data collection techniques in qualitative and quantitative research

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

Surveys : List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

Interviews : Asking open-ended questions verbally to respondents.
Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

Use quantitative research if you want to confirm or test something (a theory or hypothesis )
Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

Average scores ( means )
The number of times a particular answer was given
The correlation or causation between two or more variables
The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
Thematic analysis : Closely examining the data to identify the main themes and patterns
Discourse analysis : Studying how communication works in social contexts

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
Quantitative research
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 .

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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.

There are various approaches to qualitative data analysis , but they all share five steps in common:

Prepare and organize your data.
Review and explore your data.
Develop a data coding system.
Assign codes to the data.
Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Opinion Stage » survey » Data Collection Methods

Data Collection Methods: Qualitative vs Quantitative Research

In this article, we will talk about the importance of collecting data and show you how you can get started with data collection. We’ll also go over the main types of data collection and discuss how to use them in the best possible way.

The Importance of Collecting Data

Data helps you make informed decisions. The more relevant data you have, the more likely you are to make good decisions when managing various areas of your business.

Data can help you:

Gain a better understanding of your audience – Collecting data allows you to learn more about your audience, their needs, pain points, and expectations.
Find areas for improvement – Data provides you with insight into how well your company is doing and enables you to find areas that can be improved.
Identify patterns – Collecting and analyzing data allows you to identify patterns and predict future trends.
Personalize your messaging – Data can provide you with various insights that will allow you to tailor your content and messaging for each specific member of your target audience.

Note that it’s crucial to collect enough data to ensure that its statistical significance. It’s also important to do so properly since bad data can harm your business more than good data can benefit it. Focusing on statistical significance and data quality will ensure that your findings and conclusions are valid. It will also enable you to generalize your conclusions across populations.

How to Collect Data

Now that you understand the importance of collecting data, let’s talk about how you should go about it.

Decide what information you need

The first step to collecting data is deciding what information you need. You’ll need to figure out what topics you’re interested in and who you’ll collect the data from. Additionally, you’ll need to decide how much data you need and what you want to accomplish by collecting and using your data.

Set a time frame

You should set a time frame for collecting data. Depending on the type of data you are collecting, you may need to collect it continuously, or only over a defined period.

If you’re looking to collect data over a defined period, you’ll need to set a specific start and end time.

Choose a data collection method

It’s crucial that you pick the right method for collecting data. Since data collection is costly, you can’t afford to be haphazard about it. Choosing the right data collection method will ensure that you collect relevant, high-quality data. When selecting a data collection method, take into account the type of information you’re looking for, as well as the time frame over which you’re going to collect it.

Collect data

Once you’ve settled on a particular method of data collection, find an efficient way to store your data, and regularly check on your data collection progress to make sure there are no issues like unsolicited data or data breaches . If conditions change during the data collection process, you’ll need to make appropriate updates to your method.

Qualitative vs Quantitative Research

Research can be divided into two main groups: qualitative and quantitative. These two types of research differ in their objectives, the way they approach data collection, their flexibility, and the type of data they produce.

Qualitative research

Qualitative research answers questions such as why and how. It allows you to collect detailed information on a particular topic and formulate a hypothesis that you can then test using quantitative research. Qualitative research is usually exploratory and unstructured. It allows for more flexibility and encourages discussion. It involves in-depth interactions with a smaller number of respondents.

When conducting qualitative research, researchers aren’t interested in coming to objective statistical conclusions. Instead, they’re looking to gain a detailed insight into a particular topic. They use written responses, notes, and other types of output generated by qualitative research. They also look for patterns and attempt to draw conclusions. Qualitative data is descriptive and harder to measure compared to quantitative data. It includes opinions and descriptive phrases. It relies on the researcher to make subjective conclusions. While less objective than quantitative data, it allows for a greater depth of understanding of respondents’ opinions and motivations.

Quantitative research

Compared to qualitative research, quantitative research is usually more affordable to execute. It also requires a lesser investment of time. Quantitative research is standardized, which makes it easy to compare findings. Quantitative data deals with numbers and includes values and quantities. It’s objective and reliable. It answers questions such as who, what, when, where, and how many.

Quantitative research allows you to quantify behaviors, opinions, and attitudes. It also enables you to make generalizations based on the gathered results. It usually requires more planning compared to qualitative research. Most frequently, quantitative research takes more time to perform. However, analyzing quantitative data is a lot more straightforward than analyzing data gathered from qualitative research. You can transform findings from quantitative research into charts and graphs.

Deciding between qualitative vs quantitative research

Whether you should use qualitative or quantitative research to collect data depends on your specific needs. What kind of question are you looking to get answered? What resources do you have at your disposal? Do you have access to a sufficient number of respondents? Note that quantitative and qualitative research methods don’t conflict with each other. In fact, they’re best used in combination. Start by using qualitative research to discover a particular problem or opportunity. Devise a hypothesis and then use quantitative research to test it.

Data Collection Methods

Face-to-face interviews.

Face-to-face interviews are the most common data collection method used in qualitative research. In face-to-face interviews, researchers collect data directly from subjects through one-on-one interaction. This type of data collection is personal and highly personalized.

The interview itself is usually unstructured and informal. Most questions used in the interview are spontaneous and unplanned. They’re focused on getting an understanding of an individual’s perspectives and experiences. They can reveal respondents’ feelings, values, and beliefs. This type of approach is useful for getting a detailed understanding of the subject matter. However, it makes processing the collected data time-consuming and somewhat difficult.

Qualitative questionnaires

Qualitative questionnaires usually consist of short, open-ended questions. There are no predefined answers offered in these types of questionnaires. Instead, respondents are asked to provide detailed answers in their own words. This gives respondents much more flexibility and freedom in expressing their own opinions.

Questionnaires are highly useful for gathering data from a large number of respondents. Online questionnaires, in particular, make data collection very quick and easy. Data collected from qualitative questionnaires can be difficult to analyze because there are no standard answer options. You also need to pay special attention to the number of questions you’re going to include. Open-ended questions can be very time-consuming to answer. This makes it crucial that you don’t use too many questions in your survey and overwhelm respondents.

Focus groups

Collecting data through focus groups is similar to conducting interviews, except it’s done in a group setting. A focus group needs to consist of a moderator and at least three people. At a maximum, it should include ten people. All group members need to have something in common that’s related to the data you’re looking to gather.

When collecting data through focus groups, you’ll need a moderator that will start a discussion on a particular topic. Group members can then state their opinions on it as well as debate each other. The success of focus group data collection depends heavily on the moderator’s capability to guide the discussion and control interactions within the group. The nature of the method allows researchers to get detailed and descriptive data on a particular topic.

Observation

Observation allows researchers to collect qualitative data by observing respondents in their natural settings. In qualitative observation, researchers participate in the process. They immerse themselves in the setting along with respondents, all while taking notes.

The two main types of observation include:

Covert observation – This type of observation involves the researcher being concealed during the observation process.
Overt observation – Respondents that participate in overt observation are aware that they’re being observed.

Data collected through qualitative observation is more reliable since researchers are participating in the process themselves. However, the attitude of researchers towards the data may be subjective.

There’s also the issue of researchers’ participation interfering with the natural state of the setting. Respondents might act differently because they are being observed, which leads to impaired results.

Longitudinal studies

A longitudinal study is a type of data collection that’s performed repeatedly over an extended period on the same data sources. It can last for years or even decades. It’s used to uncover correlations through an observational or empirical study of subjects that share a common characteristic or trait. Longitudinal studies are ideal for gathering data that are supposed to establish a pattern for a specific variable over a defined period. They’re very effective in finding relationships of cause and effect. The main disadvantage of longitudinal studies is the long period that’s necessary to carry them out. There’s also the issue of data being diluted due to subjects changing their opinions and attitudes over the duration of a study.

Case studies

Case studies involve taking a close look at a particular case – an individual, a group of individuals, or an organization. This type of data collection is very versatile and can be used to analyze simple and complex subjects. Case studies tend to provide detailed, in-depth information. Researchers analyzing a case study might use other methods to collect data. They might take advantage of questionnaires, interviews, or group discussions.

Quantitative surveys

There are different types of surveys that are built to help you gain insight and feedback in various situations. Many of them are quantitative surveys. These surveys consist of a list of queries that respondents can answer by choosing the appropriate answer from a list of responses. Questions used in quantitative surveys are necessarily close-ended to ensure measurability. They can either be categorical (e.g., yes/no questions) or interval/ratio questions (e.g., on a scale of 1 to 10). Quantitative survey questions need to be straightforward and easy to understand. There should be no hint of ambiguity in these types of questions.

Quantitative surveys are standardized, which gives researchers the ability to make reliable generalizations based on the results. Findings can be presented in the form of charts and graphs, which makes them easier to understand. One can conduct a survey online, in person, or over the phone. Out of these three methods, online surveys are certainly the easiest to conduct. You can host an online survey on your own website or with a third-party provider. Then you can share the link to your survey in a variety of places. For example, you can easily create a Whatsapp survey , an email survey, or a Linkedin survey , depending on what makes the most sense for your target audience of responders.

Pro tip: An easy way to get started with creating an online survey is to use the Opinion Stage Survey Maker . You can also use our survey templates , which are professionally built to help you create and publish an online survey within minutes.

Create your own interactive survey

Experiments

An experiment is a type of quantitative data collection method that relies on the manipulation of a single independent variable while maintaining control over a number of other, usually dependent, variables. The data gathered through experiments is most frequently used to analyze relationships and determine correlations.

The three main types of experiments include:

Laboratory experiments – These types of experiments take place in a controlled environment, with researchers having strict control over all the variables involved.
Field experiments – Take place in a natural environment where full control of variables might not be available.
Natural experiments – In these types of experiments, researchers have no control over variables, and data is collected by letting variables occur naturally.

Collecting Data Through Qualitative and Quantitative Research

Data allows you to make better, informed decisions. It can help you gain a better understanding of your audience, find areas for improvement in your organization, identify patterns, and personalize your business’ messaging. When looking to collect data, decide what information you need, set a time frame for data collection, and choose a data collection method.

Once you’ve decided on the above, you can proceed to collect data. After you’ve finished the data collection process, you can go on to analyze the data and draw conclusions.

The two main types of data collection methods you have at your disposal are qualitative and quantitative.

Qualitative data collection methods rely on opinions, descriptive phrases, and researchers’ subjective conclusions. Face-to-face interviews, qualitative questionnaires, focus groups, observation, longitudinal studies, and case studies are examples of qualitative data collection methods.

Quantitative data collection methods, on the other hand, deal with numbers and are highly standardized. Examples of quantitative data collection methods include quantitative surveys, interviews, quantitative observations, and experiments.

Your choice of data collection method should depend on the topic of research, as well as the resources you have. In most cases, a combination of quantitative and qualitative data collection methods is the best way to go.

Start by using qualitative research to discover issues that need to be addressed and form a hypothesis. Then, use quantitative research to test your hypothesis and come up with results that can be analyzed easily.

Finally, use the results you’ve obtained from your quantitative research to discover relationships and correlations that can help you understand the issue better and potentially solve it.

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Qualitative Data Collection: What it is + Methods to do it

Qualitative data collection is vital in qualitative research. It helps researchers understand individuals’ attitudes, beliefs, and behaviors in a specific context.

Several methods are used to collect qualitative data, including interviews, surveys, focus groups, and observations. Understanding the various methods used for gathering qualitative data is essential for successful qualitative research.

In this post, we will discuss qualitative data and its collection methods of it.

Content Index

What is Qualitative Data?

What is qualitative data collection, what is the need for qualitative data collection, effective qualitative data collection methods, qualitative data analysis, advantages of qualitative data collection.

Qualitative data is defined as data that approximates and characterizes. It can be observed and recorded.

This data type is non-numerical in nature. This type of data is collected through methods of observations, one-to-one interviews, conducting focus groups, and similar methods.

Qualitative data in statistics is also known as categorical data – data that can be arranged categorically based on the attributes and properties of a thing or a phenomenon.

It’s pretty easy to understand the difference between qualitative and quantitative data. Qualitative data does not include numbers in its definition of traits, whereas quantitative research data is all about numbers.

The cake is orange, blue, and black in color (qualitative).
Females have brown, black, blonde, and red hair (qualitative).

Qualitative data collection is gathering non-numerical information, such as words, images, and observations, to understand individuals’ attitudes, behaviors, beliefs, and motivations in a specific context. It is an approach used in qualitative research. It seeks to understand social phenomena through in-depth exploration and analysis of people’s perspectives, experiences, and narratives. In statistical analysis , distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

The data collected through qualitative methods are often subjective, open-ended, and unstructured and can provide a rich and nuanced understanding of complex social phenomena.

Qualitative research is a type of study carried out with a qualitative approach to understand the exploratory reasons and to assay how and why a specific program or phenomenon operates in the way it is working. A researcher can access numerous qualitative data collection methods that he/she feels are relevant.

LEARN ABOUT: Best Data Collection Tools

Qualitative data collection methods serve the primary purpose of collecting textual data for research and analysis , like the thematic analysis. The collected research data is used to examine:

Knowledge around a specific issue or a program, experience of people.
Meaning and relationships.
Social norms and contextual or cultural practices demean people or impact a cause.

The qualitative data is textual or non-numerical. It covers mostly the images, videos, texts, and written or spoken words by the people. You can opt for any digital data collection methods , like structured or semi-structured surveys, or settle for the traditional approach comprising individual interviews, group discussions, etc.

Data at hand leads to a smooth process ensuring all the decisions made are for the business’s betterment. You will be able to make informed decisions only if you have relevant data.

Well! With quality data, you will improve the quality of decision-making. But you will also enhance the quality of the results expected from any endeavor.

Qualitative data collection methods are exploratory. Those are usually more focused on gaining insights and understanding the underlying reasons by digging deeper.

Although quantitative data cannot be quantified, measuring it or analyzing qualitative data might become an issue. Due to the lack of measurability, collection methods of qualitative data are primarily unstructured or structured in rare cases – that too to some extent.

Let’s explore the most common methods used for the collection of qualitative data:

data collection techniques in qualitative and quantitative research

Individual interview

It is one of the most trusted, widely used, and familiar qualitative data collection methods primarily because of its approach. An individual or face-to-face interview is a direct conversation between two people with a specific structure and purpose.

The interview questionnaire is designed in the manner to elicit the interviewee’s knowledge or perspective related to a topic, program, or issue.

At times, depending on the interviewer’s approach, the conversation can be unstructured or informal but focused on understanding the individual’s beliefs, values, understandings, feelings, experiences, and perspectives on an issue.

More often, the interviewer chooses to ask open-ended questions in individual interviews. If the interviewee selects answers from a set of given options, it becomes a structured, fixed response or a biased discussion.

The individual interview is an ideal qualitative data collection method. Particularly when the researchers want highly personalized information from the participants. The individual interview is a notable method if the interviewer decides to probe further and ask follow-up questions to gain more insights.

Qualitative surveys

To develop an informed hypothesis, many researchers use qualitative research surveys for data collection or to collect a piece of detailed information about a product or an issue. If you want to create questionnaires for collecting textual or qualitative data, then ask more open-ended questions .

LEARN ABOUT: Research Process Steps

To answer such qualitative research questions , the respondent has to write his/her opinion or perspective concerning a specific topic or issue. Unlike other collection methods, online surveys have a wider reach. People can provide you with quality data that is highly credible and valuable.

Paper surveys

Online surveys, focus group discussions.

Focus group discussions can also be considered a type of interview, but it is conducted in a group discussion setting. Usually, the focus group consists of 8 – 10 people (the size may vary depending on the researcher’s requirement). The researchers ensure appropriate space is given to the participants to discuss a topic or issue in a context. The participants are allowed to either agree or disagree with each other’s comments.

With a focused group discussion, researchers know how a particular group of participants perceives the topic. Researchers analyze what participants think of an issue, the range of opinions expressed, and the ideas discussed. The data is collected by noting down the variations or inconsistencies (if any exist) in the participants, especially in terms of belief, experiences, and practice.

The participants of focused group discussions are selected based on the topic or issues for which the researcher wants actionable insights. For example, if the research is about the recovery of college students from drug addiction. The participants have to be college students studying and recovering from drug addiction.

Other parameters such as age, qualification, financial background, social presence, and demographics are also considered, but not primarily, as the group needs diverse participants. Frequently, the qualitative data collected through focused group discussion is more descriptive and highly detailed.

Record keeping

This method uses reliable documents and other sources of information that already exist as the data source. This information can help with the new study. It’s a lot like going to the library. There, you can look through books and other sources to find information that can be used in your research.

Case studies

In this method, data is collected by looking at case studies in detail. This method’s flexibility is shown by the fact that it can be used to analyze both simple and complicated topics. This method’s strength is how well it draws conclusions from a mix of one or more qualitative data collection methods.

Observations

Observation is one of the traditional methods of qualitative data collection. It is used by researchers to gather descriptive analysis data by observing people and their behavior at events or in their natural settings. In this method, the researcher is completely immersed in watching people by taking a participatory stance to take down notes.

There are two main types of observation:

Covert: In this method, the observer is concealed without letting anyone know that they are being observed. For example, a researcher studying the rituals of a wedding in nomadic tribes must join them as a guest and quietly see everything.
Overt: In this method, everyone is aware that they are being watched. For example, A researcher or an observer wants to study the wedding rituals of a nomadic tribe. To proceed with the research, the observer or researcher can reveal why he is attending the marriage and even use a video camera to shoot everything around him.

Observation is a useful method of qualitative data collection, especially when you want to study the ongoing process, situation, or reactions on a specific issue related to the people being observed.

When you want to understand people’s behavior or their way of interaction in a particular community or demographic, you can rely on the observation data. Remember, if you fail to get quality data through surveys, qualitative interviews , or group discussions, rely on observation.

It is the best and most trusted collection method of qualitative data to generate qualitative data as it requires equal to no effort from the participants.

LEARN ABOUT: Behavioral Research

You invested time and money acquiring your data, so analyze it. It’s necessary to avoid being in the dark after all your hard work. Qualitative data analysis starts with knowing its two basic techniques, but there are no rules.

Deductive Approach: The deductive data analysis uses a researcher-defined structure to analyze qualitative data. This method is quick and easy when a researcher knows what the sample population will say.
Inductive Approach: The inductive technique has no structure or framework. When a researcher knows little about the event, an inductive approach is applied.

Whether you want to analyze qualitative data from a one-on-one interview or a survey, these simple steps will ensure a comprehensive qualitative data analysis.

Step 1: Arrange your Data

After collecting all the data, it is mostly unstructured and sometimes unclear. Arranging your data is the first stage in qualitative data analysis. So, researchers must transcribe data before analyzing it.

Step 2: Organize all your Data

After transforming and arranging your data, the next step is to organize it. One of the best ways to organize the data is to think back to your research goals and then organize the data based on the research questions you asked.

Step 3: Set a Code to the Data Collected

Setting up appropriate codes for the collected data gets you one step closer. Coding is one of the most effective methods for compressing a massive amount of data. It allows you to derive theories from relevant research findings.

Step 4: Validate your Data

Qualitative data analysis success requires data validation. Data validation should be done throughout the research process, not just once. There are two sides to validating data:

The accuracy of your research design or methods.
Reliability—how well the approaches deliver accurate data.

Step 5: Concluding the Analysis Process

Finally, conclude your data in a presentable report. The report should describe your research methods, their pros and cons, and research limitations. Your report should include findings, inferences, and future research.

QuestionPro is a comprehensive online survey software that offers a variety of qualitative data analysis tools to help businesses and researchers in making sense of their data. Users can use many different qualitative analysis methods to learn more about their data.

Users of QuestionPro can see their data in different charts and graphs, which makes it easier to spot patterns and trends. It can help researchers and businesses learn more about their target audience, which can lead to better decisions and better results.

LEARN ABOUT: Steps in Qualitative Research

Qualitative data collection has several advantages, including:

In-depth understanding: It provides in-depth information about attitudes and behaviors, leading to a deeper understanding of the research.
Flexibility: The methods allow researchers to modify questions or change direction if new information emerges.
Contextualization: Qualitative research data is in context, which helps to provide a deep understanding of the experiences and perspectives of individuals.
Rich data: It often produces rich, detailed, and nuanced information that cannot capture through numerical data.
Engagement: The methods, such as interviews and focus groups, involve active meetings with participants, leading to a deeper understanding.
Multiple perspectives: This can provide various views and a rich array of voices, adding depth and complexity.
Realistic setting: It often occurs in realistic settings, providing more authentic experiences and behaviors.

LEARN ABOUT: 12 Best Tools for Researchers

Qualitative research is one of the best methods for identifying the behavior and patterns governing social conditions, issues, or topics. It spans a step ahead of quantitative data as it fails to explain the reasons and rationale behind a phenomenon, but qualitative data quickly does.

Qualitative research is one of the best tools to identify behaviors and patterns governing social conditions. It goes a step beyond quantitative data by providing the reasons and rationale behind a phenomenon that cannot be explored quantitatively.

With QuestionPro, you can use it for qualitative data collection through various methods. Using Our robust suite correctly, you can enhance the quality and integrity of the collected data.

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Chapter 10. Introduction to Data Collection Techniques

Introduction.

Now that we have discussed various aspects of qualitative research, we can begin to collect data. This chapter serves as a bridge between the first half and second half of this textbook (and perhaps your course) by introducing techniques of data collection. You’ve already been introduced to some of this because qualitative research is often characterized by the form of data collection; for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos. Thus, some of this chapter will operate as a review of material already covered, but we will be approaching it from the data-collection side rather than the tradition-of-inquiry side we explored in chapters 2 and 4.

Revisiting Approaches

There are four primary techniques of data collection used in qualitative research: interviews, focus groups, observations, and document review. [1] There are other available techniques, such as visual analysis (e.g., photo elicitation) and biography (e.g., autoethnography) that are sometimes used independently or supplementarily to one of the main forms. Not to confuse you unduly, but these various data collection techniques are employed differently by different qualitative research traditions so that sometimes the technique and the tradition become inextricably entwined. This is largely the case with observations and ethnography. The ethnographic tradition is fundamentally based on observational techniques. At the same time, traditions other than ethnography also employ observational techniques, so it is worthwhile thinking of “tradition” and “technique” separately (see figure 10.1).

Figure 10.1. Data Collection Techniques

Each of these data collection techniques will be the subject of its own chapter in the second half of this textbook. This chapter serves as an orienting overview and as the bridge between the conceptual/design portion of qualitative research and the actual practice of conducting qualitative research.

Overview of the Four Primary Approaches

Interviews are at the heart of qualitative research. Returning to epistemological foundations, it is during the interview that the researcher truly opens herself to hearing what others have to say, encouraging her interview subjects to reflect deeply on the meanings and values they hold. Interviews are used in almost every qualitative tradition but are particularly salient in phenomenological studies, studies seeking to understand the meaning of people’s lived experiences.

Focus groups can be seen as a type of interview, one in which a group of persons (ideally between five and twelve) is asked a series of questions focused on a particular topic or subject. They are sometimes used as the primary form of data collection, especially outside academic research. For example, businesses often employ focus groups to determine if a particular product is likely to sell. Among qualitative researchers, it is often used in conjunction with any other primary data collection technique as a form of “triangulation,” or a way of increasing the reliability of the study by getting at the object of study from multiple directions. [2] Some traditions, such as feminist approaches, also see the focus group as an important “consciousness-raising” tool.

If interviews are at the heart of qualitative research, observations are its lifeblood. Researchers who are more interested in the practices and behaviors of people than what they think or who are trying to understand the parameters of an organizational culture rely on observations as their primary form of data collection. The notes they make “in the field” (either during observations or afterward) form the “data” that will be analyzed. Ethnographers, those seeking to describe a particular ethnos, or culture, believe that observations are more reliable guides to that culture than what people have to say about it. Observations are thus the primary form of data collection for ethnographers, albeit often supplemented with in-depth interviews.

Some would say that these three—interviews, focus groups, and observations—are really the foundational techniques of data collection. They are far and away the three techniques most frequently used separately, in conjunction with one another, and even sometimes in mixed methods qualitative/quantitative studies. Document review, either as a form of content analysis or separately, however, is an important addition to the qualitative researcher’s toolkit and should not be overlooked (figure 10.1). Although it is rare for a qualitative researcher to make document review their primary or sole form of data collection, including documents in the research design can help expand the reach and the reliability of a study. Document review can take many forms, from historical and archival research, in which the researcher pieces together a narrative of the past by finding and analyzing a variety of “documents” and records (including photographs and physical artifacts), to analyses of contemporary media content, as in the case of compiling and coding blog posts or other online commentaries, and content analysis that identifies and describes communicative aspects of media or documents.

In addition to these four major techniques, there are a host of emerging and incidental data collection techniques, from photo elicitation or photo voice, in which respondents are asked to comment upon a photograph or image (particularly useful as a supplement to interviews when the respondents are hesitant or unable to answer direct questions), to autoethnographies, in which the researcher uses his own position and life to increase our understanding about a phenomenon and its historical and social context.

Taken together, these techniques provide a wide range of practices and tools with which to discover the world. They are particularly suited to addressing the questions that qualitative researchers ask—questions about how things happen and why people act the way they do, given particular social contexts and shared meanings about the world (chapter 4).

Triangulation and Mixed Methods

Because the researcher plays such a large and nonneutral role in qualitative research, one that requires constant reflectivity and awareness (chapter 6), there is a constant need to reassure her audience that the results she finds are reliable. Quantitative researchers can point to any number of measures of statistical significance to reassure their audiences, but qualitative researchers do not have math to hide behind. And she will also want to reassure herself that what she is hearing in her interviews or observing in the field is a true reflection of what is going on (or as “true” as possible, given the problem that the world is as large and varied as the elephant; see chapter 3). For those reasons, it is common for researchers to employ more than one data collection technique or to include multiple and comparative populations, settings, and samples in the research design (chapter 2). A single set of interviews or initial comparison of focus groups might be conceived as a “pilot study” from which to launch the actual study. Undergraduate students working on a research project might be advised to think about their projects in this way as well. You are simply not going to have enough time or resources as an undergraduate to construct and complete a successful qualitative research project, but you may be able to tackle a pilot study. Graduate students also need to think about the amount of time and resources they have for completing a full study. Masters-level students, or students who have one year or less in which to complete a program, should probably consider their study as an initial exploratory pilot. PhD candidates might have the time and resources to devote to the type of triangulated, multifaceted research design called for by the research question.

We call the use of multiple qualitative methods of data collection and the inclusion of multiple and comparative populations and settings “triangulation.” Using different data collection methods allows us to check the consistency of our findings. For example, a study of the vaccine hesitant might include a set of interviews with vaccine-hesitant people and a focus group of the same and a content analysis of online comments about a vaccine mandate. By employing all three methods, we can be more confident of our interpretations from the interviews alone (especially if we are hearing the same thing throughout; if we are not, then this is a good sign that we need to push a little further to find out what is really going on). [3] Methodological triangulation is an important tool for increasing the reliability of our findings and the overall success of our research.

Methodological triangulation should not be confused with mixed methods techniques, which refer instead to the combining of qualitative and quantitative research methods. Mixed methods studies can increase reliability, but that is not their primary purpose. Mixed methods address multiple research questions, both the “how many” and “why” kind, or the causal and explanatory kind. Mixed methods will be discussed in more detail in chapter 15.

Let us return to the three examples of qualitative research described in chapter 1: Cory Abramson’s study of aging ( The End Game) , Jennifer Pierce’s study of lawyers and discrimination ( Racing for Innocence ), and my own study of liberal arts college students ( Amplified Advantage ). Each of these studies uses triangulation.

Abramson’s book is primarily based on three years of observations in four distinct neighborhoods. He chose the neighborhoods in such a way to maximize his ability to make comparisons: two were primarily middle class and two were primarily poor; further, within each set, one was predominantly White, while the other was either racially diverse or primarily African American. In each neighborhood, he was present in senior centers, doctors’ offices, public transportation, and other public spots where the elderly congregated. [4] The observations are the core of the book, and they are richly written and described in very moving passages. But it wasn’t enough for him to watch the seniors. He also engaged with them in casual conversation. That, too, is part of fieldwork. He sometimes even helped them make it to the doctor’s office or get around town. Going beyond these interactions, he also interviewed sixty seniors, an equal amount from each of the four neighborhoods. It was in the interviews that he could ask more detailed questions about their lives, what they thought about aging, what it meant to them to be considered old, and what their hopes and frustrations were. He could see that those living in the poor neighborhoods had a more difficult time accessing care and resources than those living in the more affluent neighborhoods, but he couldn’t know how the seniors understood these difficulties without interviewing them. Both forms of data collection supported each other and helped make the study richer and more insightful. Interviews alone would have failed to demonstrate the very real differences he observed (and that some seniors would not even have known about). This is the value of methodological triangulation.

Pierce’s book relies on two separate forms of data collection—interviews with lawyers at a firm that has experienced a history of racial discrimination and content analyses of news stories and popular films that screened during the same years of the alleged racial discrimination. I’ve used this book when teaching methods and have often found students struggle with understanding why these two forms of data collection were used. I think this is because we don’t teach students to appreciate or recognize “popular films” as a legitimate form of data. But what Pierce does is interesting and insightful in the best tradition of qualitative research. Here is a description of the content analyses from a review of her book:

In the chapter on the news media, Professor Pierce uses content analysis to argue that the media not only helped shape the meaning of affirmative action, but also helped create white males as a class of victims. The overall narrative that emerged from these media accounts was one of white male innocence and victimization. She also maintains that this narrative was used to support “neoconservative and neoliberal political agendas” (p. 21). The focus of these articles tended to be that affirmative action hurt white working-class and middle-class men particularly during the recession in the 1980s (despite statistical evidence that people of color were hurt far more than white males by the recession). In these stories fairness and innocence were seen in purely individual terms. Although there were stories that supported affirmative action and developed a broader understanding of fairness, the total number of stories slanted against affirmative action from 1990 to 1999. During that time period negative stories always outnumbered those supporting the policy, usually by a ratio of 3:1 or 3:2. Headlines, the presentation of polling data, and an emphasis in stories on racial division, Pierce argues, reinforced the story of white male victimization. Interestingly, the news media did very few stories on gender and affirmative action. The chapter on the film industry from 1989 to 1999 reinforces Pierce’s argument and adds another layer to her interpretation of affirmative action during this time period. She sampled almost 60 Hollywood films with receipts ranging from four million to 184 million dollars. In this chapter she argues that the dominant theme of these films was racial progress and the redemption of white Americans from past racism. These movies usually portrayed white, elite, and male experiences. People of color were background figures who supported the protagonist and “anointed” him as a savior (p. 45). Over the course of the film the protagonists move from “innocence to consciousness” concerning racism. The antagonists in these films most often were racist working-class white men. A Time to Kill , Mississippi Burning , Amistad , Ghosts of Mississippi , The Long Walk Home , To Kill a Mockingbird , and Dances with Wolves receive particular analysis in this chapter, and her examination of them leads Pierce to conclude that they infused a myth of racial progress into America’s cultural memory. White experiences of race are the focus and contemporary forms of racism are underplayed or omitted. Further, these films stereotype both working-class and elite white males, and underscore the neoliberal emphasis on individualism. ( Hrezo 2012 )

With that context in place, Pierce then turned to interviews with attorneys. She finds that White male attorneys often misremembered facts about the period in which the law firm was accused of racial discrimination and that they often portrayed their firms as having made substantial racial progress. This was in contrast to many of the lawyers of color and female lawyers who remembered the history differently and who saw continuing examples of racial (and gender) discrimination at the law firm. In most of the interviews, people talked about individuals, not structure (and these are attorneys, who really should know better!). By including both content analyses and interviews in her study, Pierce is better able to situate the attorney narratives and explain the larger context for the shared meanings of individual innocence and racial progress. Had this been a study only of films during this period, we would not know how actual people who lived during this period understood the decisions they made; had we had only the interviews, we would have missed the historical context and seen a lot of these interviewees as, well, not very nice people at all. Together, we have a study that is original, inventive, and insightful.

My own study of how class background affects the experiences and outcomes of students at small liberal arts colleges relies on mixed methods and triangulation. At the core of the book is an original survey of college students across the US. From analyses of this survey, I can present findings on “how many” questions and descriptive statistics comparing students of different social class backgrounds. For example, I know and can demonstrate that working-class college students are less likely to go to graduate school after college than upper-class college students are. I can even give you some estimates of the class gap. But what I can’t tell you from the survey is exactly why this is so or how it came to be so . For that, I employ interviews, focus groups, document reviews, and observations. Basically, I threw the kitchen sink at the “problem” of class reproduction and higher education (i.e., Does college reduce class inequalities or make them worse?). A review of historical documents provides a picture of the place of the small liberal arts college in the broader social and historical context. Who had access to these colleges and for what purpose have always been in contest, with some groups attempting to exclude others from opportunities for advancement. What it means to choose a small liberal arts college in the early twenty-first century is thus different for those whose parents are college professors, for those whose parents have a great deal of money, and for those who are the first in their family to attend college. I was able to get at these different understandings through interviews and focus groups and to further delineate the culture of these colleges by careful observation (and my own participation in them, as both former student and current professor). Putting together individual meanings, student dispositions, organizational culture, and historical context allowed me to present a story of how exactly colleges can both help advance first-generation, low-income, working-class college students and simultaneously amplify the preexisting advantages of their peers. Mixed methods addressed multiple research questions, while triangulation allowed for this deeper, more complex story to emerge.

In the next few chapters, we will explore each of the primary data collection techniques in much more detail. As we do so, think about how these techniques may be productively joined for more reliable and deeper studies of the social world.

Advanced Reading: Triangulation

Denzin ( 1978 ) identified four basic types of triangulation: data, investigator, theory, and methodological. Properly speaking, if we use the Denzin typology, the use of multiple methods of data collection and analysis to strengthen one’s study is really a form of methodological triangulation. It may be helpful to understand how this differs from the other types.

Data triangulation occurs when the researcher uses a variety of sources in a single study. Perhaps they are interviewing multiple samples of college students. Obviously, this overlaps with sample selection (see chapter 5). It is helpful for the researcher to understand that these multiple data sources add strength and reliability to the study. After all, it is not just “these students here” but also “those students over there” that are experiencing this phenomenon in a particular way.

Investigator triangulation occurs when different researchers or evaluators are part of the research team. Intercoding reliability is a form of investigator triangulation (or at least a way of leveraging the power of multiple researchers to raise the reliability of the study).

Theory triangulation is the use of multiple perspectives to interpret a single set of data, as in the case of competing theoretical paradigms (e.g., a human capital approach vs. a Bourdieusian multiple capital approach).

Methodological triangulation , as explained in this chapter, is the use of multiple methods to study a single phenomenon, issue, or problem.

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Data Collection Methods | Step-by-Step Guide & Examples

Data Collection Methods | Step-by-Step Guide & Examples

Published on 4 May 2022 by Pritha Bhandari .

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental, or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

The aim of the research
The type of data that you will collect
The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address, and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

Quantitative data is expressed in numbers and graphs and is analysed through statistical methods .
Qualitative data is expressed in words and analysed through interpretations and categorisations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data.

If you have several aims, you can use a mixed methods approach that collects both types of data.

Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

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Based on the data you want to collect, decide which method is best suited for your research.

Experimental research is primarily a quantitative method.
Interviews , focus groups , and ethnographies are qualitative methods.
Surveys , observations, archival research, and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design .

Operationalisation

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalisation means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness, and dependability.
You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and time frame of the data collection.

Standardising procedures

If multiple researchers are involved, write a detailed manual to standardise data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorise observations.

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organise and store your data.

If you are collecting data from people, you will likely need to anonymise and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimise distortion.
You can prevent loss of data by having an organisation system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1 to 5. The data produced is numerical and can be statistically analysed for averages and patterns.

To ensure that high-quality data is recorded in a systematic way, here are some best practices:

Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
Double-check manual data entry for errors.
If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

When conducting research, collecting original data has significant advantages:

You can tailor data collection to your specific research aims (e.g., understanding the needs of your consumers or user testing your website).
You can control and standardise the process for high reliability and validity (e.g., choosing appropriate measurements and sampling methods ).

However, there are also some drawbacks: data collection can be time-consuming, labour-intensive, and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

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.

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

Operationalisation 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, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

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

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Published: 22 March 2008

Methods of data collection in qualitative research: interviews and focus groups

P. Gill 1 ,
K. Stewart 2 ,
E. Treasure 3 &
B. Chadwick 4

British Dental Journal volume 204 , pages 291–295 ( 2008 ) Cite this article

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Interviews and focus groups are the most common methods of data collection used in qualitative healthcare research

Interviews can be used to explore the views, experiences, beliefs and motivations of individual participants

Focus group use group dynamics to generate qualitative data

Qualitative research in dentistry

Conducting qualitative interviews with school children in dental research

Analysing and presenting qualitative data

This paper explores the most common methods of data collection used in qualitative research: interviews and focus groups. The paper examines each method in detail, focusing on how they work in practice, when their use is appropriate and what they can offer dentistry. Examples of empirical studies that have used interviews or focus groups are also provided.

You have full access to this article via your institution.

Interviews in the social sciences

Eleanor Knott, Aliya Hamid Rao, … Chana Teeger

A cross-verified database of notable people, 3500BC-2018AD

Morgane Laouenan, Palaash Bhargava, … Etienne Wasmer

Principal component analysis

Michael Greenacre, Patrick J. F. Groenen, … Elena Tuzhilina

Introduction

Having explored the nature and purpose of qualitative research in the previous paper, this paper explores methods of data collection used in qualitative research. There are a variety of methods of data collection in qualitative research, including observations, textual or visual analysis (eg from books or videos) and interviews (individual or group). 1 However, the most common methods used, particularly in healthcare research, are interviews and focus groups. 2 , 3

The purpose of this paper is to explore these two methods in more detail, in particular how they work in practice, the purpose of each, when their use is appropriate and what they can offer dental research.

Qualitative research interviews

There are three fundamental types of research interviews: structured, semi-structured and unstructured. Structured interviews are, essentially, verbally administered questionnaires, in which a list of predetermined questions are asked, with little or no variation and with no scope for follow-up questions to responses that warrant further elaboration. Consequently, they are relatively quick and easy to administer and may be of particular use if clarification of certain questions are required or if there are likely to be literacy or numeracy problems with the respondents. However, by their very nature, they only allow for limited participant responses and are, therefore, of little use if 'depth' is required.

Conversely, unstructured interviews do not reflect any preconceived theories or ideas and are performed with little or no organisation. 4 Such an interview may simply start with an opening question such as 'Can you tell me about your experience of visiting the dentist?' and will then progress based, primarily, upon the initial response. Unstructured interviews are usually very time-consuming (often lasting several hours) and can be difficult to manage, and to participate in, as the lack of predetermined interview questions provides little guidance on what to talk about (which many participants find confusing and unhelpful). Their use is, therefore, generally only considered where significant 'depth' is required, or where virtually nothing is known about the subject area (or a different perspective of a known subject area is required).

Semi-structured interviews consist of several key questions that help to define the areas to be explored, but also allows the interviewer or interviewee to diverge in order to pursue an idea or response in more detail. 2 This interview format is used most frequently in healthcare, as it provides participants with some guidance on what to talk about, which many find helpful. The flexibility of this approach, particularly compared to structured interviews, also allows for the discovery or elaboration of information that is important to participants but may not have previously been thought of as pertinent by the research team.

For example, in a recent dental public heath study, 5 school children in Cardiff, UK were interviewed about their food choices and preferences. A key finding that emerged from semi-structured interviews, which was not previously thought to be as highly influential as the data subsequently confirmed, was the significance of peer-pressure in influencing children's food choices and preferences. This finding was also established primarily through follow-up questioning (eg probing interesting responses with follow-up questions, such as 'Can you tell me a bit more about that?') and, therefore, may not have emerged in the same way, if at all, if asked as a predetermined question.

The purpose of research interviews

The purpose of the research interview is to explore the views, experiences, beliefs and/or motivations of individuals on specific matters (eg factors that influence their attendance at the dentist). Qualitative methods, such as interviews, are believed to provide a 'deeper' understanding of social phenomena than would be obtained from purely quantitative methods, such as questionnaires. 1 Interviews are, therefore, most appropriate where little is already known about the study phenomenon or where detailed insights are required from individual participants. They are also particularly appropriate for exploring sensitive topics, where participants may not want to talk about such issues in a group environment.

Examples of dental studies that have collected data using interviews are 'Examining the psychosocial process involved in regular dental attendance' 6 and 'Exploring factors governing dentists' treatment philosophies'. 7 Gibson et al . 6 provided an improved understanding of factors that influenced people's regular attendance with their dentist. The study by Kay and Blinkhorn 7 provided a detailed insight into factors that influenced GDPs' decision making in relation to treatment choices. The study found that dentists' clinical decisions about treatments were not necessarily related to pathology or treatment options, as was perhaps initially thought, but also involved discussions with patients, patients' values and dentists' feelings of self esteem and conscience.

There are many similarities between clinical encounters and research interviews, in that both employ similar interpersonal skills, such as questioning, conversing and listening. However, there are also some fundamental differences between the two, such as the purpose of the encounter, reasons for participating, roles of the people involved and how the interview is conducted and recorded. 8

The primary purpose of clinical encounters is for the dentist to ask the patient questions in order to acquire sufficient information to inform decision making and treatment options. However, the constraints of most consultations are such that any open-ended questioning needs to be brought to a conclusion within a fairly short time. 2 In contrast, the fundamental purpose of the research interview is to listen attentively to what respondents have to say, in order to acquire more knowledge about the study topic. 9 Unlike the clinical encounter, it is not to intentionally offer any form of help or advice, which many researchers have neither the training nor the time for. Research interviewing therefore requires a different approach and a different range of skills.

The interview

When designing an interview schedule it is imperative to ask questions that are likely to yield as much information about the study phenomenon as possible and also be able to address the aims and objectives of the research. In a qualitative interview, good questions should be open-ended (ie, require more than a yes/no answer), neutral, sensitive and understandable. 2 It is usually best to start with questions that participants can answer easily and then proceed to more difficult or sensitive topics. 2 This can help put respondents at ease, build up confidence and rapport and often generates rich data that subsequently develops the interview further.

As in any research, it is often wise to first pilot the interview schedule on several respondents prior to data collection proper. 8 This allows the research team to establish if the schedule is clear, understandable and capable of answering the research questions, and if, therefore, any changes to the interview schedule are required.

The length of interviews varies depending on the topic, researcher and participant. However, on average, healthcare interviews last 20-60 minutes. Interviews can be performed on a one-off or, if change over time is of interest, repeated basis, 4 for example exploring the psychosocial impact of oral trauma on participants and their subsequent experiences of cosmetic dental surgery.

Developing the interview

Before an interview takes place, respondents should be informed about the study details and given assurance about ethical principles, such as anonymity and confidentiality. 2 This gives respondents some idea of what to expect from the interview, increases the likelihood of honesty and is also a fundamental aspect of the informed consent process.

Wherever possible, interviews should be conducted in areas free from distractions and at times and locations that are most suitable for participants. For many this may be at their own home in the evenings. Whilst researchers may have less control over the home environment, familiarity may help the respondent to relax and result in a more productive interview. 9 Establishing rapport with participants prior to the interview is also important as this can also have a positive effect on the subsequent development of the interview.

When conducting the actual interview it is prudent for the interviewer to familiarise themselves with the interview schedule, so that the process appears more natural and less rehearsed. However, to ensure that the interview is as productive as possible, researchers must possess a repertoire of skills and techniques to ensure that comprehensive and representative data are collected during the interview. 10 One of the most important skills is the ability to listen attentively to what is being said, so that participants are able to recount their experiences as fully as possible, without unnecessary interruptions.

Other important skills include adopting open and emotionally neutral body language, nodding, smiling, looking interested and making encouraging noises (eg, 'Mmmm') during the interview. 2 The strategic use of silence, if used appropriately, can also be highly effective at getting respondents to contemplate their responses, talk more, elaborate or clarify particular issues. Other techniques that can be used to develop the interview further include reflecting on remarks made by participants (eg, 'Pain?') and probing remarks ('When you said you were afraid of going to the dentist what did you mean?'). 9 Where appropriate, it is also wise to seek clarification from respondents if it is unclear what they mean. The use of 'leading' or 'loaded' questions that may unduly influence responses should always be avoided (eg, 'So you think dental surgery waiting rooms are frightening?' rather than 'How do you find the waiting room at the dentists?').

At the end of the interview it is important to thank participants for their time and ask them if there is anything they would like to add. This gives respondents an opportunity to deal with issues that they have thought about, or think are important but have not been dealt with by the interviewer. 9 This can often lead to the discovery of new, unanticipated information. Respondents should also be debriefed about the study after the interview has finished.

All interviews should be tape recorded and transcribed verbatim afterwards, as this protects against bias and provides a permanent record of what was and was not said. 8 It is often also helpful to make 'field notes' during and immediately after each interview about observations, thoughts and ideas about the interview, as this can help in data analysis process. 4 , 8

Focus groups

Focus groups share many common features with less structured interviews, but there is more to them than merely collecting similar data from many participants at once. A focus group is a group discussion on a particular topic organised for research purposes. This discussion is guided, monitored and recorded by a researcher (sometimes called a moderator or facilitator). 11 , 12

Focus groups were first used as a research method in market research, originating in the 1940s in the work of the Bureau of Applied Social Research at Columbia University. Eventually the success of focus groups as a marketing tool in the private sector resulted in its use in public sector marketing, such as the assessment of the impact of health education campaigns. 13 However, focus group techniques, as used in public and private sectors, have diverged over time. Therefore, in this paper, we seek to describe focus groups as they are used in academic research.

When focus groups are used

Focus groups are used for generating information on collective views, and the meanings that lie behind those views. They are also useful in generating a rich understanding of participants' experiences and beliefs. 12 Suggested criteria for using focus groups include: 13

As a standalone method, for research relating to group norms, meanings and processes

In a multi-method design, to explore a topic or collect group language or narratives to be used in later stages

To clarify, extend, qualify or challenge data collected through other methods

To feedback results to research participants.

Morgan 12 suggests that focus groups should be avoided according to the following criteria:

If listening to participants' views generates expectations for the outcome of the research that can not be fulfilled

If participants are uneasy with each other, and will therefore not discuss their feelings and opinions openly

If the topic of interest to the researcher is not a topic the participants can or wish to discuss

If statistical data is required. Focus groups give depth and insight, but cannot produce useful numerical results.

Conducting focus groups: group composition and size

The composition of a focus group needs great care to get the best quality of discussion. There is no 'best' solution to group composition, and group mix will always impact on the data, according to things such as the mix of ages, sexes and social professional statuses of the participants. What is important is that the researcher gives due consideration to the impact of group mix (eg, how the group may interact with each other) before the focus group proceeds. 14

Interaction is key to a successful focus group. Sometimes this means a pre-existing group interacts best for research purposes, and sometimes stranger groups. Pre-existing groups may be easier to recruit, have shared experiences and enjoy a comfort and familiarity which facilitates discussion or the ability to challenge each other comfortably. In health settings, pre-existing groups can overcome issues relating to disclosure of potentially stigmatising status which people may find uncomfortable in stranger groups (conversely there may be situations where disclosure is more comfortable in stranger groups). In other research projects it may be decided that stranger groups will be able to speak more freely without fear of repercussion, and challenges to other participants may be more challenging and probing, leading to richer data. 13

Group size is an important consideration in focus group research. Stewart and Shamdasani 14 suggest that it is better to slightly over-recruit for a focus group and potentially manage a slightly larger group, than under-recruit and risk having to cancel the session or having an unsatisfactory discussion. They advise that each group will probably have two non-attenders. The optimum size for a focus group is six to eight participants (excluding researchers), but focus groups can work successfully with as few as three and as many as 14 participants. Small groups risk limited discussion occurring, while large groups can be chaotic, hard to manage for the moderator and frustrating for participants who feel they get insufficient opportunities to speak. 13

Preparing an interview schedule

Like research interviews, the interview schedule for focus groups is often no more structured than a loose schedule of topics to be discussed. However, in preparing an interview schedule for focus groups, Stewart and Shamdasani 14 suggest two general principles:

Questions should move from general to more specific questions

Question order should be relative to importance of issues in the research agenda.

There can, however, be some conflict between these two principles, and trade offs are often needed, although often discussions will take on a life of their own, which will influence or determine the order in which issues are covered. Usually, less than a dozen predetermined questions are needed and, as with research interviews, the researcher will also probe and expand on issues according to the discussion.

Moderating a focus group looks easy when done well, but requires a complex set of skills, which are related to the following principles: 15

Participants have valuable views and the ability to respond actively, positively and respectfully. Such an approach is not simply a courtesy, but will encourage fruitful discussions

Moderating without participating: a moderator must guide a discussion rather than join in with it. Expressing one's own views tends to give participants cues as to what to say (introducing bias), rather than the confidence to be open and honest about their own views

Be prepared for views that may be unpalatably critical of a topic which may be important to you

It is important to recognise that researchers' individual characteristics mean that no one person will always be suitable to moderate any kind of group. Sometimes the characteristics that suit a moderator for one group will inhibit discussion in another

Be yourself. If the moderator is comfortable and natural, participants will feel relaxed.

The moderator should facilitate group discussion, keeping it focussed without leading it. They should also be able to prevent the discussion being dominated by one member (for example, by emphasising at the outset the importance of hearing a range of views), ensure that all participants have ample opportunity to contribute, allow differences of opinions to be discussed fairly and, if required, encourage reticent participants. 13

Other relevant factors

The venue for a focus group is important and should, ideally, be accessible, comfortable, private, quiet and free from distractions. 13 However, while a central location, such as the participants' workplace or school, may encourage attendance, the venue may affect participants' behaviour. For example, in a school setting, pupils may behave like pupils, and in clinical settings, participants may be affected by any anxieties that affect them when they attend in a patient role.

Focus groups are usually recorded, often observed (by a researcher other than the moderator, whose role is to observe the interaction of the group to enhance analysis) and sometimes videotaped. At the start of a focus group, a moderator should acknowledge the presence of the audio recording equipment, assure participants of confidentiality and give people the opportunity to withdraw if they are uncomfortable with being taped. 14

A good quality multi-directional external microphone is recommended for the recording of focus groups, as internal microphones are rarely good enough to cope with the variation in volume of different speakers. 13 If observers are present, they should be introduced to participants as someone who is just there to observe, and sit away from the discussion. 14 Videotaping will require more than one camera to capture the whole group, as well as additional operational personnel in the room. This is, therefore, very obtrusive, which can affect the spontaneity of the group and in a focus group does not usually yield enough additional information that could not be captured by an observer to make videotaping worthwhile. 15

The systematic analysis of focus group transcripts is crucial. However, the transcription of focus groups is more complex and time consuming than in one-to-one interviews, and each hour of audio can take up to eight hours to transcribe and generate approximately 100 pages of text. Recordings should be transcribed verbatim and also speakers should be identified in a way that makes it possible to follow the contributions of each individual. Sometimes observational notes also need to be described in the transcripts in order for them to make sense.

The analysis of qualitative data is explored in the final paper of this series. However, it is important to note that the analysis of focus group data is different from other qualitative data because of their interactive nature, and this needs to be taken into consideration during analysis. The importance of the context of other speakers is essential to the understanding of individual contributions. 13 For example, in a group situation, participants will often challenge each other and justify their remarks because of the group setting, in a way that perhaps they would not in a one-to-one interview. The analysis of focus group data must therefore take account of the group dynamics that have generated remarks.

Focus groups in dental research

Focus groups are used increasingly in dental research, on a diverse range of topics, 16 illuminating a number of areas relating to patients, dental services and the dental profession. Addressing a special needs population difficult to access and sample through quantitative measures, Robinson et al . 17 used focus groups to investigate the oral health-related attitudes of drug users, exploring the priorities, understandings and barriers to care they encounter. Newton et al . 18 used focus groups to explore barriers to services among minority ethnic groups, highlighting for the first time differences between minority ethnic groups. Demonstrating the use of the method with professional groups as subjects in dental research, Gussy et al . 19 explored the barriers to and possible strategies for developing a shared approach in prevention of caries among pre-schoolers. This mixed method study was very important as the qualitative element was able to explain why the clinical trial failed, and this understanding may help researchers improve on the quantitative aspect of future studies, as well as making a valuable academic contribution in its own right.

Interviews and focus groups remain the most common methods of data collection in qualitative research, and are now being used with increasing frequency in dental research, particularly to access areas not amendable to quantitative methods and/or where depth, insight and understanding of particular phenomena are required. The examples of dental studies that have employed these methods also help to demonstrate the range of research contexts to which interview and focus group research can make a useful contribution. The continued employment of these methods can further strengthen many areas of dentally related work.

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Professor of Paediatric Dentistry, School of Dentistry, Dental Health and Biological Sciences, School of Dentistry, Cardiff University, Heath Park, Cardiff, CF14 4XY,

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Gill, P., Stewart, K., Treasure, E. et al. Methods of data collection in qualitative research: interviews and focus groups. Br Dent J 204 , 291–295 (2008). https://doi.org/10.1038/bdj.2008.192

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Qualitative and Quantitative data collection methods in M&E

June 17, 2021.

Data is the heart and soul of monitoring and evaluation (M&E). Valid, reliable and accurate data can reveal and improve the performance and impact of your intervention and support decision making and learning, while enhancing your credibility and accountability. Data is divided into different categories based on how you source them and the techniques you employ to gather and analyse them.

In this article, we will explore:

Q ualitative data collection approach
Quantitative data collection approach
Key differences between qualitative and quantitative data collection approach and how they are used in M&E.
Some common qualitative data collection tools and methods and quantitative data collection tools and methods to help you choose the ones that best fit your project needs.
Plus, why a mixed-method approach might be your best option.

Primary and secondary data

All data are categorized as either ‘primary data’ or ‘secondary data,’ based on how you source them. Primary data are those that you and your team collect directly from the main sources, whereas, secondary data are those that were collected by other organisations, government agencies, or independent research institutions and individuals and are available for use. Secondary data could be censuses, surveys, organizational records or other previous research, extracted from books, journals, reports, newspapers, magazines, data archives, databases etc.

Once you are ready to collect your data, you will have to decide upon the data collection tools and methods. This will depend on a number of things, including the purpose of the data, the local context, cost, timeline, availability of skills and resources, and most importantly, the indicators and key questions you have identified and how the collected data will be utilized.

All data are further divided into two broad categories based on the techniques employed in the field to gather and analyse them – ‘qualitative data’ and ‘quantitative data.’ Stay with us as we walk you through each approach and explain their key differences.

Qualitative data collection approach

Qualitative data collection plays an important role in monitoring and evaluation as it helps you delve deeper into a particular problem and gain a human perspective on it. It provides in depth information on some of the more intangible factors like experiences, opinions, motivations, behaviours or descriptions of a process, event or a particular context relevant to your project. So, in other words, a qualitative approach uses people’s stories, experiences and feelings to measure change.

Compared to a quantitative approach, a qualitative approach is more open, informal and unstructured or semi-structured, and it provides more flexibility in how data is collected. Qualitative research is investigative in nature and the data collected through this process answers the question ‘why’ or ‘how’ – how do people feel about a situation, or why are health care facilities underutilized? This approach relies more heavily on interactive interviews, discussions and deeper conversations. While using this approach, many researchers also use triangulation or mixed methods to increase the credibility and authenticity of their findings. Data is often recorded in the form of field notes, sketches, audiotapes, photographs and other suitable means.

Usually the findings drawn from qualitative research are not generalizable to any specific population, rather each case study produces a unique piece of evidence that can help identify patterns among different studies of the same issue. The results produced from this approach can be subjective and as such can be subject to bias in their interpretation. Analyzing such data can also be quite complex and time-consuming which can make it an expensive process .

Quantitative data collection approach

The quantitative approach uses numbers and statistics to quantify change and is often expressed in the form of digits, units, ratios, percentages, proportions, etc. Compared to the qualitative approach, the quantitative approach is more structured, straightforward and formal. Quantitative approach is utilized to derive answers to the questions ‘how much’ or ‘how many’ – how many people attended the workshop or how often do people visit the health center.

Quantitative research is useful for multi-site and cluster evaluations that involve a large group of respondents or sample population. This approach relies heavily on random sampling and structured data collection instruments that fit diverse experiences into predetermined response categories. Typical quantitative data gathering strategies include, experiments or clinical trials, gathering relevant data from management information systems, administering surveys with closed-ended questions or observing and recording well-defined events.

Because quantitative methods are not about gaining an in-depth understanding but rather grasping a general understanding of a particular context with precise results, quantitative data is easier to collect and analyse and there are less chances of bias in the result interpretation. Results are numerical, objective, conclusive and to the point, so the results are easier to summarize and generalize and are useful for making comparisons across different sites or interventions.

Differences between qualitative and quantitative data collection approach

Qualitative and quantitative data collection methods

Below, we have summarized key data collection methods and tools used in monitoring and evaluation (M&E). Most methods and tools can be used in combination with other tools and methods and are applicable in both qualitative and quantitative research. However, this list is not complete, as tools and techniques continually evolve and new tools and techniques keep emerging in M&E.

The list is adapted from ‘Project/Programme Monitoring and Evaluation (M&E) Guide from the International Federation of Red Cross and Red Crescent Societies (IFRC), 2011.’

The most common qualitative data collection methods and tools

Open-Ended Surveys: allow for a systematic collection of information from a defined population, usually by means of interviews or questionnaires administered to a sample of units in the population. Qualitative surveys include a set of open-ended questions that aim to gather information from people about their characteristics, knowledge, attitudes, values, behaviours, experiences and opinions on relevant topics. Surveys can be collected via pen/paper forms or digitally via online/offline data collection apps.
Open ended interviews: are useful when you want an in-depth understanding of experiences, opinions or individual descriptions of a process. Can be done individually or in groups. In groups, you will ask fewer questions than in an individual interview since everyone has to have the opportunity to answer and there are limits to how long people are willing to sit still. In-person interviews can be longer and more in-depth.
Community interviews/meeting: is a form of public meeting open to all community members. Interaction is between the participants and the interviewer, who moderates the meeting and asks questions following a prepared interview guide. This is ideal for interacting with and gathering insights from a big group of people.
Focus group discussions (FGDs): is ideal when you want to interview a small group of people (6-12 individuals) to informally discuss specific topics relevant to the issues being examined. A moderator introduces the topic and uses a prepared interview guide to lead the discussion and extract insights, opinions and reactions but s/he can improvise with probes or additional questions as warranted by the situation. The composition of people in an FGD depends upon the purpose of the research, some are homogenous, others diverse. FGDs tend to elicit more information than individual interviews because people express different views, beliefs and opinions and engage in a dialogue with one another.
Case study: is an in-depth analysis of individuals, organisations, events, projects, communities, time periods or a story. As it involves data collection from multiple sources, a case study is particularly useful in evaluating complex situations and exploring qualitative impact. A case study can also be combined with other case studies or methods to illustrate findings and comparisons. They are usually presented in written forms, but can also be presented as photographs, films or videos.
Observation: It is a good technique for collecting data on behavioural patterns, physical surroundings, activities and processes as it entails recording what observers see and hear at a specified site. An observation guide is often used to look for consistent criteria, behaviours, or patterns. Observations can be obtrusive or unobtrusive. It is ‘obtrusive’ when observations are made with the participant’s knowledge and ‘unobtrusive’ when observations are done without the knowledge of the participant.
Ethnography: Ethnographic research involves observing and studying research topics in a specific geographic location to understand cultures, behaviors, trends, patterns and problems in a natural setting. Geographic location can range from a small entity to a big country. Researchers must spend a considerable amount of time, usually several weeks or months, with the group being studied to interact with them as a participant in their community. This makes it a time-consuming and challenging research method and cannot be limited to a specific period.
Visual techniques: in this method, participants are prompted to construct visual responses to questions posed by the interviewers, the visual content can be maps, diagrams, calendars, timelines and other visual displays to examine the study topics. This technique is especially effective where verbal methods can be problematic due to low-literate or mixed-language target populations, or in situations where the desired information is not easily expressed in either words or numbers.
Literature review and document review: is a review of secondary data which can be either qualitative or quantitative in nature e.g. project records and reports, administrative databases, training materials, correspondence, legislation and policy documents, as well as videos, electronic data or photos that are relevant to your project. This technique can provide cost-effective and timely baseline information and a historical perspective of the project or intervention.
Oral histories: it’s the process of establishing historical information by interviewing a select group of informants and drawing on their memories of the past. Oral history strives to obtain interesting and provoking historic information from different perspectives, most of which cannot be found in written sources. The insights from oral history can be discussed, debated, and utilized in numerous capacities.

The most common quantitative data collection methods and tools

A structured closed-ended interview: this type of interview systematically follows carefully organised questions that only allow a limited range of answers, such as “yes/no” or expressed by a rating/number on a scale. For quantitative interviews to be effective, each question must be asked the same way to each respondent, with little to no input from the interviewer.
Closed ended surveys and questionnaires: is an ideal choice when you want simple, quick feedback which can easily translate into statistics for analysis. In quantitative research, surveys are structured questionnaires with a limited number of closed-ended questions and rating scales used to generate numerical data or data that can be separated under ‘yes’ or ‘no’ categories. These can be collected and analysed quickly using statistics such as percentages.
Experimental research: is guided by hypotheses that state an expected relationship between two or more variables, so an experiment is conducted to support or disconfirm this experimental hypothesis. Usually, one set of variables is manipulated (treatment group) and applied to the other set of dependent variables (control group) to measure their effect on the latter. The effect of the independent variables on the dependent variables is observed and recorded to draw a reasonable conclusion regarding the relationship between the two groups. This research is mainly used in natural sciences.
Correlational research: is a non-experimental research that studies the relationship between two or more variables that are similar and interdependent and assesses their statistical relationship – how one variable affects the other and vice versa but with no influence from any extraneous variable. It uses mathematical analysis to analyse collected data and the results are presented in a diagram or generated in statistics.
Causal-comparative: also known as quasi-experimental research, compares two variables that are not related. Variables are not manipulated. One variable is dependent and the other independent. Variables not randomly assigned.
Statistical data review: entails a review of population censuses, research studies and other sources of statistical data.
Laboratory testing: are precise measurement of a specific objective phenomenon, e.g. infant weight or water quality test.

Why a mixed method approach might be your best option for data collection

Each data collection tool and method has its own advantages but development projects are complex and their intricate dynamics cannot be disentangled through one method or one data collection tool alone. Therefore, mixing qualitative and quantitative methods and using different data collection techniques is recommended as it could add value to the monitoring and evaluation of your development projects.

Using a combination of quantitative and qualitative methods, which is often called a mixed-method approach, enables researchers to gain a more holistic understanding of the intervention and why it is or it isn’t manifesting the expected outcomes. It also addresses the shortcomings and limitations of each method to provide more coherent, reliable and useful conclusions and increases the overall confidence in the validity of the evaluation results.

Using mixed methods helps to capture a wider range of perspectives and in some cases, one method can be used to help guide the use of another method, or to explain the findings from that method. You can measure what happened with quantitative data and examine how and why it happened with qualitative data. Qualitative methods also help to uncover issues during the early stages of an intervention that can then be further investigated using quantitative methods, or quantitative methods can highlight particular issues that can be examined in-depth with qualitative methods. Some may point out that mixed methods could be costly and time-consuming but research shows that the benefits almost always far exceed the costs.

Want to learn more about mixed-method approaches? Check out this World Bank’s document on “Combining Quantitative and Qualitative Methods for Program Monitoring and Evaluation: Why Are Mixed Method Designs Best?”

We hope you found our article on qualitative and quantitative data collection methods helpful. As you see, both methods have their own advantages and disadvantages but when used in a balanced combination, they can really provide reliable evidence to unfold the progress and shortcomings of your development projects and help you make data-driven decisions for timely improvement and enhancement. However, the choices of methods depend on the nature of your project. But one thing to keep in mind, no matter which approach you may choose to utilize, it is important to observe the ethical principles of research for all data collection methods and tools.

For more on the ethics of data collection, check out this short report by INTRAC – Principles of Data Collection.

Key References

Conducting mixed method evaluations, USAID
Project/programme monitoring and evaluation (M&E) guide
Basic tools for data collection by INTRAC

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5 Methods of Data Collection for Quantitative Research

mrx glossary quantitative data collection

In this blog, read up on five different data collection techniques for quantitative research studies.

Quantitative research forms the basis for many business decisions. But what is quantitative data collection, why is it important, and which data collection methods are used in quantitative research?

What is quantitative data collection?

Quantitative data collection is the gathering of numeric data that puts consumer insights into a quantifiable context. It typically involves a large number of respondents - large enough to extract statistically reliable findings that can be extrapolated to a larger population.

The actual data collection process for quantitative findings is typically done using a quantitative online questionnaire that asks respondents yes/no questions, ranking scales, rating matrices, and other quantitative question types. With these results, researchers can generate data charts to summarize the quantitative findings and generate easily digestible key takeaways.

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The importance of quantitative data collection

Quantitative data collection can confirm or deny a brand's hypothesis, guide product development, tailor marketing materials, and much more. It provides brands with reliable information to make decisions off of (i.e. 86% like lemon-lime flavor or just 12% are interested in a cinnamon-scented hand soap).

Compared to qualitative data collection, quantitative data allows for comparison between insights given higher base sizes which leads to the ability to have statistical significance. Brands can cut and analyze their dataset in a variety of ways, looking at their findings among different demographic groups, behavioral groups, and other ways of interest. It's also generally easier and quicker to collect quantitative data than it is to gather qualitative feedback, making it an important data collection tool for brands that need quick, reliable, concrete insights.

In order to make justified business decisions from quantitative data, brands need to recruit a high-quality sample that's reflective of their true target market (one that's comprised of all ages/genders rather than an isolated group). For example, a study into usage and attitudes around orange juice might include consumers who buy and/or drink orange juice at a certain frequency or who buy a variety of orange juice brands from different outlets.

Methods used for quantitative data collection

So knowing what quantitative data collection is and why it's important , how does one go about researching a large, high-quality, representative sample ?

Below are five examples of how to conduct your study through various data collection methods :

Online quantitative surveys

Online surveys are a common and effective way of collecting data from a large number of people. They tend to be made up of closed-ended questions so that responses across the sample are comparable; however, a small number of open-ended questions can be included as well (i.e. questions that require a written response rather than a selection of answers in a close-ended list). Open-ended questions are helpful to gather actual language used by respondents on a certain issue or to collect feedback on a view that might not be shown in a set list of responses).

Online surveys are quick and easy to send out, typically done so through survey panels. They can also appear in pop-ups on websites or via a link embedded in social media. From the participant’s point of view, online surveys are convenient to complete and submit, using whichever device they prefer (mobile phone, tablet, or computer). Anonymity is also viewed as a positive: online survey software ensures respondents’ identities are kept completely confidential.

To gather respondents for online surveys, researchers have several options. Probability sampling is one route, where respondents are selected using a random selection method. As such, everyone within the population has an equal chance of getting selected to participate.

There are four common types of probability sampling .

Simple random sampling is the most straightforward approach, which involves randomly selecting individuals from the population without any specific criteria or grouping.
Stratified random sampling divides the population into subgroups (strata) and selects a random sample from each stratum. This is useful when a population includes subgroups that you want to be sure you cover in your research.
Cluster sampling divides the population into clusters and then randomly selects some of the clusters to sample in their entirety. This is useful when a population is geographically dispersed and it would be impossible to include everyone.
Systematic sampling begins with a random starting point and then selects every nth member of the population after that point (i.e. every 15th respondent).

Learn how to leverage AI to help generate your online quantitative survey inputs:

While online surveys are by far the most common way to collect quantitative data in today’s modern age, there are still some harder-to-reach respondents where other mediums can be beneficial; for example, those who aren’t tech-savvy or who don’t have a stable internet connection. For these audiences, offline surveys may be needed.

Offline quantitative surveys

Offline surveys (though much rarer to come across these days) are a way of gathering respondent feedback without digital means. This could be something like postal questionnaires that are sent out to a sample population and asked to return the questionnaire by mail (like the Census) or telephone surveys where questions are asked of respondents over the phone.

Offline surveys certainly take longer to collect data than online surveys and they can become expensive if the population is difficult to reach (requiring a higher incentive). As with online surveys, anonymity is protected, assuming the mail is not intercepted or lost.

Despite the major difference in data collection to an online survey approach, offline survey data is still reported on in an aggregated, numeric fashion.

In-person interviews are another popular way of researching or polling a population. They can be thought of as a survey but in a verbal, in-person, or virtual face-to-face format. The online format of interviews is becoming more popular nowadays, as it is cheaper and logistically easier to organize than in-person face-to-face interviews, yet still allows the interviewer to see and hear from the respondent in their own words.

Though many interviews are collected for qualitative research, interviews can also be leveraged quantitatively; like a phone survey, an interviewer runs through a survey with the respondent, asking mainly closed-ended questions (yes/no, multiple choice questions, or questions with rating scales that ask how strongly the respondent agrees with statements). The advantage of structured interviews is that the interviewer can pace the survey, making sure the respondent gives enough consideration to each question. It also adds a human touch, which can be more engaging for some respondents. On the other hand, for more sensitive issues, respondents may feel more inclined to complete a survey online for a greater sense of anonymity - so it all depends on your research questions, the survey topic, and the audience you're researching.

Observations

Observation studies in quantitative research are similar in nature to a qualitative ethnographic study (in which a researcher also observes consumers in their natural habitats), yet observation studies for quant research remain focused on the numbers - how many people do an action, how much of a product consumer pick up, etc.

For quantitative observations, researchers will record the number and types of people who do a certain action - such as choosing a specific product from a grocery shelf, speaking to a company representative at an event, or how many people pass through a certain area within a given timeframe. Observation studies are generally structured, with the observer asked to note behavior using set parameters. Structured observation means that the observer has to hone in on very specific behaviors, which can be quite nuanced. This requires the observer to use his/her own judgment about what type of behavior is being exhibited (e.g. reading labels on products before selecting them; considering different items before making the final choice; making a selection based on price).

Document reviews and secondary data sources

A fifth method of data collection for quantitative research is known as secondary research : reviewing existing research to see how it can contribute to understanding a new issue in question. This is in contrast to the primary research methods above, which is research that is specially commissioned and carried out for a research project.

There are numerous secondary data sources that researchers can analyze such as public records, government research, company databases, existing reports, paid-for research publications, magazines, journals, case studies, websites, books, and more.

Aside from using secondary research alone, secondary research documents can also be used in anticipation of primary research, to understand which knowledge gaps need to be filled and to nail down the issues that might be important to explore further in a primary research study. Back to Table of Contents

Example of a survey showing quantitative data

The below study shows what quantitative data might look like in a final study dashboard, taken from quantilope's Sneaker category insights study .

The study includes a variety of usage and attitude metrics around sneaker wear, sneaker purchases, seasonality of sneakers, and more. Check out some of the data charts below showing these quantitative data findings - the first of which even cuts the quantitative data findings by demographics.

Beyond these basic usage and attitude (or, descriptive) data metrics, quantitative data also includes advanced methods - such as implicit association testing. See what these quantitative data charts look like from the same sneaker study below:

These are just a few examples of how a researcher or insights team might show their quantitative data findings. However, there are many ways to visualize quantitative data in an insights study, from bar charts, column charts, pie charts, donut charts, spider charts, and more, depending on what best suits the story your data is telling. Back to Table of Contents

Strengths and weaknesses of quantitative data collection

quantitative data is a great way to capture informative insights about your brand, product, category, or competitors. It's relatively quick, depending on your sample audience, and more affordable than other data collection methods such as qualitative focus groups. With quantitative panels, it's easy to access nearly any audience you might need - from something as general as the US population to something as specific as cannabis users . There are many ways to visualize quantitative findings, making it a customizable form of insights - whether you want to show the data in a bar chart, pie chart, etc.

For those looking for quick, affordable, actionable insights, quantitative studies are the way to go.

quantitative data collection, despite the many benefits outlined above, might also not be the right fit for your exact needs. For example, you often don't get as detailed and in-depth answers quantitatively as you would with an in-person interview, focus group, or ethnographic observation (all forms of qualitative research). When running a quantitative survey, it’s best practice to review your data for quality measures to ensure all respondents are ones you want to keep in your data set. Fortunately, there are a lot of precautions research providers can take to navigate these obstacles - such as automated data cleaners and data flags. Of course, the first step to ensuring high-quality results is to use a trusted panel provider. Back to Table of Contents

Quantitative research typically needs to undergo statistical analysis for it to be useful and actionable to any business. It is therefore crucial that the method of data collection, sample size, and sample criteria are considered in light of the research questions asked.

quantilope’s online platform is ideal for quantitative research studies. The online format means a large sample can be reached easily and quickly through connected respondent panels that effectively reach the desired target audience. Response rates are high, as respondents can take their survey from anywhere, using any device with internet access.

Surveys are easy to build with quantilope’s online survey builder. Simply choose questions to include from pre-designed survey templates or build your own questions using the platform’s drag & drop functionality (of which both options are fully customizable). Once the survey is live, findings update in real-time so that brands can get an idea of consumer attitudes long before the survey is complete. In addition to basic usage and attitude questions, quantilope’s suite of advanced research methodologies provides an AI-driven approach to many types of research questions. These range from exploring the features of products that drive purchase through a Key Driver Analysis , compiling the ideal portfolio of products using a TURF , or identifying the optimal price point for a product or service using a Price Sensitivity Meter (PSM) .

Depending on the type of data sought it might be worth considering a mixed-method approach, including both qual and quant in a single research study. Alongside quantitative online surveys, quantilope’s video research solution - inColor , offers qualitative research in the form of videoed responses to survey questions. inColor’s qualitative data analysis includes an AI-drive read on respondent sentiment, keyword trends, and facial expressions.

To find out more about how quantilope can help with any aspect of your research design and to start conducting high-quality, quantitative research, get in touch below:

Get in touch to learn more about quantitative research studies!

Related posts, what are brand perceptions and how can you measure them, how can brands build, measure, and manage brand equity, how to use a brand insights tool to improve your branding strategy, quantilope's 5th consecutive year as a 'fastest growing tech company'.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human. Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things. However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms.

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
The research aims for objectivity (i.e., without bias) and is separated from the data.
The design of the study is determined before it begins.
For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
Useful for testing and validating already constructed theories.
Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

Designing qualitative research
Methods of data collection and analysis
Introduction to quantitative and qualitative research
Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
Qualitative research in health care: Analysing qualitative data
Qualitative data analysis: the framework approach
Using the framework method for the analysis of
Qualitative data in multi-disciplinary health research
Content Analysis
Grounded Theory
Thematic Analysis

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Perspect Clin Res
v.14(1); Jan-Mar 2023
PMC10003579

Introduction to qualitative research methods – Part I

Shagufta bhangu.

Department of Global Health and Social Medicine, King's College London, London, United Kingdom

Fabien Provost

Carlo caduff.

Qualitative research methods are widely used in the social sciences and the humanities, but they can also complement quantitative approaches used in clinical research. In this article, we discuss the key features and contributions of qualitative research methods.

INTRODUCTION

Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures. In this article, we describe the strengths and role of qualitative research methods and how these can be employed in clinical research.

Although frequently employed in the social sciences and humanities, qualitative research methods can complement clinical research. These techniques can contribute to a better understanding of the social, cultural, political, and economic dimensions of health and illness. Social scientists and scholars in the humanities rely on a wide range of methods, including interviews, surveys, participant observation, focus groups, oral history, and archival research to examine both structural conditions and lived experience [ Figure 1 ]. Such research can not only provide robust and reliable data but can also humanize and add richness to our understanding of the ways in which people in different parts of the world perceive and experience illness and how they interact with medical institutions, systems, and therapeutics.

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Examples of qualitative research techniques

Qualitative research methods should not be seen as tools that can be applied independently of theory. It is important for these tools to be based on more than just method. In their research, social scientists and scholars in the humanities emphasize social theory. Departing from a reductionist psychological model of individual behavior that often blames people for their illness, social theory focuses on relations – disease happens not simply in people but between people. This type of theoretically informed and empirically grounded research thus examines not just patients but interactions between a wide range of actors (e.g., patients, family members, friends, neighbors, local politicians, medical practitioners at all levels, and from many systems of medicine, researchers, policymakers) to give voice to the lived experiences, motivations, and constraints of all those who are touched by disease.

PHILOSOPHICAL FOUNDATIONS OF QUALITATIVE RESEARCH METHODS

In identifying the factors that contribute to the occurrence and persistence of a phenomenon, it is paramount that we begin by asking the question: what do we know about this reality? How have we come to know this reality? These two processes, which we can refer to as the “what” question and the “how” question, are the two that all scientists (natural and social) grapple with in their research. We refer to these as the ontological and epistemological questions a research study must address. Together, they help us create a suitable methodology for any research study[ 1 ] [ Figure 2 ]. Therefore, as with quantitative methods, there must be a justifiable and logical method for understanding the world even for qualitative methods. By engaging with these two dimensions, the ontological and the epistemological, we open a path for learning that moves away from commonsensical understandings of the world, and the perpetuation of stereotypes and toward robust scientific knowledge production.

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Developing a research methodology

Every discipline has a distinct research philosophy and way of viewing the world and conducting research. Philosophers and historians of science have extensively studied how these divisions and specializations have emerged over centuries.[ 1 , 2 , 3 ] The most important distinction between quantitative and qualitative research techniques lies in the nature of the data they study and analyze. While the former focus on statistical, numerical, and quantitative aspects of phenomena and employ the same in data collection and analysis, qualitative techniques focus on humanistic, descriptive, and qualitative aspects of phenomena.[ 4 ]

For the findings of any research study to be reliable, they must employ the appropriate research techniques that are uniquely tailored to the phenomena under investigation. To do so, researchers must choose techniques based on their specific research questions and understand the strengths and limitations of the different tools available to them. Since clinical work lies at the intersection of both natural and social phenomena, it means that it must study both: biological and physiological phenomena (natural, quantitative, and objective phenomena) and behavioral and cultural phenomena (social, qualitative, and subjective phenomena). Therefore, clinical researchers can gain from both sets of techniques in their efforts to produce medical knowledge and bring forth scientifically informed change.

KEY FEATURES AND CONTRIBUTIONS OF QUALITATIVE RESEARCH METHODS

In this section, we discuss the key features and contributions of qualitative research methods [ Figure 3 ]. We describe the specific strengths and limitations of these techniques and discuss how they can be deployed in scientific investigations.

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Key features of qualitative research methods

One of the most important contributions of qualitative research methods is that they provide rigorous, theoretically sound, and rational techniques for the analysis of subjective, nebulous, and difficult-to-pin-down phenomena. We are aware, for example, of the role that social factors play in health care but find it hard to qualify and quantify these in our research studies. Often, we find researchers basing their arguments on “common sense,” developing research studies based on assumptions about the people that are studied. Such commonsensical assumptions are perhaps among the greatest impediments to knowledge production. For example, in trying to understand stigma, surveys often make assumptions about its reasons and frequently associate it with vague and general common sense notions of “fear” and “lack of information.” While these may be at work, to make such assumptions based on commonsensical understandings, and without conducting research inhibit us from exploring the multiple social factors that are at work under the guise of stigma.

In unpacking commonsensical understandings and researching experiences, relationships, and other phenomena, qualitative researchers are assisted by their methodological commitment to open-ended research. By open-ended research, we mean that these techniques take on an unbiased and exploratory approach in which learnings from the field and from research participants, are recorded and analyzed to learn about the world.[ 5 ] This orientation is made possible by qualitative research techniques that are particularly effective in learning about specific social, cultural, economic, and political milieus.

Second, qualitative research methods equip us in studying complex phenomena. Qualitative research methods provide scientific tools for exploring and identifying the numerous contributing factors to an occurrence. Rather than establishing one or the other factor as more important, qualitative methods are open-ended, inductive (ground-up), and empirical. They allow us to understand the object of our analysis from multiple vantage points and in its dispersion and caution against predetermined notions of the object of inquiry. They encourage researchers instead to discover a reality that is not yet given, fixed, and predetermined by the methods that are used and the hypotheses that underlie the study.

Once the multiple factors at work in a phenomenon have been identified, we can employ quantitative techniques and embark on processes of measurement, establish patterns and regularities, and analyze the causal and correlated factors at work through statistical techniques. For example, a doctor may observe that there is a high patient drop-out in treatment. Before carrying out a study which relies on quantitative techniques, qualitative research methods such as conversation analysis, interviews, surveys, or even focus group discussions may prove more effective in learning about all the factors that are contributing to patient default. After identifying the multiple, intersecting factors, quantitative techniques can be deployed to measure each of these factors through techniques such as correlational or regression analyses. Here, the use of quantitative techniques without identifying the diverse factors influencing patient decisions would be premature. Qualitative techniques thus have a key role to play in investigations of complex realities and in conducting rich exploratory studies while embracing rigorous and philosophically grounded methodologies.

Third, apart from subjective, nebulous, and complex phenomena, qualitative research techniques are also effective in making sense of irrational, illogical, and emotional phenomena. These play an important role in understanding logics at work among patients, their families, and societies. Qualitative research techniques are aided by their ability to shift focus away from the individual as a unit of analysis to the larger social, cultural, political, economic, and structural forces at work in health. As health-care practitioners and researchers focused on biological, physiological, disease and therapeutic processes, sociocultural, political, and economic conditions are often peripheral or ignored in day-to-day clinical work. However, it is within these latter processes that both health-care practices and patient lives are entrenched. Qualitative researchers are particularly adept at identifying the structural conditions such as the social, cultural, political, local, and economic conditions which contribute to health care and experiences of disease and illness.

For example, the decision to delay treatment by a patient may be understood as an irrational choice impacting his/her chances of survival, but the same may be a result of the patient treating their child's education as a financial priority over his/her own health. While this appears as an “emotional” choice, qualitative researchers try to understand the social and cultural factors that structure, inform, and justify such choices. Rather than assuming that it is an irrational choice, qualitative researchers try to understand the norms and logical grounds on which the patient is making this decision. By foregrounding such logics, stories, fears, and desires, qualitative research expands our analytic precision in learning about complex social worlds, recognizing reasons for medical successes and failures, and interrogating our assumptions about human behavior. These in turn can prove useful in arriving at conclusive, actionable findings which can inform institutional and public health policies and have a very important role to play in any change and transformation we may wish to bring to the societies in which we work.

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COMMENTS

Qualitative vs. Quantitative Research
When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge. Quantitative research. Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions.
PDF Methods of Data Collection in Quantitative, Qualitative, and Mixed Research
In the second kind of mixing, intramethod mixing, both quantitative and qualitative data are obtained through the creative use of a single method (i.e., using just one of the six major methods of data collection). In short, you can use a quantitative, qualitative, or a mixed version of each of the six methods of data collection.
Data Collection Methods: Qualitative vs Quantitative Research
Quantitative research. Compared to qualitative research, quantitative research is usually more affordable to execute. It also requires a lesser investment of time. Quantitative research is standardized, which makes it easy to compare findings. Quantitative data deals with numbers and includes values and quantities.
A Practical Guide to Writing Quantitative and Qualitative Research
Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes.2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ...
How to use and assess qualitative research methods
The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [1, 14, 16, 17]. ... Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand ...
Qualitative Research: Data Collection, Analysis, and Management
Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management.
Best Practices in Data Collection and Preparation: Recommendations for
We offer best-practice recommendations for journal reviewers, editors, and authors regarding data collection and preparation. Our recommendations are applicable to research adopting different epistemological and ontological perspectives—including both quantitative and qualitative approaches—as well as research addressing micro (i.e., individuals, teams) and macro (i.e., organizations ...
Qualitative Data Collection: What it is + Methods to do it
Qualitative data collection is vital in qualitative research. It helps researchers understand individuals' attitudes, beliefs, and behaviors in a specific context. Several methods are used to collect qualitative data, including interviews, surveys, focus groups, and observations. Understanding the various methods used for gathering ...
Chapter 10. Introduction to Data Collection Techniques
Figure 10.1. Data Collection Techniques. Each of these data collection techniques will be the subject of its own chapter in the second half of this textbook. This chapter serves as an orienting overview and as the bridge between the conceptual/design portion of qualitative research and the actual practice of conducting qualitative research.
Data Collection Methods
Depending on your research questions, you might need to collect quantitative or qualitative data: Quantitative data is expressed in numbers and graphs and is analysed through statistical methods. Qualitative data is expressed in words and analysed through interpretations and categorisations.
Data Collection
Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences. Quantitative Data Collection. Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods.
Methods of data collection in qualitative research: interviews and
There are a variety of methods of data collection in qualitative research, including observations, textual or visual analysis (eg from books or videos) and interviews (individual or group). 1 ...
Qualitative and Quantitative data collection methods in M&E
Quantitative data collection approach. The quantitative approach uses numbers and statistics to quantify change and is often expressed in the form of digits, units, ratios, percentages, proportions, etc. Compared to the qualitative approach, the quantitative approach is more structured, straightforward and formal.
5 Methods of Data Collection for Quantitative Research
The importance of quantitative data collection . Quantitative data collection can confirm or deny a brand's hypothesis, guide product development, tailor marketing materials, and much more. It provides brands with reliable information to make decisions off of (i.e. 86% like lemon-lime flavor or just 12% are interested in a cinnamon-scented hand ...
The Strategies for Quantitative and Qualitative Remote Data Collection
Background. The COVID-19 pandemic is transforming the landscape of clinical research. The pandemic has necessitated the unexpected adaptation of ongoing clinical research activities to web-based or blended design (ie, part web-based, part in-person) models [] and has rapidly accelerated a shift within clinical research toward web-based study designs.
Qualitative vs Quantitative Research: What's the Difference?
The main difference between quantitative and qualitative research is the type of data they collect and analyze. Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms.
Qualitative and quantitative research methods
The way data are collected usually dependent on the question being asked. Data collection can be in the form of surveys, case studies, visual observations, experiments, graphs, or open-ended response questions. All these methods can be classified as either quantative or qualitative. Quantitative data collection methods. 1.
A Comprehensive Guide to Quantitative Research Methods: Design, Data
3. Data Collection in Quantitative Research: Data collection in quantitative research involves gathering numerical data to analyze and test hypotheses. Here are some common methods and techniques used in quantitative data collection: 1. Surveys: Surveys involve collecting data through questionnaires or structured interviews. They can be ...
(PDF) Data Collection Methods and Tools for Research; A Step-by-Step
PDF | Learn how to choose the best data collection methods and tools for your research project, with examples and tips from ResearchGate experts. | Download and read the full-text PDF.
Introduction to qualitative research methods
INTRODUCTION. Qualitative research methods refer to techniques of investigation that rely on nonstatistical and nonnumerical methods of data collection, analysis, and evidence production. Qualitative research techniques provide a lens for learning about nonquantifiable phenomena such as people's experiences, languages, histories, and cultures.

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Qualitative vs. Quantitative Research | Differences, Examples & Methods

Table of contents

Quantitative data collection methods

Qualitative data collection methods

Quantitative research approach

Qualitative research approach

Mixed methods approach

Analyzing quantitative data

Analyzing qualitative data

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Raimo Streefkerk

Data Collection Methods: Qualitative vs Quantitative Research

The Importance of Collecting Data

How to Collect Data

Decide what information you need

Set a time frame

Choose a data collection method

Collect data

Qualitative vs Quantitative Research

Qualitative research

Quantitative research

Deciding between qualitative vs quantitative research

Data Collection Methods

Qualitative questionnaires

Focus groups

Observation

Longitudinal studies

Case studies

Quantitative surveys

Experiments

Collecting Data Through Qualitative and Quantitative Research

Popular Resources

Qualitative Data Collection: What it is + Methods to do it

What is Qualitative Data?

Individual interview

Qualitative surveys

Paper surveys

Record keeping

Case studies

Observations

Step 1: Arrange your Data

Step 2: Organize all your Data

Step 3: Set a Code to the Data Collected

Step 4: Validate your Data

Step 5: Concluding the Analysis Process

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Chapter 10. Introduction to Data Collection Techniques

Revisiting Approaches

Overview of the Four Primary Approaches

Triangulation and Mixed Methods

Advanced Reading: Triangulation

Further Readings

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Data Collection Methods | Step-by-Step Guide & Examples

Table of contents

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Operationalisation

Standardising procedures

Creating a data management plan

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Pritha Bhandari

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Interviews in the social sciences

A cross-verified database of notable people, 3500BC-2018AD

Principal component analysis

Introduction

Qualitative research interviews

The purpose of research interviews

The interview

Developing the interview

Focus groups

When focus groups are used

Conducting focus groups: group composition and size