Meh, this software is okay [Product Quality] but cheaper competitors [Price] are just as good with much better customer service [Customer Service].”

Step 3: Assign sentiments to the sample. For more in-depth analysis, use a numerical scale. We’ll use 1-5 in this example, with 1 being the lowest satisfaction and 5 being the highest.

• Product Quality:
• “This software is amazing, does exactly what I need it to do” [5/5]
• “Love the product” [5/5]
• “Meh, this software is okay [⅖]
• Customer Service:
• “Honestly I can’t deal with the terrible customer service anymore [⅕]
• “...Much better customer service,” [⅖]
• “However, I do wish they’d stop raising prices every year as it’s starting to get a little out of my budget.” [⅗]
• “Cheaper competitors are just as good.” [⅖]

Step 4: After confirming that the established category and sentiment tags are accurate, continue steps 1-3 for the rest of your data, adding tags where necessary.

Step 5: Identify recurring patterns using data analysis. You can combine your insights with other types of data , like demographic and psychographic customer profiles.

Step 6: Take action based on what you find! For example, you may discover that customers aged 20-30 were the most likely to provide negative feedback on your customer service team, equating to ⅖ or ⅕ on your coding scale. You may be able to conclude that younger customers need a more streamlined way to communicate with your company, perhaps through an automated chatbot service.

Step 7: Repeat this process with more specific research goals in mind to continue digging deeper into what your customers are thinking and feeling . For example, if you uncover the above insight through coding qualitative data from online reviews, you could send out a customer feedback survey specifically asking free-form questions about how your customers would feel interacting with a chatbot instead.

How AI tools help with Coding Qualitative Data

Now that you understand the work that goes into coding qualitative data, you’re probably wondering if there’s an easier solution than manually sorting through every response.

The good news is that, yes, there is. Advanced AI-backed tools are available to help companies quickly and accurately analyse qualitative data at scale, such as customer surveys and online reviews.

These tools can not only code data based on a set of rules you determine, but they can even do their own inductive coding to determine themes and create the most accurate tags as they go.

These capabilities allow business owners to make accurate decisions about their business based on actual data and free up the necessary time and employee bandwidth to act on these insights.

The infographic below gives a visual summary of how to code qualitative data and why it’s essential for businesses to learn how:

Try Chattermill today today to learn how our AI-powered software can help you make smarter business decisions.

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The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

• Handling qualitative data
• Transcripts
• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Introduction

Qualitative data

Coding qualitative data, coding methods, using atlas.ti for qualitative data coding, automated coding tools in atlas.ti.

• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative data analysis software

Coding qualitative data for valuable insights

Qualitative researchers, at one point or another, will inevitably find themselves involved in coding their data. The coding process can be arduous and time-consuming, so it's essential to understand how coding contributes to the understanding of knowledge in qualitative research .

Qualitative research tends to work with unstructured data that requires some systematic organization to facilitate insights relevant to your research inquiry. Suppose you need to determine the most critical aspects for deciding what hotel to stay in when you go on vacation. The decision process that goes into choosing the "best" hotel can be located in various and separate places (e.g., travel websites, blogs, personal conversations) and scattered among pieces of information that may not be relevant to you. In qualitative research, one of the goals prior to data analysis is to identify what information is important, find that information, and sort that information in a way that makes it easy for you to come to a decision.

Qualitative coding is almost always a necessary part of the qualitative data analysis process . Coding provides a way to make the meaning of the data clear to you and to your research audience.

What is a code?

A code in the context of qualitative data analysis is a summary of a larger segment of text. Imagine applying a couple of sticky notes to a collection of recipes, marking each section with short labels like "ingredients," "directions," and "advice." Afterward, someone can page through those recipes and easily locate the section they are looking for, thanks to those sticky notes.

Now, suppose you have different colors of sticky notes, where each color denotes a particular cuisine (e.g., Italian, Chinese, vegetarian). Now, with two ways to organize the data in front of you, you can look at all of the ingredient sections of all the recipes belonging to a cuisine to get a sense of the items that are commonly used for such recipes.

As illustrated in this example, one reason someone might apply sticky notes to a recipe is to help the reader save time in getting the desired information from that text, which is essentially the goal of qualitative coding. Coding allows a reader to get the information they are looking for to facilitate the analysis process. Moreover, this process of categorizing the different pieces of data helps researchers see what is going on in their data and identify emerging dimensions and patterns.

The use of codes also has a purpose beyond simply establishing a convenient means to draw meaning from the data . When presenting qualitative research to an audience, researchers could rely on a narrative summary of the data, but such narratives might be too lengthy to grasp or difficult to convey to others.

As a result, researchers in all fields tend to rely on data visualizations to illustrate their data analysis . Naturally, suppose such visualizations rely on tables and figures like bar charts and diagrams to convey meaning. In that case, researchers need to find ways to "count" the data along established data points, which is a role that coding can fulfill. While a strictly numerical understanding of qualitative research may overlook the finer aspects of social phenomena, researchers ultimately benefit from an analysis of the frequency of codes, combinations of codes, and patterns of codes that can contribute to theory generation. In addition, codes can be visualized in numerous ways to present qualitative insights. From flow charts to semantic networks, codes provide researchers with almost limitless possibilities in choosing how to present their rich qualitative data to different audiences.

Applying codes

To engage in coding, a researcher looks at the data line-by-line and develops a codebook by identifying data segments that can be represented by words or short phrases.

In the example above, a set of three paragraphs is represented by one code displayed in green in the right margin. Without codes, the researcher might have to re-read all of the text to remind themselves what the data is about. Indeed, any researcher who examines the codebook of a project can glean a sense of the data and analysis.

Analyzing codes

Think of a simple example to illustrate the importance of analyzing codes. Suppose you are analyzing survey responses for people's preferences for shopping in brick-and-mortar stores and shopping online. In that case, you might think about marking each survey response as either "prefers shopping in-person" or "prefers shopping online." Once you have applied the relevant codes to each survey response, you can compare the frequencies of both codes to determine where the population as a whole stands on the subject.

Among other things, codes can be analyzed by their frequency or their connection to other codes (or co-occurrence with other codes). In the example above, you may also decide to code the data for the reasons that inform people's shopping habits, applying labels such as "convenience," "value," and "service." Then, the analysis process is simply a matter of determining how often each reason co-occurs with preferences for in-person shopping and online shopping by analyzing the codes applied to the data.

As a result, qualitative coding transforms raw data into a form that facilitates the generation of deeper insights through empirical analysis.

That said, coding is a time-consuming, albeit necessary, task in qualitative research and one that researchers have developed into an array of established methods that are worth briefly looking at.

Years of development of qualitative research methods have yielded multiple methods for assigning codes to data. While all qualitative coding approaches essentially seek to summarize large amounts of information succinctly, there are various approaches you can apply to your coding process.

Inductive coding

Probably the most basic form of coding is to look at the data and reduce it to its salient points of information through coding. Any inductive approach to research involves generating knowledge from the ground up. Inductive coding, as a result, looks to generate insights from the qualitative data itself.

Inductive coding benefits researchers who need to look at the data primarily for its inherent meaning rather than for how external frameworks of knowledge might look at it. Inductive coding can also provide a new perspective that established theory has yet to consider, which would make a theory-driven approach inappropriate.

Deductive coding

A deductive approach to coding is also useful in qualitative research . In contrast with inductive coding, a deductive coding approach applies an existing research framework or previous research study to new data. This means that the researcher applies a set of predefined codes based on established research to the new data.

Researchers can benefit from using both approaches in tandem if their research questions call for a synthesized analysis . Returning to the example of a cookbook, a person may mark the different sections of each recipe because they have prior knowledge about what a typical recipe might look like. On the other hand, if they come across a non-typical recipe (e.g., a recipe that may not have an ingredients section), they might need to create new codes to identify parts of the recipe that seem unusual or novel.

Employing both inductive coding and deductive coding , as a result, can help you achieve a more holistic analysis of your data by building on existing knowledge of a phenomenon while generating new knowledge about the less familiar aspects.

Thematic analysis coding

Whether you decide to apply an inductive coding or deductive coding approach to qualitative data, the coding should also be relevant to your research inquiry in order to be useful and avoid a cumbersome amount of coding that might defeat the purpose of summarizing your data. Let's look at a series of more specific approaches to qualitative coding to get a wider sense of how coding has been applied to qualitative research.

The goal of a thematic analysis arising from coding , as the name suggests, is to identify themes revolving around a particular concept or phenomenon. While concepts in the natural sciences, such as temperature and atomic weight, can be measured with numerical data, concepts in the social sciences often escape easy numerical analysis. Rather than reduce the beauty of a work of art or proficiency in a foreign language down to a number, thematic analysis coding looks to describe these phenomena by various aspects that can be grouped together within common themes.

Looking at the recipe again, we can describe a typical recipe by the sections that appear the most often. The same is true for describing a sport (e.g., rules, strategies, equipment) or a car (e.g., type, price, fuel efficiency, safety rating). While later analysis might be able to numerically measure these themes if they are particular enough, the role of coding along the lines of themes provides a good starting point for recognizing and analyzing relevant concepts.

Process coding

Processes are phenomena that are characterized by action. Think about the act of driving a car rather than describing the car itself. In this case, process coding can be thought of as an extension of thematic coding, except that the major aspects of a process can also be identified by sequences and patterns, on the assumption that some actions may follow other actions. After all, drivers typically turn the key in the ignition before releasing the parking brake or shifting to drive. Capturing the specific phases and sequences is a key objective in process coding.

Structural coding

The "structure" of a recipe in a cookbook is different from that of an essay or a newspaper article. Also, think about how an interview for research might be structured differently from an interview for a TV news program. Researchers can employ structural coding to organize the data according to its distinct structural elements, such as specific elements, the ordering of information, or the purpose behind different structures. This kind of analysis could help, for instance, to achieve a greater understanding of how cultures shape a particular piece of writing or social practice.

Longitudinal coding

Studies that observe people or practices over time do so to capture and understand changes in dynamic environments. The role of longitudinal coding is to also code for relevant contextual or temporal aspects. These can then be analyzed together with other codes to assess how frequencies and patterns change from one observation or interview to the next. This will help researchers empirically illustrate differences or changes over time.

Whatever your approach, code your data with ATLAS.ti

Powerful tools for manual coding and automated coding. Check them out with a free trial.

Qualitative data analysis software should effectively facilitate qualitative coding. Researchers can choose between manual coding and automated coding , where tools can be employed to suggest and apply codes to save time. ATLAS.ti is ideal for both approaches to suit researchers of all needs and backgrounds.

Manual coding

At the core of any qualitative data analysis software is the interface that allows researchers the freedom of assigning codes to qualitative data . ATLAS.ti's interface for viewing data makes it easy to highlight data segments and apply new codes or existing codes quickly and efficiently.

In vivo coding

Interpreting qualitative data to create codes is often a part of the coding process. This can mean that the names of codes may differ from the actual text of the data itself.

However, the best names for codes sometimes come from the textual data itself, as opposed to some interpretation of the text. As a result, there may be a particular word or short phrase that stands out to you in your data set, compelling you to incorporate that word or phrase into your qualitative codes. Think about how social media has slang or acronyms like "YOLO" or "YMMV" which condense a lot of meaning or convey something of importance in the context of the research. Rather than obscuring participants’ meanings or experiences within another layer of interpretation, researchers can build meaningful and rich insights by using participants’ own words to create in vivo codes .

In vivo coding is a handy feature in ATLAS.ti for when you come across a key term or phrase that you want to create a code out of. Simply highlight the desired text and click on "Code in Vivo" to create a new code instantly.

Code Manager

One of the biggest challenges of coding qualitative data is keeping track of dozens or even hundreds of codes, because a lack of organization may hinder researchers in the main objective of succinctly summarizing qualitative data.

Once you have developed and applied a set of codes to your project data, you can open the Code Manager to gain a bird's eye view of all of your codes so you can develop and reorganize them, into hierarchies, groups, or however you prefer. Your list of codes can also be exported to share with others or use in other qualitative or quantitative analysis software .

Use ATLAS.ti for efficient and insightful coding

Intuitive tools to help you code and analyze your data, available starting with a free trial.

Traditionally, qualitative researchers would perform this coding on their data manually by hand, which involves carefully reading each piece of data and attaching codes. For qualitative researchers using pen and paper, they can use highlighters or bookmark flags to mark the key points in their data for later reference. Qualitative researchers also have powerful qualitative data analysis software they can rely on to facilitate all aspects of the coding process.

Although researchers can use qualitative data analysis software to engage in manual coding, there is also now a range of software tools that can even automate the coding process . A number of automated coding tools in ATLAS.ti such as AI Coding, Sentiment Analysis, and Opinion Mining use machine learning and natural language processing to apply useful codes for later analysis. Moreover, other tools in ATLAS.ti rely on pattern recognition to facilitate the creation of descriptive codes throughout your project.

One of the most exciting implications of recent advances in artificial intelligence is its potential for facilitating the research process, especially in qualitative research. The use of machine learning to understand the salient points in data can be especially useful to researchers in all fields.

AI Coding , available in both the Desktop platforms and Web version of ATLAS.ti, performs comprehensive descriptive coding on your qualitative data . It processes data through OpenAI's language models to suggest and apply codes to your project in a fraction of the time that it would take to do manually.

Sentiment Analysis

Participants may often express sentiments that are positive or negative in nature. If you are interested in analyzing the feelings expressed in your data, you can analyze these sentiments . To conduct automated coding for these sentiments, ATLAS.ti employs machine learning to process your data quickly and suggest codes to be applied to relevant data segments.

Opinion Mining

If you want to understand both what participants talked about and how they felt about it, you can conduct Opinion Mining. This tool synthesizes key phrases in your textual data according to whether they are being talked about in a positive or negative manner. The codes generated from Opinion Mining can provide a useful illustration of how language in interviews, focus groups, and surveys is used when discussing certain topics or phenomena.

Code qualitative data with ATLAS.ti

Download a free trial of ATLAS.ti and code your data with ease.

Coding qualitative data: a synthesis guiding the novice

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Conducting and Writing Quantitative and Qualitative Research

Edward barroga.

1 Department of Medical Education, Showa University School of Medicine, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

Atsuko Furuta

Makiko arima, shizuma tsuchiya, chikako kawahara, yusuke takamiya.

Comprehensive knowledge of quantitative and qualitative research systematizes scholarly research and enhances the quality of research output. Scientific researchers must be familiar with them and skilled to conduct their investigation within the frames of their chosen research type. When conducting quantitative research, scientific researchers should describe an existing theory, generate a hypothesis from the theory, test their hypothesis in novel research, and re-evaluate the theory. Thereafter, they should take a deductive approach in writing the testing of the established theory based on experiments. When conducting qualitative research, scientific researchers raise a question, answer the question by performing a novel study, and propose a new theory to clarify and interpret the obtained results. After which, they should take an inductive approach to writing the formulation of concepts based on collected data. When scientific researchers combine the whole spectrum of inductive and deductive research approaches using both quantitative and qualitative research methodologies, they apply mixed-method research. Familiarity and proficiency with these research aspects facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Graphical Abstract

INTRODUCTION

Novel research studies are conceptualized by scientific researchers first by asking excellent research questions and developing hypotheses, then answering these questions by testing their hypotheses in ethical research. 1 , 2 , 3 Before they conduct novel research studies, scientific researchers must possess considerable knowledge of both quantitative and qualitative research. 2

In quantitative research, researchers describe existing theories, generate and test a hypothesis in novel research, and re-evaluate existing theories deductively based on their experimental results. 1 , 4 , 5 In qualitative research, scientific researchers raise and answer research questions by performing a novel study, then propose new theories by clarifying their results inductively. 1 , 6

RATIONALE OF THIS ARTICLE

When researchers have a limited knowledge of both research types and how to conduct them, this can result in substandard investigation. Researchers must be familiar with both types of research and skilled to conduct their investigations within the frames of their chosen type of research. Thus, meticulous care is needed when planning quantitative and qualitative research studies to avoid unethical research and poor outcomes.

Understanding the methodological and writing assumptions 7 , 8 underpinning quantitative and qualitative research, especially by non-Anglophone researchers, is essential for their successful conduct. Scientific researchers, especially in the academe, face pressure to publish in international journals 9 where English is the language of scientific communication. 10 , 11 In particular, non-Anglophone researchers face challenges related to linguistic, stylistic, and discourse differences. 11 , 12 Knowing the assumptions of the different types of research will help clarify research questions and methodologies, easing the challenge and help.

SEARCH FOR RELEVANT ARTICLES

To identify articles relevant to this topic, we adhered to the search strategy recommended by Gasparyan et al. 7 We searched through PubMed, Scopus, Directory of Open Access Journals, and Google Scholar databases using the following keywords: quantitative research, qualitative research, mixed-method research, deductive reasoning, inductive reasoning, study design, descriptive research, correlational research, experimental research, causal-comparative research, quasi-experimental research, historical research, ethnographic research, meta-analysis, narrative research, grounded theory, phenomenology, case study, and field research.

AIMS OF THIS ARTICLE

This article aims to provide a comparative appraisal of qualitative and quantitative research for scientific researchers. At present, there is still a need to define the scope of qualitative research, especially its essential elements. 13 Consensus on the critical appraisal tools to assess the methodological quality of qualitative research remains lacking. 14 Framing and testing research questions can be challenging in qualitative research. 2 In the healthcare system, it is essential that research questions address increasingly complex situations. Therefore, research has to be driven by the kinds of questions asked and the corresponding methodologies to answer these questions. 15 The mixed-method approach also needs to be clarified as this would appear to arise from different philosophical underpinnings. 16

This article also aims to discuss how particular types of research should be conducted and how they should be written in adherence to international standards. In the US, Europe, and other countries, responsible research and innovation was conceptualized and promoted with six key action points: engagement, gender equality, science education, open access, ethics and governance. 17 , 18 International ethics standards in research 19 as well as academic integrity during doctoral trainings are now integral to the research process. 20

POTENTIAL BENEFITS FROM THIS ARTICLE

This article would be beneficial for researchers in further enhancing their understanding of the theoretical, methodological, and writing aspects of qualitative and quantitative research, and their combination.

Moreover, this article reviews the basic features of both research types and overviews the rationale for their conduct. It imparts information on the most common forms of quantitative and qualitative research, and how they are carried out. These aspects would be helpful for selecting the optimal methodology to use for research based on the researcher’s objectives and topic.

This article also provides information on the strengths and weaknesses of quantitative and qualitative research. Such information would help researchers appreciate the roles and applications of both research types and how to gain from each or their combination. As different research questions require different types of research and analyses, this article is anticipated to assist researchers better recognize the questions answered by quantitative and qualitative research.

Finally, this article would help researchers to have a balanced perspective of qualitative and quantitative research without considering one as superior to the other.

TYPES OF RESEARCH

Research can be classified into two general types, quantitative and qualitative. 21 Both types of research entail writing a research question and developing a hypothesis. 22 Quantitative research involves a deductive approach to prove or disprove the hypothesis that was developed, whereas qualitative research involves an inductive approach to create a hypothesis. 23 , 24 , 25 , 26

In quantitative research, the hypothesis is stated before testing. In qualitative research, the hypothesis is developed through inductive reasoning based on the data collected. 27 , 28 For types of data and their analysis, qualitative research usually includes data in the form of words instead of numbers more commonly used in quantitative research. 29

Quantitative research usually includes descriptive, correlational, causal-comparative / quasi-experimental, and experimental research. 21 On the other hand, qualitative research usually encompasses historical, ethnographic, meta-analysis, narrative, grounded theory, phenomenology, case study, and field research. 23 , 25 , 28 , 30 A summary of the features, writing approach, and examples of published articles for each type of qualitative and quantitative research is shown in Table 1 . 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43

QUANTITATIVE RESEARCH

Deductive approach.

The deductive approach is used to prove or disprove the hypothesis in quantitative research. 21 , 25 Using this approach, researchers 1) make observations about an unclear or new phenomenon, 2) investigate the current theory surrounding the phenomenon, and 3) hypothesize an explanation for the observations. Afterwards, researchers will 4) predict outcomes based on the hypotheses, 5) formulate a plan to test the prediction, and 6) collect and process the data (or revise the hypothesis if the original hypothesis was false). Finally, researchers will then 7) verify the results, 8) make the final conclusions, and 9) present and disseminate their findings ( Fig. 1A ).

Types of quantitative research

The common types of quantitative research include (a) descriptive, (b) correlational, c) experimental research, and (d) causal-comparative/quasi-experimental. 21

Descriptive research is conducted and written by describing the status of an identified variable to provide systematic information about a phenomenon. A hypothesis is developed and tested after data collection, analysis, and synthesis. This type of research attempts to factually present comparisons and interpretations of findings based on analyses of the characteristics, progression, or relationships of a certain phenomenon by manipulating the employed variables or controlling the involved conditions. 44 Here, the researcher examines, observes, and describes a situation, sample, or variable as it occurs without investigator interference. 31 , 45 To be meaningful, the systematic collection of information requires careful selection of study units by precise measurement of individual variables 21 often expressed as ranges, means, frequencies, and/or percentages. 31 , 45 Descriptive statistical analysis using ANOVA, Student’s t -test, or the Pearson coefficient method has been used to analyze descriptive research data. 46

Correlational research is performed by determining and interpreting the extent of a relationship between two or more variables using statistical data. This involves recognizing data trends and patterns without necessarily proving their causes. The researcher studies only the data, relationships, and distributions of variables in a natural setting, but does not manipulate them. 21 , 45 Afterwards, the researcher establishes reliability and validity, provides converging evidence, describes relationship, and makes predictions. 47

Experimental research is usually referred to as true experimentation. The researcher establishes the cause-effect relationship among a group of variables making up a study using the scientific method or process. This type of research attempts to identify the causal relationships between variables through experiments by arbitrarily controlling the conditions or manipulating the variables used. 44 The scientific manuscript would include an explanation of how the independent variable was manipulated to determine its effects on the dependent variables. The write-up would also describe the random assignments of subjects to experimental treatments. 21

Causal-comparative/quasi-experimental research closely resembles true experimentation but is conducted by establishing the cause-effect relationships among variables. It may also be conducted to establish the cause or consequences of differences that already exist between, or among groups of individuals. 48 This type of research compares outcomes between the intervention groups in which participants are not randomized to their respective interventions because of ethics- or feasibility-related reasons. 49 As in true experiments, the researcher identifies and measures the effects of the independent variable on the dependent variable. However, unlike true experiments, the researchers do not manipulate the independent variable.

In quasi-experimental research, naturally formed or pre-existing groups that are not randomly assigned are used, particularly when an ethical, randomized controlled trial is not feasible or logical. 50 The researcher identifies control groups as those which have been exposed to the treatment variable, and then compares these with the unexposed groups. The causes are determined and described after data analysis, after which conclusions are made. The known and unknown variables that could still affect the outcome are also included. 7

QUALITATIVE RESEARCH

Inductive approach.

Qualitative research involves an inductive approach to develop a hypothesis. 21 , 25 Using this approach, researchers answer research questions and develop new theories, but they do not test hypotheses or previous theories. The researcher seldom examines the effectiveness of an intervention, but rather explores the perceptions, actions, and feelings of participants using interviews, content analysis, observations, or focus groups. 25 , 45 , 51

Distinctive features of qualitative research

Qualitative research seeks to elucidate about the lives of people, including their lived experiences, behaviors, attitudes, beliefs, personality characteristics, emotions, and feelings. 27 , 30 It also explores societal, organizational, and cultural issues. 30 This type of research provides a good story mimicking an adventure which results in a “thick” description that puts readers in the research setting. 52

The qualitative research questions are open-ended, evolving, and non-directional. 26 The research design is usually flexible and iterative, commonly employing purposive sampling. The sample size depends on theoretical saturation, and data is collected using in-depth interviews, focus groups, and observations. 27

In various instances, excellent qualitative research may offer insights that quantitative research cannot. Moreover, qualitative research approaches can describe the ‘lived experience’ perspectives of patients, practitioners, and the public. 53 Interestingly, recent developments have looked into the use of technology in shaping qualitative research protocol development, data collection, and analysis phases. 54

Qualitative research employs various techniques, including conversational and discourse analysis, biographies, interviews, case-studies, oral history, surveys, documentary and archival research, audiovisual analysis, and participant observations. 26

Conducting qualitative research

To conduct qualitative research, investigators 1) identify a general research question, 2) choose the main methods, sites, and subjects, and 3) determine methods of data documentation access to subjects. Researchers also 4) decide on the various aspects for collecting data (e.g., questions, behaviors to observe, issues to look for in documents, how much (number of questions, interviews, or observations), 5) clarify researchers’ roles, and 6) evaluate the study’s ethical implications in terms of confidentiality and sensitivity. Afterwards, researchers 7) collect data until saturation, 8) interpret data by identifying concepts and theories, and 9) revise the research question if necessary and form hypotheses. In the final stages of the research, investigators 10) collect and verify data to address revisions, 11) complete the conceptual and theoretical framework to finalize their findings, and 12) present and disseminate findings ( Fig. 1B ).

Types of qualitative research

The different types of qualitative research include (a) historical research, (b) ethnographic research, (c) meta-analysis, (d) narrative research, (e) grounded theory, (f) phenomenology, (g) case study, and (h) field research. 23 , 25 , 28 , 30

Historical research is conducted by describing past events, problems, issues, and facts. The researcher gathers data from written or oral descriptions of past events and attempts to recreate the past without interpreting the events and their influence on the present. 6 Data is collected using documents, interviews, and surveys. 55 The researcher analyzes these data by describing the development of events and writes the research based on historical reports. 2

Ethnographic research is performed by observing everyday life details as they naturally unfold. 2 It can also be conducted by developing in-depth analytical descriptions of current systems, processes, and phenomena or by understanding the shared beliefs and practices of a particular group or culture. 21 The researcher collects extensive narrative non-numerical data based on many variables over an extended period, in a natural setting within a specific context. To do this, the researcher uses interviews, observations, and active participation. These data are analyzed by describing and interpreting them and developing themes. A detailed report of the interpreted data is then provided. 2 The researcher immerses himself/herself into the study population and describes the actions, behaviors, and events from the perspective of someone involved in the population. 23 As examples of its application, ethnographic research has helped to understand a cultural model of family and community nursing during the coronavirus disease 2019 outbreak. 56 It has also been used to observe the organization of people’s environment in relation to cardiovascular disease management in order to clarify people’s real expectations during follow-up consultations, possibly contributing to the development of innovative solutions in care practices. 57

Meta-analysis is carried out by accumulating experimental and correlational results across independent studies using a statistical method. 21 The report is written by specifying the topic and meta-analysis type. In the write-up, reporting guidelines are followed, which include description of inclusion criteria and key variables, explanation of the systematic search of databases, and details of data extraction. Meta-analysis offers in-depth data gathering and analysis to achieve deeper inner reflection and phenomenon examination. 58

Narrative research is performed by collecting stories for constructing a narrative about an individual’s experiences and the meanings attributed to them by the individual. 9 It aims to hear the voice of individuals through their account or experiences. 17 The researcher usually conducts interviews and analyzes data by storytelling, content review, and theme development. The report is written as an in-depth narration of events or situations focused on the participants. 2 , 59 Narrative research weaves together sequential events from one or two individuals to create a “thick” description of a cohesive story or narrative. 23 It facilitates understanding of individuals’ lives based on their own actions and interpretations. 60

Grounded theory is conducted by engaging in an inductive ground-up or bottom-up strategy of generating a theory from data. 24 The researcher incorporates deductive reasoning when using constant comparisons. Patterns are detected in observations and then a working hypothesis is created which directs the progression of inquiry. The researcher collects data using interviews and questionnaires. These data are analyzed by coding the data, categorizing themes, and describing implications. The research is written as a theory and theoretical models. 2 In the write-up, the researcher describes the data analysis procedure (i.e., theoretical coding used) for developing hypotheses based on what the participants say. 61 As an example, a qualitative approach has been used to understand the process of skill development of a nurse preceptor in clinical teaching. 62 A researcher can also develop a theory using the grounded theory approach to explain the phenomena of interest by observing a population. 23

Phenomenology is carried out by attempting to understand the subjects’ perspectives. This approach is pertinent in social work research where empathy and perspective are keys to success. 21 Phenomenology studies an individual’s lived experience in the world. 63 The researcher collects data by interviews, observations, and surveys. 16 These data are analyzed by describing experiences, examining meanings, and developing themes. The researcher writes the report by contextualizing and reporting the subjects’ experience. This research approach describes and explains an event or phenomenon from the perspective of those who have experienced it. 23 Phenomenology understands the participants’ experiences as conditioned by their worldviews. 52 It is suitable for a deeper understanding of non-measurable aspects related to the meanings and senses attributed by individuals’ lived experiences. 60

Case study is conducted by collecting data through interviews, observations, document content examination, and physical inspections. The researcher analyzes the data through a detailed identification of themes and the development of narratives. The report is written as an in-depth study of possible lessons learned from the case. 2

Field research is performed using a group of methodologies for undertaking qualitative inquiries. The researcher goes directly to the social phenomenon being studied and observes it extensively. In the write-up, the researcher describes the phenomenon under the natural environment over time with no implantation of controls or experimental conditions. 45

DIFFERENCES BETWEEN QUANTITATIVE AND QUALITATIVE RESEARCH

Scientific researchers must be aware of the differences between quantitative and qualitative research in terms of their working mechanisms to better understand their specific applications. This knowledge will be of significant benefit to researchers, especially during the planning process, to ensure that the appropriate type of research is undertaken to fulfill the research aims.

In terms of quantitative research data evaluation, four well-established criteria are used: internal validity, external validity, reliability, and objectivity. 23 The respective correlating concepts in qualitative research data evaluation are credibility, transferability, dependability, and confirmability. 30 Regarding write-up, quantitative research papers are usually shorter than their qualitative counterparts, which allows the latter to pursue a deeper understanding and thus producing the so-called “thick” description. 29

Interestingly, a major characteristic of qualitative research is that the research process is reversible and the research methods can be modified. This is in contrast to quantitative research in which hypothesis setting and testing take place unidirectionally. This means that in qualitative research, the research topic and question may change during literature analysis, and that the theoretical and analytical methods could be altered during data collection. 44

Quantitative research focuses on natural, quantitative, and objective phenomena, whereas qualitative research focuses on social, qualitative, and subjective phenomena. 26 Quantitative research answers the questions “what?” and “when?,” whereas qualitative research answers the questions “why?,” “how?,” and “how come?.” 64

Perhaps the most important distinction between quantitative and qualitative research lies in the nature of the data being investigated and analyzed. Quantitative research focuses on statistical, numerical, and quantitative aspects of phenomena, and employ the same data collection and analysis, whereas qualitative research focuses on the humanistic, descriptive, and qualitative aspects of phenomena. 26 , 28

Structured versus unstructured processes

The aims and types of inquiries determine the difference between quantitative and qualitative research. In quantitative research, statistical data and a structured process are usually employed by the researcher. Quantitative research usually suggests quantities (i.e., numbers). 65 On the other hand, researchers typically use opinions, reasons, verbal statements, and an unstructured process in qualitative research. 63 Qualitative research is more related to quality or kind. 65

In quantitative research, the researcher employs a structured process for collecting quantifiable data. Often, a close-ended questionnaire is used wherein the response categories for each question are designed in which values can be assigned and analyzed quantitatively using a common scale. 66 Quantitative research data is processed consecutively from data management, then data analysis, and finally to data interpretation. Data should be free from errors and missing values. In data management, variables are defined and coded. In data analysis, statistics (e.g., descriptive, inferential) as well as central tendency (i.e., mean, median, mode), spread (standard deviation), and parameter estimation (confidence intervals) measures are used. 67

In qualitative research, the researcher uses an unstructured process for collecting data. These non-statistical data may be in the form of statements, stories, or long explanations. Various responses according to respondents may not be easily quantified using a common scale. 66

Composing a qualitative research paper resembles writing a quantitative research paper. Both papers consist of a title, an abstract, an introduction, objectives, methods, findings, and discussion. However, a qualitative research paper is less regimented than a quantitative research paper. 27

Quantitative research as a deductive hypothesis-testing design

Quantitative research can be considered as a hypothesis-testing design as it involves quantification, statistics, and explanations. It flows from theory to data (i.e., deductive), focuses on objective data, and applies theories to address problems. 45 , 68 It collects numerical or statistical data; answers questions such as how many, how often, how much; uses questionnaires, structured interview schedules, or surveys 55 as data collection tools; analyzes quantitative data in terms of percentages, frequencies, statistical comparisons, graphs, and tables showing statistical values; and reports the final findings in the form of statistical information. 66 It uses variable-based models from individual cases and findings are stated in quantified sentences derived by deductive reasoning. 24

In quantitative research, a phenomenon is investigated in terms of the relationship between an independent variable and a dependent variable which are numerically measurable. The research objective is to statistically test whether the hypothesized relationship is true. 68 Here, the researcher studies what others have performed, examines current theories of the phenomenon being investigated, and then tests hypotheses that emerge from those theories. 4

Quantitative hypothesis-testing research has certain limitations. These limitations include (a) problems with selection of meaningful independent and dependent variables, (b) the inability to reflect subjective experiences as variables since variables are usually defined numerically, and (c) the need to state a hypothesis before the investigation starts. 61

Qualitative research as an inductive hypothesis-generating design

Qualitative research can be considered as a hypothesis-generating design since it involves understanding and descriptions in terms of context. It flows from data to theory (i.e., inductive), focuses on observation, and examines what happens in specific situations with the aim of developing new theories based on the situation. 45 , 68 This type of research (a) collects qualitative data (e.g., ideas, statements, reasons, characteristics, qualities), (b) answers questions such as what, why, and how, (c) uses interviews, observations, or focused-group discussions as data collection tools, (d) analyzes data by discovering patterns of changes, causal relationships, or themes in the data; and (e) reports the final findings as descriptive information. 61 Qualitative research favors case-based models from individual characteristics, and findings are stated using context-dependent existential sentences that are justifiable by inductive reasoning. 24

In qualitative research, texts and interviews are analyzed and interpreted to discover meaningful patterns characteristic of a particular phenomenon. 61 Here, the researcher starts with a set of observations and then moves from particular experiences to a more general set of propositions about those experiences. 4

Qualitative hypothesis-generating research involves collecting interview data from study participants regarding a phenomenon of interest, and then using what they say to develop hypotheses. It involves the process of questioning more than obtaining measurements; it generates hypotheses using theoretical coding. 61 When using large interview teams, the key to promoting high-level qualitative research and cohesion in large team methods and successful research outcomes is the balance between autonomy and collaboration. 69

Qualitative data may also include observed behavior, participant observation, media accounts, and cultural artifacts. 61 Focus group interviews are usually conducted, audiotaped or videotaped, and transcribed. Afterwards, the transcript is analyzed by several researchers.

Qualitative research also involves scientific narratives and the analysis and interpretation of textual or numerical data (or both), mostly from conversations and discussions. Such approach uncovers meaningful patterns that describe a particular phenomenon. 2 Thus, qualitative research requires skills in grasping and contextualizing data, as well as communicating data analysis and results in a scientific manner. The reflective process of the inquiry underscores the strengths of a qualitative research approach. 2

Combination of quantitative and qualitative research

When both quantitative and qualitative research methods are used in the same research, mixed-method research is applied. 25 This combination provides a complete view of the research problem and achieves triangulation to corroborate findings, complementarity to clarify results, expansion to extend the study’s breadth, and explanation to elucidate unexpected results. 29

Moreover, quantitative and qualitative findings are integrated to address the weakness of both research methods 29 , 66 and to have a more comprehensive understanding of the phenomenon spectrum. 66

For data analysis in mixed-method research, real non-quantitized qualitative data and quantitative data must both be analyzed. 70 The data obtained from quantitative analysis can be further expanded and deepened by qualitative analysis. 23

In terms of assessment criteria, Hammersley 71 opined that qualitative and quantitative findings should be judged using the same standards of validity and value-relevance. Both approaches can be mutually supportive. 52

Quantitative and qualitative research must be carefully studied and conducted by scientific researchers to avoid unethical research and inadequate outcomes. Quantitative research involves a deductive process wherein a research question is answered with a hypothesis that describes the relationship between independent and dependent variables, and the testing of the hypothesis. This investigation can be aptly termed as hypothesis-testing research involving the analysis of hypothesis-driven experimental studies resulting in a test of significance. Qualitative research involves an inductive process wherein a research question is explored to generate a hypothesis, which then leads to the development of a theory. This investigation can be aptly termed as hypothesis-generating research. When the whole spectrum of inductive and deductive research approaches is combined using both quantitative and qualitative research methodologies, mixed-method research is applied, and this can facilitate the construction of novel hypotheses, development of theories, or refinement of concepts.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Data curation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Formal analysis: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C.
• Investigation: Barroga E, Matanguihan GJ, Takamiya Y, Izumi M.
• Methodology: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Project administration: Barroga E, Matanguihan GJ.
• Resources: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Supervision: Barroga E.
• Validation: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.
• Visualization: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ, Furuta A, Arima M, Tsuchiya S, Kawahara C, Takamiya Y, Izumi M.

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• SPSS Tutorials
• Creating a Codebook

SPSS Tutorials: Creating a Codebook

• The SPSS Environment
• The Data View Window
• Using SPSS Syntax
• Data Creation in SPSS
• Importing Data into SPSS
• Variable Types
• Date-Time Variables in SPSS
• Defining Variables
• Computing Variables
• Computing Variables: Mean Centering
• Computing Variables: Recoding Categorical Variables
• Computing Variables: Recoding String Variables into Coded Categories (Automatic Recode)
• rank transform converts a set of data values by ordering them from smallest to largest, and then assigning a rank to each value. In SPSS, the Rank Cases procedure can be used to compute the rank transform of a variable." href="https://libguides.library.kent.edu/SPSS/RankCases" style="" >Computing Variables: Rank Transforms (Rank Cases)
• Weighting Cases
• Sorting Data
• Grouping Data
• Descriptive Stats for One Numeric Variable (Explore)
• Descriptive Stats for One Numeric Variable (Frequencies)
• Descriptive Stats for Many Numeric Variables (Descriptives)
• Descriptive Stats by Group (Compare Means)
• Frequency Tables
• Working with "Check All That Apply" Survey Data (Multiple Response Sets)
• Chi-Square Test of Independence
• Pearson Correlation
• One Sample t Test
• Paired Samples t Test
• Independent Samples t Test
• One-Way ANOVA
• How to Cite the Tutorials

Sample Data Files

Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below:

• Data definitions (*.pdf)
• Data - Comma delimited (*.csv)
• Data - Tab delimited (*.txt)
• Data - Excel format (*.xlsx)
• Data - SAS format (*.sas7bdat)
• Data - SPSS format (*.sav)

A codebook is a document containing information about each of the variables in your dataset, such as:

• The name assigned to the variable
• What the variable represents (i.e., its label)
• How the variable was measured (e.g. nominal, ordinal, scale)
• How the variable was actually recorded in the raw data (i.e. numeric, string; how many characters wide it is; how many decimal places it has)
• For scale variables: The variable's units of measurement
• For categorical variables: If coded numerically, the numeric codes and what they represent

Codebooks can also contain documentation about when and how the data was created. A good codebook allows you to communicate your research data to others clearly and succinctly, and ensures that the data is understood and interpreted properly.

Many codebooks are created manually; however, in SPSS, it's possible to generate a codebook from an existing SPSS data file.

To get the most out of the Codebooks procedure in SPSS, your dataset should already have variable labels and value labels applied before you run the Codebooks procedure. If you are not familiar with variable properties, such as labels or measurement levels, or concepts like value labeling of category codes in SPSS, you should read the Defining Variables tutorial before continuing.

Creating a Codebook from an SPSS Datafile

Simple codebook.

This codebook method prints most of the information found in the Variable View window. It gives the names, labels, measurement levels, widths, formats, and any assigned missing values labels for every variable in the dataset. It also prints a table with the assigned value labels for categorical variables.

You can generate this simple codebook using the point-and-click menus, or using syntax.

Using the Menus

• Open the SPSS datafile.
• Click File > Display Data File Information > Working File .
• The codebook will print to the Output Viewer window.

Using Syntax

Detailed codebook.

This codebook method includes all of the same information as the simple method, but also includes options for printing summary statistics as well. Unlike the simple method, you can choose which variables are included in the codebook, and you can choose which variable properties are included in the summary. Also unlike the simple method, the summary information for each variable will be printed in its own table.

You can generate this detailed codebook using the Codebooks dialog window, or using syntax.

Note: This procedure was introduced in SPSS version 17 ( source: SPSS v23 Command Syntax Reference ). If you are using an older version of SPSS, this command is not available - it will not appear in the menus, and running the syntax will return error messages.

Using the Codebooks Dialog Window

• Click Analyze > Reports > Codebook .
• In the Variables tab: Add the variables you want in the codebook to the Codebook Variables box. To include all variables, click inside the Variables box, press Ctrl + A , then click the arrow button.
• Variable information : By default, includes Position, Label, Type, Format, Measurement level, Role, Value labels, Missing values, and Custom attributes.
• File information : None included by default.
• Variable display order : By default, ordered identically to how the variables are ordered in the file. Can also order alphabetically, by file, or by measurement level.
• Maximum number of categories : By default, limits to 200 categories.
• In the Statistics tab: (Optional) Choose what statistics you want in the codebook. By default, counts and percents will be printed for nominal and ordinal variables, and mean, standard deviation, and quartiles will be printed for scale variables.
• When finished, click OK .

Note: When listing the variable names in the syntax, the assigned measurement level must be given in brackets after each variable name: [s] for scale, [n] for nominal, [o] for ordinal.

Example: Simple codebook for sample data

To reproduce this example, download the sample SPSS dataset and SPSS syntax file. Run the syntax file on the sample data. This will add all of the appropriate variable labels and value labels for this dataset.

Problem Statement

When sharing your data with others, it's important that your variables are properly documented. This includes having succinct but descriptive labels for your variables, and labels for any numeric codes used for categories.

If you receive a dataset from a collaborator, you can get an overview of its contents by running the Display Dictionary procedure.

Running the Procedure

To generate a simple codebook for the sample data, click File > Display Data File Information > Working File .

The first table is the Variable Information table. This table summarizes variable-level information, including:

• Variable name
• Position (i.e., the order of the columns)
• Variable label
• Measurement level (nominal, ordinal, scale)
• Column width
• Print format
• Write format

The second table is the Variable Values table. This table will only appear if you have value labels defined for at least one variable in your dataset; otherwise, it is omitted. This table prints the name of each variable with defined value labels, and lists each code and associated label for that variable.

Qualtrics users: This procedure works well with survey data that you've downloaded from Qualtrics in SPSS format. Use it to check the coding of your multiple choice items!

• << Previous: Defining Variables
• Next: Working with Data >>
• Last Updated: Jul 10, 2024 11:08 AM
• URL: https://libguides.library.kent.edu/SPSS

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Qualitative Data Coding 101

How to code qualitative data, the smart way (with examples).

By: Jenna Crosley (PhD) | Reviewed by:Dr Eunice Rautenbach | December 2020

As we’ve discussed previously , qualitative research makes use of non-numerical data – for example, words, phrases or even images and video. To analyse this kind of data, the first dragon you’ll need to slay is  qualitative data coding  (or just “coding” if you want to sound cool). But what exactly is coding and how do you do it? 

Overview: Qualitative Data Coding

In this post, we’ll explain qualitative data coding in simple terms. Specifically, we’ll dig into:

• What exactly qualitative data coding is
• What different types of coding exist
• How to code qualitative data (the process)
• Moving from coding to qualitative analysis
• Tips and tricks for quality data coding

What is qualitative data coding?

Let’s start by understanding what a code is. At the simplest level,  a code is a label that describes the content  of a piece of text. For example, in the sentence:

“Pigeons attacked me and stole my sandwich.”

You could use “pigeons” as a code. This code simply describes that the sentence involves pigeons.

So, building onto this,  qualitative data coding is the process of creating and assigning codes to categorise data extracts.   You’ll then use these codes later down the road to derive themes and patterns for your qualitative analysis (for example, thematic analysis ). Coding and analysis can take place simultaneously, but it’s important to note that coding does not necessarily involve identifying themes (depending on which textbook you’re reading, of course). Instead, it generally refers to the process of  labelling and grouping similar types of data  to make generating themes and analysing the data more manageable. 

Makes sense? Great. But why should you bother with coding at all? Why not just look for themes from the outset? Well, coding is a way of making sure your  data is valid . In other words, it helps ensure that your  analysis is undertaken systematically  and that other researchers can review it (in the world of research, we call this transparency). In other words, good coding is the foundation of high-quality analysis.

What are the different types of coding?

Now that we’ve got a plain-language definition of coding on the table, the next step is to understand what overarching types of coding exist – in other words, coding approaches . Let’s start with the two main approaches, inductive and deductive .

With deductive coding, you, as the researcher, begin with a set of  pre-established codes  and apply them to your data set (for example, a set of interview transcripts). Inductive coding on the other hand, works in reverse, as you create the set of codes based on the data itself – in other words, the codes emerge from the data. Let’s take a closer look at both.

Deductive coding 101

With deductive coding, we make use of pre-established codes, which are developed before you interact with the present data. This usually involves drawing up a set of  codes based on a research question or previous research . You could also use a code set from the codebook of a previous study.

For example, if you were studying the eating habits of college students, you might have a research question along the lines of 

“What foods do college students eat the most?”

As a result of this research question, you might develop a code set that includes codes such as “sushi”, “pizza”, and “burgers”.  

Deductive coding allows you to approach your analysis with a very tightly focused lens and quickly identify relevant data . Of course, the downside is that you could miss out on some very valuable insights as a result of this tight, predetermined focus. 

Inductive coding 101 

But what about inductive coding? As we touched on earlier, this type of coding involves jumping right into the data and then developing the codes  based on what you find  within the data. 

For example, if you were to analyse a set of open-ended interviews , you wouldn’t necessarily know which direction the conversation would flow. If a conversation begins with a discussion of cats, it may go on to include other animals too, and so you’d add these codes as you progress with your analysis. Simply put, with inductive coding, you “go with the flow” of the data.

Inductive coding is great when you’re researching something that isn’t yet well understood because the coding derived from the data helps you explore the subject. Therefore, this type of coding is usually used when researchers want to investigate new ideas or concepts , or when they want to create new theories. 

A little bit of both… hybrid coding approaches

If you’ve got a set of codes you’ve derived from a research topic, literature review or a previous study (i.e. a deductive approach), but you still don’t have a rich enough set to capture the depth of your qualitative data, you can  combine deductive and inductive  methods – this is called a  hybrid  coding approach. 

To adopt a hybrid approach, you’ll begin your analysis with a set of a priori codes (deductive) and then add new codes (inductive) as you work your way through the data. Essentially, the hybrid coding approach provides the best of both worlds, which is why it’s pretty common to see this in research.

Need a helping hand?

How to code qualitative data

Now that we’ve looked at the main approaches to coding, the next question you’re probably asking is “how do I actually do it?”. Let’s take a look at the  coding process , step by step.

Both inductive and deductive methods of coding typically occur in two stages:  initial coding  and  line by line coding . 

In the initial coding stage, the objective is to get a general overview of the data by reading through and understanding it. If you’re using an inductive approach, this is also where you’ll develop an initial set of codes. Then, in the second stage (line by line coding), you’ll delve deeper into the data and (re)organise it according to (potentially new) codes. 

Step 1 – Initial coding

The first step of the coding process is to identify  the essence  of the text and code it accordingly. While there are various qualitative analysis software packages available, you can just as easily code textual data using Microsoft Word’s “comments” feature. 

Let’s take a look at a practical example of coding. Assume you had the following interview data from two interviewees:

What pets do you have?

I have an alpaca and three dogs.

Only one alpaca? They can die of loneliness if they don’t have a friend.

I didn’t know that! I’ll just have to get five more. 

I have twenty-three bunnies. I initially only had two, I’m not sure what happened. 

In the initial stage of coding, you could assign the code of “pets” or “animals”. These are just initial,  fairly broad codes  that you can (and will) develop and refine later. In the initial stage, broad, rough codes are fine – they’re just a starting point which you will build onto in the second stage. 

How to decide which codes to use

But how exactly do you decide what codes to use when there are many ways to read and interpret any given sentence? Well, there are a few different approaches you can adopt. The  main approaches  to initial coding include:

• In vivo coding 

Process coding

• Open coding

Descriptive coding

Structural coding.

• Value coding

Let’s take a look at each of these:

In vivo coding

When you use in vivo coding , you make use of a  participants’ own words , rather than your interpretation of the data. In other words, you use direct quotes from participants as your codes. By doing this, you’ll avoid trying to infer meaning, rather staying as close to the original phrases and words as possible. 

In vivo coding is particularly useful when your data are derived from participants who speak different languages or come from different cultures. In these cases, it’s often difficult to accurately infer meaning due to linguistic or cultural differences. 

For example, English speakers typically view the future as in front of them and the past as behind them. However, this isn’t the same in all cultures. Speakers of Aymara view the past as in front of them and the future as behind them. Why? Because the future is unknown, so it must be out of sight (or behind us). They know what happened in the past, so their perspective is that it’s positioned in front of them, where they can “see” it. 

In a scenario like this one, it’s not possible to derive the reason for viewing the past as in front and the future as behind without knowing the Aymara culture’s perception of time. Therefore, in vivo coding is particularly useful, as it avoids interpretation errors.

Next up, there’s process coding , which makes use of  action-based codes . Action-based codes are codes that indicate a movement or procedure. These actions are often indicated by gerunds (words ending in “-ing”) – for example, running, jumping or singing.

Process coding is useful as it allows you to code parts of data that aren’t necessarily spoken, but that are still imperative to understanding the meaning of the texts. 

An example here would be if a participant were to say something like, “I have no idea where she is”. A sentence like this can be interpreted in many different ways depending on the context and movements of the participant. The participant could shrug their shoulders, which would indicate that they genuinely don’t know where the girl is; however, they could also wink, showing that they do actually know where the girl is. 

Simply put, process coding is useful as it allows you to, in a concise manner, identify the main occurrences in a set of data and provide a dynamic account of events. For example, you may have action codes such as, “describing a panda”, “singing a song about bananas”, or “arguing with a relative”.

Descriptive coding aims to summarise extracts by using a  single word or noun  that encapsulates the general idea of the data. These words will typically describe the data in a highly condensed manner, which allows the researcher to quickly refer to the content. 

Descriptive coding is very useful when dealing with data that appear in forms other than traditional text – i.e. video clips, sound recordings or images. For example, a descriptive code could be “food” when coding a video clip that involves a group of people discussing what they ate throughout the day, or “cooking” when coding an image showing the steps of a recipe. 

Structural coding involves labelling and describing  specific structural attributes  of the data. Generally, it includes coding according to answers to the questions of “ who ”, “ what ”, “ where ”, and “ how ”, rather than the actual topics expressed in the data. This type of coding is useful when you want to access segments of data quickly, and it can help tremendously when you’re dealing with large data sets. 

For example, if you were coding a collection of theses or dissertations (which would be quite a large data set), structural coding could be useful as you could code according to different sections within each of these documents – i.e. according to the standard  dissertation structure . What-centric labels such as “hypothesis”, “literature review”, and “methodology” would help you to efficiently refer to sections and navigate without having to work through sections of data all over again. 

Structural coding is also useful for data from open-ended surveys. This data may initially be difficult to code as they lack the set structure of other forms of data (such as an interview with a strict set of questions to be answered). In this case, it would useful to code sections of data that answer certain questions such as “who?”, “what?”, “where?” and “how?”.

Let’s take a look at a practical example. If we were to send out a survey asking people about their dogs, we may end up with a (highly condensed) response such as the following: 

Bella is my best friend. When I’m at home I like to sit on the floor with her and roll her ball across the carpet for her to fetch and bring back to me. I love my dog.

In this set, we could code  Bella  as “who”,  dog  as “what”,  home  and  floor  as “where”, and  roll her ball  as “how”. 

Values coding

Finally, values coding involves coding that relates to the  participant’s worldviews . Typically, this type of coding focuses on excerpts that reflect the values, attitudes, and beliefs of the participants. Values coding is therefore very useful for research exploring cultural values and intrapersonal and experiences and actions.   

To recap, the aim of initial coding is to understand and  familiarise yourself with your data , to  develop an initial code set  (if you’re taking an inductive approach) and to take the first shot at  coding your data . The coding approaches above allow you to arrange your data so that it’s easier to navigate during the next stage, line by line coding (we’ll get to this soon). 

While these approaches can all be used individually, it’s important to remember that it’s possible, and potentially beneficial, to  combine them . For example, when conducting initial coding with interviews, you could begin by using structural coding to indicate who speaks when. Then, as a next step, you could apply descriptive coding so that you can navigate to, and between, conversation topics easily. You can check out some examples of various techniques here .

Step 2 – Line by line coding

Once you’ve got an overall idea of our data, are comfortable navigating it and have applied some initial codes, you can move on to line by line coding. Line by line coding is pretty much exactly what it sounds like – reviewing your data, line by line,  digging deeper  and assigning additional codes to each line. 

With line-by-line coding, the objective is to pay close attention to your data to  add detail  to your codes. For example, if you have a discussion of beverages and you previously just coded this as “beverages”, you could now go deeper and code more specifically, such as “coffee”, “tea”, and “orange juice”. The aim here is to scratch below the surface. This is the time to get detailed and specific so as to capture as much richness from the data as possible. 

In the line-by-line coding process, it’s useful to  code everything  in your data, even if you don’t think you’re going to use it (you may just end up needing it!). As you go through this process, your coding will become more thorough and detailed, and you’ll have a much better understanding of your data as a result of this, which will be incredibly valuable in the analysis phase.

Moving from coding to analysis

Once you’ve completed your initial coding and line by line coding, the next step is to  start your analysis . Of course, the coding process itself will get you in “analysis mode” and you’ll probably already have some insights and ideas as a result of it, so you should always keep notes of your thoughts as you work through the coding.  

When it comes to qualitative data analysis, there are  many different types of analyses  (we discuss some of the  most popular ones here ) and the type of analysis you adopt will depend heavily on your research aims, objectives and questions . Therefore, we’re not going to go down that rabbit hole here, but we’ll cover the important first steps that build the bridge from qualitative data coding to qualitative analysis.

When starting to think about your analysis, it’s useful to  ask yourself  the following questions to get the wheels turning:

• What actions are shown in the data? 
• What are the aims of these interactions and excerpts? What are the participants potentially trying to achieve?
• How do participants interpret what is happening, and how do they speak about it? What does their language reveal?
• What are the assumptions made by the participants? 
• What are the participants doing? What is going on? 
• Why do I want to learn about this? What am I trying to find out? 
• Why did I include this particular excerpt? What does it represent and how?

Code categorisation

Categorisation is simply the process of reviewing everything you’ve coded and then  creating code categories  that can be used to guide your future analysis. In other words, it’s about creating categories for your code set. Let’s take a look at a practical example.

If you were discussing different types of animals, your initial codes may be “dogs”, “llamas”, and “lions”. In the process of categorisation, you could label (categorise) these three animals as “mammals”, whereas you could categorise “flies”, “crickets”, and “beetles” as “insects”. By creating these code categories, you will be making your data more organised, as well as enriching it so that you can see new connections between different groups of codes. 

Theme identification

From the coding and categorisation processes, you’ll naturally start noticing themes. Therefore, the logical next step is to  identify and clearly articulate the themes  in your data set. When you determine themes, you’ll take what you’ve learned from the coding and categorisation and group it all together to develop themes. This is the part of the coding process where you’ll try to draw meaning from your data, and start to  produce a narrative . The nature of this narrative depends on your research aims and objectives, as well as your research questions (sounds familiar?) and the  qualitative data analysis method  you’ve chosen, so keep these factors front of mind as you scan for themes. 

Tips & tricks for quality coding

Before we wrap up, let’s quickly look at some general advice, tips and suggestions to ensure your qualitative data coding is top-notch.

• Before you begin coding,  plan out the steps  you will take and the coding approach and technique(s) you will follow to avoid inconsistencies. 
• When adopting deductive coding, it’s useful to  use a codebook  from the start of the coding process. This will keep your work organised and will ensure that you don’t forget any of your codes. 
• Whether you’re adopting an inductive or deductive approach,  keep track of the meanings  of your codes and remember to revisit these as you go along.
• Avoid using synonyms  for codes that are similar, if not the same. This will allow you to have a more uniform and accurate coded dataset and will also help you to not get overwhelmed by your data.
• While coding, make sure that you  remind yourself of your aims  and coding method. This will help you to  avoid  directional drift , which happens when coding is not kept consistent. 
• If you are working in a team, make sure that everyone has  been trained and understands  how codes need to be assigned. 

32 Comments

I appreciated the valuable information provided to accomplish the various stages of the inductive and inductive coding process. However, I would have been extremely satisfied to be appraised of the SPECIFIC STEPS to follow for: 1. Deductive coding related to the phenomenon and its features to generate the codes, categories, and themes. 2. Inductive coding related to using (a) Initial (b) Axial, and (c) Thematic procedures using transcribe data from the research questions

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Hello, I am doing qualitative research, please assist with example of coding format.

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deductive data analysis of my Focus group discussion results very helpful

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Methodology

• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

• What is the demographic makeup of Singapore in 2020?
• How has the average temperature changed globally over the last century?
• Does environmental pollution affect the prevalence of honey bees?
• Does working from home increase productivity for people with long commutes?

Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Bhandari, P. (2023, June 22). What Is Quantitative Research? | Definition, Uses & Methods. Scribbr. Retrieved September 9, 2024, from https://www.scribbr.com/methodology/quantitative-research/

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