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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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Qualitative vs Quantitative research: Similarities, differences, pros, and cons

Amirah Khan • 2023-05-15

Qualitative and quantitative research are two popular approaches to data collection and analysis. Both are essential research approaches that are utilised across disciplines, including psychology, business, user research, computer science, and more. In this article, we’ll share the key features, research methods, pros and cons, and use cases of qualitative and quantitative research.

What is Qualitative Research?

Qualitative research aims to use non-numerical data to understand, explore, and interpret the way people think, behaviour, and feel. This includes examining experiences, attitudes, and beliefs that exist in our subjective social reality. Qualitative research uses descriptive data to draw rich, in-depth insights into problems, topics, and phenomena. This kind of research focuses on making sense of the subjective, dynamic, and evolving nature of real life. Using this research approach, it is possible to generate new ideas for research, including hypotheses and theories that are rooted in natural settings. 

Key Features

Non-Numerical Data: Qualitative data focuses on rich, subjective sources of information including images, videos, text, and audio. This could be documents, observation notes, interview transcripts, audio recordings, video interviews, diaries, personal logs, photographs, and many more descriptive data sources. 

Inductive Reasoning: Rather than test existing theories and hypotheses, qualitative research aims to generate new ideas for research. The goal is to take a bottom-up approach and extract rich, in-depth meaning from a specific dataset. Researchers examine unique experiences and aim to draw out common themes or categories to make sense of the topic at hand. 

Flexible Research Design: Qualitative research studies have a flexible and emergent design that is data-driven. The research design, including the methods of data collection and analysis, can change throughout the study as findings emerge. This allows the design to develop alongside the study, as long as the research question is answered. 

Qualitative Researchers: Due to the subjective nature of qualitative research, the qualitative researchers are considered instruments in the process. This is because their beliefs, attitudes, personal characteristics, and experiences can influence the interpretive data collection and analysis process. 

Small Scale: Qualitative research methods can be time-consuming, and the subject matter can sometimes be very specific to a certain group of people. This means qualitative research often features a small sample of participants to be observed, interviewed, or given questionnaires. 

Open-Ended Questions: To gather the rich, in-depth data needed for qualitative research, open-ended questions are used throughout the research methods. These kinds of questions allow participants to answer how they want in detail, rather than having to select from a limited range of pre-determined answers. 

Qualitative Research Methods

For qualitative research, there are five common research methods used for data collection. Researchers often use multiple methods collect data and this depends on their chosen research approach:

Surveys can often be a time-saving, complementary method of data collection. Researchers can collect data using questionnaires with open-ended questions. These can be distributed online or in-person and allows participants to provide detailed responses in their own time. 

In-depth interviews are used to collect in-depth insights into a person’s perspective on a problem, event, or topic. Researchers ask open-ended questions in a one-to-one conversation, and can deep-dive into the participants' answers with follow-up questions. 

Focus groups are ideal for collecting data from multiple participants in the form of a group discussion. Researchers generate and facilitate discussion using open-ended questions. This research method is good for understanding complex social topics, and examining beliefs and opinions. 

Observations occur when researchers go out into natural settings of interest to create records of what they saw, heard, or encountered. This is documented in detailed field notes, and focuses on understanding how people behave. 

Secondary data involves using existing data, such as documents, photos, and videos to conduct qualitative research. This can be a more efficient way to approach a research topic, rather than collecting new data. 

Pros and Cons of Qualitative Research 

Qualitative research produces rich, in-depth insights into problems, issues, and phenomena. The research findings are often full of meaning that explore the ‘why’, ‘how’, and ‘what’ behind processes, behaviours, thoughts, feelings, attitudes, and experiences. This is something that can be hard to obtain from quantitative research. Qualitative research also focuses on real-life settings and people, which can provide a more accurate representation than laboratory based experiments. Finally, the inductive approach of qualitative research allows for new possibilities to be discovered and explored. 

However, the subjective nature of qualitative research makes it hard to replicate. Researchers are also key instruments in the process which further reduces replicability. This limits how reliable qualitative findings are, Qualitative research can also be time-consuming, especially during data analysis. Despite using a small sample, there’s often large amounts of data to prepare and analyse. These smaller samples can also make it harder for researchers to generalise their findings beyond their current participants.  

When to use Qualitative Research?

Qualitative research is ideal if you want to:

• Extract rich, in-depth, and meaningful insights into problems and topics
• Understand how people perceive their own experiences
• Explore a person’s thoughts, feelings, and behaviours
• Gain insight into social realities of specific individuals, groups, and cultures 
• Examine controversial social issues and topics 
• Generate new research ideas and possibilities 
• Learn about attitudes, beliefs, and opinions 

Qualitative Research Questions 

• Why are customers unsatisfied with their new product?
• How do teachers feel about students using artificial intelligence?
• What are teenagers' experiences of para-social relationships with influencers? 

What is Quantitative Research?

Quantitative research focuses on testing hypotheses and theories using numerical data. The aim is to use maths, statistics, and deductive logic to establish facts about behaviour or a phenomena of interest. This type of research aims to understand and measure the causal or correlational relationships between quantifiable variables. Quantitative research data can be transformed into useful graphs and tables using statistics. 

Specifically, descriptive statistics are used to summarise data, and describe the relationships or connections between variables. Inferential statistics establish the statistical significance of the given groups of data. For this reason, quantitative research requires a large sample of participants, and a carefully planned research design. This is important for conducting statistical analyses that are reliable and generalisable.  

Here are the key features of quantitative research that contrast with the features of qualitative research: 

Numerical Data : Quantitative data focuses on variables that can be quantified, measured, and analysed through statistics. This data, which is rooted in numbers and maths, can be displayed using graphs and tables. 

Deductive Reasoning: Quantitative research aims to test whether existing theories, hypotheses, or observations can hold up in specific conditions. This allows researchers to determine whether a theory or hypotheses should be confirmed or rejected for that particular condition. 

Fixed Research Design: Quantitative research follows a structured process that is well-established. The research design, including the research questions, research methods, and data analysis techniques are often decided at the beginning and rarely changed during the study. 

Quantitative Researchers: For quantitative researchers, their approach to the world is objective, and focuses on the quantifiable, measurable aspects of reality. Their goal is to remain as objective as possible and produce results that can be generalised beyond the specific environment of the study. 

Large Scale: Statistical analyses require a large amount of data to produce significant and reliable results. For this reason, quantitative research often involves a large sample of participants. This larger sample allows results to be generalised and enables researchers to account for erroneous data. 

Close-ended Questions: Quantitative data collection methods use close-ended questions to collect quantifiable, measurable data. Close-ended questions have predetermined responses for people to pick from. This can include yes/no questions, multiple-choice answers, and rating scales of all kinds. 

Quantitative Research Methods

Experiments involve manipulating an independent variable and measuring a dependent variable. This is to examine how changes to the independent variable affect the dependent variable. Researchers can use experiments to identify cause and effect relationships between variables. 

Observations are used to watch, understand, and investigate quantifiable variables. Instead of manipulating variables, this method focuses on measuring variables. For example, weight, size, and noting the number of times something occurs are measurements. Observations are used for descriptive and correlational research designs . 

Surveys are a common and popular research method, also used for descriptive and correlational research designs. This method uses close-ended questions, such as multiple choice, or rating scales to collect data. Surveys can be used to understand how something changes over time, or to get a snapshot of the current moment. 

Pros and Cons of Quantitative Research 

Quantitative research follows structured, unambiguous, standardised processes that can be easily replicated. This improves the reliability of the study, allowing it to be replicated and proven using the same approach. Unlike qualitative research, quantitative research can be both quick and scientifically objective. Researchers can study phenomena in a timely manner, and utilise sophisticated softwares for rapid, statistical analyses. This allows researchers to process large amounts of data in an efficient way, and produce findings that are generalisable. 

If researchers are unable to obtain an adequate sample size, or end up with data that cannot be used, this limits the accuracy and generalisability of the findings. Researchers also require statistical expertise in order to conduct statistical analyses in an accurate manner. Finally, quantitative research can lack meaning and be subject to confirmation bias. That is, researchers can miss emerging phenomena because they are focused on testing a theory of hypothesis. 

When to use Quantitative Research?

Quantitative research is best used when you want to:

• Measure or quantify data 
• Establish trends and relationships between variables
• Test existing hypotheses and theories 
• Describe and predict casual relationships
• Investigate correlational relationships
• Understand the characteristics of a population or phenomena 
• Produce visual displays of information, such as graphs or tables 

Quantitative Research Questions 

• What are the demographics of my target audience on social media?
• How satisfied are customers with my products and services?
• Can mindfulness improve a student's ability to recall information?

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• Qualitative vs Quantitative Data:15 Differences & Similarities

• Data Collection

Research and statistics are two important things that are not mutually exclusive as they go hand in hand in most cases. The role of statistics in research is to function as a tool in designing research, analysing data and drawing conclusions from there. 

On the other hand, the basis of statistics is data, making most research studies result in large volumes of data. This data is measured, collected and reported, and analysed (making it information), whereupon it can be visualised using graphs, images or other analysis tools

In this article, we will be discussing data, a very important aspect of statistics and research. We will be touching on its meaning, types and working with them in research and statistics. 

What is Data?  

Data is a group of raw facts or information collected for research, reference or analysis. They are individual units of information that has been transformed into an efficient form, for easy movement and/or processing. 

The plural of the word Datum, which describes a single quantity or quality of an object or phenomenon. It is applicable in different fields of research, business and statistics.

In the case of data analysis, we define it as the process of inspecting, editing, transforming and modelling data to discover useful information, informing conclusion and supporting decision-making. An important part of performing data analysis is knowing the different types of data we have. 

There are two types of data, namely; quantitative and qualitative data;

What is Quantitative Data? 

Quantitative data is the type of data whose value is measured in the form of numbers or counts, with a unique numerical value associated with each data set. Also known as Numerical data , this data type describes numeric variables. 

It has various uses in research and most especially statistics because of its compatibility with most statistical analysis methods. There are different methods of analysing quantitative data depending on its type.

Quantitative data is divided into two types , namely; discrete data and continuous data. Continuous data is then further divided into interval data and ratio data. 

What is Qualitative Data?

Qualitative data is the type of data that describes information. Its is a descriptive statistical data type, making it a data that is expressed with groups and categories rather than numbers. 

It is also known as categorical data . This data type is relevant to a large extent in research with limited use in statistics due to its incompatibility with most statistical methods. 

Qualitative data is divided into two categories, namely; nominal data and ordinal data . Nominal data names or define variables while ordinal data scales them. 

Here are the 15 Key differences between quantitative & qualitative data; 

• Definitions

Quantitative data is a group of quantifiable information that can be used for mathematical computations and statistical analysis which informs real-life decisions while qualitative data is a group of data that describes information.

Quantitative data is a combination of numeric values which depict relevant information. Qualitative data, on the other hand, uses descriptive approach towards expressing information. 

• Another name

Quantitative data is also known as numerical data while qualitative data is also known as categorical data. This is because quantitative data are measured in the form of numbers or counts.for qualitative data, they are grouped into categories.

Quantitative data are of two types namely; discrete data and continuous data. Continuous data is further divided into interval data and ratio data.

Qualitative data, on the other hand, is also divided into two types, namely; nominal data and ordinal data. However, ordinal data is classified as quantitative in some cases. 

Some examples of quantitative data include Likert scale, interval sale etc. The Likert scale is a commonly used example of ordinal data and is of different types — 5 point to 7-point Likert scale .

Some qualitative data examples include name, gender, phone number etc. This data can be collected through open-ended questions, multiple-choice or closed open-ended questions. 

• Characteristics

The characteristics of quantitative data include the following; it takes the numeric value with numeric properties, it has a standardised order scale, it is visualised using scatter plots, and dot plot, etc.

Qualitative data, on the other hand, may take numeric values but without numeric properties, does not have a standardised order scale And is visualised using a bar chart and pie chart. 

Quantitative data analysis is grouped into two, namely; descriptive and inferential statistics. The methods include measures of central tendency, turf analysis, text analysis, conjoint analysis, trend analysis, etc.

Quantitative data analysis methods are however straightforward, where only mean and median analysis can be performed. In some cases, ordinal data analysis use univariate statistics, bivariate statistics, regression analysis etc. which are close substitutes to calculating some mean and standard deviation analysis. 

During the collection of qualitative data , researchers use tools like surveys, interviews, focus groups and observations, while Qualitative data is usually collected through surveys and interviews in a few cases. For example, when calculating the average height of students in a class, the students may be interviewed on what their height is instead of measuring the heights again. 

• Collection Methods

Quantitative data is collected through closed-ended methods while qualitative data uses open-ended questions, multiple-choice questions, closed-ended and closed open-ended approach. This gives qualitative data a broader collection mode.

Quantitative data is mostly used to carry out statistical calculations involving the use of arithmetic operations. Calculating the CGPA of a student, for example, will require finding the average of all grades.

Quantitative data, on the other hand, deals with descriptive information without adding or performing any operation with it. It is mainly used to collect personal information. 

Quantitative data is compatible with most statistical analysis methods and as such is mostly used by researchers. Qualitative data, on the other hand, is only compatible with median and mode, making it have restricted applications.

Although, in some cases, alternative tests are carried out on ordinal data. For example, we use univariate statistics, bivariate statistics, regression analysis etc. as alternatives. 

• Disadvantages :

Although very applicable in most statistical analysis, its standardised environment may limit the proper investigation. Quantitative research is strictly based on the researcher’s point of view, thus limiting freedom of expression on the respondent’s end.

This is not the case for qualitative research. Nominal data captures human emotions to an extent through open-ended questions. This may, however, cause the researcher to deal with irrelevant data. 

• Question Samples: Quantitative research questions always have preset answers . This is not always the case in qualitative data. 

Qualitative question example

In which of the following interval does your height fall in centimetres? 

This is an interval data example . 

Quantitative question example 2

Kindly enter your National identification number below. 

This is a nominal data example . 

• Examples: Below are some examples of quantitative data and qualitative data. 

Quantitative Data Examples

• Mean height in a class
• Measurement of physical objects
• The probability of an event occurring
• Random number generation
• Calculation of student’s CGPA

Qualitative Data Examples

• Likert scale
• Data collected from a competitive analysis survey.
• Oral-job interview responses.
• Student biodata .
• Phone number
• Statistical compatibility

Quantitative data is compatible with most statistical methods, but qualitative data isn’t. This may pose issues for researchers when performing data analysis.

This is part of the reason why researchers prefer using quantitative data for research. 

• User-friendliness

Quantitative data collection methods are more user-friendly compared to that of qualitative data. Although open-ended questions may give the researchers much-needed information, it may get stressful for respondents.

Respondents like spending as little time as possible filling out surveys, and when it takes time, they may abandon it. 

Are there any similarities between quantitative & qualitative data? 

Both quantitative and qualitative data has an order or scale to it. That is while ordinal data is sometimes classified under quantitative data. Qualitative data do not, however, have a standardised scale.

Quantitative and qualitative data are both used for research and statistical analysis. Although, through different approaches, they can both be used for the same thing. Consider two organisations investigating the purchasing power of its target audience through the method below.

Organisation A

What is your monthly income?  ____

Organisation B

In which interval does your monthly income fall? 

• €1000 – €5000
• €5001 – €10000
• €10001 – €15000

The first is a qualitative data collection example while the second is a quantitative data collection example. 

• Quantitative Value

Both quantitative data and qualitative data takes a numeric value. Qualitative data takes numeric values like phone number, postal code, national identification number, etc. The difference, however, is that arithmetic operations cannot be performed on qualitative data.

• Collection tools

Both qualitative and quantitative data can be collected through surveys/questionnaires and interviews . Although through different approaches, they use similar tools.

When to Choose Quantitative Over Qualitative Data 

The different types of data have their usefulness and advantages over the other. These advantages are why they are chosen over the other in some cases depending on the purpose of data collection. Here are some cases where quantitative data should be chosen over qualitative data. 

• When conducting scientific research

Quantitative data is more suitable for scientific research due to its compatibility with most statistical analysis methods. It also has numerical properties which allow for the performance of arithmetic operations on it.

• When replicating research

Quantitative research has a standardised procedure to it. Hence, it is easy to replicate past research, build on it and even edit research procedures.

• When dealing with large data

Large data sets are best analysed using quantitative data. This is why some researchers turn qualitative data into quantitative data before analysis.

It is called the quantification of qualitative data. This way, they don’t have to be sweeping through a large string of texts for analysis. 

• During laboratory-based research

Due to its standard procedure of analysis, it is the most suitable data type for laboratory analysis.

• When dealing with sensitive data

Research that involve sensitive data is best processed using quantitative data. This helps eliminate cases of bias due to familiarity or leaking sensitive information.

When to Choose Qualitative Over Quantitative Data 

Although not compatible with most statistical analysis methods, qualitative data is preferable in certain cases. It is mostly preferred when collecting data for real-life research processes. Here are some cases where qualitative data should be chosen over quantitative data. 

• During customer experience research

The main purpose of customer experience research is to know how customers feel about an organisation’s service and get information on what they can do to improve their service. Therefore, to achieve this, organisations need to assess human feelings and emotions. This is something that can only be done with qualitative data.

• Job interviews

Especially with this ever-changing workplace culture, recruiters are now more interested in the applicant’s attitude, emotional intelligence, etc. than the skills they have to offer. For them to properly assess these traits, qualitative data about the applicant should be collected through an interview.

• Competitive analysis

Organisations perform competitive analysis to assess their competition’s popularity and what they did to gain such popularity. Quantitative data do not give detailed information about this unlike how qualitative does.

• Security questions

Many web-based companies ask personal questions like, “What is your pet’s name?” or “What is your mother’s maiden name?” as a means of extra security on user’s account. Numbers are usually hard to memorise, which is why some people to find it difficult to memorise their phone number to date. Personal questions (qualitative data) like this is hard to forget and therefore better for security questions.

• Dating website

Dating websites collect personal Information (usually nominal data) of users to properly match them with their type.

What is the best tool to collect quantitative and qualitative data? 

Formplus as a data collection tool was built with the notion that proper data collection is the first step towards efficient and reliable research. Therefore, the makers of Formplus form builder software have added necessary features to help you collect your data. 

Quantitative and qualitative data is best collected with Formplus because it not only helps you collect proper data but also arrange them for analysis. You no longer have to deal with data that is difficult to read when performing data validation process. 

Each data is properly matched to the corresponding variables, making it easy to identify missing or inconsistent data. 

How to collect Qualitative and Quantitative Data with Formplus Survey Tools

To collect qualitative data using Formplus builder, follow these steps:

Step 1: Register or Sign up

• Visit www.formpl.us on your desktop or mobile device.
• Sign up through your Email, Google or Facebook in less than 30 seconds…

Step 2: Start Creating Forms: Formplus gives you a 21-day free trial to test all features and start collecting quantitative data from online surveys. Pricing plan starts after trial expiration at $20 monthly, with reasonable discounts for Education and Non-Governmental Organizations.

• Click on the Create form button to start creating forms for free.
• You can also click on the Upgrade Now button to upgrade to a pricing plan at $20 monthly.

Step 3: Collect Qualitative Data

We will be creating a sample qualitative data collection form that inputs name (nominal data) and happiness level (ordinal data) of a respondent. 

• Edit form title and click on the input section of the form builder menu.
• The input sections let you insert features such as small texts for names, numbers, date, email, long text for general feedback. Click on the Name tab and edit in the settings

• Click on the choice options section of the form builder menu. Then, click on the Radio tab.
• the choice options let respondents choose from different options. Use Radio choice to ask your respondents to choose a single option from a shortlist.

Step 4: Collect Quantitative Data

We will be creating a sample quantitative data collection form that inputs the courses offered by a student and their score, then output their average score. 

• Click on the Advanced inputs section of the builder menu, then click on the Table tab.

• Click on the Labeled Text tab in the inputs section to output the result of our quantitative data calculation.

• Click on Add Calculations in the Advanced inputs tab and use the formula  Score/COUNT() to calculate the average score.

The Add Calculations tab lets you perform arithmetic operations on numerical data.  

Conclusion  

Qualitative and quantitative data do have their key differences and similarities, and understanding them is very important as it helps in choosing the best data type to work with. It also helps in proper identification, so as not to miscategorise data.

These two data types also have their unique advantages over the other, which is why researchers use a particular data type for research and use the other for another research. However, quantitative data remains the more popular data type when compared to qualitative data. 

As we have done in this article, understanding data types are the first step towards proper usage. 

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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

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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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Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing 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. 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.

Table of contents

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

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

Prevent plagiarism, run a free check.

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, observations 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 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 organisation 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 analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths 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
• 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

Analysing qualitative data

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

Some common approaches to analysing 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

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.

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 analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are 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 organisations.

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

• Prepare and organise 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 .

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Introduction: Considering Qualitative, Quantitative and Mixed Methods Research

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In this introduction we will explore some of the differences and similarities between quantitative and qualitative research, and dispel some of the perceived mysteries within research. We will briefly introduce some of the advantages and disadvantages of both approaches. There will also be an introduction to some of the philosophical assumptions that underpin quantitative and qualitative research methods, with specific mention made of ontological and epistemological considerations. These about the nature of existence (ontology) and how we might gain knowledge about the nature of existence (epistemology). We will explore the difference between positivist and interpretivist research, idiographic versus nomothetic, and inductive and deductive perspectives. Finally, we will also distinguish between qualitative, quantitative and mixed method s research, gaining familiarity with attempts to bridge divides between disciplines and research approaches. Throughout this book, the issue of research-supported practice will remain an underlying theme. This chapter aims to support a research-based practice, aided by considering the multiple routes into research. The chapter encourages you to familiarise yourself with approaches ranging from phenomenological experiences to more nomothetic, generalising and comparing foci like outcome measuring and random control trials (RCTs), understood with a basic knowledge of statistics. The book introduces you to a range of research, guided by interest in separate approaches but also inductive—deductive combinations, as in grounded theory together with pluralistic and mixed methods approaches, all with a shared interest in providing support in the field of mental health and emotional wellbeing. Primarily, we hope that the chapter will encourage you to start considering your own research. Enjoy!

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Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

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Qualitative Vs. Quantitative Research — A step-wise guide to conduct research

A research study includes the collection and analysis of data. In quantitative research, the data are analyzed with numbers and statistics, and in qualitative research, the data analyzed are non-numerical and perceive the meaning of social reality.

What Is Qualitative Research?

Qualitative research observes and describes a phenomenon to gain a deeper understanding of a subject. It is also used to generate hypotheses for further studies. In general, qualitative research is explanatory and helps understands how an individual perceives non-numerical data, like video, photographs, or audio recordings. The qualitative data is collected from diary accounts or interviews and analyzed by grounded theory or thematic analysis.

When to Use Qualitative Research?

Qualitative research is used when the outcome of the research study is to disseminate knowledge and understand concepts, thoughts, and experiences. This type of research focuses on creating ideas and formulating theories or hypotheses .

Benefits of Qualitative Research

• Unlike quantitative research, which relies on numerical data, qualitative research relies on data collected from interviews, observations, and written texts.
• It is often used in fields such as sociology and anthropology, where the goal is to understand complex social phenomena.
• Qualitative research is considered to be more flexible and adaptive, as it is used to study a wide range of social aspects.
• Additionally, qualitative research often leads to deeper insights into the research study. This helps researchers and scholars in designing their research methods .

Qualitative Research Example

In research, to understand the culture of a pharma company, one could take an ethnographic approach. With an experience in the company, one could gather data based on the —

• Field notes with observations, and reflections on one’s experiences of the company’s culture
• Open-ended surveys for employees across all the company’s departments via email to find out variations in culture across teams and departments
• Interview sessions with employees and gather information about their experiences and perspectives.

What Is Quantitative Research?

Quantitative research is for testing hypotheses and measuring relationships between variables. It follows the process of objectively collecting data and analyzing it numerically, to determine and control variables of interest. This type of research aims to test causal relationships between variables and provide generalized results. These results determine if the theory proposed for the research study could be accepted or rejected.

When to Use Quantitative Research?

Quantitative research is used when a research study needs to confirm or test a theory or a hypothesis. When a research study is focused on measuring and quantifying data, using a quantitative approach is appropriate. It is often used in fields such as economics, marketing, or biology, where researchers are interested in studying trends and relationships between variables .

Benefits of Quantitative Research

• Quantitative data is interpreted with statistical analysis . The type of statistical study is based on the principles of mathematics and it provides a fast, focused, scientific and relatable approach.
• Quantitative research creates an ability to replicate the test and results of research. This approach makes the data more reliable and less open to argument.
• After collecting the quantitative data, expected results define which statistical tests are applicable and results provide a quantifiable conclusion for the research hypothesis
• Research with complex statistical analysis is considered valuable and impressive. Quantitative research is associated with technical advancements like computer modeling and data-based decisions.

Quantitative Research Example

An organization wishes to conduct a customer satisfaction (CSAT) survey by using a survey template. From the survey, the organization can acquire quantitative data and metrics on the brand or the organization based on the customer’s experience. Various parameters such as product quality, pricing, customer experience, etc. could be used to generate data in the form of numbers that is statistically analyzed.

Data Collection Methods

1. qualitative data collection methods.

Qualitative data is collected from interview sessions, discussions with focus groups, case studies, and ethnography (scientific description of people and cultures with their customs and habits). The collection methods involve understanding and interpreting social interactions.

Qualitative research data also includes respondents’ opinions and feelings, which is conducted face-to-face mostly in focus groups. Respondents are asked open-ended questions either verbally or through discussion among a group of people, related to the research topic implemented to collect opinions for further research.

2. Quantitative Data Collection Methods

Quantitative research data is acquired from surveys, experiments, observations, probability sampling, questionnaire observation, and content review. Surveys usually contain a list of questions with multiple-choice responses relevant to the research topic under study. With the availability of online survey tools, researchers can conduct a web-based survey for quantitative research.

Quantitative data is also assimilated from research experiments. While conducting experiments, researchers focus on exploring one or more independent variables and studying their effect on one or more dependent variables.

A Step-wise Guide to Conduct Qualitative and Quantitative Research

• Understand the difference between types of research — qualitative, quantitative, or mixed-methods-based research.
• Develop a research question or hypothesis. This research approach will define which type of research one could choose.
• Choose a method for data collection. Depending on the process of data collection, the type of research could be determined.
• Analyze and interpret the collected data. Based on the analyzed data, results are reported.
• If observed results are not equivalent to expected results, consider using an unbiased research approach or choose both qualitative and quantitative research methods for preferred results.

Qualitative Vs. Quantitative Research – A Comparison

With an awareness of qualitative vs. quantitative research and the different data collection methods , researchers could use one or both types of research approaches depending on their preferred results. Moreover, to implement unbiased research and acquire meaningful insights from the research study, it is advisable to consider both qualitative and quantitative research methods .

Through this article, you would have understood the comparison between qualitative and quantitative research. However, if you have any queries related to qualitative vs. quantitative research, do comment below or email us.

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Quantitative and Qualitative Research Methods: Similarities and Differences Compare & Contrast Essay

Introduction.

The aim of this paper is to analyze and to compare quantitative and qualitative research methods. The analysis will begin with the definition and description of the two methods. This will be followed by a discussion on the various aspects of the two research methods.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics, data collection methods, data analysis methods, and the validity issues associated with them, as well as, their strengths and weaknesses.

Definition and Description

Qualitative research method is a technique of “studying phenomena by collecting and analyzing data in non-numeric form”. It focuses on exploring the topic of the study by finding as much detail as possible. The characteristics of qualitative research include the following.

First, it focuses on studying the behavior of individuals in their natural settings. Thus, it does not use artificial experiments. This helps researchers to avoid interfering with the participants’ normal way of life.

Second, qualitative research focuses on meanings, perspectives, and understandings. It aims at finding out the meanings that the subjects of the study “attach to their behavior, how they interpret situations, and what their perspectives are on particular issues”.

Concisely, it is concerned with the processes that explain why and how things happen.

Quantitative research is “explaining phenomena by collecting numerical data that are analyzed using mathematical techniques such as statistics”.

It normally uses experiments to answer research questions. Control is an important aspect of the experiments because it enables the researcher to find unambiguous answers to research questions.

Quantitative research also uses operational definitions. Concisely, the terms used in a quantitative study must be defined according to the operations employed to measure them in order to avoid confusion in meaning or communication.

Moreover, the results of quantitative research are considered to be reliable only if they are replicable. This means that the same results must be produced if the research is repeated using the same techniques.

Hypothesis testing is also an integral part of quantitative research. Concisely, hypotheses enable the researcher to concentrate on a specific aspect of a problem, and to identify the methods for solving it.

The similarities and differences between quantitative and qualitative research methods can be seen in their characteristics

Quantitative and qualitative studies are similar in the following ways. To begin with, qualitative research is normally used to generate theory. Similarly, quantitative studies can be used to explore new areas, thereby creating a new theory.

Even though qualitative research focuses on generating theory, it can also be used to test hypotheses and existing theories. In this regard, it is similar to quantitative studies that mainly focus on testing theories and hypotheses.

Both qualitative and quantitative studies use numeric and non-numeric data. For instance, the use of statements such as less than normally involves the use of quantitative data in qualitative studies.

Similarly, quantitative studies can use questionnaires with open-ended questions to collect qualitative data.

Despite these similarities, quantitative and qualitative studies differ in the following ways. To begin with, the purpose of qualitative research is to facilitate understanding of fundamental meanings, reasons, and motives.

It also aims at providing valuable insights concerning a problem through determination of common trends in thought and generation of ideas.

On the other hand, the purpose of quantitative research is to quantify data and to use the results obtained from a sample to make generalizations on a particular population.

The sample used in qualitative research is often small and non-representative of the population. On the contrary, quantitative research uses large samples that represent the population. In this regard, it uses random sampling techniques to select a representative sample.

Qualitative research uses unstructured or semi-structured data collection techniques such as focus group discussions, whereas quantitative research uses structured techniques such as questionnaires.

Moreover, qualitative research uses non-statistical data analysis techniques, whereas quantitative uses statistical methods to analyze data. Finally, the results of qualitative research are normally exploratory and inconclusive, whereas the results of quantitative research are usually conclusive.

The similarities and differences between quantitative and qualitative research methods can be seen in their data collection methods

The main data collection methods in qualitative research include observations, interviews, content review, and questionnaires. The researcher can use participant or systematic observation to collect data.

In participant observation, the researcher engages actively in the activities of the subjects of the study. Researchers prefer this technique because it enables them to avoid disturbing the natural settings of the study.

In systematic observation, schedules are used to observe the behaviors of the participants at regular intervals. This technique enhances objectivity and reduces bias during data collection.

Most qualitative studies use unstructured interviews in which the interviewer uses general ideas to guide the interview and prompts to solicit more information.

Content review involves reading official documents such as diaries, journals, and minutes of meetings in order to obtain data. The importance of this technique is that it enables the researcher to reconstruct events and to describe social relationships.

Questionnaires are often used when the sample size is too large to be reached through face-to-face interviews. However, its use is discouraged in qualitative research because it normally influences the way participants respond, rather than allowing them to act naturally during data collection.

Quantitative research mainly uses surveys for data collection. This involves the use of questionnaires and interviews with closed-ended questions to enable the researcher to obtain data that can be analyzed with the aid of statistical techniques.

The questionnaires can be mailed or they can be administered directly to the respondents.

Observations are also used to collect data in quantitative studies. For example, the researcher can count the number of customers queuing at a point of sale in a retail shop.

Finally, quantitative researchers use management information systems to collect data. This involves reviewing documents such as financial reports to obtain quantitative data.

The similarities and differences between quantitative and qualitative research methods can be seen in their data analysis methods

Qualitative researchers often start the analysis process during the data collection and preparation stage in order to discover emerging themes and patterns. This involves continuous examination of data in order to identify important points, contradictions, inconsistencies, and common themes.

After this preliminary analysis, qualitative data is usually organized through systematic categorization and concept formation. This involves summarizing data under major categories that appear in the data set.

Data can also be summarized through tabulation in order to reveal its underlying features. The summaries usually provide descriptions that are used to generate theories. Concisely, the data is used to develop theories that explain the causes of the participants’ behavior.

Theories are also developed through comparative analysis. This involves comparing observations “across a range of situations over a period of time among different participants through a variety of techniques”.

Continuous comparisons provide clues on why participants behave in a particular manner, thereby facilitating theory formulation.

Quantitative analysis begins with the identification of the level of measurement that is appropriate for the collected data. After identifying the measurement level, data is usually summarized under different categories in tables by calculating frequencies and percentage distributions.

A frequency distribution indicates the number of observations or scores in each category of data, whereas a percentage distribution indicates the proportion of the subjects of the study who are represented in each category.

Descriptive statistics help the researcher to describe quantitative data. It involves calculating the mean and median, as well as, minimum and maximum values. Other analytical tools include correlation, regression, and analysis of variance.

Correlation analysis reveals the direction and strength of the relationship associated with two variables. Analysis of variance tests the statistical significance of the independent variables. Regression analysis helps the researcher to determine whether the independent variables are predictors of the dependent variables.

The similarities and differences between quantitative and qualitative research methods can be seen in their validity issues

Validity refers to the “degree to which the evidence proves that the interpretations of the data are correct and appropriate”. Validity is achieved if the measurement instrument is reliable. Replicability is the most important aspect of reliability in quantitative research.

This is because the results of quantitative research can only be approved if they are replicable. In quantitative research, validity is established through experiment review, data triangulation, and participant feedback, as well as, regression and statistical analyses.

In qualitative research, validity depends on unobtrusive measures, respondent validation, and triangulation. The validity of the results is likely to improve if the researcher is unobtrusive. This is because the presence of the researcher will not influence the responses of the participants.

Respondent validation involves obtaining feedback from the respondents concerning the accuracy of the data in order to ensure reliability. Triangulation involves collecting data using different methods at different periods from different people in order to ensure reliability.

The similarities and differences between quantitative and qualitative research methods can be seen in their strengths and weaknesses

The strengths of qualitative research include the following. First, it enables the researcher to pay attention to detail, as well as, to understand meanings and complexities of phenomena.

Second, it enables respondents to convey their views, feelings, and experiences without the influence of the researcher.

Third, qualitative research involves contextualization of behavior within situations and time. This improves the researcher’s understanding, thereby enhancing the reliability of the conclusions made from the findings.

Finally, the findings of qualitative research are generalizable through the theory developed in the study.

Qualitative research has the following weaknesses. Participant observation can lead to interpretation of phenomena based only on particular situations, while ignoring external factors that may influence the behavior of participants.

This is likely to undermine the validity of the research. Additionally, conducting a qualitative research is usually difficult due to the amount of time and resources required to negotiate access, to build trust, and to collect data from the respondents.

Finally, qualitative research is associated with high levels of subjectivity and bias.

Quantitative research has the following strengths. First, it has high levels of precision, which is achieved through reliable measures.

Second, it uses controlled experiments, which enable the researcher to determine cause and effect relationships.

Third, the use of advanced statistical techniques such as regression analysis facilitates accurate and sophisticated analysis of data.

Despite these strengths, quantitative research is criticized because it ignores the fact that individuals are able to interpret their experiences, as well as, to develop their own meanings.

Furthermore, control of variables often leads to trivial findings, which may not explain the phenomena that are being studied. Finally, quantitative research cannot be used to study phenomena that are not quantifiable.

The aim of this paper was to analyze quantitative and qualitative research methods by comparing and contrasting them. The main difference between qualitative and quantitative research is that the former uses non-numeric data, whereas the later mainly uses numeric data.

The main similarity between them is that they can be used to test existing theories and hypothesis. Qualitative and quantitative research methods have strengths and weaknesses. The results obtained through these methods can be improved if the researcher addresses their weaknesses.

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Wallnau, L., & Gravetter, F. (2009). Statistics for the behavioral sciences. London, England: Macmillan.

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IvyPanda. (2019, July 2). Quantitative and Qualitative Research Methods: Similarities and Differences. https://ivypanda.com/essays/qualitative-and-quantitative-research-methods/

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Navigating the Common Ground: Exploring Similarities Between Qualitative and Quantitative Research

Qualitative and quantitative research are two distinct yet interconnected approaches to studying phenomena. In this article, we will delve into the fundamental concepts of both methods, explore their differences, and uncover the common ground they share. By understanding the similarities between qualitative and quantitative research, researchers can gain valuable insights and enhance the rigor of their studies.

Key Takeaways

• Understanding the fundamental concepts of qualitative and quantitative research is essential for researchers to make informed methodological choices.
• While qualitative and quantitative research differ in their approaches, they share common ground in terms of rigor, validity, and reliability.
• Choosing the right research approach depends on the nature of the research question and the phenomena being studied.
• Data collection techniques in qualitative and quantitative research may differ, but both aim to gather accurate and meaningful data for analysis.
• Interpreting findings from qualitative and quantitative research involves identifying patterns, drawing conclusions, and considering implications for practice.

Understanding Qualitative and Quantitative Research

Exploring the Basics

At the heart of any research lies a fundamental need to understand phenomena, whether they be natural, social, or technological. Qualitative research delves into the quality and human behavior aspects of a subject, often utilizing methods like interviews and observation to gather rich, narrative data. On the other hand, quantitative research seeks to quantify variables , aiming to uncover patterns through statistical analysis.

Qualitative and quantitative research methods are often viewed as polar opposites, but this is a misconception. Both approaches strive to answer questions and solve problems, albeit through different lenses. For example:

• Qualitative: Understanding the reasons behind student engagement in classrooms.
• Quantitative: Measuring the rate of student engagement using numerical data.

While each method has its unique strengths, they share a common goal: to reveal insights and truths about the world around us.

Choosing between qualitative and quantitative research often depends on the nature of the question at hand and the type of data needed to answer it. It’s crucial to recognize that both methods can complement each other, providing a more comprehensive understanding of the research topic.

Key Differences

While both qualitative and quantitative research methods are integral to scientific inquiry, they serve different purposes and have distinct characteristics. Quantitative research is often associated with numerical data and statistical analysis, aiming to quantify the problem by way of generating numerical data or data that can be transformed into useable statistics. It is structured and can often be represented in tables and charts. On the other hand, qualitative research is primarily exploratory research. It is used to gain an understanding of underlying reasons, opinions, and motivations, providing insights into the problem or helping to develop ideas or hypotheses for potential quantitative research.

Qualitative research answers the "how" and "why" of a phenomenon, delving into the subjective experiences of participants. Quantitative research, in contrast, answers "how much" questions, seeking to measure and predict elements of the research subject.

The choice between qualitative and quantitative research methods should be driven by the research question, not by the preference or comfort of the researcher.

Here is a simple list to summarize the key differences:

• Quantitative research deals with numbers and statistics.
• Qualitative research deals with words and meanings.
• Quantitative research seeks to confirm hypotheses about phenomena.
• Qualitative research seeks to explore phenomena.
• Quantitative methods include experiments, surveys, and statistical analysis.
• Qualitative methods include interviews, focus groups, and observations.

Common Misconceptions

One common misconception is that quantitative research is inherently more objective and scientific than qualitative research. Both methods have their own rigor and validity , and it’s crucial to understand that neither is superior to the other. Qualitative research is sometimes unfairly characterized as non-scientific or purely subjective, but this overlooks the structured and systematic approaches that qualitative researchers employ to ensure reliability and depth in their findings.

Misconceptions can lead to an underappreciation of the richness that qualitative research brings to our understanding of complex issues.

To clarify these misconceptions, consider the following points:

• Quantitative research often uses statistical tools to analyze data, which can be mistakenly seen as the only ‘scientific’ approach.
• Qualitative research, while interpretative, employs rigorous methods such as thematic analysis to uncover patterns in data.
• Both approaches can be verifiable and factual when conducted properly.

By recognizing the strengths and limitations of each method, researchers can choose the most appropriate approach for their study, leading to more comprehensive and insightful results.

Applying Research Methods

Choosing the Right Approach

Selecting the appropriate research method is crucial to the success of any study. Both qualitative and quantitative research have their place , and understanding when to use each can greatly enhance the quality of your findings. Qualitative research is often best suited for exploring complex issues in depth, while quantitative research excels in measuring and quantifying variables.

When deciding on a method, consider the following factors:

• The nature of your research question
• The type of data you need
• Your resources, including time and budget
• The desired outcome of your study

It’s essential to align your research approach with your objectives to ensure that the data you collect is both relevant and actionable. This alignment will also guide your choice of data collection techniques and analysis methods.

Remember, the goal is to choose a method that will provide the most insightful and reliable results for your specific research question. Sometimes, a mixed-methods approach, which combines elements of both qualitative and quantitative research, may be the most effective way to explore your topic comprehensively.

Data Collection Techniques

In the realm of research, data collection is a pivotal step that determines the quality and reliability of the findings. Both qualitative and quantitative research methods require meticulous planning to ensure that the data collected is relevant and robust. For qualitative research, techniques such as interviews, focus groups, and observations are paramount. These methods allow for a deeper understanding of the participants’ perspectives and experiences.

Quantitative research, on the other hand, often relies on structured methods like surveys and experiments to gather numerical data. This data is then subject to statistical analysis, which can reveal patterns and correlations. Below is a list of common data collection techniques used in both research paradigms:

• Focus Groups
• Observations
• Experiments

It’s essential to choose the right data collection technique based on the research question and objectives. The method should align with the goals of the study and the nature of the data required.

Analyzing Results

Once data collection is complete, the focus shifts to analyzing results , a critical phase where researchers make sense of the data gathered. For quantitative research, this often involves statistical analysis to test hypotheses and measure relationships between variables. A common tool for presenting this data is a table, like the one below:

In qualitative research, analysis is more interpretive. Researchers look for themes and patterns within the data, which may include interview transcripts, observations, or other textual materials. Here, a list can help organize the findings:

• Identifying recurring themes
• Categorizing data into groups
• Developing a narrative to connect the data points

It’s essential to approach data analysis with an open mind and a rigorous method, ensuring that the conclusions drawn are both credible and valuable.

Whether dealing with numbers or narratives, the goal is to distill the essence of the data into actionable insights. This process requires careful consideration and often benefits from collaborative review to enhance the validity of the findings.

Interpreting Findings

Identifying Patterns

In both qualitative and quantitative research, identifying patterns is crucial for interpreting data. Patterns can emerge in various forms, such as trends, themes, or repeated behaviors, and recognizing these can provide valuable insights into the research question.

For qualitative studies, patterns might be identified through a process of coding , where data is categorized based on recurring themes or concepts. In quantitative research, statistical methods are often used to detect patterns. For instance, a researcher might use a table to summarize the frequency of certain responses:

Once patterns are identified, they serve as a foundation for deeper analysis and understanding of the data.

It’s important to approach pattern identification without bias, ensuring that the patterns observed are truly reflective of the data and not the researcher’s preconceptions.

Drawing Conclusions

After analyzing the data, whether qualitative or quantitative, the next critical step is drawing conclusions . This involves synthesizing the information to understand what the data tells us about the research question. For qualitative research, this means looking for themes and patterns that emerge from the data, often requiring a level of interpretation that is subjective but should be grounded in the data collected.

In quantitative research, conclusions are typically drawn from statistical analysis, which provides a more objective basis for interpretation. However, it’s important to remember that numbers can be misleading without proper context. That’s why it’s crucial to support quantitative findings with qualitative insights to ensure a well-rounded understanding of the results.

Interpretation of data is not the final step; it’s a precursor to action. The conclusions drawn should lead to practical implications for the field of study. For instance:

• Identifying areas for further research
• Suggesting changes to policy or practice
• Recommending improvements to theoretical frameworks

The goal of drawing conclusions is not just to summarize data but to provide meaningful insights that can drive progress and innovation in the respective field.

Implications for Practice

The synthesis of qualitative and quantitative research findings can lead to more robust and practical outcomes. Incorporating both methods enhances the depth and breadth of understanding, allowing practitioners to make well-informed decisions. For instance, quantitative data might indicate a trend, while qualitative insights provide the narrative behind the numbers.

When translating research into practice, it’s essential to consider the context in which the findings will be applied. A one-size-fits-all approach rarely works, as different settings and populations may require tailored interventions. Here’s a simple list to ensure effective implementation:

• Assess the relevance of research findings to your specific context.
• Adapt the strategies to meet the unique needs of your target population.
• Monitor and evaluate the impact of these strategies regularly.
• Be prepared to modify your approach based on feedback and results.

By thoughtfully applying research findings, practitioners can optimize outcomes and contribute to the ongoing cycle of evidence-based practice.

In conclusion, both qualitative and quantitative research methods offer valuable insights and contribute to a comprehensive understanding of complex phenomena. By recognizing their similarities and complementing each other’s strengths, researchers can navigate the common ground and enhance the rigor and depth of their studies.

Frequently Asked Questions

What are the main differences between qualitative and quantitative research.

Qualitative research focuses on understanding the meaning and context of phenomena, while quantitative research focuses on measuring and analyzing numerical data.

How do I choose the right research approach for my study?

The choice between qualitative and quantitative research depends on the research question, the nature of the phenomenon being studied, and the desired outcomes of the study.

What are some common misconceptions about qualitative and quantitative research?

A common misconception is that qualitative research is subjective and lacks rigor, while quantitative research is purely objective. In reality, both approaches have their strengths and limitations.

What are some effective data collection techniques for qualitative research?

Data collection techniques in qualitative research include interviews, focus groups, observations, and document analysis. These methods help capture rich, detailed information about the research topic.

How can I identify patterns in qualitative data?

Identifying patterns in qualitative data involves thematic analysis, coding, and categorization. Researchers look for recurring themes and connections within the data.

What are the implications of research findings for practice?

Research findings can inform and guide practical applications in various fields. Understanding the implications of research findings is crucial for making informed decisions and implementing effective strategies.

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Quantitative vs. Qualitative Research in Psychology

Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Similarities between Qualitative Research and Quantitative Research

December 4, 2017 , Victoria Jones , Leave a comment

What does Qualitative Research mean?

The qualitative research came up as an alternative form of research over the quantitative research methodology and was often conceptualized as the polar opposite of quantitative research. Qualitative research mainly relies on the collection of qualitative data. This research is supported by the interpretive paradigm which describes a world in which reality is socially constructed, complex and ever changing. This research method a researcher prefers for the theory to come up from the data itself. This enhances the researcher to understand and come up with possible explanations for the phenomenon which is consistent with its occurrence in the social world. The collection of characteristics that comes to make up the qualitative research came up as a result of the natural and agricultural sciences.

Few characteristics of qualitative research

• The research is mainly inductive. The researcher mainly comes up with new hypotheses and grounded theory from the data collected during fieldwork.
• In qualitative research, human behaviour is mainly fluid, dynamic, situational, contextual and personal.
• Most common research objectives in qualitative research are descriptive, exploratory, and discovery.
• Its area of focus is wide angle. It examines the breadth and depth of phenomena to learn more about them.
• The nature of observation in qualitative research is through studying behaviours in natural environments. They study the context through which behaviours occurs.
• The nature of data is mainly images categories and words.
• Form of final report in qualitative research is narrative report with contextual description and direct quotations from research participants

What does Quantitative research mean?

Quantitative methods are majorly supported by the positivists who lead us to regard that the world is made up of observable, measurable facts. This theory grew up from the works of social anthropology and sociology. In this research methodology, the researchers are mainly concerned with discovering, verifying or identifying casual relationships among concepts that are derived from a prior theoretical scheme. The assignments of subjects are of greater concerns and much effort is used to use other random assignments so as to minimize intervening variables that could have an impact on the results of the research.

Few characteristics of quantitative research

• It is mainly deductive or “top down”. The researcher mainly uses data to test hypotheses and theories.
• In quantitative research, the human behaviour is more regular and predictable.
• In quantitative research the most common research objectives are descriptive, explanatory and predictable.
• Quantitative research methodology focuses on testing specific hypotheses i.e. narrow-angle lens.
• Quantitative research attempts to study behaviour through observation under controlled conditions.
• Quantitative research in nature of reality proposes that different observers agree on what is observed.
• Form of data collected in quantitative research is based on precise measurement using structured and validated data collection instruments like rating scales.
• Form of final report in quantitative research is mainly statistical report which contains correlations, comparisons of means and reporting of statistical significance of findings.
• Both the qualitative and quantitative researchers do use similar elements when carrying out their work.
• They both rely on a theoretical framework and are much concerned with rigour.
• Both the qualitative research and quantitative research require a plan for carrying out an investigation
• Both qualitative and sometimes quantitative researchers collect data and carry out their research in natural settings i.e. through observation.
• Quantitative research is mostly used to test a theory, but can also be used to generate hypotheses and theory. Qualitative theory can be used to test hypotheses and theories even though it is used to generate theories.
• Qualitative data often includes quantification i.e. statements. Quantitative research can use questionnaire to collect data through open ended questions.

Few Crucial points on Qualitative  Research and Quantitative Research

• It is important to emphasize that one method is not necessary better than the other. It all relies on what the researcher is studying or wants to find out.
• The best method that the one is answers the research questions efficiently and with most inference quality.
• In qualitative research, most researchers tend to employ an inductive mode of development, and their placement of theory tends to be towards the end of the study.
• Qualitative design is used when observing and interpreting reality with the aim of developing a theory that will explain what was experienced.
• Quantitative research is needed when one begins with a theory and tests for confirmation and disconfirmation of that theory.

Author: Victoria Jones

Victoria Jones has a degree in psychology from UK. She is an expert with over 4 years experience in writing and content strategy. She has written over 50 articles, some of which have been featured in local daily’s and magazines.

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CRO Guide   >  Chapter 3.1

Qualitative Research: Definition, Methodology, Limitation & Examples

Qualitative research is a method focused on understanding human behavior and experiences through non-numerical data. Examples of qualitative research include:

• One-on-one interviews,
• Focus groups, Ethnographic research,
• Case studies,
• Record keeping,
• Qualitative observations

In this article, we’ll provide tips and tricks on how to use qualitative research to better understand your audience through real world examples and improve your ROI. We’ll also learn the difference between qualitative and quantitative data.

Table of Contents

Marketers often seek to understand their customers deeply. Qualitative research methods such as face-to-face interviews, focus groups, and qualitative observations can provide valuable insights into your products, your market, and your customers’ opinions and motivations. Understanding these nuances can significantly enhance marketing strategies and overall customer satisfaction.

What is Qualitative Research

Qualitative research is a market research method that focuses on obtaining data through open-ended and conversational communication. This method focuses on the “why” rather than the “what” people think about you. Thus, qualitative research seeks to uncover the underlying motivations, attitudes, and beliefs that drive people’s actions. 

Let’s say you have an online shop catering to a general audience. You do a demographic analysis and you find out that most of your customers are male. Naturally, you will want to find out why women are not buying from you. And that’s what qualitative research will help you find out.

In the case of your online shop, qualitative research would involve reaching out to female non-customers through methods such as in-depth interviews or focus groups. These interactions provide a platform for women to express their thoughts, feelings, and concerns regarding your products or brand. Through qualitative analysis, you can uncover valuable insights into factors such as product preferences, user experience, brand perception, and barriers to purchase.

Types of Qualitative Research Methods

Qualitative research methods are designed in a manner that helps reveal the behavior and perception of a target audience regarding a particular topic.

The most frequently used qualitative analysis methods are one-on-one interviews, focus groups, ethnographic research, case study research, record keeping, and qualitative observation.

1. One-on-one interviews

Conducting one-on-one interviews is one of the most common qualitative research methods. One of the advantages of this method is that it provides a great opportunity to gather precise data about what people think and their motivations.

Spending time talking to customers not only helps marketers understand who their clients are, but also helps with customer care: clients love hearing from brands. This strengthens the relationship between a brand and its clients and paves the way for customer testimonials.

• A company might conduct interviews to understand why a product failed to meet sales expectations.
• A researcher might use interviews to gather personal stories about experiences with healthcare.

These interviews can be performed face-to-face or on the phone and usually last between half an hour to over two hours. 

When a one-on-one interview is conducted face-to-face, it also gives the marketer the opportunity to read the body language of the respondent and match the responses.

2. Focus groups

Focus groups gather a small number of people to discuss and provide feedback on a particular subject. The ideal size of a focus group is usually between five and eight participants. The size of focus groups should reflect the participants’ familiarity with the topic. For less important topics or when participants have little experience, a group of 10 can be effective. For more critical topics or when participants are more knowledgeable, a smaller group of five to six is preferable for deeper discussions.

The main goal of a focus group is to find answers to the “why”, “what”, and “how” questions. This method is highly effective in exploring people’s feelings and ideas in a social setting, where group dynamics can bring out insights that might not emerge in one-on-one situations.

• A focus group could be used to test reactions to a new product concept.
• Marketers might use focus groups to see how different demographic groups react to an advertising campaign.

One advantage that focus groups have is that the marketer doesn’t necessarily have to interact with the group in person. Nowadays focus groups can be sent as online qualitative surveys on various devices.

Focus groups are an expensive option compared to the other qualitative research methods, which is why they are typically used to explain complex processes.

3. Ethnographic research

Ethnographic research is the most in-depth observational method that studies individuals in their naturally occurring environment.

This method aims at understanding the cultures, challenges, motivations, and settings that occur.

• A study of workplace culture within a tech startup.
• Observational research in a remote village to understand local traditions.

Ethnographic research requires the marketer to adapt to the target audiences’ environments (a different organization, a different city, or even a remote location), which is why geographical constraints can be an issue while collecting data.

This type of research can last from a few days to a few years. It’s challenging and time-consuming and solely depends on the expertise of the marketer to be able to analyze, observe, and infer the data.

4. Case study research

The case study method has grown into a valuable qualitative research method. This type of research method is usually used in education or social sciences. It involves a comprehensive examination of a single instance or event, providing detailed insights into complex issues in real-life contexts.  

• Analyzing a single school’s innovative teaching method.
• A detailed study of a patient’s medical treatment over several years.

Case study research may seem difficult to operate, but it’s actually one of the simplest ways of conducting research as it involves a deep dive and thorough understanding of the data collection methods and inferring the data.

5. Record keeping

Record keeping is similar to going to the library: you go over books or any other reference material to collect relevant data. This method uses already existing reliable documents and similar sources of information as a data source.

• Historical research using old newspapers and letters.
• A study on policy changes over the years by examining government records.

This method is useful for constructing a historical context around a research topic or verifying other findings with documented evidence.

6. Qualitative observation

Qualitative observation is a method that uses subjective methodologies to gather systematic information or data. This method deals with the five major sensory organs and their functioning, sight, smell, touch, taste, and hearing.

• Sight : Observing the way customers visually interact with product displays in a store to understand their browsing behaviors and preferences.
• Smell : Noting reactions of consumers to different scents in a fragrance shop to study the impact of olfactory elements on product preference.
• Touch : Watching how individuals interact with different materials in a clothing store to assess the importance of texture in fabric selection.
• Taste : Evaluating reactions of participants in a taste test to identify flavor profiles that appeal to different demographic groups.
• Hearing : Documenting responses to changes in background music within a retail environment to determine its effect on shopping behavior and mood.

Below we are also providing real-life examples of qualitative research that demonstrate practical applications across various contexts:

Qualitative Research Real World Examples

Let’s explore some examples of how qualitative research can be applied in different contexts.

1. Online grocery shop with a predominantly male audience

Method used: one-on-one interviews.

Let’s go back to one of the previous examples. You have an online grocery shop. By nature, it addresses a general audience, but after you do a demographic analysis you find out that most of your customers are male.

One good method to determine why women are not buying from you is to hold one-on-one interviews with potential customers in the category.

Interviewing a sample of potential female customers should reveal why they don’t find your store appealing. The reasons could range from not stocking enough products for women to perhaps the store’s emphasis on heavy-duty tools and automotive products, for example. These insights can guide adjustments in inventory and marketing strategies.

2. Software company launching a new product

Method used: focus groups.

Focus groups are great for establishing product-market fit.

Let’s assume you are a software company that wants to launch a new product and you hold a focus group with 12 people. Although getting their feedback regarding users’ experience with the product is a good thing, this sample is too small to define how the entire market will react to your product.

So what you can do instead is holding multiple focus groups in 20 different geographic regions. Each region should be hosting a group of 12 for each market segment; you can even segment your audience based on age. This would be a better way to establish credibility in the feedback you receive.

3. Alan Pushkin’s “God’s Choice: The Total World of a Fundamentalist Christian School”

Method used: ethnographic research.

Moving from a fictional example to a real-life one, let’s analyze Alan Peshkin’s 1986 book “God’s Choice: The Total World of a Fundamentalist Christian School”.

Peshkin studied the culture of Bethany Baptist Academy by interviewing the students, parents, teachers, and members of the community alike, and spending eighteen months observing them to provide a comprehensive and in-depth analysis of Christian schooling as an alternative to public education.

The study highlights the school’s unified purpose, rigorous academic environment, and strong community support while also pointing out its lack of cultural diversity and openness to differing viewpoints. These insights are crucial for understanding how such educational settings operate and what they offer to students.

Even after discovering all this, Peshkin still presented the school in a positive light and stated that public schools have much to learn from such schools.

Peshkin’s in-depth research represents a qualitative study that uses observations and unstructured interviews, without any assumptions or hypotheses. He utilizes descriptive or non-quantifiable data on Bethany Baptist Academy specifically, without attempting to generalize the findings to other Christian schools.

4. Understanding buyers’ trends

Method used: record keeping.

Another way marketers can use quality research is to understand buyers’ trends. To do this, marketers need to look at historical data for both their company and their industry and identify where buyers are purchasing items in higher volumes.

For example, electronics distributors know that the holiday season is a peak market for sales while life insurance agents find that spring and summer wedding months are good seasons for targeting new clients.

5. Determining products/services missing from the market

Conducting your own research isn’t always necessary. If there are significant breakthroughs in your industry, you can use industry data and adapt it to your marketing needs.

The influx of hacking and hijacking of cloud-based information has made Internet security a topic of many industry reports lately. A software company could use these reports to better understand the problems its clients are facing.

As a result, the company can provide solutions prospects already know they need.

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Qualitative Research Approaches

Once the marketer has decided that their research questions will provide data that is qualitative in nature, the next step is to choose the appropriate qualitative approach.

The approach chosen will take into account the purpose of the research, the role of the researcher, the data collected, the method of data analysis , and how the results will be presented. The most common approaches include:

• Narrative : This method focuses on individual life stories to understand personal experiences and journeys. It examines how people structure their stories and the themes within them to explore human existence. For example, a narrative study might look at cancer survivors to understand their resilience and coping strategies.
• Phenomenology : attempts to understand or explain life experiences or phenomena; It aims to reveal the depth of human consciousness and perception, such as by studying the daily lives of those with chronic illnesses.
• Grounded theory : investigates the process, action, or interaction with the goal of developing a theory “grounded” in observations and empirical data. 
• Ethnography : describes and interprets an ethnic, cultural, or social group;
• Case study : examines episodic events in a definable framework, develops in-depth analyses of single or multiple cases, and generally explains “how”. An example might be studying a community health program to evaluate its success and impact.

How to Analyze Qualitative Data

Analyzing qualitative data involves interpreting non-numerical data to uncover patterns, themes, and deeper insights. This process is typically more subjective and requires a systematic approach to ensure reliability and validity. 

1. Data Collection

Ensure that your data collection methods (e.g., interviews, focus groups, observations) are well-documented and comprehensive. This step is crucial because the quality and depth of the data collected will significantly influence the analysis.

2. Data Preparation

Once collected, the data needs to be organized. Transcribe audio and video recordings, and gather all notes and documents. Ensure that all data is anonymized to protect participant confidentiality where necessary.

3. Familiarization

Immerse yourself in the data by reading through the materials multiple times. This helps you get a general sense of the information and begin identifying patterns or recurring themes.

Develop a coding system to tag data with labels that summarize and account for each piece of information. Codes can be words, phrases, or acronyms that represent how these segments relate to your research questions.

• Descriptive Coding : Summarize the primary topic of the data.
• In Vivo Coding : Use language and terms used by the participants themselves.
• Process Coding : Use gerunds (“-ing” words) to label the processes at play.
• Emotion Coding : Identify and record the emotions conveyed or experienced.

5. Thematic Development

Group codes into themes that represent larger patterns in the data. These themes should relate directly to the research questions and form a coherent narrative about the findings.

6. Interpreting the Data

Interpret the data by constructing a logical narrative. This involves piecing together the themes to explain larger insights about the data. Link the results back to your research objectives and existing literature to bolster your interpretations.

7. Validation

Check the reliability and validity of your findings by reviewing if the interpretations are supported by the data. This may involve revisiting the data multiple times or discussing the findings with colleagues or participants for validation.

8. Reporting

Finally, present the findings in a clear and organized manner. Use direct quotes and detailed descriptions to illustrate the themes and insights. The report should communicate the narrative you’ve built from your data, clearly linking your findings to your research questions.

Limitations of qualitative research

The disadvantages of qualitative research are quite unique. The techniques of the data collector and their own unique observations can alter the information in subtle ways. That being said, these are the qualitative research’s limitations:

1. It’s a time-consuming process

The main drawback of qualitative study is that the process is time-consuming. Another problem is that the interpretations are limited. Personal experience and knowledge influence observations and conclusions.

Thus, qualitative research might take several weeks or months. Also, since this process delves into personal interaction for data collection, discussions often tend to deviate from the main issue to be studied.

2. You can’t verify the results of qualitative research

Because qualitative research is open-ended, participants have more control over the content of the data collected. So the marketer is not able to verify the results objectively against the scenarios stated by the respondents. For example, in a focus group discussing a new product, participants might express their feelings about the design and functionality. However, these opinions are influenced by individual tastes and experiences, making it difficult to ascertain a universally applicable conclusion from these discussions.

3. It’s a labor-intensive approach

Qualitative research requires a labor-intensive analysis process such as categorization, recording, etc. Similarly, qualitative research requires well-experienced marketers to obtain the needed data from a group of respondents.

4. It’s difficult to investigate causality

Qualitative research requires thoughtful planning to ensure the obtained results are accurate. There is no way to analyze qualitative data mathematically. This type of research is based more on opinion and judgment rather than results. Because all qualitative studies are unique they are difficult to replicate.

5. Qualitative research is not statistically representative

Because qualitative research is a perspective-based method of research, the responses given are not measured.

Comparisons can be made and this can lead toward duplication, but for the most part, quantitative data is required for circumstances that need statistical representation and that is not part of the qualitative research process.

While doing a qualitative study, it’s important to cross-reference the data obtained with the quantitative data. By continuously surveying prospects and customers marketers can build a stronger database of useful information.

Quantitative vs. Qualitative Research

Image source

Quantitative and qualitative research are two distinct methodologies used in the field of market research, each offering unique insights and approaches to understanding consumer behavior and preferences.

As we already defined, qualitative analysis seeks to explore the deeper meanings, perceptions, and motivations behind human behavior through non-numerical data. On the other hand, quantitative research focuses on collecting and analyzing numerical data to identify patterns, trends, and statistical relationships.  

Let’s explore their key differences: 

Nature of Data:

• Quantitative research : Involves numerical data that can be measured and analyzed statistically.
• Qualitative research : Focuses on non-numerical data, such as words, images, and observations, to capture subjective experiences and meanings.

Research Questions:

• Quantitative research : Typically addresses questions related to “how many,” “how much,” or “to what extent,” aiming to quantify relationships and patterns.
• Qualitative research: Explores questions related to “why” and “how,” aiming to understand the underlying motivations, beliefs, and perceptions of individuals.

Data Collection Methods:

• Quantitative research : Relies on structured surveys, experiments, or observations with predefined variables and measures.
• Qualitative research : Utilizes open-ended interviews, focus groups, participant observations, and textual analysis to gather rich, contextually nuanced data.

Analysis Techniques:

• Quantitative research: Involves statistical analysis to identify correlations, associations, or differences between variables.
• Qualitative research: Employs thematic analysis, coding, and interpretation to uncover patterns, themes, and insights within qualitative data.

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• Last modified: January 3, 2023
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Combining qualitative and quantitative research within mixed method research designs: A methodological review

Ulrika Östlund.

a Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

b Institute for Applied Health Research/School of Health, Glasgow Caledonian University, United Kingdom

Yvonne Wengström

c Division of Nursing, Department or Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

Neneh Rowa-Dewar

d Public Health Sciences, University of Edinburgh, United Kingdom

It has been argued that mixed methods research can be useful in nursing and health science because of the complexity of the phenomena studied. However, the integration of qualitative and quantitative approaches continues to be one of much debate and there is a need for a rigorous framework for designing and interpreting mixed methods research. This paper explores the analytical approaches (i.e. parallel, concurrent or sequential) used in mixed methods studies within healthcare and exemplifies the use of triangulation as a methodological metaphor for drawing inferences from qualitative and quantitative findings originating from such analyses.

This review of the literature used systematic principles in searching CINAHL, Medline and PsycINFO for healthcare research studies which employed a mixed methods approach and were published in the English language between January 1999 and September 2009.

In total, 168 studies were included in the results. Most studies originated in the United States of America (USA), the United Kingdom (UK) and Canada. The analytic approach most widely used was parallel data analysis. A number of studies used sequential data analysis; far fewer studies employed concurrent data analysis. Very few of these studies clearly articulated the purpose for using a mixed methods design. The use of the methodological metaphor of triangulation on convergent, complementary, and divergent results from mixed methods studies is exemplified and an example of developing theory from such data is provided.

A trend for conducting parallel data analysis on quantitative and qualitative data in mixed methods healthcare research has been identified in the studies included in this review. Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings, help researchers to clarify their theoretical propositions and the basis of their results. This can offer a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and develop new theory.

What is already known about the topic?

• • Mixed methods research, where quantitative and qualitative methods are combined, is increasingly recognized as valuable, because it can potentially capitalize on the respective strengths of quantitative and qualitative approaches.
• • There is a lack of pragmatic guidance in the research literature as how to combine qualitative and quantitative approaches and how to integrate qualitative and quantitative findings.
• • Analytical approaches used in mixed-methods studies differ on the basis of the sequence in which the components occur and the emphasis given to each, e.g. parallel, sequential or concurrent.

What this paper adds

• • A trend for conducting parallel analysis on quantitative and qualitative data in healthcare research is apparent within the literature.
• • Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings and help researchers to clearly present both their theoretical propositions and the basis of their results.
• • Using triangulation as a methodological metaphor may also support a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and aid the development of new theory.

1. Introduction

Mixed methods research has been widely used within healthcare research for a variety of reasons. The integration of qualitative and quantitative approaches is an interesting issue and continues to be one of much debate ( Bryman, 2004 , Morgan, 2007 , Onwuegbuzie and Leech, 2005 ). In particular, the different epistemological and ontological assumptions and paradigms associated with qualitative and quantitative research have had a major influence on discussions on whether the integration of the two is feasible, let alone desirable ( Morgan, 2007 , Sale et al., 2002 ). Proponents of mixed methods research suggest that the purist view, that quantitative and qualitative approaches cannot be merged, poses a threat to the advancement of science ( Onwuegbuzie and Leech, 2005 ) and that while epistemological and ontological commitments may be associated with certain research methods, the connections are not necessary deterministic ( Bryman, 2004 ). Mixed methods research can be viewed as an approach which draws upon the strengths and perspectives of each method, recognising the existence and importance of the physical, natural world as well as the importance of reality and influence of human experience ( Johnson and Onquegbuzie, 2004 ). Rather than continue these debates in this paper, we aim to explore the approaches used to integrate qualitative and quantitative data within healthcare research to date. Accordingly, this paper focuses on the practical issues of conducting mixed methods studies and the need to develop a rigorous framework for designing and interpreting mixed methods studies to advance the field. In this paper, we will attempt to offer some guidance for those interested in mixed methods research on ways to combine qualitative and quantitative methods.

The concept of mixing methods was first introduced by Jick (1979) , as a means for seeking convergence across qualitative and quantitative methods within social science research ( Creswell, 2003 ). It has been argued that mixed methods research can be particularly useful in healthcare research as only a broader range of perspectives can do justice to the complexity of the phenomena studied ( Clarke and Yaros, 1988 , Foss and Ellefsen, 2002 , Steckler et al., 1992 ). By combining qualitative and quantitative findings, an overall or negotiated account of the findings can be forged, not possible by using a singular approach ( Bryman, 2007 ). Mixed methods can also help to highlight the similarities and differences between particular aspects of a phenomenon ( Bernardi et al., 2007 ). Interest in, and expansion of, the use of mixed methods designs have most recently been fuelled by pragmatic issues: the increasing demand for cost effective research and the move away from theoretically driven research to research which meets policymakers’ and practitioners’ needs and the growing competition for research funding ( Brannen, 2009 , O’Cathain et al., 2007 ).

Tashakkori and Creswell (2007) broadly define mixed methods research as “research in which the investigator collects and analyses data, integrates the findings and draws inferences using both qualitative and quantitative approaches” (2007:3). In any mixed methods study, the purpose of mixing qualitative and quantitative methods should be clear in order to determine how the analytic techniques relate to one another and how, if at all, the findings should be integrated ( O’Cathain et al., 2008 , Onwuegbuzie and Teddlie, 2003 ). It has been argued that a characteristic of truly mixed methods studies are those which involve integration of the qualitative and quantitative findings at some stage of the research process, be that during data collection, analysis or at the interpretative stage of the research ( Kroll and Neri, 2009 ). An example of this is found in mixed methods studies which use a concurrent data analysis approach, in which each data set is integrated during the analytic stage to provide a complete picture developed from both data sets after data has been qualitised or quantitised (i.e. where both forms of data have been converted into either qualitative or quantitative data so that it can be easily merged) ( Onwuegbuzie and Teddlie, 2003 ). Other analytic approaches have been identified including; parallel data analysis, in which collection and analysis of both data sets is carried out separately and the findings are not compared or consolidated until the interpretation stage, and finally sequential data analysis, in which data are analysed in a particular sequence with the purpose of informing, rather than being integrated with, the use of, or findings from, the other method ( Onwuegbuzie and Teddlie, 2003 ). An example of sequential data analysis might be where quantitative findings are intended to lead to theoretical sampling in an in depth qualitative investigation or where qualitative data is used to generate items for the development of quantitative measures.

When qualitative and quantitative methods are mixed in a single study, one method is usually given priority over the other. In such cases, the aim of the study, the rationale for employing mixed methods, and the weighting of each method determine whether, and how, the empirical findings will be integrated. This is less challenging in sequential mixed methods studies where one approach clearly informs the other, however, guidance on combining qualitative and quantitative data of equal weight, for example, in concurrent mixed methods studies, is rather less clear ( Foss and Ellefsen, 2002 ). This is made all the more challenging by a common flaw which is to insufficiently and inexplicitly identify the relationships between the epistemological and methodological concepts in a particular study and the theoretical propositions about the nature of the phenomena under investigation ( Kelle, 2001 ).

One approach to combining different data of equal weight and which facilitate clear identification of the links between the different levels of theory, epistemology, and methodology could be to frame triangulation as a ‘methodological metaphor’, as argued by Erzberger and Kelle (2003) . This can help to; describe the logical relations between the qualitative and quantitative findings and the theoretical concepts in a study; demonstrate the way in which both qualitative and quantitative data can be combined to facilitate an improved understanding of particular phenomena; and, can also be used to help generate new theory ( Erzberger and Kelle, 2003 ) (see Fig. 1 ). The points of the triangle represent theoretical propositions and empirical findings from qualitative and quantitative data while the sides of the triangle represent the logical relationships between these propositions and findings. The nature and use of the triangle depends upon the outcome from the analysis, whether that be convergent , where qualitative and quantitative findings lead to the same conclusion; complementary, where qualitative and quantitative results can be used to supplement each other or; divergent , where the combination of qualitative and quantitative results provides different (and at times contradictory) findings. Each of these outcomes requires a different way of using the triangulation metaphor to link theoretical propositions to empirical findings ( Erzberger and Kelle, 2003 ).

Illustrating the triangulation triangle ( Erzberger and Kelle, 2003 )

1.1. Purpose of this paper

In the following paper, we identify the analytical approaches used in mixed methods healthcare research and exemplify the use of triangulation ( Erzberger and Kelle, 2003 ) as a methodological metaphor for drawing inferences from qualitative and quantitative findings. Papers reporting on mixed methods studies within healthcare research were reviewed to (i) determine the type of analysis approach used, i.e. parallel, concurrent, or sequential data analysis and, (ii) identify studies which could be used to illustrate the use of the methodological metaphor of triangulation suggested by Erzberger and Kelle (2003) . Four papers were selected to illustrate the application of the triangulation metaphor on complementary, convergent and divergent outcomes and to develop theory.

This literature review has used systematic principles ( Cochrane, 2009 , Khan, 2001 ) to search for mixed methods studies within healthcare research. The first search was conducted in September 2009 in the data bases CINAHL, Medline and PsycINFO on papers published in English language between 1999 and 2009. To identify mixed methods studies, the search terms (used as keywords and where possible as MeSH terms) were: “mixed methods”, “mixed research methods”, “mixed research”, “triangulation”, “complementary methods”, “concurrent mixed analysis” and “multi-strategy research.” These terms were searched individually and then combined (with OR). This resulted in 1896 hits in CINAHL, 1177 in Medline and 1943 in PsycINFO.

To focus on studies within, or relevant to, a healthcare context the following search terms were used (as keywords or as MeSH terms and combined with OR): “health care research”; “health services research”; and “health”. These limits applied to the initial search (terms combined with AND) resulted in 205 hits in Medline and 100 hits in PsycINFO. Since this combination in CINAHL only limited the search results to 1017; a similar search was conducted but without using the search term triangulation to capture mixed methods papers; resulting in 237 hits. In CINAHL the search result on 1017 papers was further limited by using “interventions” as a keyword resulting in 160 papers also selected to be reviewed. Moreover; in Medline the mixed methods data set was limited by the MeSH term “research” resulting in 218 hits and in PsycINFO with “intervention” as keyword or MeSH term resulting in 178 hits.

When duplicates were removed the total numbers of papers identified were 843. The abstracts were then reviewed by each author and those identified as relevant to the review were selected to be retrieved and reviewed in full text. Papers were selected based on the following inclusion criteria: empirical studies; published in peer review journals; healthcare research (for the purpose of this paper defined as any study focussing on participants in receipt, or involved in the delivery, of healthcare or a study conducted within a healthcare setting, e.g. different kinds of care, health economics, decision making, and professionals’ role development); and using mixed methods (defined as a study in which both qualitative and quantitative data were collected and analysed ( Halcomb et al., 2009b ). To maintain rigour, a random sample (10%) of the full text papers was reviewed jointly by two authors. Any disagreements or uncertainties that arose between the reviewers regarding their inclusion or in determining the type of analytic approach used were resolved through discussion between the authors.

In addition to the criteria outlined above, papers were excluded if the qualitative element constituted a few open-ended questions in a questionnaire, as we would agree with previous authors who have argued such studies do not strictly constitute a mixed methods design ( Kroll and Neri, 2009 ). Papers were also excluded if they could not be retrieved in full text via the library services at the University of Edinburgh, Glasgow Caledonian University or the Karolinska Institutet, or did not adequately or clearly describe their analytic strategy, for example, failing to report how the qualitative and quantitative data sets were analysed individually and, where relevant, how these were integrated. See Table 1 for reasons for the exclusion of subsequent papers.

Reasons for exclusion.

A second search was conducted within the databases of Medline, PsychInfo and Cinahl to identify studies which have specifically used Erzberger and Kelle's (2003) triangulation metaphor to frame the description and interpretation of their findings. The term ‘triangulation metaphor’ (as keywords) and author searches on ‘Christian Erzberger’ and ‘Udo Kelle’ were conducted. Three papers, published by Christian Erzberger and Udo Kelle, were identified in the PsychInfo databases but none of these were relevant to the purpose of this review. There were no other relevant papers identified in the other two databases.

168 Papers were included in the final review and reviewed to determine the type of mixed analysis approach used, i.e. parallel, concurrent, or sequential mixed analysis. Four of these papers (identified from the first search on mixed methods studies and healthcare research) were also used to exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ). Data was extracted from included papers accordingly in relation to these purposes.

In total, 168 papers were included in our review. The majority of these studies originated in the USA ( n  = 63), the UK ( n  = 39) and Canada ( n  = 19), perhaps reflecting the considerable interest and expertise in mixed methods research within these countries. The focus of the studies included in the review varied significantly and the populations studied included both patients and healthcare professionals.

3.1. Analytic approaches

Table 2 illustrates the types of analytic approaches adopted in each of the studies included in the review. The most widely used analytic approach ( n  = 98) was parallel analysis ( Creswell and Plano Clark, 2007 ). However, very few of the studies employing parallel analysis clearly articulate their purpose for mixing qualitative and quantitative data, the weighting (or priority) given to the qualitative and quantitative data or the expected outcomes from doing so, mirroring previous research findings ( O’Cathain et al., 2008 ). The weighting, or priority, of the qualitative and quantitative data in a mixed methods study is dependent upon various factors including; the aims of the study and whether the purpose is, for example, to contextualise quantitative data using qualitative data or to use qualitative data to inform a larger quantitative approach such as a survey. Nonetheless, the omission of this statement makes it difficult to determine which data set the conclusions have been drawn from and the role of, or emphasis on, each approach. Therefore, is of importance for authors to clearly state this in their papers ( Creswell and Plano Clark, 2007 ). A number of studies had also used sequential data analysis ( n  = 46), where qualitative approaches were visibly used to inform the development of both clinical tools (e.g. Canales and Rakowski, 2006 ) and research measures and surveys (e.g. Beatty et al., 2004 ) or where quantitative surveys were supplemented by and issues further explored using qualitative approaches (e.g. Abadia and Oviedo, 2009 , Cheng, 2004 , Halcomb et al., 2008 ).

Included papers illustrating their analytical approach and country of origin.

Most notably, with only 20 included studies using a concurrent approach to data analysis, this was the least common design employed. Compared to the studies using a parallel or sequential approach, the authors of concurrent studies more commonly provided an explanation for their purpose of using a mixed methods design in their study, e.g. how it addressed a gap or would facilitate and advance the state of knowledge (e.g. Bussing et al., 2005 , Kartalova-O’Doherty and Tedstone Doherty, 2009 ). Despite this, there remained a lack of clarity within these studies about the weighting given to, and priority of, each method. Consequently, the importance and relevance of the findings produced by each approach and how these have informed their conclusions and interpretation is lacking. In four of the included papers a combination of approaches to data analysis (i.e. sequential and concurrent, parallel and concurrent, or sequential and parallel) were used. In the next section, we have selected papers to illustrate the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ).

3.2. Using the methodological metaphor of triangulation

We have selected four papers from our review ( Lukkarinen, 2005 , Midtgaard et al., 2006 , Shipman et al., 2008 , Skilbeck et al., 2005 ) to illustrate how the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) can be applied to mixed methods studies. Each of these studies has been used to illustrate how the metaphor of triangulation can be applied to studies producing: (i) complementary findings, (ii) convergent findings, and (iii) divergent findings. In the following section, we demonstrate how the application of the metaphor can be used as a framework both to develop theory and to facilitate the interpretation of the findings from mixed methods studies and their conclusions in each of these scenarios, and how using the metaphor can help to promote greater clarity of the study's purpose, its theoretical propositions, and the links between data sets.

3.2.1. Triangulating complementary results

To exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from complementary results, we have drawn on the results of a UK based study by Shipman et al. (2008) ( Fig. 2 ). In the UK, members of district nursing teams (DNs) provide most nursing care to people at home in the last year of life. Following concerns that inadequate education might limit the confidence of some DNs to support patients and their carers’ at home, and that low home death rates may in part be related to this, the Department of Health (DH) identified good examples of palliative care educational initiatives for DNs and invested in a 3-year national education and support programme in the principles and practice of palliative care. Shipman et al.’s study evaluates whether the programme had measurable effects on DN knowledge and confidence in competency in the principles and practice of palliative care. The study had two parts, a summative (concerned with outcomes) quantitative component which included ‘before and after’ postal questionnaires which measured effects on DNs’ ( n  = 1280) knowledge, confidence and perceived competence in the principles and practice of palliative care and a formative (concerned with process) qualitative component which included semi-structured focus groups and interviews with a sub-sample of DNs ( n  = 39).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on complementary results in the study by Shipman et al. (2008) .

While their theoretical proposition may not be explicitly stated by the authors, there is clearly an implicit theoretical proposition that the educational intervention would improve DNs knowledge and confidence (theoretical proposition 1, Fig. 2 ). This was supported by the quantitative findings which showed significant improvement in the district nurses confidence in their professional competence post intervention. Qualitative results supported and complemented the quantitative findings as the district nurses described several benefits from the program including greater confidence in tackling complex problems and better communication with patient and carers’ because of greater understanding of the reasons for symptoms. Thus, a complementary theoretical proposition (theoretical proposition 2, Fig. 2 ) can be deduced from the qualitative findings: the DN's better understanding of factors contributing to complex problems and underlying reasons for symptoms led to improved confidence in competence raised from district nurses increased understanding.

Fig. 2 illustrates the theoretical propositions, the empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 is supported by the quantitative findings. From qualitative findings, a complementary theoretical proposition (theoretical proposition 2) can be stated explaining the process that led to the DNs improved confidence in competence.

3.2.2. Triangulating convergent results

To illustrate how the methodological metaphor of triangulation can be used to draw inferences from convergent findings, we have drawn on the example of a Danish study by Midtgaard et al. (2006) ( Fig. 3 ). This study was conducted to explore experiences of group cohesion and changes in quality of life (QoL) among people ( n  = 55) who participated in a weekly physical exercise intervention (for six weeks) during treatment for cancer. The study, conducted in a Danish hospital, involved the use of structured QoL questionnaires, administered at baseline and post intervention (at six weeks) to determine changes in QoL and health status, and qualitative focus groups, conducted post intervention (at six weeks), to explore aspects of cohesion within the group. With regards to the theoretical proposition of the study ( Fig. 3 ), group cohesion was seen as essential to understand the processes within the group that facilitated the achievement of desired outcomes and the satisfaction of affective needs as well as promoting a sense of belonging to the group itself.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on convergent results in the study by Midtgaard et al. (2006) .

This proposition was deductively tested in an intervention where patients exercised in mixed gender groups of seven to nine members during a nine hour weekly session over a six week period and was supported by both the empirical quantitative and qualitative findings. The quantitative data showed significant improvements in peoples’ emotional functioning, social functioning and mental health. The qualitative data showed how the group setting motivated the individuals to pursue personal endeavors beyond physical limitations, that patients used each others as role models during ‘down periods’ and how exercising in a group made individuals feel a sense of obligation to train and to do their best. This subsequently helped to improve their social functioning which in turn satisfied their affective needs, improving their improved emotional functioning and mental health.

Fig. 3 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Both the quantitative and qualitative findings, demonstrating improvements in participants’ emotional and social functioning and their mental health, can be attributed to the nature of group cohesion within the programme as expected.

3.2.3. Triangulating divergent results

Qualitative and quantitative results that seem to contradict each other are often explained as resulting from methodological error. However, instead of a methodological flaw, a divergent result could be a consequence of the inadequacy of the theoretical concepts used. This may indicate the need for changing or developing the theoretical concepts involved ( Erzberger and Kelle, 2003 ). The following example of using the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from divergent results is intended as an example rather than an attempt to change the theoretical concept involved. In a study by Skilbeck et al. (2005) ( Fig. 4 ), some results were found to be divergent which was explained as resulting from the use of inadequate questionnaires. We do not wish to critique their conclusion; rather we intend to simply offer an alternative interpretation for their findings.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on divergent results using the study by Skilbeck et al. (2005) .

The study aimed to explore family carers’ expectations and experiences of respite services provided by one independent hospice in North England. This hospice provides inpatient respite beds specifically for planned respite admission for a two-week period. Referrals were predominated from general practitioners and patients and their carers were offered respite care twice a year, during the study this was reduced to once a year for each patient. Data was collected prior to respite admission and post respite care by semi-structured interviews and using the Relative Stress Scale inventory (RSSI), a validated scale to measure relative distress in relation to caring. Twenty-five carers were included but pre- and post-data were completed by 12 carers. Qualitative data was analysed by using a process of constant comparison and quantitative data by descriptive and comparative statistical analysis.

No clear theoretical proposition was stated by the authors, but from the definition of respite care it is possible to deduce that ‘respite care is expected to provide relief from care-giving to the primary care provider’ (theoretical proposition 1, Fig. 4 ). This proposition was tested quantitatively by pre- and post-test using the RSSI showing that the majority of carers experienced either a negative or no change in scores following the respite stay (no test of significance was stated). Accordingly, the theoretical proposition was not supported by the quantitative empirical data. The qualitative empirical results, however, were supportive in showing that most of the carers considered respite care to be important as it enabled them to have a break and a rest from ongoing care-responsibilities. From this divergent empirical data it could be suggested changing or developing the original theoretical proposition. It seems that respite care gave the carers relief from their care-responsibilities but not from the distress carers experienced in relation to caring (measured by the used scale). We would therefore suggest that in order to lessen distress related to caring, other types of support is also needed which would change the theoretical proposition as suggested (theoretical proposition 2).

Fig. 4 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 was not supported by the quantitative findings (indicated in Fig. 4 by the broken arrow), but the qualitative findings supported this proposition. From these divergent empirical findings, the theoretical proposition could accordingly be changed and developed. Respite care seemed to provide relief from carers’ on-going care-responsibilities, but other types of support need to be added to provide relief from distress experienced (theoretical proposition 2).

3.2.4. Triangulation to produce theoretical propositions

Methodological triangulation has also been applied to illustrate how theoretical propositions can be produced by drawing on the findings from a Finnish study by Lukkarinen (2005) ( Fig. 5 ). The purpose of this longitudinal study was to describe, explain and understand the subjective health related quality of life (QoL) and life course of people with coronary artery disease (CAD). A longitudinal quantitative study was undertaken during the year post treatment and 19 individuals also attended thematic interviews one year after treatment. This study is one of the few studies that clearly defines theoretical underpinnings for both the selected methods and their purpose, namely “to obtain quantitatively abundant average information about the QoL of CAD patients and the changes in it as well as the patients’ individual, unique experiences of their respective life situations” ( Lukkarinen, 2005 :622).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) to develop theory from the study by Lukkarinen (2005) .

The results of the quantitative analysis showed that the male and female CAD patients in the youngest age group had the poorest QoL. While patients’ QoL improved in the dimensions of pain, energy and mobility it deteriorated on dimensions of social isolation, sleep and emotional reactions. From the viewpoint of methodological triangulation used in the study the aim of the quantitative approach was to observe changes in QoL at the group level and also explore correlations of background factors to QoL. The qualitative approach generated information concerning both QoL in the individuals’ life situation and life course and the individuals’ rehabilitation. Both the quantitative and the qualitative analysis showed the youngest CAD patients to have the poorest psychosocial QoL. The results obtained using qualitative methods explained the quantitative findings and offered new insight into the factors related to poor psychosocial QoL, which could be used to help develop theoretical propositions around these. Patients at risk of poorer QoL were those with an acute onset of illness at a young age that led to an unexpected termination of career, resulting in financial problems, and worries about family. This group also experienced lack of emotional support (especially the females with CAD) and were concerned for the illness that was not alleviated by treatment. The interviews and the method of phenomenological psychology therefore helped to gain insight into the participants’ situational experience of QoL and life course, not detectable by the use of a questionnaire.

Fig. 5 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the relationships between these. The use of the mixed methods approach enabled a clearer understanding to emerge in relation to the lived experience of CAD patients and the factors that were related to poor QoL. This understanding allows new theoretical propositions about these issues to be developed and further explored, as depicted at the theoretical level.

4. Discussion

As the need for, and use of, mixed methods research continues to grow, the issue of quality within mixed methods studies is becoming increasingly important ( O’Cathain et al., 2008 , O’Cathain et al., 2007 ). Similarly, the need for guidance on the analysis and integration of qualitative and quantitative data is a prominant issue ( Bazeley, 2009 ). This paper firstly intended to review the types of analytic approaches (parallel, concurrent or sequential data analysis) that have been used in mixed methods studies within healthcare research. As identified in previous research ( O’Cathain et al., 2008 ), we found that the majority of studies included in our review employed parallel data analysis in which the different analyses are not compared or consolidated until the full analysis of both data sets have been completed. A trend to conduct separate analysis on quantitative and qualitative data is apparent in mixed methods healthcare studies, despite the fact that if the data were correlated, a more complete picture of a particular phenomenon may be produced ( Onwuegbuzie and Teddlie, 2003 ). If qualitative and quantitative data are not integrated during data collection or analysis, the findings may be integrated at the stage of interpretation and conclusion.

Although little pragmatic guidance exists within the wider literature, Erzberger and Kelle (2003) have published some practical advice, on the integration of mixed methods findings. For mixed methodologists, the ‘triangulation metaphor’ offers a framework to facilitate a description of the relationships between data sets and theoretical concepts and can also assist in the integration of qualitative and quantitative data ( Erzberger and Kelle, 2003 ). Yet despite the fact that the framework was published in 2003 within Tashakkori and Teddlie's (2003) seminal work, the Handbook for Mixed Methods in Social and Behavioural Research, our search revealed that it has received little application within the published body of work around mixed methods studies since its publication. This is surprising since mixed methodologists are acutely aware of the lack of guidance with regards to the pragmatics and practicalities of conducting mixed methods research ( Bryman, 2006 , Leech et al., 2010 ). Furthermore, there have been frequent calls to move the field of mixed methods away from the “should we or shouldn’t we” debate towards the practical application, analysis and integration of mixed methods and its’ findings and what we can learn from each other's work and advice. Consequently, we have a state of ambiguity and instability in the field of mixed methods in which mixed methodologists find themselves lacking appropriate sources or evidence to draw upon with which to facilitate the future design, conduct and interpretation of mixed methods studies. It is for these reasons that we, in this paper, also intended to identify and select studies that could be used as examples for the application of Erzberger and Kelle's (2003) triangulation metaphor.

When reviewing the studies it was clear that the majority of theoretical assumptions were implicit, rather than explicitly stated by authors. Wu and Volker (2009) previously acknowledged that while studies undoubtedly have a theoretical basis in their literature reviews and the nature of their research questions, they often fail to clearly articulate a particular theoretical framework. This is unfortunate as theory can help researchers to clarify their ideas and also help data collection, analysis and to improve the study's rigour ( Wu and Volker, 2009 ). When using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ), researchers are encouraged to articulate their theoretical propositions and to validate their conclusions in relation to the chosen theories. Theory can also guide researchers when defining outcome measures . Should the findings not support the chosen theory, as shown in our examples on complementary and divergent results, researchers can modify or expand their theory accordingly and new theory may be developed ( Wu and Volker, 2009 ). It is therefore our belief that using triangulation as a methodological metaphor in mixed methods research can also benefit the design of mixed method studies.

Like other researchers ( O’Cathain et al., 2008 ), we have also found that most of the papers reviewed lacked clarity in whether the reported results primarily stemmed from qualitative or quantitative findings. Many of the papers were even less clear when discussing their results and the basis of their conclusions. The reporting of mixed methods studies is notoriously challenging, but clarity and transparency are, at the very least, crucial in such reports ( O’Cathain, 2009 ). Using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) may be one way of addressing this lack of clarity by explicitly showing the types of data that researchers have based their interpretations on. It may even help address some of the issues raised in the debate on the feasibility of integrating research methods and results stemming from different epistemological and ontological assumptions and paradigms ( Morgan, 2007 , Sale et al., 2002 ). In order to carry out methodological triangulation researchers also need to identify and observe the consistency and adequacy of the two methods, positivistic and phenomenological regarding the research questions, data collection, methods of analysis and conclusions.

While we used systematic principles in our search for mixed methods studies in healthcare research, we cannot claim to have included all such studies. In many cases, reports of mixed methods studies are subjected to ‘salami slicing’ by researchers and hence the conduct of, and findings from, individual approaches are addressed in separate papers. Since these papers are often not indexed as a ‘mixed method’ study, they are undoubtedly more difficult to identify. Furthermore, different terminologies are used to describe and index mixed methods studies within the electronic databases ( Halcomb and Andrew, 2009a ), making it challenging to be certain that all relevant studies were captured in this review. However, the studies included in this review should give a sufficient overview of the use of mixed analysis in healthcare research and most importantly, they enable us to make suggestions for the future design, conduct, interpretation and reporting of mixed methods studies. It is also important to emphasise that we have based our triangulation examples on the data published but have no further knowledge of the analysis and findings undertaken by the authors. The examples should thus be taken as examples and not alternative explanations or interpretations.

Mixed methods research within healthcare remains an emerging field and its use is subject to much debate. It is therefore particularly important that researchers clearly describe their use of the approach and the conclusions made to improve transparency and quality within mixed methods research. The use of triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) can help researchers not only to present their theoretical propositions but also the origin of their results in an explicit way and to understand the links between theory, epistemology and methodology in relation to their topic area. Furthermore it has the potential to make valid inferences, challenge existing theoretical assumptions and to develop or create new ones.

Conflict of interest

None declared.

Ethical approval

Not required.

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• Open access
• Published: 27 May 2024

Challenges to the implementation of a multi-level intervention to reduce mistreatment of women during childbirth in Iran: a qualitative study using the Consolidated Framework for Implementation Research

• Marjan Mirzania 1 ,
• Elham Shakibazadeh 1 ,
• Meghan A. Bohren 2 ,
• Sedigheh Hantoushzadeh 3 ,
• Abdoljavad Khajavi 4 &
• Abbas Rahimi Foroushani 5  

Reproductive Health volume  21 , Article number:  70 ( 2024 ) Cite this article

Metrics details

Mistreatment during childbirth is a growing concern worldwide, especially in developing countries, such as Iran. In response, we launched a comprehensive implementation research (IR) project to reduce mistreatment during childbirth and enhance positive birth experiences in birth facilities. This study identified the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth using the Consolidated Framework for Implementation Research (CFIR).

An exploratory qualitative study, involving 30 in-depth interviews, was conducted between July 2022 and February 2023. Participants included a purposive sample of key stakeholders at different levels of the health system (macro: Ministry of Health and Medical Education; meso: universities of medical sciences and health services; and micro: hospitals) with sufficient knowledge, direct experience, and/or collaboration in the implementation of the studied interventions. Interviews were transcribed verbatim and coded using directed qualitative content analysis (CFIR constructs) in MAXQDA 18.

The identified challenges were: (1) individual level (childbirth preparation classes: e.g., adaptability, design quality and packaging, cosmopolitanism; presence of birth companions: e.g., patient needs and resources, structural characteristics, culture); (2) healthcare provider level (integrating respectful maternity care into in-service training: e.g., relative priority, access to knowledge and information, reflecting and evaluating); (3) hospital level (evaluating the performance of maternity healthcare providers: e.g., executing, external policies and incentives); and (4) national health system level (implementation of pain relief during childbirth guidelines: e.g., networks and communications, patient needs and resources, executing, reflecting and evaluating).

Conclusions

This study provides a clear understanding of the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth and highlights potential implications for policy makers and practitioners of maternal health programs. We encourage them to take the lessons learned from this study and revise their current programs and policies regarding the quality of maternity care by focusing on the identified challenges.

Plain English summary

Evidence suggests that mistreatment during childbirth is a growing concern worldwide, especially in developing countries, such as Iran. In this qualitative study, through 30 in-depth interviews with key stakeholders at different levels of the health system (macro: Ministry of Health and Medical Education; meso: universities of medical sciences and health services; and micro: hospitals), we identified the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth using the Consolidated Framework for Implementation Research (CFIR). The data were analyzed using directed content analysis and a deductive approach in MAXQDA 18 software. The identified challenges were: (1) individual level (childbirth preparation classes: e.g., adaptability; presence of birth companions: e.g., patient needs and resources); (2) healthcare provider level (integrating respectful maternity care into in-service training: e.g., relative priority); (3) hospital level (evaluating the performance of maternity healthcare providers: e.g., executing, external policies and incentives); and (4) national health system level (implementation of pain relief childbirth guidelines: e.g., networks and communications). This study provides a clear understanding of the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth; and highlights potential implications for policy makers and practitioners of maternal health programs.

Peer Review reports

Despite the recognition of every woman's right to enjoy the highest attainable standard of health, including the right to dignified and respectful care [ 1 ], evidence shows that mistreatment during childbirth is a common experience among women worldwide [ 2 , 3 ]. It is increasingly recognized as an urgent public health priority and a poor quality of care index [ 1 , 4 ], and is a critical determinant of women's decisions regarding place of birth, mode of birth, lactation, mother-child bonding, and childbirth experiences [ 5 , 6 ]. The prevalence of mistreatment among women seeking maternity care varies across different settings, from 43% in Latin America and the Caribbean [ 7 ] to 76.3% in Europe (Germany and the Netherlands) [ 8 ]. The prevalence in Iran is likewise high, reported as 75.7% [ 9 ] and 100% [ 10 ]. Women in Iran have experienced verbal abuse, frequent and painful vaginal examinations, lack of continuity of care, empathy, participation in decision-making, choice of preferred birth position, privacy, and birth companions [ 11 , 12 , 13 , 14 ].

In recent years, some interventions have been developed, implemented, and showed promising results on reducing mistreatment and promoting respectful care for all women [ 15 , 16 , 17 , 18 ]. The Heshima project reported reductions in most forms of disrespect and abuse (D&A) in 13 health facilities in Kenya [ 15 ]. A study by Kujawski et al. (2017) in two hospitals in Tanzania (Staha project) showed a 66% reduction in the odds of women experiencing D&A after the intervention [ 17 ]. Asfa et al.'s (2020) study in Ethiopia showed that the intervention led to an 18% reduction in the number of mistreatment components [ 18 ].

In Iran, the Ministry of Health and Medical Education (MOHME) has developed a list of programs and practices to ensure maternal dignity during childbirth, such as the mother's bill of rights, maternal dignity training package, maternal dignity seminars for maternity healthcare providers (MHCPs) [ 19 , 20 ], and emphasis on respectful maternity care (RMC) in the national guidelines for normal childbirth [ 21 ]. However, these actions did not make effective changes in the maternity quality of care. It seems that the programs implemented by the MOHME were not developed using context- and evidence-based approaches. There were also lacks of precise guidance on their effective implementation. Furthermore, the available research evidence on respectful/disrespectful maternity care in Iran has focused on the prevalence [ 9 , 10 ], development and psychometrics of instruments [ 22 , 23 ], and descriptions of women and healthcare providers’ experiences [ 24 , 25 ], and few interventional studies have been conducted to reduce D&A or promote RMC, including workshops for midwives [ 26 , 27 ]. It seems that healthcare providers training alone is not a sufficient solution [ 28 ]. In response, we launched a comprehensive implementation research (IR) project to reduce mistreatment during childbirth and enhance positive birth experiences in health facilities.

Prior to implementing any evidence-based intervention/innovation (EBI), it is important to identify the factors affecting its implementation in “real-world” settings to increase its adoption, scale-up, and sustainability [ 29 ]. It has been shown that many interventions that were effective in “in-vitro” and controlled conditions or small-scale fail in the real world due to contextual factors that acted against the implementation [ 30 , 31 ]. Implementation science (IS) helps to identify factors that can support or inhibit implementation and to optimize intervention implementation. Therefore, although it is necessary to prove the effectiveness of interventions in trials, this is not sufficient to ensure successful implementation at scale. Therefore, it is necessary to understand why intervention works, how, for whom, and in what settings, and what strategies are needed to improve its implementation [ 32 , 33 ].

In recent years, several models, theories, and implementation frameworks have been developed. The Consolidated Framework for Implementation Research (CFIR) [ 31 ] was developed by combining 19 theories on dissemination, innovation, implementation, organizational change, knowledge translation, and research uptake [ 34 ]. The CFIR is a “determinant framework” that consists of five domains, including the intervention characteristics (key features of an intervention), outer setting (features of the external context such as economic, political, and social environments of the intervention), inner setting (features of the organization such as structural, political, and cultural environments), characteristics of individuals involved (features of implementers such as cultural, organizational, and professional norms), and process of implementation (strategies or tactics that might influence the success of implementation) with 39 constructs/sub-constructs [ 34 , 35 ] (Additional file 1 : CFIR).

Despite attention to intervention studies to promote RMC or prevent mistreatment during childbirth, few studies have examined the implementation process of such interventions, and there is little insight into how the contextual conditions surrounding the implementation of these interventions contribute to their success or failure. To address this gap, we chose a qualitative method to obtain the experiences and perspectives of key stakeholders on the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth in Iran using CFIR. Qualitative research methods are appropriate when seeking an in-depth understanding of participants' perspectives.

This qualitative study was part of a larger implementation research project focusing on the development and implementation of a context-specific intervention to reduce disrespectful maternity care and evaluation of strategies to improve implementation. The project was initiated in October 2021 in five public teaching hospitals in Tehran, Iran, and consists of five phases: (1) needs assessment (to assess knowledge, attitudes and practices of maternity healthcare providers about mistreatment of women during labour and childbirth, and the manifestations of mistreatment and its influencing factors), (2) identifying interventions to reduce mistreatment of women during childbirth, (3) identifying the implementation challenges of interventions, (4) designing implementation strategies for the intervention, and (5) testing implementation strategies in a real-life setting. The findings of phase 1 of the project are described elsewhere [ 11 , 36 ].

Identifying interventions to reduce mistreatment of women during childbirth

Based on the findings of phase 1 of the project, we created a logical model of the mistreatment problem based on the PRECEDE health-planning model [ 37 ]. According to the determinants of mistreatment based on the model, multi-level intervention was identified to address mistreatment drivers (Fig. 1 ). In this phase 3 of the project, we selected interventions from each level (individual, healthcare provider, hospital, and national health system) that are currently being implemented in Iran's health system to gain in-depth understanding of the challenges that affect proper implementation. Interventions implemented at each level are presented in Table 1 . This study investigated the implementation challenges of these interventions.

Logic model of the study

Study design and participants

We conducted an exploratory qualitative study consisting of individual in-depth interviews between July 2022 and February 2023 in Tehran, Iran. Participants included key stakeholders at different levels of the health system (including healthcare providers, managers, experts, policy makers, and decision makers) with sufficient knowledge, direct experience, and/or collaboration in the implementation of each of the studied interventions. We selected participants using purposive sampling to obtain diverse perspectives and experiences and then used the snowball method to recruit more participants. We aimed for maximum variation among participants according to age, education, organizational role, and work experience. Key stakeholders were selected from three levels: macro (Ministry of Health and Medical Education (MOHME): four participants), meso (universities of medical sciences and health services: 12 participants), and micro (hospitals: 14 participants). These individuals were invited to participate by phone calls and/or in-person. The eligibility criteria for this study were familiarity and/or executive responsibility in any of the studied interventions and having at least five years of work experience.

Data collection

We developed the initial semi-structured interview guide based on sample interviews at http://cfirguide.org [ 42 ]. Damschroder et al. (2009) recommend that researchers try to select constructs from CFIR that are most related to their study setting [ 34 ]. Therefore, the interview guide was revised using study-related constructs (Additional file 2 : interview guide). We then pilot-tested this by conducting two initial interviews, which were not analyzed. Interviews were conducted in Persian by the lead author (M.M.), a female PhD candidate in Health Education and Promotion with previous experience in qualitative studies who had no prior interactions with the participants. To prepare participants for the interview, interview guide questions were sent to them in advance via email. Additionally, at the beginning of the interviews, the purpose of the study, guarantee of confidentiality and anonymity of information, nature of voluntary participation, and the possibility of withdrawing from the study at any time were explained to the participants. All participants provided written informed consent and permission for audio recordings. The interviews were conducted in participants' workplaces (in a private room) and during their preferred accommodation. The duration of the interviews ranged from 40 to 60 min, during which the interviewer made field notes. The demographic characteristics of the participants (including age, gender, education, organizational role, and number of years of work experience) were recorded at the end of each interview. The interviews continued until data saturation was reached. Saturation was obtained after the 28th interview; however, to ensure that no new information emerged, data collection continued until the 30th interview. All the invited individuals participated in the interviews, and no repeat interviews were conducted.

Data analysis

Data analysis was conducted simultaneously with data collection, using directed content analysis [ 43 ] and a deductive approach. After each interview, M.M. listened to the recorded audios several times, transcribed verbatim in Persian, and returned to the participants for comments and/or corrections. E.Sh. (female professor in Health Education and Promotion; an experienced qualitative researcher) checked the transcripts for accuracy and consistency. Prior to coding the data, a categorization matrix was developed based on the interview guide (i.e., CFIR constructs). Next, two authors (M.M. and E.Sh.) independently analyzed the data. We marked and color-coded the significant segments of the text. We put those color-coded text segments together and gave codes. We categorized the codes according to their differences and similarities, and linked them to pre-specified categorizations in sub-themes and themes. If disagreements arose in coding, the authors discussed until consensus was reached. The MAXQDA 18 software was used to manage the data [ 44 ]. We translated selected quotes into English to support the themes developed throughout the analysis.

The trustworthiness of this study was tested based on the four criteria of Lincoln and Guba [ 45 ]. The credibility of the data was ensured through prolonged engagement with the data, applying a sampling technique with maximum variation, multiple data sources (including field notes, audio recordings, and transcripts), and providing initial codes to the three participants for approval. To enhance the transferability of the data, we conducted interviews with participants who had the most experience and knowledge of each of the studied interventions. Furthermore, dependability was obtained by analyzing the data separately by the two members of the research team. To assess confirmability, a qualitative research specialist, who did not participate in this study, confirmed the data analysis process. This paper was reported in accordance with the consolidated criteria for reporting qualitative research (COREQ) checklist [ 46 ] (Additional file 3 : COREQ Checklist).

Characteristics of participants

Thirty in-depth interviews were conducted with the key stakeholders. The mean age of the participants was 49.5 years (range: 35-65 years). Most participants (73.4%) held an MD or PhD degree. Four participants worked in MOHME, 12 in medical universities, and 14 in hospitals; more than half had over 20 years of work experience (Table 2 ).

The identified challenges

The challenges of implementing each intervention (currently implemented in the system) were identified and categorized using the domains and constructs/sub-constructs of the CFIR (Table 3 ).

Individual-level interventions

At the individual level, two interventions were listed according to the determinants of mistreatment based on the model: childbirth preparation classes and the presence of birth companions (Fig.  1 ). Both interventions are implemented in the system; however, there were serious challenges in the settings, as outline below.

Training of pregnant women about the process of labour and childbirth, respectful care and their rights during childbirth

In our study, participants shared opinions about the challenges of implementing childbirth preparation classes in five CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals involved, and process of implementation).

Intervention characteristics

Adaptability.

The level of adaptability of the intervention (childbirth preparation classes) was described as a key barrier to its implementation by most participants. They believed that non-compliance of the conditions and facilities of maternity hospitals with the educational content of the classes, improper timing of the start of classes (from the 20th week of pregnancy), and poor announcements can weaken the implementation of the intervention. The participants suggested that for effective childbirth preparation classes, the situations of facilities of maternity hospitals can be tailored and refined according to the educational content of the classes. Additionally, classes should be held in the early phases of pregnancy and widely announced.

“The training that women receive in classes is different from that implemented in maternity hospitals. For example, we teach that they can move during labour, take their preferred position during childbirth, and have a chosen companion. However, in practice, this has not been implemented in maternity hospitals ...” (Reproductive Health Specialist, University level) “Announcing about childbirth preparation classes in hospitals and health centers is poor. Only 18% of the pregnant women participated in classes. We did not announce them correctly…” (Health Policy Specialist, MOHME level)

Design quality and packaging

Weakness in the design quality and packaging of childbirth preparation classes prevent their successful implementation. Some participants (obstetricians) reported a lack of a multidisciplinary team in holding classes as a barrier to implementation. They believed that classes should be managed by a team and should not be exclusive to midwives. However, the midwives stated that the content of the classes was such that it could be handled by them, but the presence of a psychologist in some sessions could play an important role in the success of the classes.

“We must accept that midwives cannot cover all sessions. Psychologists, nutritionists, and obstetricians can be used in these classes.” (Obstetrician, Hospital level)

Outer setting

Patient needs and resources.

Lack of training about RMC was also considered a fundamental factor. Most participants highlighted that women do not understand respectful care principles and their rights during childbirth, and this should be integrated into the content of childbirth preparation classes.

“… They should be aware of their rights during childbirth. This should be integrated into the content of the childbirth preparation classes.” (Obstetrician, MOHME level)

Cosmopolitanism

A crucial factor affecting childbirth preparation class implementation was the poor collaboration of the private sector to hold classes. Participants reported that since most pregnant women receive their care from the private sector (obstetricians and/or midwives' offices), there is a need to establish efficient mechanisms for more support and collaboration of these sectors in holding classes.

“Participation of the private sector is essential because 70% of pregnant women receive their care from obstetricians and midwives.” (Midwife, University level)

Inner setting

Organizational incentives and rewards.

A few participants expressed concerns about the poor implementation of childbirth preparation classes following the low participation of pregnant women in classes. They believed that setting enough incentives could affect women’s degree of engagement and commitment to participate in classes.

“Between 9-10% of pregnant women attend our classes (health centers), and this rate is very low ... If incentives are provided, they are more motivated to participate.” (Reproductive Health Specialist, University level)

Available resources

Participants reported that poor physical environment and staff shortages were barriers to implementing childbirth preparation classes.

“In some hospitals, there is no standard space to hold classes, especially in private hospitals.” (Midwife, Hospital level) “Dedicated instructors should be considered in these classes. Here, they appoint one person as an instructor, and at the same time, she has to work shifts in the maternity hospital because they do not have staff.” (Midwife, Hospital level)

Characteristics of individuals involved

Other personal attributes.

Instructors’ skill and interest was another challenge that was highlighted by some participants: “Unfortunately, some of our midwives (as instructors of classes) are rarely interested in training or do not have enough skills …” (Reproductive Health Specialist, Hospital level)

Process of implementation

Poor execution of childbirth preparation classes, especially during the COVID-19 pandemic, was an important challenge discussed by participants. They also believed that focusing on quantity and neglecting the quality of the classes made them not have the proper efficiency, and their goal was rarely reached: “The classes are implemented, but they are not implemented according to plan and properly ... Unfortunately, we focused on the quantity of the classes, for example, the forms we have to complete and the statistics we have to give to the MOHME.” (Reproductive Health Specialist, University level)

Reflecting and evaluating

Supervising implementation and continuous evaluation were crucial factors emphasized by the participants. They acknowledged that the MOHME should supervise the implementation of childbirth preparation classes in hospitals and health centers through regular inspections. In addition, evaluate the progress and quality of their implementation through an external evaluation.

“I think the biggest challenge of childbirth preparation classes is that there is no supervision of their implementation … There should be a monitoring and auditing system.” (Reproductive Health Specialist, MOHME level)

Presence of birth companions

In this study, the challenges of implementing birth companions in four CFIR domains (outer setting, inner setting, characteristics of individuals involved, and process of implementation) were discussed by the participants.

According to the participants, the lack of knowledge of companions could be a barrier to their attendance at maternity hospitals. Some participants believed that a person going to be a birth companion should be required to participate in childbirth preparation classes and receive training:

“Companions have limited knowledge. I think birth companions should be required to participate in childbirth preparation classes because those who are trained in these classes are helpful to both labouring women and us providers.” (Reproductive Health Specialist, Hospital level)

Structural characteristics

The lack of physical space in some maternity hospitals was another factor that some participants stated: “Some of our maternity hospitals do not have a standard structure, for example, Hospital X, which is a hall with 12 beds and set up some extra beds because of the high visits, so there will be no place for the presence of a birth companion.” (Health Policy Specialist, MOHME level)

The participants also reported cultural issues as barriers to the implementation of birth companions. They noted that most of the time, if the companion is a partner, due to the feminine environment of maternity hospitals and female providers’ unwillingness to be accompanied by men in the delivery room; they are not allowed to be accompanied.

“The companion is not allowed to enter the maternity hospital; why? Because my colleague (midwife or doctor) does not like a man to be in the labour room, she says, 'No, sir, you go out and let a woman come.” (Reproductive Health Specialist, MOHME level)

Compatibility

One potential barrier to implementation was concern about the compatibility of the presence of birth companions with the existing workflows of maternity staff. The participants agreed that the interference of birth companions in the clinical duties of staff was a major factor for not allowing a companion.

“As a midwife who worked in a maternity hospital for several years and was strongly against the presence of birth companions, I say that our main challenge was the interference of companions. For example, when a labouring woman's serum runs out, the companion comes many times and warns …” (Midwife, University level)

Some participants believed that the unwillingness of staff was an important barrier. They mentioned that staff prevents the presence of birth companions because of the perception that the companion is witnessing their performance as an advocate for the woman, which may cause them to expect more attention to labouring women.

“The companion is like an advocate; it is like a hidden camera. Why do some staff members not like companions to enter maternity hospitals? This is because it controls their performance …” (Obstetrician, Hospital level)

Another factor was related to lack of supervision. The participants highlighted the need for continuous supervision of the implementation of birth companion guidelines in hospitals: “The presence of birth companions has a guideline that has been communicated to all hospitals, but in many hospitals, especially public hospitals, it is not implemented because it is not supervising ...” (Obstetrician, University level)

Healthcare provider-level intervention

At this level, five interventions were listed according to the determinants of mistreatment based on the model (Fig. 1 ). However, one of them (integrating RMC into the in-service training of maternity staff) is implemented in the system. The challenges of this intervention were identified as follows:

Integrating RMC into in-service training of maternity staff

Participants in this study reported intervention implementation challenges in the four CFIR domains (outer setting, inner setting, characteristics of individuals involved, and process of implementation).

External policies and incentives

Regulations and guidelines related to in-service training of staff affect the quality and efficiency of courses. Weakness in some regulations and guidelines has caused staff to be given a quantitative view, which means that many of them participate in the training course to obtain a certificate, rather than improve their knowledge, skills, and behavior, and/or increase the organization's productivity.

“… Unfortunately, our regulations and guidelines are quantitative; that is, they dictate that if a person spends X hours in a year, it will be included in his/her evaluation and career promotion. Therefore, staff members only participate in courses to complete their duty hours and obtain a certificate.” (Midwife, University level)

Relative priority

Obtaining a license to hold an in-service training course was one of the challenges mentioned by some of the participants. They expressed the belief that the necessity of holding a respectful care training course should be clarified in the steering committee of training and empowerment of human resources in such a way that the course is included in the specialized and mandatory training of employees, not general and optional; thus, it is effective in their career development and they have sufficient motivation to participate in the course.

“One of the challenges is to obtain a license to hold the course. You must justify the necessity of holding a respectful care training course in such a way that the course is included in the job description of the maternity staff.” (Public Health- related manager, University level)

Allocation of an insufficient budget for staff training was an important challenge reported by some participants. They found that staff participation in training courses required more financial support: “Unfortunately, the investment in training staff is very low. The per capita education budget for healthcare staff training this year is 800,000 Iranian rials (IRR), is very small.” (Public Health- related manager, MOHME level)

Similarly, the lack of experienced instructors is considered a challenge. When the instructor of an in-service training course does not have specialized knowledge and teaching ability, the course does not have the necessary efficiency and is not welcomed.

Access to knowledge and information

The participants also believed that informing the staff about the value and importance of the training course played an important role in its successful implementation. They highlighted that information and materials about the importance of RMC should already be provided to the maternity staff. A participant said: “First, it clarifies the importance of respectful care training for the maternity staff. They need to know how much their behavior with labouring women can affect their mental health status as well as their decisions for future pregnancies.” (Health Services Management Specialist, University level)

Knowledge and beliefs about the intervention

Managers do not believe in in-service training for staff, and lack of support for them has caused the need for this training to not be included in the organization's plans and priorities.

“Some managers do not support participation in training courses, and they do not believe that these courses have many benefits for the individual and organization.” (Public Health- related manager, University level)

Another major challenge was the weakness of evaluating the effectiveness of the training courses. The participants acknowledged that, although the evaluation of the effectiveness of courses is done using Kirkpatrick's model [ 47 ], it is often incomplete or limited to the first two levels of this model, and the third and fourth levels are not done because of problems and complexity.

“... Our current evaluation method is flawed, and we do not evaluate almost any of our courses at the level of behavior; therefore, we cannot be sure if the person who participated in the course acquired the expected capabilities.” (Reproductive Health Specialist, University level)

Hospital-level intervention

At the hospital level, four interventions were listed based on the model (Fig. 1 ). Of these, the evaluation of the performance of MHCPs is implemented in the system. The identified challenges for this intervention were as follows:

Evaluating the performance of MHCPs about mistreatment and/or RMC

In our study, participants discussed the intervention implementation challenges in two CFIR domains (outer setting and process of implementation).

Some participants complained of weakness in laws and regulations. They stated that to supervise the performance of MHCPs in laws and regulations (including the Support of Family and Youth Population Act), the merit pay of providers dependent on the satisfaction of pregnant women is defined. However, they are not included in the payment systems of all MHCPs. Furthermore, participants expressed concern that these laws (such as reducing merit pay or warnings) were not very effective in supervising the performance of the providers.

“Currently, in the Support of Family and Youth Population Act, merit pay of the providers depends on the satisfaction of pregnant women, but unfortunately not for all providers (including obstetricians or residents). We are pursuing this to be modified.” (Health Policy Specialist, MOHME level)

Poor execution of the intervention (mother’s satisfaction questionnaire) was considered important. Participants stated that, although all hospitals were required to implement and provide feedback to the MOHME, the providers often completed the questionnaire. To solve this problem, an electronic satisfaction questionnaire is currently being designed, whose links will be sent to women, and their satisfaction reports will be registered in the Ministry of Health's portal. However, owing to the poor support of the Information Technology (IT) unit, it has not yet been implemented.

“… Unfortunately, the questionnaires were completed by the providers, without the mother being informed. Currently, an electronic questionnaire is designed, the report of which will be registered in the Ministry of Health's portal, but it has not yet been implemented.” (Reproductive Health Specialist, Hospital level)

National health system-level intervention

At the national health system level, the implementation of pain relief during childbirth guidelines was listed based on the model (Fig. 1 ). This intervention is implemented in the system, and its challenges were as follows:

Implementation of pain relief during childbirth guidelines

In this study, the participants identified implementation challenges in the four CFIR domains (outer setting, inner setting, characteristics of individuals involved, and process of implementation).

Participants mentioned the lack of knowledge of pregnant women as an important challenge in implementing pain relief during childbirth. They believed that most women are unaware of the option of pain relief during childbirth. Pregnancy is an important time to inform and prepare women about pain relief options during childbirth; however, women are unaware of this right and do not demand it.

“… Pregnant women do not have sufficient information regarding pain relief during childbirth … so they do not demand ... Information about this should be provided during pregnancy (for example, in childbirth preparation classes), but when labouring women come to the maternity hospital, we have to go and explain … I think that this is not the right time for training.” (Anesthesiologist, Hospital level)

Some participants also pointed out that a large number of their clients are Afghan women who refuse pain relief, because they do not have insurance coverage and would be required to pay out-of-pocket.

“... Most of our clients are Afghan women. They do not have insurance and have to pay for it. Therefore, they do not do (pain relief during childbirth).” (Obstetrician, Hospital level)

The presence of good networking and relationships with external organizations, such as insurance organizations, to modify pain relief during childbirth tariffs and motivate staff was described by participants as an effective factor in implementation.

“The support of insurance organizations is also crucial for the implementation of pain relief during childbirth; tariffs should be revised, but unfortunately, they do not collaborate.” (Obstetrician, MOHME level)

MOHME policies and support were critical for the successful implementation of pain relief during childbirth. Some participants believed that being free of charge for pain relief during childbirth in public hospitals was one of the factors facilitating its implementation. However, the participants reported that some measures of the MOHME, including the absence of on-call anesthesiologists in hospitals were another challenge for the implementation of the program.

“... The hospital should have an on-call anesthetist, which unfortunately the MOHME took it away ... Therefore; we do not have the possibility of pain relief during childbirth at night because we there is not have an on-call anesthesiologist. There is an aesthesia resident, but it is normal that she/he does not spend X hours on pain relief during childbirth and quickly performs a caesarean section.” (Obstetrician, Hospital level)

Networks and communications

Poor working relationships between obstetricians and anesthesiologists were key barriers. Some participants believed that obstetricians are the primary decision-makers for pain relief during childbirth, and if they approve, labouring woman will be referred to anesthesiologists, but unfortunately, they do not collaborate enough in this regard. A participant stated:

“Obstetricians should select labouring women based on the criteria and then refer to them. Unfortunately, they do not collaborate with us …” (Anesthesiologist, Hospital level)

The availability of resources during the implementation process was critical for success. The participants complained about the low tariff allocated to pain relief during childbirth and considered it a fundamental barrier to non-collaboration of anesthesiologists in the implementation of the program. Furthermore, the lack of staff (anesthesiologists and nurse anesthetists) to offer top-ups and continuous monitoring adds to this factor.

“Pain relief during childbirth is a time-consuming process, but the tariff is so low that the anesthesiologist does not want to perform it. However, there is a shortage of anesthesiologists and nurse anesthetists in most hospitals.” (Anesthesiologist, Hospital level)

Similarly, limited access to knowledge and information about pain relief during childbirth for the provider team was considered another challenge. The participants identified a lack of adequate training for providers prior to implementing the program as a contributing factor.

“Prior to the implementation of this program (pain relief during childbirth), sufficient training should have been provided to all team members (including anesthesiologists, obstetricians, and midwifes), and the purpose and importance of the program were well introduced. We were not justified at all as to why we wanted to do this program ...” (Obstetrician, Hospital level)

Another challenge was the lack of knowledge and misconceptions of providers (obstetricians and midwives) regarding pain relief during childbirth. For example, it is believed that pain relief during childbirth is associated with an increased risk of prolonged labour, poor maternal and infant outcomes, and an increased chance of cesarean section. The participants believed that there was a serious need to spread awareness and cultivate a positive attitude among providers about the benefits of pain relief during childbirth and eliminate misconceptions by holding training courses.

“I think the most important challenge is misconceptions. Still, many obstetricians do not agree with pain relief during childbirth; it is believed that it prolongs the labour process or may have complications for the mother and/or the infant; all this is due to lack of knowledge. This belief needs to be corrected.” (Anesthesiologist, Hospital level)

According to most participants, the lack of expertise and skills of anesthesiologists was another barrier to implementation. They acknowledged that pain relief during childbirth is one of the important abilities that anesthesia residents should acquire, which has not been considered in their educational curriculum. Anesthesiology residents spend a short period of one month in the maternity ward, so they do not acquire enough skills.

“Pain relief during childbirth requires expertise and skill ... However, it has not been considered an important topic in the educational curriculum of anesthesiologists.” (Midwife, University level)

Some participants felt that pain relief during childbirth had not been implemented according to the implementation plan. They emphasized the identification of program problems and the importance of proper planning: “At first, the process of pain relief during childbirth in our hospitals was increasing; for example, in our hospital, we had about 500 pain relief during childbirths per month, but currently we do not have four ... We were weak in execution; we have implemented the program since 2014, but unfortunately, I can say that we have been unsuccessful thus far.” (Anesthesiologist, Hospital level)

In addition, supervising the implementation of pain relief during childbirth in hospitals was another factor mentioned by some participants. They stated that internal and external inspections should be used to supervise the performance of the team in providing pain relief during childbirth.

“There must be supervision ... If it is not done (pain relief during childbirth), it is not supervised that why was it not done? The mother did not request or you (providers) did not?” (Health Education and Promotion Specialist, University level)

In this study, using the CFIR, we identified perspectives of key stakeholders from different levels of the health system regarding the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth in Iran. Overall, the findings showed that through the lens of CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals involved, and process of implementation), there are several challenges to successfully implementing current interventions. Documenting the findings of such studies can help formulate appropriate strategies to improve the implementation of interventions to reduce mistreatment during childbirth as well as the development of high-quality maternity care guidelines in similar settings.

In our study, the most identified challenges to successfully implementing the interventions were related to the outer- and inner-setting domains. The key role of the outer setting [ 48 , 49 ] and inner setting [ 50 ], which emphasize the external influences on the intervention and characteristics of the implementing organization, has been highlighted in other studies for successful implementation. Our findings showed that all proposed interventions were influenced by factors from the outer setting. Patients’ needs and resources could challenge the implementation of childbirth preparation classes, birth companionship, and pain relief during childbirth. Participants reported that women were not trained in childbirth preparation classes about respectful care principles and their rights during childbirth, birth companions were not trained, and most women were unaware of pain relief during childbirth. Previous studies are in agreement with our findings and reflect the need for respectful care education for women [ 51 , 52 ], the presence of a trained birth companion [ 25 , 53 ], and training programs to increase women's awareness of pain relief options during childbirth [ 54 ]. In this study, poor collaboration with external organizations was also identified as a barrier to the implementation of childbirth preparation classes and pain relief during childbirth. The Heshima project in Kenya showed that participatory design of interventions at the policy, facility, and community levels played a significant role in the public acceptance of maternity care and health rights; therefore, the successful implementation and sustainability of the RMC intervention requires the formation of partnerships with external organizations [ 55 ]. Furthermore, our findings showed that external policies (including weakness in regulations and guidelines) challenge the implementation of interventions (including integrating RMC into in-service training, evaluating the performance of MHCPs, and pain relief during childbirth). Similar to our findings, Warren et al. (2017) reported that the free maternity care policy in Kenya affected the quality of care by increasing the demand for health facilities, delays in financing, augmented provider workloads and shortages, and posed challenges to the implementation of RMC [ 55 ]. In another study (2021), the existing policy in the West Bank to prevent the presence of birth companions in public facilities was reported by participants as a factor for mistreatment during childbirth [ 56 ].

Inner setting factors that affected the implementation of interventions were structural characteristics, networks and communications, culture, compatibility, relative priority, organizational incentives and rewards, and readiness for implementation (available resources and access to knowledge and information). Participants acknowledged that structural characteristics (including a lack of physical space), cultural issues, and incompatibility can act as barriers to birth companion intervention. Studies support the findings of our study that the limitations of the physical structure of hospitals make it difficult to allow birth companions [ 28 , 57 ]. In addition, in our study, as in some cultures, the presence of male partners was not socially acceptable, especially during childbirth [ 58 ], and there were concerns about the interference of birth companions in healthcare providers’ medical decisions [ 58 ]. Moreover, in this study, participants stressed the importance of holding a respectful care training course for providers. Healthcare providers have been shown to have a negative attitude toward respectful care [ 59 ], which is often less important than other aspects of care [ 60 ]. Poor working relationships between providers were another factor affecting implementation. Similarly, poor teamwork among obstetricians, midwives, and anesthesiologists was highlighted as an important barrier to implementing labour analgesia in Wu et al.'s study [ 61 ]. Moreover, in our study, lack of resources (including physical space, human resources, money, and training) was described as a potential barrier to readiness for implementation. In addition, limited access to knowledge and information about the intervention was another barrier to readiness for implementation. Our findings are consistent with previous studies that have examined how inner setting characteristics such as readiness for implementation [ 55 ] and organizational rewards and incentives [ 62 ] influence implementation.

The intervention characteristics domain, which emphasizes the importance of the need to adapt the intervention to enhance its fit with the context [ 63 ], is a critical determinant of the success of implementation [ 34 , 64 , 65 ]. In our study, adaptability and design quality and packaging were seen as important factors in the implementation of childbirth preparation classes. The findings showed that the situations of facilities of maternity hospitals do not adapt the content of the classes, the timing of the start of the classes are not appropriate, and they are not announced correctly. Previous studies have assessed the factors influencing childbirth preparation classes; for example, a study conducted by Otogara (2017) reported the need for the presence of a psychologist consultant as well as appropriate timing and information for successful implementation of classes [ 66 ].

The domain of the characteristics of individuals involved in the intervention is also crucial to ensure the success of implementation [ 34 ]. Our findings showed that the personal attributes of individuals within the organization (such as interest, skills, and expertise), as well as their knowledge and beliefs about the intervention, are other key factors that can hinder implementation. Other studies have similarly shown that competent, skilled, and motivated service providers are important for RMC provision [ 67 ]. Moreover, providers' knowledge and understanding of RMC are reported to be important in designing interventions to address mistreatment in maternity care [ 68 ]. In Mexico, training and enabling healthcare providers to promote respectful delivery care have been noted [ 69 ].

In our study, the implementation process domain was identified as a key factor in the implementation of all the studied interventions. Participants noted suboptimal execution, lack of supervision, and weakness in evaluating posed challenges for implementing interventions. Previous studies have revealed the key role of monitoring and evaluation interventions in the success of RMC implementation [ 15 , 70 ]. This was implemented in the Hashima project by applying mechanisms to report cases of disrespect, such as customer service desks, suggestion boxes and supervisory visits at the facility level [ 15 ].

Overall, our study has potential implications for practice and research. This study highlights practical benefits for policy makers and practitioners of maternal health programs in Iran and other contexts. We suggest that they consider the findings of this study when implementing their current programs and policies regarding the quality of maternity care. Moreover, intervention studies focusing on RMC and/or mistreatment during childbirth appear to be relatively limited in high-income countries (HICs), and research and implementation efforts in these settings must continue. The implementation process of these interventions has been inadequately explored, thus affecting their comparability. Using the CFIR, this study provides important insights into how the contextual conditions surrounding the implementation of multi-level interventions to reduce mistreatment during childbirth contribute to their success or failure. Therefore, the findings of this study can provide evidence for formulating effective strategies with the potential to increase the positive experiences of childbirth for women.

Strengths and limitations

To the best of our knowledge, this is the first attempt to identify the challenges of implementing a multi-level intervention to reduce mistreatment of women during childbirth in Iran and provides important insights into the contextual conditions around the implementation of each of the interventions. Our findings can be useful for other developing countries (LMICs) in similar contexts, especially those in the Eastern Mediterranean region. Reflecting the perspectives of key stakeholders from the micro- to macro-level of the health system was another strength of our study. Furthermore, the use of CFIR as the most common framework in IS allowed us to comprehensively identify the effective factors in the implementation of each intervention. However, given that the interviews were conducted with key stakeholders involved in the interventions, there is a possibility of a social desirability bias (underreporting of actual experiences and challenges due to their roles). We tried to mitigate this limitation by guaranteeing the confidentiality and anonymity of information as well as, conducting interviews in a private room. Also, this study focused on the interventions that are currently implemented in Iran's health system; and further research is needed to explore the implementation challenges of other interventions intended to reduce mistreatment during childbirth.

Our findings revealed potential challenges for implementing a multi-level intervention to reduce mistreatment of women during childbirth in the domains of intervention characteristics, outer setting, inner setting, characteristics of individuals involved, and process of implementation of the CFIR. Addressing these challenges is necessary to improve the implementation of interventions to reduce mistreatment during childbirth in Iran.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to privacy restrictions of the participants but are available from the corresponding author on reasonable request.

Abbreviations

Disrespect and Abuse

Respectful Maternity Care

Evidence-Based Intervention/Innovation

Implementation Science

Consolidated Framework for Implementation Research

Ministry of Health and Medical Education

Maternity Healthcare Providers

Consolidated Criteria for Reporting Qualitative Research

Iranian Rials

Information Technology

High-Income Countries

Low and Middle Income Countries

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Acknowledgments

This study was part of a PhD dissertation. We thank the Tehran University of Medical Sciences (TUMS) and the Health Information Management Research Center, TUMS for their financial support. We appreciate the sincere collaboration of all participants who provided valuable information in the interviews.

This study received funding from the Deputy for Education at Tehran University of Medical Sciences (TUMS) (9811108001) and Health Information Management Research Center, TUMS (1401-3-208-62407). The role of the funders is to monitor the corresponding study planning and progression.

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Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Marjan Mirzania & Elham Shakibazadeh

Gender and Women’s Health Unit, Nossal Institute for Global Health, School of Population and Global Health, University of Melbourne, Carlton, VIC, Australia

Meghan A. Bohren

Department of Obstetrics and Gynecology, School of Medicine, Vali-E-Asr Reproductive Health research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran

Sedigheh Hantoushzadeh

Department of Social Medicine, School of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran

Abdoljavad Khajavi

Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Abbas Rahimi Foroushani

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Contributions

E.Sh. and M.M. conceived and designed the project with input from all authors. M.M. developed the interview guide, conducted the interviews, coded and analyzed the data, and drafted the manuscript. E.Sh. contributed to the development of the interview guide, coding and analysis the data, and drafting of the manuscript. M.B., S.H., A.Kh., and A.RF. participated in the revision of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Elham Shakibazadeh .

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The study was reviewed and approved by the Ethics Committee of Tehran University of Medical Sciences (code number: IR.TUMS.SPH.REC.1400.169). Written informed consent was obtained from all participants prior to the interviews. This study was performed in accordance with the principles set out in the Declaration of Helsinki.

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Mirzania, M., Shakibazadeh, E., Bohren, M.A. et al. Challenges to the implementation of a multi-level intervention to reduce mistreatment of women during childbirth in Iran: a qualitative study using the Consolidated Framework for Implementation Research. Reprod Health 21 , 70 (2024). https://doi.org/10.1186/s12978-024-01813-1

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Received : 10 May 2023

Accepted : 14 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12978-024-01813-1

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