qualitative versus quantitative nursing research

Complexity, contextual, inductive logic, discovery, exploration

Experiment, random assignment, independent/dependent variable, causal/correlational, validity, deductive logic

Understand a phenomenon

Discover causal relationships or describe a phenomenon

Purposive sample, small

Random sample, large

Focus groups, interviews, field observation

Tests, surveys, questionnaires

Phenomenological, grounded theory, ethnographic, case study, historical/narrative research, participatory research, clinical research

Experimental, quasi-experimental, descriptive, methodological, exploratory, comparative, correlational, developmental (cross-sectional, longitudinal/prospective/cohort, retrospective/ex post facto/case control)

Examples: focus groups, interviews, open-ended surveys 

(unstructured responses)

Examples: close-ended surveys, data analysis

(structured responses) 

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• Correspondence to Kate Seers RCN Research Institute, School of Health & Social Studies, University of Warwick, Coventry, CV4 7AL, Warwick, UK; kate.seers{at}warwick.ac.uk

https://doi.org/10.1136/ebnurs.2011.100352

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Good qualitative research uses a systematic and rigorous approach that aims to answer questions concerned with what something is like (such as a patient experience), what people think or feel about something that has happened, and it may address why something has happened as it has. Qualitative data often takes the form of words or text and can include images.

Qualitative research covers a very broad range of philosophical underpinnings and methodological approaches. Each has its own particular way of approaching all stages of the research process, including analysis, and has its own terms and techniques, but there are some common threads that run across most of these approaches. This Research Made Simple piece will focus on some of these common threads in the analysis of qualitative research.

So you have collected all your qualitative data – you may have a pile of interview transcripts, field-notes, documents and notes from observation. The process of analysis is described by Richards and Morse 1 as one of transformation and interpretation.

It is easy to be overwhelmed by the volume of data – novice qualitative researchers are sometimes told not to worry and the themes will emerge from the data. This suggests some sort of epiphany, (which is how it happens sometimes!) but generally it comes from detailed work and reflection on the data and what it is telling you. There is sometimes a fine line between being immersed in the data and drowning in it!

A first step is to sort and organise the data, by coding it in some way. For example, you could read through a transcript, and identify that in one paragraph a patient is talking about two things; first is fear of surgery and second is fear of unrelieved pain. The codes for this paragraph could be ‘fear of surgery’ and ‘fear of pain’. In other areas of the transcript fear may arise again, and perhaps these codes will be merged into a category titled ‘fear’. Other concerns may emerge in this and other transcripts and perhaps best be represented by the theme ‘lack of control’. Themes are thus more abstract concepts, reflecting your interpretation of patterns across your data. So from codes, categories can be formed, and from categories, more encompassing themes are developed to describe the data in a form which summarises it, yet retains the richness, depth and context of the original data. Using quotations to illustrate categories and themes helps keep the analysis firmly grounded in the data. You need to constantly ask yourself ‘what is happening here?’ as you code and move from codes, to categories and themes, making sure you have data to support your decisions. Analysis inevitably involves subjective choices, and it is important to document what you have done and why, so a clear audit trail is provided. The coding example above describes codes inductively coming from the data. Some researchers may use a coding framework derived from, for example, the literature, their research questions or interview prompts, (Ritchie and Spencer 2 ) or a combination of both approaches.

Qualitative data, such as transcripts from an interview, are often routed in the interaction between the participant and the researcher. Reflecting on how you, as a researcher, may have influenced both the data collected and the analysis is an important part of the analysis.

As well as keeping your brain very much in gear, you need to be really organised. You may use highlighting pens and paper to keep track of your analysis, or use qualitative software to manage your data (such as NVivio or Atlas Ti). These programmes help you organise your data – you still have to do all the hard work to analyse it! Whatever you choose, it is important that you can trace your data back from themes to categories to codes. There is nothing more frustrating than looking for that illustrative patient quote, and not being able to find it.

If your qualitative data are part of a mixed methods study, (has both quantitative and qualitative data) careful thought has to be given to how you will analyse and present findings. Refer to O’Caithain et al 3 for more details.

There are many books and papers on qualitative analysis, a very few of which are listed below. 4 , – , 6 Working with someone with qualitative expertise is also invaluable, as you can read about it, but doing it really brings it alive.

• Richards L ,
• Ritchie J ,
• O'Cathain ,
• Bradley EH ,
• Huberman AM

Competing interests None.

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Difference between Qualitative vs. Quantitative Research

Qualitative research allows you to discover the why and how of people and activities. The abstract from this dissertation shows  the study: 

• Asks open-ended questions
• Examines people's activities
• Gives a narrative conclusion 
• Observations and Interviews 
• Understanding and interpreting social interactions and behaviors 
• Uses a small group of subjects 
• Uses words 

Quantitative research is based on things that can be accurately and precisely measured. The abstract shows that this dissertation:

• Describes with statistics
• Gives a statistical result 
• Makes predictions 
• Objective 
• Studies a randomly selected group 
• Tests a theory 
• Uses numbers
• Uses a survey 

Identifying Quantitative Research- Example

This abstract has several indications that this is a  quantitative  study:

• the goal of the study was examining relationships between several variables
• the researchers used statistical methods (logistic regression models)
• subjects completed questionnaires
• the study included a large number of subjects

Based on Eastern Michigan University Library Libguide Quantitative and Qualitative Research 

Identifying Qualitative Research-Example

This abstract has several indications that this is a qualitative study: 

• the goal of the study was to explore the subjects' experiences. 
• the researchers conducted open-ended interviews. 
• the researchers used thematic analysis when reviewing the interviews. 

Based on Eastern Michigan University Library Libguide  Quantitative and Qualitative Research 

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

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What is Quantitative Research?

There are two kinds of research: Quantitative and Qualitative

Quantitative is research that generates numerical data. If it helps, think of the root of the word "Quantitative." The word "Quantity" is at its core, and quantity just means "amount" or "how many." Heart rates, blood cell counts, how many people fainted at the jazz festival-- these are all examples of quantitative measures.

Qualitative , on the other hand, is a more subjective measurement. Think of the root of the word again, this time it's "Quality." If someone is called a quality person or someone's selling a high quality product, they're being measured in subjective terms, rather than concrete, objective terms (like numbers.) Qualitative research includes things like interviews or focus groups.

How do I tell if my article has Quantitative Research?

Just like when we examine whether or not our article is an example of Primary Research, the best way to examine what kind of data your article uses is by reading the article's Abstract, Methodologies, and Results sections. That will tell you how the research was conducted and what kind of data (qualitative or quantitative) was collected.

An example of what to look for in the Abstract can be seen here:

You can see that data was evaluated (66% of students were in compliance with school immunization requirements), a strategy was implemented (letters and emails were sent to student's parents/guardians), and at the end of the study, new quantitative data is reported (99.6% of students were in compliance with vaccination requirements).

Qualitative Research

Finding qualitative research can be trickier, since it can often take more time to collect. Examples of qualitative data include things like interview transcripts, focus group feedback, and journal entries detailing people's experiences and feelings. The easiest way to search for a qualitative study is to include the word "qualitative" as a keyword in your database search along with the search terms about the topic you're interested in.

Check out the video below to see an example of searching for qualitative research in CINAHL.

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

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• BMJ Glob Health
• v.4(Suppl 1); 2019

Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods

1 School of Social Sciences, Bangor University, Wales, UK

Andrew Booth

2 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

Graham Moore

3 School of Social Sciences, Cardiff University, Wales, UK

Kate Flemming

4 Department of Health Sciences, The University of York, York, UK

Özge Tunçalp

5 Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland

Elham Shakibazadeh

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

Associated Data

bmjgh-2018-000893supp001.pdf

bmjgh-2018-000893supp002.pdf

bmjgh-2018-000893supp003.pdf

bmjgh-2018-000893supp005.pdf

bmjgh-2018-000893supp004.pdf

Guideline developers are increasingly dealing with more difficult decisions concerning whether to recommend complex interventions in complex and highly variable health systems. There is greater recognition that both quantitative and qualitative evidence can be combined in a mixed-method synthesis and that this can be helpful in understanding how complexity impacts on interventions in specific contexts. This paper aims to clarify the different purposes, review designs, questions, synthesis methods and opportunities to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of guidelines developed by WHO, which incorporated quantitative and qualitative evidence, are used to illustrate possible uses of mixed-method reviews and evidence. Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Consideration is given to the opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence. Recommendations are made concerning the future development of methods to better address questions in systematic reviews and guidelines that adopt a complexity perspective.

Summary box

• When combined in a mixed-method synthesis, quantitative and qualitative evidence can potentially contribute to understanding how complex interventions work and for whom, and how the complex health systems into which they are implemented respond and adapt.
• The different purposes and designs for combining quantitative and qualitative evidence in a mixed-method synthesis for a guideline process are described.
• Questions relevant to gaining an understanding of the complexity of complex interventions and the wider health systems within which they are implemented that can be addressed by mixed-method syntheses are presented.
• The practical methodological guidance in this paper is intended to help guideline producers and review authors commission and conduct mixed-method syntheses where appropriate.
• If more mixed-method syntheses are conducted, guideline developers will have greater opportunities to access this evidence to inform decision-making.

Introduction

Recognition has grown that while quantitative methods remain vital, they are usually insufficient to address complex health systems related research questions. 1 Quantitative methods rely on an ability to anticipate what must be measured in advance. Introducing change into a complex health system gives rise to emergent reactions, which cannot be fully predicted in advance. Emergent reactions can often only be understood through combining quantitative methods with a more flexible qualitative lens. 2 Adopting a more pluralist position enables a diverse range of research options to the researcher depending on the research question being investigated. 3–5 As a consequence, where a research study sits within the multitude of methods available is driven by the question being asked, rather than any particular methodological or philosophical stance. 6

Publication of guidance on designing complex intervention process evaluations and other works advocating mixed-methods approaches to intervention research have stimulated better quality evidence for synthesis. 1 7–13 Methods for synthesising qualitative 14 and mixed-method evidence have been developed or are in development. Mixed-method research and review definitions are outlined in box 1 .

Defining mixed-method research and reviews

Pluye and Hong 52 define mixed-methods research as “a research approach in which a researcher integrates (a) qualitative and quantitative research questions, (b) qualitative research methods* and quantitative research designs, (c) techniques for collecting and analyzing qualitative and quantitative evidence, and (d) qualitative findings and quantitative results”.A mixed-method synthesis can integrate quantitative, qualitative and mixed-method evidence or data from primary studies.† Mixed-method primary studies are usually disaggregated into quantitative and qualitative evidence and data for the purposes of synthesis. Thomas and Harden further define three ways in which reviews are mixed. 53

• The types of studies included and hence the type of findings to be synthesised (ie, qualitative/textual and quantitative/numerical).
• The types of synthesis method used (eg, statistical meta-analysis and qualitative synthesis).
• The mode of analysis: theory testing AND theory building.

*A qualitative study is one that uses qualitative methods of data collection and analysis to produce a narrative understanding of the phenomena of interest. Qualitative methods of data collection may include, for example, interviews, focus groups, observations and analysis of documents.

†The Cochrane Qualitative and Implementation Methods group coined the term ‘qualitative evidence synthesis’ to mean that the synthesis could also include qualitative data. For example, qualitative data from case studies, grey literature reports and open-ended questions from surveys. ‘Evidence’ and ‘data’ are used interchangeably in this paper.

This paper is one of a series that aims to explore the implications of complexity for systematic reviews and guideline development, commissioned by WHO. This paper is concerned with the methodological implications of including quantitative and qualitative evidence in mixed-method systematic reviews and guideline development for complex interventions. The guidance was developed through a process of bringing together experts in the field, literature searching and consensus building with end users (guideline developers, clinicians and reviewers). We clarify the different purposes, review designs, questions and synthesis methods that may be applicable to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of WHO guidelines that incorporated quantitative and qualitative evidence are used to illustrate possible uses of mixed-method reviews and mechanisms of integration ( table 1 , online supplementary files 1–3 ). Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process are presented. Specific considerations when using an evidence to decision framework such as the Developing and Evaluating Communication strategies to support Informed Decisions and practice based on Evidence (DECIDE) framework 15 or the new WHO-INTEGRATE evidence to decision framework 16 at the review design and evidence to decision stage are outlined. See online supplementary file 4 for an example of a health systems DECIDE framework and Rehfuess et al 16 for the new WHO-INTEGRATE framework. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence in guidelines of complex interventions that take a complexity perspective and health systems focus.

Designs and methods and their use or applicability in guidelines and systematic reviews taking a complexity perspective

Supplementary data

Taking a complexity perspective.

The first paper in this series 17 outlines aspects of complexity associated with complex interventions and health systems that can potentially be explored by different types of evidence, including synthesis of quantitative and qualitative evidence. Petticrew et al 17 distinguish between a complex interventions perspective and a complex systems perspective. A complex interventions perspective defines interventions as having “implicit conceptual boundaries, representing a flexible, but common set of practices, often linked by an explicit or implicit theory about how they work”. A complex systems perspective differs in that “ complexity arises from the relationships and interactions between a system’s agents (eg, people, or groups that interact with each other and their environment), and its context. A system perspective conceives the intervention as being part of the system, and emphasises changes and interconnections within the system itself”. Aspects of complexity associated with implementation of complex interventions in health systems that could potentially be addressed with a synthesis of quantitative and qualitative evidence are summarised in table 2 . Another paper in the series outlines criteria used in a new evidence to decision framework for making decisions about complex interventions implemented in complex systems, against which the need for quantitative and qualitative evidence can be mapped. 16 A further paper 18 that explores how context is dealt with in guidelines and reviews taking a complexity perspective also recommends using both quantitative and qualitative evidence to better understand context as a source of complexity. Mixed-method syntheses of quantitative and qualitative evidence can also help with understanding of whether there has been theory failure and or implementation failure. The Cochrane Qualitative and Implementation Methods Group provide additional guidance on exploring implementation and theory failure that can be adapted to address aspects of complexity of complex interventions when implemented in health systems. 19

Health-system complexity-related questions that a synthesis of quantitative and qualitative evidence could address (derived from Petticrew et al 17 )

It may not be apparent which aspects of complexity or which elements of the complex intervention or health system can be explored in a guideline process, or whether combining qualitative and quantitative evidence in a mixed-method synthesis will be useful, until the available evidence is scoped and mapped. 17 20 A more extensive lead in phase is typically required to scope the available evidence, engage with stakeholders and to refine the review parameters and questions that can then be mapped against potential review designs and methods of synthesis. 20 At the scoping stage, it is also common to decide on a theoretical perspective 21 or undertake further work to refine a theoretical perspective. 22 This is also the stage to begin articulating the programme theory of the complex intervention that may be further developed to refine an understanding of complexity and show how the intervention is implemented in and impacts on the wider health system. 17 23 24 In practice, this process can be lengthy, iterative and fluid with multiple revisions to the review scope, often developing and adapting a logic model 17 as the available evidence becomes known and the potential to incorporate different types of review designs and syntheses of quantitative and qualitative evidence becomes better understood. 25 Further questions, propositions or hypotheses may emerge as the reviews progress and therefore the protocols generally need to be developed iteratively over time rather than a priori.

Following a scoping exercise and definition of key questions, the next step in the guideline development process is to identify existing or commission new systematic reviews to locate and summarise the best available evidence in relation to each question. For example, case study 2, ‘Optimising health worker roles for maternal and newborn health through task shifting’, included quantitative reviews that did and did not take an additional complexity perspective, and qualitative evidence syntheses that were able to explain how specific elements of complexity impacted on intervention outcomes within the wider health system. Further understanding of health system complexity was facilitated through the conduct of additional country-level case studies that contributed to an overall understanding of what worked and what happened when lay health worker interventions were implemented. See table 1 online supplementary file 2 .

There are a few existing examples, which we draw on in this paper, but integrating quantitative and qualitative evidence in a mixed-method synthesis is relatively uncommon in a guideline process. Box 2 includes a set of key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in mixed-methods design might ask. Subsequent sections provide more information and signposting to further reading to help address these key questions.

Key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in a mixed-methods design might ask

Compound questions requiring both quantitative and qualitative evidence?

Questions requiring mixed-methods studies?

Separate quantitative and qualitative questions?

Separate quantitative and qualitative research studies?

Related quantitative and qualitative research studies?

Mixed-methods studies?

Quantitative unpublished data and/or qualitative unpublished data, eg, narrative survey data?

Throughout the review?

Following separate reviews?

At the question point?

At the synthesis point?

At the evidence to recommendations stage?

Or a combination?

Narrative synthesis or summary?

Quantitising approach, eg, frequency analysis?

Qualitising approach, eg, thematic synthesis?

Tabulation?

Logic model?

Conceptual model/framework?

Graphical approach?

• WHICH: Which mixed-method designs, methodologies and methods best fit into a guideline process to inform recommendations?

Complexity-related questions that a synthesis of quantitative and qualitative evidence can potentially address

Petticrew et al 17 define the different aspects of complexity and examples of complexity-related questions that can potentially be explored in guidelines and systematic reviews taking a complexity perspective. Relevant aspects of complexity outlined by Petticrew et al 17 are summarised in table 2 below, together with the corresponding questions that could be addressed in a synthesis combining qualitative and quantitative evidence. Importantly, the aspects of complexity and their associated concepts of interest have however yet to be translated fully in primary health research or systematic reviews. There are few known examples where selected complexity concepts have been used to analyse or reanalyse a primary intervention study. Most notable is Chandler et al 26 who specifically set out to identify and translate a set of relevant complexity theory concepts for application in health systems research. Chandler then reanalysed a trial process evaluation using selected complexity theory concepts to better understand the complex causal pathway in the health system that explains some aspects of complexity in table 2 .

Rehfeuss et al 16 also recommends upfront consideration of the WHO-INTEGRATE evidence to decision criteria when planning a guideline and formulating questions. The criteria reflect WHO norms and values and take account of a complexity perspective. The framework can be used by guideline development groups as a menu to decide which criteria to prioritise, and which study types and synthesis methods can be used to collect evidence for each criterion. Many of the criteria and their related questions can be addressed using a synthesis of quantitative and qualitative evidence: the balance of benefits and harms, human rights and sociocultural acceptability, health equity, societal implications and feasibility (see table 3 ). Similar aspects in the DECIDE framework 15 could also be addressed using synthesis of qualitative and quantitative evidence.

Integrate evidence to decision framework criteria, example questions and types of studies to potentially address these questions (derived from Rehfeuss et al 16 )

GIS, Geographical Information System; RCT, randomised controlled trial.

Questions as anchors or compasses

Questions can serve as an ‘anchor’ by articulating the specific aspects of complexity to be explored (eg, Is successful implementation of the intervention context dependent?). 27 Anchor questions such as “How does intervention x impact on socioeconomic inequalities in health behaviour/outcome x” are the kind of health system question that requires a synthesis of both quantitative and qualitative evidence and hence a mixed-method synthesis. Quantitative evidence can quantify the difference in effect, but does not answer the question of how . The ‘how’ question can be partly answered with quantitative and qualitative evidence. For example, quantitative evidence may reveal where socioeconomic status and inequality emerges in the health system (an emergent property) by exploring questions such as “ Does patterning emerge during uptake because fewer people from certain groups come into contact with an intervention in the first place? ” or “ are people from certain backgrounds more likely to drop out, or to maintain effects beyond an intervention differently? ” Qualitative evidence may help understand the reasons behind all of these mechanisms. Alternatively, questions can act as ‘compasses’ where a question sets out a starting point from which to explore further and to potentially ask further questions or develop propositions or hypotheses to explore through a complexity perspective (eg, What factors enhance or hinder implementation?). 27 Other papers in this series provide further guidance on developing questions for qualitative evidence syntheses and guidance on question formulation. 14 28

For anchor and compass questions, additional application of a theory (eg, complexity theory) can help focus evidence synthesis and presentation to explore and explain complexity issues. 17 21 Development of a review specific logic model(s) can help to further refine an initial understanding of any complexity-related issues of interest associated with a specific intervention, and if appropriate the health system or section of the health system within which to contextualise the review question and analyse data. 17 23–25 Specific tools are available to help clarify context and complex interventions. 17 18

If a complexity perspective, and certain criteria within evidence to decision frameworks, is deemed relevant and desirable by guideline developers, it is only possible to pursue a complexity perspective if the evidence is available. Careful scoping using knowledge maps or scoping reviews will help inform development of questions that are answerable with available evidence. 20 If evidence of effect is not available, then a different approach to develop questions leading to a more general narrative understanding of what happened when complex interventions were implemented in a health system will be required (such as in case study 3—risk communication guideline). This should not mean that the original questions developed for which no evidence was found when scoping the literature were not important. An important function of creating a knowledge map is also to identify gaps to inform a future research agenda.

Table 2 and online supplementary files 1–3 outline examples of questions in the three case studies, which were all ‘COMPASS’ questions for the qualitative evidence syntheses.

Types of integration and synthesis designs in mixed-method reviews

The shift towards integration of qualitative and quantitative evidence in primary research has, in recent years, begun to be mirrored within research synthesis. 29–31 The natural extension to undertaking quantitative or qualitative reviews has been the development of methods for integrating qualitative and quantitative evidence within reviews, and within the guideline process using evidence to decision-frameworks. Advocating the integration of quantitative and qualitative evidence assumes a complementarity between research methodologies, and a need for both types of evidence to inform policy and practice. Below, we briefly outline the current designs for integrating qualitative and quantitative evidence within a mixed-method review or synthesis.

One of the early approaches to integrating qualitative and quantitative evidence detailed by Sandelowski et al 32 advocated three basic review designs: segregated, integrated and contingent designs, which have been further developed by Heyvaert et al 33 ( box 3 ).

Segregated, integrated and contingent designs 32 33

Segregated design.

Conventional separate distinction between quantitative and qualitative approaches based on the assumption they are different entities and should be treated separately; can be distinguished from each other; their findings warrant separate analyses and syntheses. Ultimately, the separate synthesis results can themselves be synthesised.

Integrated design

The methodological differences between qualitative and quantitative studies are minimised as both are viewed as producing findings that can be readily synthesised into one another because they address the same research purposed and questions. Transformation involves either turning qualitative data into quantitative (quantitising) or quantitative findings are turned into qualitative (qualitising) to facilitate their integration.

Contingent design

Takes a cyclical approach to synthesis, with the findings from one synthesis informing the focus of the next synthesis, until all the research objectives have been addressed. Studies are not necessarily grouped and categorised as qualitative or quantitative.

A recent review of more than 400 systematic reviews 34 combining quantitative and qualitative evidence identified two main synthesis designs—convergent and sequential. In a convergent design, qualitative and quantitative evidence is collated and analysed in a parallel or complementary manner, whereas in a sequential synthesis, the collation and analysis of quantitative and qualitative evidence takes place in a sequence with one synthesis informing the other ( box 4 ). 6 These designs can be seen to build on the work of Sandelowski et al , 32 35 particularly in relation to the transformation of data from qualitative to quantitative (and vice versa) and the sequential synthesis design, with a cyclical approach to reviewing that evokes Sandelowski’s contingent design.

Convergent and sequential synthesis designs 34

Convergent synthesis design.

Qualitative and quantitative research is collected and analysed at the same time in a parallel or complementary manner. Integration can occur at three points:

a. Data-based convergent synthesis design

All included studies are analysed using the same methods and results presented together. As only one synthesis method is used, data transformation occurs (qualitised or quantised). Usually addressed one review question.

b. Results-based convergent synthesis design

Qualitative and quantitative data are analysed and presented separately but integrated using a further synthesis method; eg, narratively, tables, matrices or reanalysing evidence. The results of both syntheses are combined in a third synthesis. Usually addresses an overall review question with subquestions.

c. Parallel-results convergent synthesis design

Qualitative and quantitative data are analysed and presented separately with integration occurring in the interpretation of results in the discussion section. Usually addresses two or more complimentary review questions.

Sequential synthesis design

A two-phase approach, data collection and analysis of one type of evidence (eg, qualitative), occurs after and is informed by the collection and analysis of the other type (eg, quantitative). Usually addresses an overall question with subquestions with both syntheses complementing each other.

The three case studies ( table 1 , online supplementary files 1–3 ) illustrate the diverse combination of review designs and synthesis methods that were considered the most appropriate for specific guidelines.

Methods for conducting mixed-method reviews in the context of guidelines for complex interventions

In this section, we draw on examples where specific review designs and methods have been or can be used to explore selected aspects of complexity in guidelines or systematic reviews. We also identify other review methods that could potentially be used to explore aspects of complexity. Of particular note, we could not find any specific examples of systematic methods to synthesise highly diverse research designs as advocated by Petticrew et al 17 and summarised in tables 2 and 3 . For example, we could not find examples of methods to synthesise qualitative studies, case studies, quantitative longitudinal data, possibly historical data, effectiveness studies providing evidence of differential effects across different contexts, and system modelling studies (eg, agent-based modelling) to explore system adaptivity.

There are different ways that quantitative and qualitative evidence can be integrated into a review and then into a guideline development process. In practice, some methods enable integration of different types of evidence in a single synthesis, while in other methods, the single systematic review may include a series of stand-alone reviews or syntheses that are then combined in a cross-study synthesis. Table 1 provides an overview of the characteristics of different review designs and methods and guidance on their applicability for a guideline process. Designs and methods that have already been used in WHO guideline development are described in part A of the table. Part B outlines a design and method that can be used in a guideline process, and part C covers those that have the potential to integrate quantitative, qualitative and mixed-method evidence in a single review design (such as meta-narrative reviews and Bayesian syntheses), but their application in a guideline context has yet to be demonstrated.

Points of integration when integrating quantitative and qualitative evidence in guideline development

Depending on the review design (see boxes 3 and 4 ), integration can potentially take place at a review team and design level, and more commonly at several key points of the review or guideline process. The following sections outline potential points of integration and associated practical considerations when integrating quantitative and qualitative evidence in guideline development.

Review team level

In a guideline process, it is common for syntheses of quantitative and qualitative evidence to be done separately by different teams and then to integrate the evidence. A practical consideration relates to the organisation, composition and expertise of the review teams and ways of working. If the quantitative and qualitative reviews are being conducted separately and then brought together by the same team members, who are equally comfortable operating within both paradigms, then a consistent approach across both paradigms becomes possible. If, however, a team is being split between the quantitative and qualitative reviews, then the strengths of specialisation can be harnessed, for example, in quality assessment or synthesis. Optimally, at least one, if not more, of the team members should be involved in both quantitative and qualitative reviews to offer the possibility of making connexions throughout the review and not simply at re-agreed junctures. This mirrors O’Cathain’s conclusion that mixed-methods primary research tends to work only when there is a principal investigator who values and is able to oversee integration. 9 10 While the above decisions have been articulated in the context of two types of evidence, variously quantitative and qualitative, they equally apply when considering how to handle studies reporting a mixed-method study design, where data are usually disaggregated into quantitative and qualitative for the purposes of synthesis (see case study 3—risk communication in humanitarian disasters).

Question formulation

Clearly specified key question(s), derived from a scoping or consultation exercise, will make it clear if quantitative and qualitative evidence is required in a guideline development process and which aspects will be addressed by which types of evidence. For the remaining stages of the process, as documented below, a review team faces challenges as to whether to handle each type of evidence separately, regardless of whether sequentially or in parallel, with a view to joining the two products on completion or to attempt integration throughout the review process. In each case, the underlying choice is of efficiencies and potential comparability vs sensitivity to the underlying paradigm.

Once key questions are clearly defined, the guideline development group typically needs to consider whether to conduct a single sensitive search to address all potential subtopics (lumping) or whether to conduct specific searches for each subtopic (splitting). 36 A related consideration is whether to search separately for qualitative, quantitative and mixed-method evidence ‘streams’ or whether to conduct a single search and then identify specific study types at the subsequent sifting stage. These two considerations often mean a trade-off between a single search process involving very large numbers of records or a more protracted search process retrieving smaller numbers of records. Both approaches have advantages and choice may depend on the respective availability of resources for searching and sifting.

Screening and selecting studies

Closely related to decisions around searching are considerations relating to screening and selecting studies for inclusion in a systematic review. An important consideration here is whether the review team will screen records for all review types, regardless of their subsequent involvement (‘altruistic sifting’), or specialise in screening for the study type with which they are most familiar. The risk of missing relevant reports might be minimised by whole team screening for empirical reports in the first instance and then coding them for a specific quantitative, qualitative or mixed-methods report at a subsequent stage.

Assessment of methodological limitations in primary studies

Within a guideline process, review teams may be more limited in their choice of instruments to assess methodological limitations of primary studies as there are mandatory requirements to use the Cochrane risk of bias tool 37 to feed into Grading of Recommendations Assessment, Development and Evaluation (GRADE) 38 or to select from a small pool of qualitative appraisal instruments in order to apply GRADE; Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) 39 to assess the overall certainty or confidence in findings. The Cochrane Qualitative and Implementation Methods Group has recently issued guidance on the selection of appraisal instruments and core assessment criteria. 40 The Mixed-Methods Appraisal Tool, which is currently undergoing further development, offers a single quality assessment instrument for quantitative, qualitative and mixed-methods studies. 41 Other options include using corresponding instruments from within the same ‘stable’, for example, using different Critical Appraisal Skills Programme instruments. 42 While using instruments developed by the same team or organisation may achieve a degree of epistemological consonance, benefits may come more from consistency of approach and reporting rather than from a shared view of quality. Alternatively, a more paradigm-sensitive approach would involve selecting the best instrument for each respective review while deferring challenges from later heterogeneity of reporting.

Data extraction

The way in which data and evidence are extracted from primary research studies for review will be influenced by the type of integrated synthesis being undertaken and the review purpose. Initially, decisions need to be made regarding the nature and type of data and evidence that are to be extracted from the included studies. Method-specific reporting guidelines 43 44 provide a good template as to what quantitative and qualitative data it is potentially possible to extract from different types of method-specific study reports, although in practice reporting quality varies. Online supplementary file 5 provides a hypothetical example of the different types of studies from which quantitative and qualitative evidence could potentially be extracted for synthesis.

The decisions around what data or evidence to extract will be guided by how ‘integrated’ the mixed-method review will be. For those reviews where the quantitative and qualitative findings of studies are synthesised separately and integrated at the point of findings (eg, segregated or contingent approaches or sequential synthesis design), separate data extraction approaches will likely be used.

Where integration occurs during the process of the review (eg, integrated approach or convergent synthesis design), an integrated approach to data extraction may be considered, depending on the purpose of the review. This may involve the use of a data extraction framework, the choice of which needs to be congruent with the approach to synthesis chosen for the review. 40 45 The integrative or theoretical framework may be decided on a priori if a pre-developed theoretical or conceptual framework is available in the literature. 27 The development of a framework may alternatively arise from the reading of the included studies, in relation to the purpose of the review, early in the process. The Cochrane Qualitative and Implementation Methods Group provide further guidance on extraction of qualitative data, including use of software. 40

Synthesis and integration

Relatively few synthesis methods start off being integrated from the beginning, and these methods have generally been subject to less testing and evaluation particularly in a guideline context (see table 1 ). A review design that started off being integrated from the beginning may be suitable for some guideline contexts (such as in case study 3—risk communication in humanitarian disasters—where there was little evidence of effect), but in general if there are sufficient trials then a separate systematic review and meta-analysis will be required for a guideline. Other papers in this series offer guidance on methods for synthesising quantitative 46 and qualitative evidence 14 in reviews that take a complexity perspective. Further guidance on integrating quantitative and qualitative evidence in a systematic review is provided by the Cochrane Qualitative and Implementation Methods Group. 19 27 29 40 47

Types of findings produced by specific methods

It is highly likely (unless there are well-designed process evaluations) that the primary studies may not themselves seek to address the complexity-related questions required for a guideline process. In which case, review authors will need to configure the available evidence and transform the evidence through the synthesis process to produce explanations, propositions and hypotheses (ie, findings) that were not obvious at primary study level. It is important that guideline commissioners, developers and review authors are aware that specific methods are intended to produce a type of finding with a specific purpose (such as developing new theory in the case of meta-ethnography). 48 Case study 1 (antenatal care guideline) provides an example of how a meta-ethnography was used to develop a new theory as an end product, 48 49 as well as framework synthesis which produced descriptive and explanatory findings that were more easily incorporated into the guideline process. 27 The definitions ( box 5 ) may be helpful when defining the different types of findings.

Different levels of findings

Descriptive findings —qualitative evidence-driven translated descriptive themes that do not move beyond the primary studies.

Explanatory findings —may either be at a descriptive or theoretical level. At the descriptive level, qualitative evidence is used to explain phenomena observed in quantitative results, such as why implementation failed in specific circumstances. At the theoretical level, the transformed and interpreted findings that go beyond the primary studies can be used to explain the descriptive findings. The latter description is generally the accepted definition in the wider qualitative community.

Hypothetical or theoretical finding —qualitative evidence-driven transformed themes (or lines of argument) that go beyond the primary studies. Although similar, Thomas and Harden 56 make a distinction in the purposes between two types of theoretical findings: analytical themes and the product of meta-ethnographies, third-order interpretations. 48

Analytical themes are a product of interrogating descriptive themes by placing the synthesis within an external theoretical framework (such as the review question and subquestions) and are considered more appropriate when a specific review question is being addressed (eg, in a guideline or to inform policy). 56

Third-order interpretations come from translating studies into one another while preserving the original context and are more appropriate when a body of literature is being explored in and of itself with broader or emergent review questions. 48

Bringing mixed-method evidence together in evidence to decision (EtD) frameworks

A critical element of guideline development is the formulation of recommendations by the Guideline Development Group, and EtD frameworks help to facilitate this process. 16 The EtD framework can also be used as a mechanism to integrate and display quantitative and qualitative evidence and findings mapped against the EtD framework domains with hyperlinks to more detailed evidence summaries from contributing reviews (see table 1 ). It is commonly the EtD framework that enables the findings of the separate quantitative and qualitative reviews to be brought together in a guideline process. Specific challenges when populating the DECIDE evidence to decision framework 15 were noted in case study 3 (risk communication in humanitarian disasters) as there was an absence of intervention effect data and the interventions to communicate public health risks were context specific and varied. These problems would not, however, have been addressed by substitution of the DECIDE framework with the new INTEGRATE 16 evidence to decision framework. A d ifferent type of EtD framework needs to be developed for reviews that do not include sufficient evidence of intervention effect.

Mixed-method review and synthesis methods are generally the least developed of all systematic review methods. It is acknowledged that methods for combining quantitative and qualitative evidence are generally poorly articulated. 29 50 There are however some fairly well-established methods for using qualitative evidence to explore aspects of complexity (such as contextual, implementation and outcome complexity), which can be combined with evidence of effect (see sections A and B of table 1 ). 14 There are good examples of systematic reviews that use these methods to combine quantitative and qualitative evidence, and examples of guideline recommendations that were informed by evidence from both quantitative and qualitative reviews (eg, case studies 1–3). With the exception of case study 3 (risk communication), the quantitative and qualitative reviews for these specific guidelines have been conducted separately, and the findings subsequently brought together in an EtD framework to inform recommendations.

Other mixed-method review designs have potential to contribute to understanding of complex interventions and to explore aspects of wider health systems complexity but have not been sufficiently developed and tested for this specific purpose, or used in a guideline process (section C of table 1 ). Some methods such as meta-narrative reviews also explore different questions to those usually asked in a guideline process. Methods for processing (eg, quality appraisal) and synthesising the highly diverse evidence suggested in tables 2 and 3 that are required to explore specific aspects of health systems complexity (such as system adaptivity) and to populate some sections of the INTEGRATE EtD framework remain underdeveloped or in need of development.

In addition to the required methodological development mentioned above, there is no GRADE approach 38 for assessing confidence in findings developed from combined quantitative and qualitative evidence. Another paper in this series outlines how to deal with complexity and grading different types of quantitative evidence, 51 and the GRADE CERQual approach for qualitative findings is described elsewhere, 39 but both these approaches are applied to method-specific and not mixed-method findings. An unofficial adaptation of GRADE was used in the risk communication guideline that reported mixed-method findings. Nor is there a reporting guideline for mixed-method reviews, 47 and for now reports will need to conform to the relevant reporting requirements of the respective method-specific guideline. There is a need to further adapt and test DECIDE, 15 WHO-INTEGRATE 16 and other types of evidence to decision frameworks to accommodate evidence from mixed-method syntheses which do not set out to determine the statistical effects of interventions and in circumstances where there are no trials.

When conducting quantitative and qualitative reviews that will subsequently be combined, there are specific considerations for managing and integrating the different types of evidence throughout the review process. We have summarised different options for combining qualitative and quantitative evidence in mixed-method syntheses that guideline developers and systematic reviewers can choose from, as well as outlining the opportunities to integrate evidence at different stages of the review and guideline development process.

Review commissioners, authors and guideline developers generally have less experience of combining qualitative and evidence in mixed-methods reviews. In particular, there is a relatively small group of reviewers who are skilled at undertaking fully integrated mixed-method reviews. Commissioning additional qualitative and mixed-method reviews creates an additional cost. Large complex mixed-method reviews generally take more time to complete. Careful consideration needs to be given as to which guidelines would benefit most from additional qualitative and mixed-method syntheses. More training is required to develop capacity and there is a need to develop processes for preparing the guideline panel to consider and use mixed-method evidence in their decision-making.

This paper has presented how qualitative and quantitative evidence, combined in mixed-method reviews, can help understand aspects of complex interventions and the systems within which they are implemented. There are further opportunities to use these methods, and to further develop the methods, to look more widely at additional aspects of complexity. There is a range of review designs and synthesis methods to choose from depending on the question being asked or the questions that may emerge during the conduct of the synthesis. Additional methods need to be developed (or existing methods further adapted) in order to synthesise the full range of diverse evidence that is desirable to explore the complexity-related questions when complex interventions are implemented into health systems. We encourage review commissioners and authors, and guideline developers to consider using mixed-methods reviews and synthesis in guidelines and to report on their usefulness in the guideline development process.

Handling editor: Soumyadeep Bhaumik

Contributors: JN, AB, GM, KF, ÖT and ES drafted the manuscript. All authors contributed to paper development and writing and agreed the final manuscript. Anayda Portela and Susan Norris from WHO managed the series. Helen Smith was series Editor. We thank all those who provided feedback on various iterations.

Funding: Funding provided by the World Health Organization Department of Maternal, Newborn, Child and Adolescent Health through grants received from the United States Agency for International Development and the Norwegian Agency for Development Cooperation.

Disclaimer: ÖT is a staff member of WHO. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO.

Competing interests: No financial interests declared. JN, AB and ÖT have an intellectual interest in GRADE CERQual; and JN has an intellectual interest in the iCAT_SR tool.

Patient consent: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: No additional data are available.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Qualitative versus quantitative nursing research

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Qualitative Vs. Quantitative Research or Qualitative and Quantitative Research?

GOODWIN, LAURA D.; GOODWIN, WILLIAM L.

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Qualitative vs quantitative research.

13 min read You’ll use both quantitative and qualitative research methods to gather survey data. What are they exactly, and how can you best use them to gain the most accurate insights?

What is qualitative research?

Qualitative research  is all about  language, expression, body language and other forms of human communication . That covers words, meanings and understanding. Qualitative research is used to describe WHY. Why do people  feel  the way they do, why do they  act  in a certain way, what  opinions  do they have and what  motivates  them?

Qualitative data is used to understand phenomena – things that happen, situations that exist, and most importantly the meanings associated with them. It can help add a ‘why’ element to factual, objective data.

Qualitative research gives breadth, depth and context to questions, although its linguistic subtleties and subjectivity can mean that results are trickier to analyse than quantitative data.

This qualitative data is called  unstructured data by researchers. This is because it has not traditionally had the type of structure that can be processed by computers, until today. It has, until recently at least, been exclusively accessible to human brains. And although our brains are highly sophisticated, they have limited processing power. What can help analyse this structured data to assist computers and the human brain?

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What is quantitative research?

Quantitative data refers to numerical information. Quantitative research gathers information that can be counted, measured, or rated numerically – AKA quantitative data. Scores, measurements, financial records, temperature charts and receipts or ledgers are all examples of quantitative data.

Quantitative data is often structured data, because it follows a consistent, predictable pattern that computers and calculating devices are able to process with ease. Humans can process it too, although we are now able to pass it over to machines to process on our behalf. This is partly what has made quantitative data so important historically, and why quantitative data – sometimes called ‘hard data’ – has dominated over qualitative data in fields like business, finance and economics.

It’s easy to ‘crunch the numbers’ of quantitative data and produce results visually in graphs, tables and on data analysis dashboards. Thanks to today’s abundance and accessibility of processing power, combined with our ability to store huge amounts of information, quantitative data has fuelled the Big Data phenomenon, putting quantitative methods and vast amounts of quantitative data at our fingertips.

As we’ve indicated, quantitative and qualitative data are entirely different and mutually exclusive categories. Here are a few of the differences between them.

1. Data collection

Data collection methods for quantitative data and qualitative data vary, but there are also some places where they overlap.

2. Data analysis

Quantitative data suits statistical analysis techniques like linear regression, T-tests and ANOVA. These are quite easy to automate, and large quantities of quantitative data can be analyzed quickly.

Analyzing qualitative data needs a higher degree of human judgement, since unlike quantitative data, non numerical data of a subjective nature has certain characteristics that inferential statistics can’t perceive. Working at a human scale has historically meant that qualitative data is lower in volume – although it can be richer in insights.

3. Strengths and weaknesses

When weighing up qualitative vs quantitative research, it’s largely a matter of choosing the method appropriate to your research goals. If you’re in the position of having to choose one method over another, it’s worth knowing the strengths and limitations of each, so that you know what to expect from your results.

Qualitative vs quantitative – the role of research questions

How do you know whether you need qualitative or quantitative research techniques? By finding out what kind of data you’re going to be collecting.

You’ll do this as you develop your research question, one of the first steps to any research program. It’s a single sentence that sums up the purpose of your research, who you’re going to gather data from, and what results you’re looking for.

As you formulate your question, you’ll get a sense of the sort of answer you’re working towards, and whether it will be expressed in numerical data or qualitative data.

For example, your research question might be “How often does a poor customer experience cause shoppers to abandon their shopping carts?” – this is a quantitative topic, as you’re looking for numerical values.

Or it might be “What is the emotional impact of a poor customer experience on regular customers in our supermarket?” This is a qualitative topic, concerned with thoughts and feelings and answered in personal, subjective ways that vary between respondents.

Here’s how to evaluate your research question and decide which method to use:

• Qualitative research:

Use this if your goal is to  understand  something – experiences, problems, ideas.

For example, you may want to understand how poor experiences in a supermarket make your customers feel. You might carry out this research through focus groups or in depth interviews (IDI’s). For a larger scale research method you could start  by surveying supermarket loyalty card holders, asking open text questions, like “How would you describe your experience today?” or “What could be improved about your experience?” This research will provide context and understanding that quantitative research will not.

• Quantitative research:

Use this if your goal is to  test or confirm  a hypothesis, or to study cause and effect relationships. For example, you want to find out what percentage of your returning customers are happy with the customer experience at your store. You can collect data to answer this via a survey.

For example, you could recruit 1,000 loyalty card holders as participants, asking them, “On a scale of 1-5, how happy are you with our store?” You can then make simple mathematical calculations to find the average score. The larger sample size will help make sure your results aren’t skewed by anomalous data or outliers, so you can draw conclusions with confidence.

Qualitative and quantitative research combined?

Do you always have to choose between qualitative or quantitative data?

In some cases you can get the best of both worlds by combining both quantitative and qualitative data.You could use pre quantitative data to understand the landscape of your research. Here you can gain  insights around a topic  and propose a  hypothesis.  Then adopt a quantitative research method to test it out. Here you’ll discover where to focus your survey appropriately or to pre-test your survey, to ensure your questions are understood as you intended. Finally, using a round of qualitative research methods to bring your insights and story to life. This mixed methods approach is becoming increasingly popular with businesses who are looking for in depth insights.

For example, in the supermarket scenario we’ve described, you could start out with a qualitative data collection phase where you use focus groups and conduct interviews with customers. You might find suggestions in your qualitative data that customers would like to be able to buy children’s clothes in the store.

In response, the supermarket might pilot a children’s clothing range. Targeted  quantitative  research could then reveal whether or not those stores selling children’s clothes achieve higher  customer satisfaction  scores  and a  rise in profits  for clothing.

Together, qualitative and quantitative data, combined with statistical analysis, have provided important insights about customer experience, and have proven the effectiveness of a solution to business problems.

Qualitative vs quantitative question types

As we’ve noted, surveys are one of the data collection methods suitable for both quantitative and qualitative research. Depending on the types of questions you choose to include, you can generate qualitative and quantitative data. Here we have summarized some of the survey question types you can use for each purpose.

Qualitative data survey questions

There are fewer survey  question  options for collecting qualitative data, since they all essentially do the same thing – provide the respondent with space to enter information in their own words. Qualitative research is not typically done with surveys alone, and researchers may use a mix of qualitative methods. As well as a survey, they might conduct in depth interviews, use observational studies or hold focus groups.

Open text ‘Other’ box (can be used with multiple choice questions)

Text box (space for short written answer)

Essay box (space for longer, more detailed written answers)

Quantitative data survey questions

These questions will yield quantitative data – i.e. a numerical value.

Net Promoter Score (NPS)

Likert Scale

Radio buttons (respondents choose just one option)

Check boxes (respondents can choose multiple options)

Sliding scale

Star rating

Analysing data (quantitative or qualitative) using technology

We are currently at an exciting point in the history of qualitative analysis. Digital analysis and other methods that were formerly exclusively used for  quantitative data  are now used for interpreting non numerical data too.

Artificial intelligence programs can now be used to analyse open text, and turn qualitative data into structured and semi structured quantitative data that relates to qualitative data topics such as emotion and sentiment, opinion and experience.

Research that in the past would have meant qualitative researchers conducting time-intensive studies using analysis methods like thematic analysis can now be done in a very short space of time. This not only saves time and money, but opens up qualitative data analysis to a much wider range of businesses and organisations.

The most advanced tools can even be used for real-time statistical analysis, forecasting and prediction, making them a powerful asset for businesses.

Qualitative or quantitative – which is better for data analysis?

Historically, quantitative data was much easier to analyse than qualitative data. But as we’ve seen, modern technology is helping qualitative analysis to catch up, making it quicker and less labor-intensive than before.

That means the choice between qualitative and quantitative studies no longer needs to factor in ease of analysis, provided you have the right tools at your disposal. With an integrated platform like Qualtrics, which incorporates data collection, data cleaning, data coding and a powerful suite of analysis tools for both qualitative and quantitative data, you have a wide range of options at your fingertips.

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