Qualitative Data Analysis

Qualitative data can be defined as non-numerical and unstructured data that is collected from qualitative research methods, such as open-ended survey questions, in-depth interviews, focus groups, direct observation, and content analysis (video, picture or document). The data collected from these methods is typically in the form of typed text, transcripts or recordings that must be examined to identify key themes and insights. The process of examining and interpreting qualitative data is known as qualitative data analysis.

The most common type of qualitative data analysis is content analysis. Content analysis refers to the process of categorizing text, verbal or behavioral data to classify, summarize and tabulate the data. For example, to analyze focus group data, researchers might review transcripts or recordings and group similar sentiments together into categories. They would then assign each of those categories a “code.”

Coding Qualitative Data

Coding is an integral part of qualitative data analysis. Coding can be defined as the labeling and organizing of qualitative data to identify themes and patterns. The purpose of coding is to provide structure to free-form data so that it can be examined in a systematic way.

A code can be a word or a phrase that represents a recurring theme or idea in the data. The code name should be meaningful and capture the essence of the free-form response or observation. For example, coding the open-ended responses to a survey question might look like the following:

Assigning codes helps capture what each qualitative response is about. Researchers can then analyze those codes and begin to build on the themes and patterns that surface to gain comprehensive insights into the data. Although the process of developing and assigning codes can be laborious and time-consuming, ultimately it helps reduce the amount of data that must be reviewed or taken into account in the final analysis.

Automated Coding of Qualitative Data

There are two methods of coding qualitative data: automated coding and manual coding.

Automated coding uses qualitative data analysis software to quickly analyze and code qualitative data. The software leverages machine learning, artificial intelligence (AI), and natural language processing to determine themes and create codes without any advance setup or pre-planning. The algorithms learn as they go.

Some of the perceived benefits of automated coding include the elimination of researcher bias, the ability to process large amounts of data, and significant time-savings. Manual coding remains popular, however, due to its perceived higher accuracy.

Manual Coding of Qualitative Data

Manual coding requires researchers to read through their data and manually develop and assign codes and themes. Although manual coding is time-consuming, it can help streamline the overall analysis process. Creating codes requires the researcher to decide which data is relevant and why, reducing the amount of data that must be considered in the final analysis.

Before starting coding, researchers have to decide if they want to use a deductive or inductive coding method.

Deductive Coding

In deductive coding, researchers start with a predefined set of codes or a codebook developed before analyzing the research data. This set could be based on the research questions or an existing research framework or theory. For example, if the research question is why a consumer purchased a specific product, the researcher might predefine a list of codes that includes price, quality, brand, etc. With this list in mind, the researcher would then read through the research data and simply assign the predefined codes.

Inductive Coding

Inductive coding involves building a list of codes or a codebook from scratch based on the research data. Rather than starting with a plan for what the codes should be, researchers allow the themes and theories to emerge from the data itself.

Inductive coding is often more difficult but can be less prone to bias than deductive coding, because the researcher does not start the analysis process with any preconceived notions about what they might read or hear.

In practice, research studies often combine deductive and inductive coding, starting with a predefined list of codes but then inductively modifying and adding to that list as analysis ensues.

Steps for Manually Coding Qualitative Data

Coding is an important step in moving from the raw data to the findings. There is no right or wrong way to code a set of data, and the process can vary significantly depending on the data collected and the objective of the research.

In general, however, it involves some variation of the following steps:

1. First pass: Researchers first read through or listen to all the data and assign codes to general phrases, ideas or categories that surface. The codes might represent the participant’s own words, a label, description, definition, or category name. The purpose of this round is to gain an overall understanding of what the data is about. This step should be relatively fast and easy since the researcher will be evolving and updating the codes in the rounds to follow.
2. Line-by-line coding: In the second pass through the data, the researcher should comb through the data line-by-line, refining the list of codes and adding detail. While the first pass at coding is fast and loose, this second round is about reanalyzing, renaming, merging codes, finding patterns, and getting closer to developing theories and concepts.
3. Creating categories and themes: After line-by-line coding, it is time to start grouping codes together into categories and developing themes. Codes might be grouped together according to similarity or if they pertain to the same topic or general concept. The researcher then looks through the categories, keeping a close eye out for any themes or patterns that emerge across the data set. Within these themes lies the overall narrative of the research.

Conduct Qualitative Data Research and Analysis with GeoPoll

GeoPoll has experience designing, administering and analyzing quantitative and qualitative research studies around the globe. Our research methods include surveys with closed-ended and open-ended question capabilities, mobile-based focus groups, concept testing, and more. We use both automated and manual coding as part of our qualitative research analysis process. To learn more about GeoPoll’s capabilities, please contact us today.

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Difference Between Quantitative and Qualitative Data

The primary difference between quantitative and qualitative data is that quantitative data represents data that can easily be measured or quantified, such as the number of people who have bought a product. Qualitative data represent opinions or feelings and cannot be represented by a numerical statistic such as an average.

For example, if a survey asked 500 respondents the question “Did you buy ice cream today?”, and 300 responded ‘yes’ while 200 responded ‘no’, we would know that 300/500 or 60% bought milk, a quantitative fact. If the same survey asked an open-ended follow-up question: “Why did you choose the brand of ice cream you bought?” you would receive qualitative insights that are unique to each respondent. One person may say, ‘I liked the packaging and label colors’ while another may state, ‘It was the first one I saw on the shelf.’ These descriptive insights cannot easily be quantified into numbers, so they are qualitative.

Qualitative vs Quantitative Analysis

Another difference between quantitative and qualitative research is how data is analyzed. While quantitative data can be analyzed statistically and calculated into averages, means, and other numerical data points, qualitative data analysis involves a more complex system.

To glean insights from qualitative data, researchers conduct a manual analysis of datasets and often code responses into categories. For example, to analyze focus group data, researchers could review transcripts or recordings and group similar sentiments together into categories. Due to this manual process, qualitative data analysis is a longer and more labor-intensive process than quantitative data analysis, which is another factor to keep in mind when deciding what type of data to collect.

While some methods such as focus groups typically collect qualitative data, other methods such as surveys often collect quantitative and qualitative data within one survey instrument, as outlined below.

Quantitative Data Examples

Quantitative data is collected through several methods, including surveys, controlled experiments, and certain observation types. Quantitative data types include:

• Yes/no questions
  • “Did you go to work today? 1) Yes 2) No”
• Single choice questions
  • “What is your favorite flavor of ice cream? 1) Vanilla 2) Chocolate 3) Cookie Dough 4) Peppermint 5) Chocolate chip”
• Multiple choice or ‘select-all-that-apply’ questions
  • “Which of the following products did you buy last week? 1) Toothpaste 2) Soap 3) Vegetables 4) Meat 5) Grains 6) Bread”
• Ranking questions
  • “Please rank the statement ‘I enjoy ice cream’ from 1: Strongly disagree to 5: Strongly agree”
• Numerical range questions
  • “How much money did you spend at the grocery store today? Please respond with a dollar amount”
• Quantitative observations
  • Observations that can be categorized or quantified, such as the number of times a person checks their phone in a given time. These observations often take place in a controlled environment.

As all of these question types collect data that fit into set categories or can be calculated into averages and other statistics, they are quantitative.

Qualitative Data Examples

Qualitative data can also be collected through certain types of survey questions, in addition to interviews and focus groups. Examples of qualitative data include:

• Open-ended survey questions
  • “Why is cookie dough your favorite flavor of ice cream?”
• Unstructured or semi-structured interviews
  • Unstructured and semi-structured interviews allow topics and questions to flow naturally, rather than only asking questions from a set question list in a specific order.
• Focus groups
  • In focus groups, multiple people have a discussion (in-person or via an online or mobile-based chat group) facilitated by a trained moderator who gives prompts to start conversations.
• Unstructured observation
  • Researchers can gather qualitative data through unstructured observations, such as observing participants as they partake in certain activities such as shopping.
• Documents or content analysis
  • Reviewing documents to better understand a particular topic or categorize elements of documents is a type of qualitative research.

Data collected from these methods and question types do not provide numerical statistics but instead, give insights that are often longer and more detailed than their quantitative counterparts.

When Should I Use Quantitative or Qualitative Research?

Once you understand the types of data provided by qualitative and quantitative research and the methods for each, it’s essential to understand how to utilize each type of data best. Generally, quantitative data is used to answer precise questions and prove or disprove hypotheses, while qualitative data provides richer insights on a smaller scale.

Qualitative research is often conducted at the beginning of a study when researchers are looking to gather broad, unstructured information on a topic to create a hypothesis, which can then be more clearly answered by quantitative research. Qualitative data collected through unstructured interviews or focus groups can also inform the development of a more structured questionnaire administered to a larger group.

For example, a focus on different ice cream brands may uncover that participants generally consider price and packaging first. That information can then be inputted into a quantitative question: “Which is more important to you when buying ice cream? 1) Price 2) Packaging” administered to a nationally representative sample.

Qualitative data may also be used as part of a mixed-methods research study to add additional context to quantitative data. A researcher may administer both a quantitative questionnaire and conduct a qualitative analysis of interviews with subject-matter experts to form a more robust conclusion.

Surveys can also be split between qualitative and quantitative; Many surveys are mostly quantitative questions that can be quickly analyzed, plus one or two qualitative questions that provide deeper insights into the topic being studied.

Quantitative vs Qualitative Data: Definitions and Uses Cheat Sheet

GeoPoll has experience designing and administering both quantitative and qualitative research studies around the globe. Our research methods include surveys with closed-ended and open-ended question capabilities, mobile-based focus groups, concept testing, and more. To learn more about GeoPoll’s capabilities, please contact us today.

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During every research project there comes a key moment – the decision around what type of data will best answer the posed research question. While quantitative data provides hard, numerical statistics on, for example, the popularity of a specific brand or the potential market size for a new venture, qualitative data is able to look beyond numbers into thoughts, feelings, and perceptions which are not so easily distilled.

Both quantitative and qualitative data have value at different stages in a project – qualitative data is often exploratory in nature, and can assist when a research question or hypothesis has not been fully fleshed out, while quantitative data allows for input from and quick analysis of large sample sizes. Once a hypothesis has been settled on, each type of data provides different inputs, and many modern researchers may lean towards mixed-methods approaches which incorporate both types of data to get a more complete view of a situation.

There are also practical implications to consider when making the decision between qualitative, quantitative, or mixed-modes research; While quantitative data from a survey or other dataset can be quickly analyzed to answer questions such as “How many consumers would buy my product tomorrow?”, qualitative data from interviews or focus groups requires a lengthier analysis process.

To glean insights from qualitative data regarding “How does the packaging of this product make you feel?”, researchers may need to manually categorize answers and create a data model that fits their needs. Quantitative methods can collect data quickly and cost-effectively from large sample sizes, while qualitative data collection often takes longer and is costlier. Despite this, the depth the qualitative data brings to researchers is extremely valuable, and below we outline some of the types of qualitative data collection, and some new, mobile-centric methods for gathering this type of data.

Types of Qualitative Data Collection

Interviews

Interviews are one of the most well-known forms of collecting qualitative data, as they allow for more in-depth responses than quantitative questions, in which answers are chosen from a set list of options. While interviews can collect quantitative data, for example if a yes/no question is posed to the interviewee, they are often used for qualitative data collection. Qualitative interviews are typically semi-structured or unstructured in format: The interviewer may have a list of general topics to discuss without specific questions, allowing the interviewee’s responses to guide the direction of the discussion, or they may ask open-ended questions which allow for rich data collection.

Focus Groups

Focus groups are similar to a group interview, in that they consist of multiple research subjects being posed questions as a group by a moderator. The key difference between interviews and focus groups comes with the interactions group members have with each other – as they are participating in a group discussion, they are able to influence each other’s thinking and bounce ideas off of each other, while still contributing at an individual level. A skilled moderator is a necessity in a focus group discussion, as the moderator guides the conversation to extract the best information possible while remaining a neutral observer. Focus groups are an excellent way for brands to gather honest feedback on new products, brand messaging, and other items that consumers often have a visceral reaction to. While focus groups are traditionally conducted in-person, virtual focus groups are becoming more popular in market research, as we explain below.

Observation

Direct observation of people in their natural environments, also called fieldwork, is one of the best ways to gather unbiased data on the habits and actions of consumers. In this method, a researcher observes the actions of their target group. Observation could take the shape of a researcher viewing consumers shopping in a store, examining a shelf of products and deciding which one to purchase, or could include analyzing the reactions of a group to a video playing on a large screen. Observation allows researchers to view decision making, social interactions, and reactions to stimuli as they occur in the real world, which can provide insights not seen in settings such as interviews. However, a drawback of observation is the amount of time and effort on the part of the researcher it requires.

Video and Picture Analysis

Analyzing videos or pictures of how individuals use or react to a product can be an excellent source of qualitative data. Using this technique, for example, a brand may realize that a new package design was not as intuitive as they had hoped, or that consumers are not reading usage instructions. Video and picture market research data can be solicited directly from consumers, or researchers may invite subjects to a location and video them themselves. This type of qualitative data can then be analyzed to identify common themes or topics among participants.

Document analysis

In the document analysis research method, researchers gather qualitative data from existing documents. These documents could include anything from diaries or journals of research subjects, to advertisements, web content, and other written documents that can provide deep insights into past events or individual’s feelings at a specific moment in time. Documents allow researchers to analyze information without needing to conduct interviews or gather new data, which can be beneficial when time or budget prohibit other research methods.

How to Gather Qualitative Data with Mobile Phones

As mobile phone technology has advanced and penetration has increased around the globe, mobile phones have become an increasingly popular way to collect qualitative data. Using a research subject’s own mobile phones as a vehicle for data collection, researchers can gather data from subjects in remote locations, and collect data more often than is feasible using in-person methodologies. In addition, leveraging mobile often reduces the costs associated with qualitative research. Some of the above methods that can be adapted to mobile include:

Mobile Phone Interviews

Interviews can be conducted by phone, through voice calls or video interviews. This enables researchers to gather qualitative data without needing to meet interviewees in person, making it more convenient for the interviewee and allowing for a higher number of interviews to be conducted.

Mobile Phone Focus Groups

Focus groups conducted virtually through a web-based chat, often referred to as Market Research Online Communities or MROCs, have grown in popularity, and a similar technique can be applied to mobile phones. Using mobile-based chat groups and a skilled moderator, participants can take part in an ongoing focus group via mobile, allowing organizations to collect rich data over a week or longer.

Mobile Phone Video and Picture Analysis

Many mobile phones include high-quality cameras, and this tool can be leveraged by researchers looking to gather qualitative picture and/or video data. Researchers can request study participants take pictures or videos of themselves interacting with products, exploring features, and using them in their everyday lives.

Conduct Qualitative Research Through Mobile Around the World

GeoPoll has experience collecting qualitative data through multiple mobile-based methods, including mobile focus groups in Africa, and would be happy to answer your questions about collecting qualitative data quickly and cost-effectively using mobile. To learn more or request a quote, contact us here.

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Focus groups provide companies with a deeper understanding of their target audience, allowing them to develop tailored products and more effective marketing campaigns. While survey research can provide the “what” when looking at consumer drivers, such as “What category do you spend most of your money on?” or “What brand of shampoo do you prefer?”, focus groups can delve into the related “why” questions, the answers of which are more complex and can be difficult to glean from quantitative data. The semi-structured nature of focus groups allows for feedback to flow naturally and can provide more organic answers than a rigid set of survey questions.

A downside of focus groups is that they are expensive and time-consuming, requiring a company to recruit participants, bring them to a central location, and hire a moderator for an in-person discussion. They are often inconvenient for participants, which makes it difficult to recruit and means incentives often have to be high. These challenges have led to the emergence of Market Research Online Communities (MROCs), in which participants can provide feedback on products and participate in unstructured interviews in an online forum that does not require them to be present in person.

How to Conduct Market Research Online Communities (MROCs) in Africa

However, even traditional MROCs can be difficult to organize in emerging markets such as Nigeria and South Africa. Many brands in these regions have a desire for qualitative data, but unreliable internet connections and the need for moderators to speak local languages and understand relevant cultural context means that recruiting participants and keeping them active in an MROC can be difficult. In addition, the reliance on online modes can skew the participant group. If the forum through which the MROC is managed is only accessible through a desktop-browser, a large segment of the population who only access the internet through mobile browsers would be excluded.

In order to provide our clients with high-quality qualitative data, GeoPoll has recently launched mobile-based Market Research Online Communities (MROCs) in our core markets throughout Africa. These MROCs are run through the mobile phone, so that participants don’t need to have desktop computers, and are moderated by GeoPoll’s country experts, who are able to lead discussions and provide directions appropriately. Participants for GeoPoll’s MROCs are recruited through our active database in each country and are provided incentives regularly during participation via mobile money, airtime credit, or PayPal to encourage ongoing participation.

GeoPoll can create one-time MROCs to facilitate feedback on a specific product or marketing campaign or can recruit participants to take part in an ongoing MROC to gather insights on multiple topics from the same audience over time. Following the MROC discussion or as it is ongoing, GeoPoll’s research team compiles a detailed report that can include photo and video content shared by participants, discussion transcripts, and a summary of insights gathered from the MROC. To learn more about GeoPoll’s MROC capabilities in Africa and other emerging regions and how we recruit participants for and manage the MROC, please contact us here.

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