research descriptive study meaning

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Descriptive Research | Definition, Types, Methods & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/descriptive-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is quantitative research | definition, uses & methods, correlational research | when & how to use, descriptive statistics | definitions, types, examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

Analyze your descriptive research

Dovetail streamlines analysis to help you uncover and share actionable insights

Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

• Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

• Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

• Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

• Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

• Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

• Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

Get started today

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 21 December 2023

Last updated: 16 December 2023

Last updated: 6 October 2023

Last updated: 5 March 2024

Last updated: 25 November 2023

Last updated: 15 February 2024

Last updated: 11 March 2024

Last updated: 12 December 2023

Last updated: 6 March 2024

Last updated: 10 April 2023

Last updated: 20 December 2023

Latest articles

Related topics, log in or sign up.

Get started for free

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

Event Feedback Software: Top 11 Best in 2024

Apr 9, 2024

Top 10 Free Market Research Tools to Boost Your Business

Best 15 Behavior Analytics Tools to Explore Your User Actions

Apr 8, 2024

Top 7 Concept Testing Tools to Elevate Your Ideas in 2024

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

Child Care and Early Education Research Connections

Descriptive research studies.

Descriptive research is a type of research that is used to describe the characteristics of a population. It collects data that are used to answer a wide range of what, when, and how questions pertaining to a particular population or group. For example, descriptive studies might be used to answer questions such as: What percentage of Head Start teachers have a bachelor's degree or higher? What is the average reading ability of 5-year-olds when they first enter kindergarten? What kinds of math activities are used in early childhood programs? When do children first receive regular child care from someone other than their parents? When are children with developmental disabilities first diagnosed and when do they first receive services? What factors do programs consider when making decisions about the type of assessments that will be used to assess the skills of the children in their programs? How do the types of services children receive from their early childhood program change as children age?

Descriptive research does not answer questions about why a certain phenomenon occurs or what the causes are. Answers to such questions are best obtained from  randomized and quasi-experimental studies . However, data from descriptive studies can be used to examine the relationships (correlations) among variables. While the findings from correlational analyses are not evidence of causality, they can help to distinguish variables that may be important in explaining a phenomenon from those that are not. Thus, descriptive research is often used to generate hypotheses that should be tested using more rigorous designs.

A variety of data collection methods may be used alone or in combination to answer the types of questions guiding descriptive research. Some of the more common methods include surveys, interviews, observations, case studies, and portfolios. The data collected through these methods can be either quantitative or qualitative. Quantitative data are typically analyzed and presenting using  descriptive statistics . Using quantitative data, researchers may describe the characteristics of a sample or population in terms of percentages (e.g., percentage of population that belong to different racial/ethnic groups, percentage of low-income families that receive different government services) or averages (e.g., average household income, average scores of reading, mathematics and language assessments). Quantitative data, such as narrative data collected as part of a case study, may be used to organize, classify, and used to identify patterns of behaviors, attitudes, and other characteristics of groups.

Descriptive studies have an important role in early care and education research. Studies such as the  National Survey of Early Care and Education  and the  National Household Education Surveys Program  have greatly increased our knowledge of the supply of and demand for child care in the U.S. The  Head Start Family and Child Experiences Survey  and the  Early Childhood Longitudinal Study Program  have provided researchers, policy makers and practitioners with rich information about school readiness skills of children in the U.S.

Each of the methods used to collect descriptive data have their own strengths and limitations. The following are some of the strengths and limitations of descriptive research studies in general.

Study participants are questioned or observed in a natural setting (e.g., their homes, child care or educational settings).

Study data can be used to identify the prevalence of particular problems and the need for new or additional services to address these problems.

Descriptive research may identify areas in need of additional research and relationships between variables that require future study. Descriptive research is often referred to as "hypothesis generating research."

Depending on the data collection method used, descriptive studies can generate rich datasets on large and diverse samples.

Limitations:

Descriptive studies cannot be used to establish cause and effect relationships.

Respondents may not be truthful when answering survey questions or may give socially desirable responses.

The choice and wording of questions on a questionnaire may influence the descriptive findings.

Depending on the type and size of sample, the findings may not be generalizable or produce an accurate description of the population of interest.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

Prevent plagiarism, run a free check.

Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Descriptive Research Design | Definition, Methods & Examples. Scribbr. Retrieved 8 April 2024, from https://www.scribbr.co.uk/research-methods/descriptive-research-design/

Is this article helpful?

Shona McCombes

Other students also liked, a quick guide to experimental design | 5 steps & examples, correlational research | guide, design & examples, qualitative vs quantitative research | examples & methods.

Bridging the Gap: Overcome these 7 flaws in descriptive research design

Descriptive research design is a powerful tool used by scientists and researchers to gather information about a particular group or phenomenon. This type of research provides a detailed and accurate picture of the characteristics and behaviors of a particular population or subject. By observing and collecting data on a given topic, descriptive research helps researchers gain a deeper understanding of a specific issue and provides valuable insights that can inform future studies.

In this blog, we will explore the definition, characteristics, and common flaws in descriptive research design, and provide tips on how to avoid these pitfalls to produce high-quality results. Whether you are a seasoned researcher or a student just starting, understanding the fundamentals of descriptive research design is essential to conducting successful scientific studies.

Table of Contents

What Is Descriptive Research Design?

The descriptive research design involves observing and collecting data on a given topic without attempting to infer cause-and-effect relationships. The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data.

Descriptive research methods can include surveys, observational studies , and case studies, and the data collected can be qualitative or quantitative . The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.

Importance of Descriptive Research in Scientific Studies

1. understanding of a population or phenomenon.

Descriptive research provides a comprehensive picture of the characteristics and behaviors of a particular population or phenomenon, allowing researchers to gain a deeper understanding of the topic.

2. Baseline Information

The information gathered through descriptive research can serve as a baseline for future research and provide a foundation for further studies.

3. Informative Data

Descriptive research can provide valuable information and insights into a particular topic, which can inform future research, policy decisions, and programs.

4. Sampling Validation

Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study.

5. Cost Effective

Descriptive research is often less expensive and less time-consuming than other research methods , making it a cost-effective way to gather information about a particular population or phenomenon.

6. Easy to Replicate

Descriptive research is straightforward to replicate, making it a reliable way to gather and compare information from multiple sources.

Key Characteristics of Descriptive Research Design

The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon.

2. Participants and Sampling

Descriptive research studies a particular population or sample that is representative of the larger population being studied. Furthermore, sampling methods can include convenience, stratified, or random sampling.

3. Data Collection Techniques

Descriptive research typically involves the collection of both qualitative and quantitative data through methods such as surveys, observational studies, case studies, or focus groups.

4. Data Analysis

Descriptive research data is analyzed to identify patterns, relationships, and trends within the data. Statistical techniques , such as frequency distributions and descriptive statistics, are commonly used to summarize and describe the data.

5. Focus on Description

Descriptive research is focused on describing and summarizing the characteristics of a particular population or phenomenon. It does not make causal inferences.

6. Non-Experimental

Descriptive research is non-experimental, meaning that the researcher does not manipulate variables or control conditions. The researcher simply observes and collects data on the population or phenomenon being studied.

When Can a Researcher Conduct Descriptive Research?

A researcher can conduct descriptive research in the following situations:

• To better understand a particular population or phenomenon
• To describe the relationships between variables
• To describe patterns and trends
• To validate sampling methods and determine the best approach for a study
• To compare data from multiple sources.

Types of Descriptive Research Design

1. survey research.

Surveys are a type of descriptive research that involves collecting data through self-administered or interviewer-administered questionnaires. Additionally, they can be administered in-person, by mail, or online, and can collect both qualitative and quantitative data.

2. Observational Research

Observational research involves observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. It can be conducted in naturalistic settings or controlled laboratory settings.

3. Case Study Research

Case study research is a type of descriptive research that focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.

4. Focus Group Research

Focus group research involves bringing together a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.

5. Ethnographic Research

Ethnographic research involves conducting detailed observations of a particular culture or community. It is often used to gain a deep understanding of the beliefs, behaviors, and practices of a particular group.

Advantages of Descriptive Research Design

1. provides a comprehensive understanding.

Descriptive research provides a comprehensive picture of the characteristics, behaviors, and attributes of a particular population or phenomenon, which can be useful in informing future research and policy decisions.

2. Non-invasive

Descriptive research is non-invasive and does not manipulate variables or control conditions, making it a suitable method for sensitive or ethical concerns.

3. Flexibility

Descriptive research allows for a wide range of data collection methods , including surveys, observational studies, case studies, and focus groups, making it a flexible and versatile research method.

4. Cost-effective

Descriptive research is often less expensive and less time-consuming than other research methods. Moreover, it gives a cost-effective option to many researchers.

5. Easy to Replicate

Descriptive research is easy to replicate, making it a reliable way to gather and compare information from multiple sources.

6. Informs Future Research

The insights gained from a descriptive research can inform future research and inform policy decisions and programs.

Disadvantages of Descriptive Research Design

1. limited scope.

Descriptive research only provides a snapshot of the current situation and cannot establish cause-and-effect relationships.

2. Dependence on Existing Data

Descriptive research relies on existing data, which may not always be comprehensive or accurate.

3. Lack of Control

Researchers have no control over the variables in descriptive research, which can limit the conclusions that can be drawn.

The researcher’s own biases and preconceptions can influence the interpretation of the data.

5. Lack of Generalizability

Descriptive research findings may not be applicable to other populations or situations.

6. Lack of Depth

Descriptive research provides a surface-level understanding of a phenomenon, rather than a deep understanding.

7. Time-consuming

Descriptive research often requires a large amount of data collection and analysis, which can be time-consuming and resource-intensive.

7 Ways to Avoid Common Flaws While Designing Descriptive Research

1. Clearly define the research question

A clearly defined research question is the foundation of any research study, and it is important to ensure that the question is both specific and relevant to the topic being studied.

2. Choose the appropriate research design

Choosing the appropriate research design for a study is crucial to the success of the study. Moreover, researchers should choose a design that best fits the research question and the type of data needed to answer it.

3. Select a representative sample

Selecting a representative sample is important to ensure that the findings of the study are generalizable to the population being studied. Researchers should use a sampling method that provides a random and representative sample of the population.

4. Use valid and reliable data collection methods

Using valid and reliable data collection methods is important to ensure that the data collected is accurate and can be used to answer the research question. Researchers should choose methods that are appropriate for the study and that can be administered consistently and systematically.

5. Minimize bias

Bias can significantly impact the validity and reliability of research findings.  Furthermore, it is important to minimize bias in all aspects of the study, from the selection of participants to the analysis of data.

6. Ensure adequate sample size

An adequate sample size is important to ensure that the results of the study are statistically significant and can be generalized to the population being studied.

7. Use appropriate data analysis techniques

The appropriate data analysis technique depends on the type of data collected and the research question being asked. Researchers should choose techniques that are appropriate for the data and the question being asked.

Have you worked on descriptive research designs? How was your experience creating a descriptive design? What challenges did you face? Do write to us or leave a comment below and share your insights on descriptive research designs!

extremely very educative

Indeed very educative and useful. Well explained. Thank you

Simple,easy to understand

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Publishing Research
• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

• Industry News
• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

Achieving Research Excellence: Checklist for good research practices

Academia is built on the foundation of trustworthy and high-quality research, supported by the pillars…

• Promoting Research

Plain Language Summary — Communicating your research to bridge the academic-lay gap

Science can be complex, but does that mean it should not be accessible to the…

Science under Surveillance: Journals adopt advanced AI to uncover image manipulation

Journals are increasingly turning to cutting-edge AI tools to uncover deceitful images published in manuscripts.…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

What should universities' stance be on AI tools in research and academic writing?

Just one more step to your free trial.

.surveysparrow.com

Already using SurveySparrow?  Login

By clicking on "Get Started", I agree to the Privacy Policy and Terms of Service .

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Enterprise Survey Software

Enterprise Survey Software to thrive in your business ecosystem

NPS Software

Turn customers into promoters

Offline Survey

Real-time data collection, on the move. Go internet-independent.

360 Assessment

Conduct omnidirectional employee assessments. Increase productivity, grow together.

Reputation Management

Turn your existing customers into raving promoters by monitoring online reviews.

Ticket Management

Build loyalty and advocacy by delivering personalized support experiences that matter.

Chatbot for Website

Collect feedback smartly from your website visitors with the engaging Chatbot for website.

Swift, easy, secure. Scalable for your organization.

Executive Dashboard

Customer journey map, craft beautiful surveys, share surveys, gain rich insights, recurring surveys, white label surveys, embedded surveys, conversational forms, mobile-first surveys, audience management, smart surveys, video surveys, secure surveys, api, webhooks, integrations, survey themes, accept payments, custom workflows, all features, customer experience, employee experience, product experience, marketing experience, sales experience, hospitality & travel, market research, saas startup programs, wall of love, success stories, sparrowcast, nps benchmarks, learning centre, apps & integrations, testimonials.

Our surveys come with superpowers ⚡

Blog General

Descriptive Research 101: Definition, Methods and Examples

Parvathi vijayamohan.

8 April 2024

Table Of Contents

• Descriptive Research 101: The Definitive Guide

What is Descriptive Research?

Key characteristics of descriptive research.

• Descriptive Research Methods: The 3 You Need to Know!

Observation

Case studies, 7 types of descriptive research, descriptive research: examples to build your next study, tips to excel at descriptive research.

Imagine you are a detective called to a crime scene. Your job is to study the scene and report whatever you find: whether that’s the half-smoked cigarette on the table or the large “RACHE” written in blood on the wall. That, in a nutshell, is  descriptive research .

Researchers often need to do descriptive research on a problem before they attempt to solve it. So in this guide, we’ll take you through:

• What is descriptive research + characteristics
• Descriptive research methods
• Types of descriptive research
• Descriptive research examples
• Tips to excel at the descriptive method

Click to jump to the section that interests you.

Definition: As its name says, descriptive research  describes  the characteristics of the problem, phenomenon, situation, or group under study.

So the goal of all descriptive studies is to  explore  the background, details, and existing patterns in the problem to fully understand it. In other words, preliminary research.

However, descriptive research can be both  preliminary and conclusive . You can use the data from a descriptive study to make reports and get insights for further planning.

What descriptive research isn’t: Descriptive research finds the  what/when/where  of a problem, not the  why/how .

Because of this, we can’t use the descriptive method to explore cause-and-effect relationships where one variable (like a person’s job role) affects another variable (like their monthly income).

• Answers the “what,” “when,” and “where”  of a research problem. For this reason, it is popularly used in  market research ,  awareness surveys , and  opinion polls .
• Sets the stage  for a research problem. As an early part of the research process, descriptive studies help you dive deeper into the topic.
• Opens the door  for further research. You can use descriptive data as the basis for more profound research, analysis and studies.
• Qualitative and quantitative . It is possible to get a balanced mix of numerical responses and open-ended answers from the descriptive method.
• No control or interference with the variables . The researcher simply observes and reports on them. However, specific research software has filters that allow her to zoom in on one variable.
• Done in natural settings . You can get the best results from descriptive research by talking to people, surveying them, or observing them in a suitable environment. For example, suppose you are a website beta testing an app feature. In that case, descriptive research invites users to try the feature, tracking their behavior and then asking their opinions .
• Can be applied to many research methods and areas. Examples include healthcare, SaaS, psychology, political studies, education, and pop culture.

Descriptive Research Methods: The Top Three You Need to Know!

In short, survey research is a brief interview or conversation with a set of prepared questions about a topic.

So you create a questionnaire, share it, and analyze the data you collect for further action. Learn about the differences between surveys and questionnaires  here .

You can access free survey templates , over 20+ question types, and pass data to 1,500+ applications with survey software, like SurveySparrow . It enables you to create surveys, share them and capture data with very little effort.

Sign up today to launch stunning surveys for free.

Please enter a valid Email ID.

14-Day Free Trial • No Credit Card Required • No Strings Attached

• Surveys can be hyper-local, regional, or global, depending on your objectives.
• Share surveys in-person, offline, via SMS, email, or QR codes – so many options!
• Easy to automate if you want to conduct many surveys over a period.

The observational method is a type of descriptive research in which you, the researcher, observe ongoing behavior.

Now, there are several (non-creepy) ways you can observe someone. In fact, observational research has three main approaches:

• Covert observation: In true spy fashion, the researcher mixes in with the group undetected or observes from a distance.
• Overt observation : The researcher identifies himself as a researcher – “The name’s Bond. J. Bond.” – and explains the purpose of the study.
• Participatory observation : The researcher participates in what he is observing to understand his topic better.
• Observation is one of the most accurate ways to get data on a subject’s behavior in a natural setting.
• You don’t need to rely on people’s willingness to share information.
• Observation is a universal method that can be applied to any area of research.

In the case study method, you do a detailed study of a specific group, person, or event over a period.

This brings us to a frequently asked question: “What’s the difference between case studies and longitudinal studies?”

A case study will go  very in-depth into the subject with one-on-one interviews, observations, and archival research. They are also qualitative, though sometimes they will use numbers and stats.

An example of longitudinal research would be a study of the health of night shift employees vs. general shift employees over a decade. An example of a case study would involve in-depth interviews with Casey, an assistant director of nursing who’s handled the night shift at the hospital for ten years now.

• Due to the focus on a few people, case studies can give you a tremendous amount of information.
• Because of the time and effort involved, a case study engages both researchers and participants.
• Case studies are helpful for ethically investigating unusual, complex, or challenging subjects. An example would be a study of the habits of long-term cocaine users.

1. Case Study: Airbnb’s Growth Strategy

In an excellent case study, Tam Al Saad, Principal Consultant, Strategy + Growth at Webprofits, deep dives into how Airbnb attracted and retained 150 million users .

“What Airbnb offers isn’t a cheap place to sleep when you’re on holiday; it’s the opportunity to experience your destination as a local would. It’s the chance to meet the locals, experience the markets, and find non-touristy places.

Sure, you can visit the Louvre, see Buckingham Palace, and climb the Empire State Building, but you can do it as if it were your hometown while staying in a place that has character and feels like a home.” – Tam al Saad, Principal Consultant, Strategy + Growth at Webprofits

2. Observation – Better Tech Experiences for the Elderly

We often think that our elders are so hopeless with technology. But we’re not getting any younger either, and tech is changing at a hair trigger! This article by Annemieke Hendricks shares a wonderful example where researchers compare the levels of technological familiarity between age groups and how that influences usage.

“It is generally assumed that older adults have difficulty using modern electronic devices, such as mobile telephones or computers. Because this age group is growing in most countries, changing products and processes to adapt to their needs is increasingly more important. “ – Annemieke Hendricks, Marketing Communication Specialist, Noldus

3. Surveys – Decoding Sleep with SurveySparrow

SRI International (formerly Stanford Research Institute) – an independent, non-profit research center – wanted to investigate the impact of stress on an adolescent’s sleep. To get those insights, two actions were essential: tracking sleep patterns through wearable devices and sending surveys at a pre-set time –  the pre-sleep period.

“With SurveySparrow’s recurring surveys feature, SRI was able to share engaging surveys with their participants exactly at the time they wanted and at the frequency they preferred.”

Read more about this project : How SRI International decoded sleep patterns with SurveySparrow

1: Answer the six Ws –

• Who should we consider?
• What information do we need?
• When should we collect the information?
• Where should we collect the information?
• Why are we obtaining the information?
• Way to collect the information

#2: Introduce and explain your methodological approach

#3: Describe your methods of data collection and/or selection.

#4: Describe your methods of analysis.

#5: Explain the reasoning behind your choices.

#6: Collect data.

#7: Analyze the data. Use software to speed up the process and reduce overthinking and human error.

#8: Report your conclusions and how you drew the results.

Wrapping Up

That’s all, folks!

Growth Marketer at SurveySparrow

Fledgling growth marketer. Cloud watcher. Aunty to a naughty beagle.

You Might Also Like

A guide to effective lead generation strategies, 6 surefire tips to grow your b2b ecommerce business, survey design: a 10-step guide with examples.

Leave us your email, we wont spam. Promise!

Start your free trial today

No Credit Card Required. 14-Day Free Trial

Request a Demo

Want to learn more about SurveySparrow? We'll be in touch soon!

Scale up your descriptive research with the best survey software

Build surveys that actually work. give surveysparrow a free try today.

14-Day Free Trial • No Credit card required • 40% more completion rate

Hi there, we use cookies to offer you a better browsing experience and to analyze site traffic. By continuing to use our website, you consent to the use of these cookies. Learn More

What is Descriptive Research and How is it Used?

Introduction

What does descriptive research mean, why would you use a descriptive research design, what are the characteristics of descriptive research, examples of descriptive research, what are the data collection methods in descriptive research, how do you analyze descriptive research data, ensuring validity and reliability in the findings.

Conducting descriptive research offers researchers a way to present phenomena as they naturally occur. Rooted in an open-ended and non-experimental nature, this type of research focuses on portraying the details of specific phenomena or contexts, helping readers gain a clearer understanding of topics of interest.

From businesses gauging customer satisfaction to educators assessing classroom dynamics, the data collected from descriptive research provides invaluable insights across various fields.

This article aims to illuminate the essence, utility, characteristics, and methods associated with descriptive research, guiding those who wish to harness its potential in their respective domains.

At its core, descriptive research refers to a systematic approach used by researchers to collect, analyze, and present data about real-life phenomena to describe it in its natural context. It primarily aims to describe what exists, based on empirical observations .

Unlike experimental research, where variables are manipulated to observe outcomes, descriptive research deals with the "as-is" scenario to facilitate further research by providing a framework or new insights on which continuing studies can build.

Definition of descriptive research

Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon.

The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.

The difference between descriptive and exploratory research

While both descriptive and exploratory research seek to provide insights into a topic or phenomenon, they differ in their focus. Exploratory research is more about investigating a topic to develop preliminary insights or to identify potential areas of interest.

In contrast, descriptive research offers detailed accounts and descriptions of the observed phenomenon, seeking to paint a full picture of what's happening.

The evolution of descriptive research in academia

Historically, descriptive research has played a foundational role in numerous academic disciplines. Anthropologists, for instance, used this approach to document cultures and societies. Psychologists have employed it to capture behaviors, emotions, and reactions.

Over time, the method has evolved, incorporating technological advancements and adapting to contemporary needs, yet its essence remains rooted in describing a phenomenon or setting as it is.

Descriptive research serves as a cornerstone in the research landscape for its ability to provide a detailed snapshot of life. Its unique qualities and methods make it an invaluable method for various research purposes. Here's why:

Benefits of obtaining a clear picture

Descriptive research captures the present state of phenomena, offering researchers a detailed reflection of situations. This unaltered representation is crucial for sectors like marketing, where understanding current consumer behavior can shape future strategies.

Facilitating data interpretation

Given its straightforward nature, descriptive research can provide data that's easier to interpret, both for researchers and their audiences. Rather than analyzing complex statistical relationships among variables, researchers present detailed descriptions of their qualitative observations . Researchers can engage in in depth analysis relating to their research question , but audiences can also draw insights from their own interpretations or reflections on potential underlying patterns.

Enhancing the clarity of the research problem

By presenting things as they are, descriptive research can help elucidate ambiguous research questions. A well-executed descriptive study can shine light on overlooked aspects of a problem, paving the way for further investigative research.

Addressing practical problems

In real-world scenarios, it's not always feasible to manipulate variables or set up controlled experiments. For instance, in social sciences, understanding cultural norms without interference is paramount. Descriptive research allows for such non-intrusive insights, ensuring genuine understanding.

Building a foundation for future research

Often, descriptive studies act as stepping stones for more complex research endeavors. By establishing baseline data and highlighting patterns, they create a platform upon which more intricate hypotheses can be built and tested in subsequent studies.

Descriptive research is distinguished by a set of hallmark characteristics that set it apart from other research methodologies . Recognizing these features can help researchers effectively design, implement , and interpret descriptive studies.

Specificity in the research question

As with all research, descriptive research starts with a well-defined research question aiming to detail a particular phenomenon. The specificity ensures that the study remains focused on gathering relevant data without unnecessary deviations.

Focus on the present situation

While some research methods aim to predict future trends or uncover historical truths, descriptive research is predominantly concerned with the present. It seeks to capture the current state of affairs, such as understanding today's consumer habits or documenting a newly observed phenomenon.

Standardized and structured methodology

To ensure credibility and consistency in results, descriptive research often employs standardized methods. Whether it's using a fixed set of survey questions or adhering to specific observation protocols, this structured approach ensures that data is collected uniformly, making it easier to compare and analyze.

Non-manipulative approach in observation

One of the standout features of descriptive research is its non-invasive nature. Researchers observe and document without influencing the research subject or the environment. This passive stance ensures that the data gathered is a genuine reflection of the phenomenon under study.

Replicability and consistency in results

Due to its structured methodology, findings from descriptive research can often be replicated in different settings or with different samples. This consistency adds to the credibility of the results, reinforcing the validity of the insights drawn from the study.

Analyze data quickly and efficiently with ATLAS.ti

Download a free trial to see how you can make sense of complex qualitative data.

Numerous fields and sectors conduct descriptive research for its versatile and detailed nature. Through its focus on presenting things as they naturally occur, it provides insights into a myriad of scenarios. Here are some tangible examples from diverse domains:

Conducting market research

Businesses often turn to data analysis through descriptive research to understand the demographics of their target market. For instance, a company launching a new product might survey potential customers to understand their age, gender, income level, and purchasing habits, offering valuable data for targeted marketing strategies.

Evaluating employee behaviors

Organizations rely on descriptive research designs to assess the behavior and attitudes of their employees. By conducting observations or surveys , companies can gather data on workplace satisfaction, collaboration patterns, or the impact of a new office layout on productivity.

Understanding consumer preferences

Brands aiming to understand their consumers' likes and dislikes often use descriptive research. By observing shopping behaviors or conducting product feedback surveys , they can gauge preferences and adjust their offerings accordingly.

Documenting historical patterns

Historians and anthropologists employ descriptive research to identify patterns through analysis of events or cultural practices. For instance, a historian might detail the daily life in a particular era, while an anthropologist might document rituals and ceremonies of a specific tribe.

Assessing student performance

Educational researchers can utilize descriptive studies to understand the effectiveness of teaching methodologies. By observing classrooms or surveying students, they can measure data trends and gauge the impact of a new teaching technique or curriculum on student engagement and performance.

Descriptive research methods aim to authentically represent situations and phenomena. These techniques ensure the collection of comprehensive and reliable data about the subject of interest.

The most appropriate descriptive research method depends on the research question and resources available for your research study.

Surveys and questionnaires

One of the most familiar tools in the researcher's arsenal, surveys and questionnaires offer a structured means of collecting data from a vast audience. Through carefully designed questions, researchers can obtain standardized responses that lend themselves to straightforward comparison and analysis in quantitative and qualitative research .

Survey research can manifest in various formats, from face-to-face interactions and telephone conversations to digital platforms. While surveys can reach a broad audience and generate quantitative data ripe for statistical analysis, they also come with the challenge of potential biases in design and rely heavily on respondent honesty.

Observations and case studies

Direct or participant observation is a method wherein researchers actively watch and document behaviors or events. A researcher might, for instance, observe the dynamics within a classroom or the behaviors of shoppers in a market setting.

Case studies provide an even deeper dive, focusing on a thorough analysis of a specific individual, group, or event. These methods present the advantage of capturing real-time, detailed data, but they might also be time-intensive and can sometimes introduce observer bias .

Interviews and focus groups

Interviews , whether they follow a structured script or flow more organically, are a powerful means to extract detailed insights directly from participants. On the other hand, focus groups gather multiple participants for discussions, aiming to gather diverse and collective opinions on a particular topic or product.

These methods offer the benefit of deep insights and adaptability in data collection . However, they necessitate skilled interviewers, and focus group settings might see individual opinions being influenced by group dynamics.

Document and content analysis

Here, instead of generating new data, researchers examine existing documents or content . This can range from studying historical records and newspapers to analyzing media content or literature.

Analyzing existing content offers the advantage of accessibility and can provide insights over longer time frames. However, the reliability and relevance of the content are paramount, and researchers must approach this method with a discerning eye.

Descriptive research data, rich in details and insights, necessitates meticulous analysis to derive meaningful conclusions. The analysis process transforms raw data into structured findings that can be communicated and acted upon.

Qualitative content analysis

For data collected through interviews , focus groups , observations , or open-ended survey questions , qualitative content analysis is a popular choice. This involves examining non-numerical data to identify patterns, themes, or categories.

By coding responses or observations , researchers can identify recurring elements, making it easier to comprehend larger data sets and draw insights.

Using descriptive statistics

When dealing with quantitative data from surveys or experiments, descriptive statistics are invaluable. Measures such as mean, median, mode, standard deviation, and frequency distributions help summarize data sets, providing a snapshot of the overall patterns.

Graphical representations like histograms, pie charts, or bar graphs can further help in visualizing these statistics.

Coding and categorizing the data

Both qualitative and quantitative data often require coding. Coding involves assigning labels to specific responses or behaviors to group similar segments of data. This categorization aids in identifying patterns, especially in vast data sets.

For instance, responses to open-ended questions in a survey can be coded based on keywords or sentiments, allowing for a more structured analysis.

Visual representation through graphs and charts

Visual aids like graphs, charts, and plots can simplify complex data, making it more accessible and understandable. Whether it's showcasing frequency distributions through histograms or mapping out relationships with networks, visual representations can elucidate trends and patterns effectively.

In the realm of research , the credibility of findings is paramount. Without trustworthiness in the results, even the most meticulously gathered data can lose its value. Two cornerstones that bolster the credibility of research outcomes are validity and reliability .

Validity: Measuring the right thing

Validity addresses the accuracy of the research. It seeks to answer the question: Is the research genuinely measuring what it aims to measure? In descriptive research, where the objective is to paint an authentic picture of the current state of affairs, ensuring validity is crucial.

For instance, if a study aims to understand consumer preferences for a product category, the questions posed should genuinely reflect those preferences and not veer into unrelated territories. Multiple forms of validity, including content, criterion, and construct validity, can be examined to ensure that the research instruments and processes are aligned with the research goals.

Reliability: Consistency in findings

Reliability, on the other hand, pertains to the consistency of the research findings. When a study demonstrates reliability, this suggests that others could repeat the study and the outcomes would remain consistent across repetitions.

In descriptive research, factors like the clarity of survey questions , the training of observers , and the standardization of interview protocols play a role in enhancing reliability. Techniques such as test-retest and internal consistency measurements can be employed to assess and improve reliability.

Make your research happen with ATLAS.ti

Analyze descriptive research with our powerful data analysis interface. Download a free trial of ATLAS.ti.

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Employee Exit Interviews
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories

Market Research

• Artificial Intelligence
• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Descriptive Research

Try Qualtrics for free

Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

Free eBook: 2024 global market research trends report

What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

Ready to learn more about Qualtrics?

• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

Connect to Formplus, Get Started Now - It's Free!

• descriptive research
• descriptive research method
• example of descriptive research
• types of descriptive research
• busayo.longe

You may also like:

Extrapolation in Statistical Research: Definition, Examples, Types, Applications

In this article we’ll look at the different types and characteristics of extrapolation, plus how it contrasts to interpolation.

Cross-Sectional Studies: Types, Pros, Cons & Uses

In this article, we’ll look at what cross-sectional studies are, how it applies to your research and how to use Formplus to collect...

Type I vs Type II Errors: Causes, Examples & Prevention

This article will discuss the two different types of errors in hypothesis testing and how you can prevent them from occurring in your research

Acceptance Sampling: Meaning, Examples, When to Use

In this post, we will discuss extensively what acceptance sampling is and when it is applied.

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

Descriptive Research: Definition, 7 Types, Examples

Definition of Descriptive Studies

Descriptive studies are those used to describe the characteristics of a population or phenomena.

Objectives of Descriptive Studies

The objective of a descriptive study is to focus on ‘who,’ ‘what,’ ‘when,’ and ‘how’ questions. The simplest descriptive study aims at

• Describing phenomena or characteristics associated with a population by univariate questions;
• Estimating the proportions of a population that have the characteristics outlined above, and
• Discovering association (but not causation) among different variables.

Descriptive studies may be carried out on a small or large scale. Such a study may often be completed within a few months, weeks, or even a few hours.

When its findings pertain to a smaller population and are of short duration, we may call it a descriptive case study or micro-study, and by nature, it is an explorative type study.

However, if one wishes to test whether the findings pertain to a larger population, a more extensive study has to be designed.

7 Types of Descriptive Studies

Cross-sectional study.

It is a single unrepeated descriptive study aimed at studying a cross-section of the population at a single point in time.

By cross-section, we mean a broad sampling of persons of different ages, different educational levels, different religions, and so on.

The cross-sectional study is sometimes referred to as the ‘snapshot approach’ because although the single study can provide a momentary representative portrait of a population, it cannot trace the process of changes.

A national census is a good example of a cross-sectional study. A population census provides enormous descriptive information on such cross-sectional characteristics as age, sex, religion, ethnicity, occupational composition, household structure, and the like.

These characteristics can be expressed in absolute terms (e.g., number of illiterate persons) or proportion (e.g., percentage of illiterate persons).

One might attempt to examine also the relationship between household structure and occupational composition by a simple cross-tabulation of these variables.

A cross-tabulation of the level of education and occupation may also reveal a close association. A census is considered a macro study since its unit of analysis is a large aggregate of persons covering a large geographical area.

A descriptive study may go much beyond the simple relationship, as we perceive above.

Such studies are more complex and involve studying inter-relationships of many factors, suggesting a multivariate analysis .

Such a descriptive study might indicate causal relationships between the dependent and independent variables. This may ultimately suggest useful hypotheses.

Longitudinal study

A descriptive study may also be longitudinal. The difference between a cross-sectional and a longitudinal study is in how they deal with time.

Longitudinal studies are repeated over an extended period to measure the rate and degree of change occurring in response patterns.

Trend study

One type of longitudinal study is the trend study, which consists of several successive surveys based on a different sample of subjects.

Such a study involves studying the same topic (for example, attitude towards the use of traditional methods of contraception) by re-interviewing over some time, but with no attempt to re-interview the same respondents each time.

Gallup polls are conducted in this way, and comparisons of the results of several different polls can be quite useful for analyzing trends.

Panel study

The panel study is also a longitudinal study designed specifically to minimize the effects of repeated sampling error as encountered in trend study.

A sample or a panel is chosen in a panel study, and the same group of respondents is re-surveyed at selected intervals.

Thus the later responses of any subject or the sample as a whole can be directly compared to responses given earlier.

Baseline study

A baseline study is a research in which data on pre-project socio­economic and business aspects are generated to assess the future impact of project intervention.

A baseline survey is conducted without available published data on various socio­economic and business aspects.

Impact Assessment study

The research, which is undertaken to measure the quantitative benefits derived from project intervention and qualitative changes that occurred due to intervention, is known as impact assessment research.

This type of research also provides information for identifying the project’s negative impact.

Assessment research primarily involves characterizations-objective description, while evaluation research involves characterizations and appraisals-determinations of merit and/or worth.

Feasibility study

This type of study is undertaken before starting any business enterprise or business-related project to assess the project’s technical, economic, market, and financial viability.

The issue of whether the project is socially desirable and environmentally acceptable is also considered.

Descriptive studies describes the characteristics of a population or phenomenon.

What are the main objectives of descriptive studies?

The objectives of descriptive studies are to focus on ‘who,’ ‘what,’ ‘when,’ and ‘how’ questions, describe phenomena or characteristics associated with a population, estimate the proportions of a population that have certain characteristics, and discover associations among different variables without establishing causation.

How does a cross-sectional study differ from a longitudinal study?

A cross-sectional study is a single unrepeated study that examines a population at a single point in time, often referred to as the ‘snapshot approach’. In contrast, a longitudinal study is repeated over an extended period to measure the rate and degree of change in response patterns.

What is a trend study in the context of descriptive research?

A trend study is a type of longitudinal study that consists of several successive surveys based on different samples of subjects. It involves studying the same topic over time through different polls or surveys without re-interviewing the same respondents each time.

How is a panel study conducted?

A sample or panel is chosen in a panel study, and the same group of respondents is re-surveyed at selected intervals. This allows for directly comparing later responses to earlier ones from the same subjects.

What is the purpose of a baseline study?

A baseline study is research where data on pre-project socioeconomic and business aspects are generated to assess the future impact of project intervention, especially when there’s no available published data on various socioeconomic and business aspects.

What is an impact assessment study?

An impact assessment study is research undertaken to measure the quantitative benefits of project intervention and the qualitative changes that occurred due to the intervention. It also identifies any negative impacts of the project.

Your email address will not be published. Required fields are marked *

Psychological Research

Descriptive Research

Learning objectives.

• Differentiate between descriptive, experimental, and correlational research
• Explain the strengths and weaknesses of case studies, naturalistic observation, and surveys

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments.

The three main categories of psychological research are descriptive, correlational, and experimental research. Research studies that do not test specific relationships between variables are called descriptive, or qualitative, studies . These studies are used to describe general or specific behaviors and attributes that are observed and measured. In the early stages of research it might be difficult to form a hypothesis, especially when there is not any existing literature in the area. In these situations designing an experiment would be premature, as the question of interest is not yet clearly defined as a hypothesis. Often a researcher will begin with a non-experimental approach, such as a descriptive study, to gather more information about the topic before designing an experiment or correlational study to address a specific hypothesis. Descriptive research is distinct from correlational research , in which psychologists formally test whether a relationship exists between two or more variables. Experimental research goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about how these conditions affect behavior. It aims to determine if one variable directly impacts and causes another. Correlational and experimental research both typically use hypothesis testing, whereas descriptive research does not.

Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions. For example, studies that rely primarily on observation produce incredible amounts of information, but the ability to apply this information to the larger population is somewhat limited because of small sample sizes. Survey research, on the other hand, allows researchers to easily collect data from relatively large samples. While this allows for results to be generalized to the larger population more easily, the information that can be collected on any given survey is somewhat limited and subject to problems associated with any type of self-reported data. Some researchers conduct archival research by using existing records. While this can be a fairly inexpensive way to collect data that can provide insight into a number of research questions, researchers using this approach have no control on how or what kind of data was collected.

Correlational research can find a relationship between two variables, but the only way a researcher can claim that the relationship between the variables is cause and effect is to perform an experiment. In experimental research, which will be discussed later in the text, there is a tremendous amount of control over variables of interest. While this is a powerful approach, experiments are often conducted in very artificial settings. This calls into question the validity of experimental findings with regard to how they would apply in real-world settings. In addition, many of the questions that psychologists would like to answer cannot be pursued through experimental research because of ethical concerns.

The three main types of descriptive studies are case studies, naturalistic observation, and surveys.

Case Studies

In 2011, the New York Times published a feature story on Krista and Tatiana Hogan, Canadian twin girls. These particular twins are unique because Krista and Tatiana are conjoined twins, connected at the head. There is evidence that the two girls are connected in a part of the brain called the thalamus, which is a major sensory relay center. Most incoming sensory information is sent through the thalamus before reaching higher regions of the cerebral cortex for processing.

Link to Learning

To learn more about Krista and Tatiana, watch this video about their lives as conjoined twins.

The implications of this potential connection mean that it might be possible for one twin to experience the sensations of the other twin. For instance, if Krista is watching a particularly funny television program, Tatiana might smile or laugh even if she is not watching the program. This particular possibility has piqued the interest of many neuroscientists who seek to understand how the brain uses sensory information.

These twins represent an enormous resource in the study of the brain, and since their condition is very rare, it is likely that as long as their family agrees, scientists will follow these girls very closely throughout their lives to gain as much information as possible (Dominus, 2011).

In observational research, scientists are conducting a clinical or case study when they focus on one person or just a few individuals. Indeed, some scientists spend their entire careers studying just 10–20 individuals. Why would they do this? Obviously, when they focus their attention on a very small number of people, they can gain a tremendous amount of insight into those cases. The richness of information that is collected in clinical or case studies is unmatched by any other single research method. This allows the researcher to have a very deep understanding of the individuals and the particular phenomenon being studied.

If clinical or case studies provide so much information, why are they not more frequent among researchers? As it turns out, the major benefit of this particular approach is also a weakness. As mentioned earlier, this approach is often used when studying individuals who are interesting to researchers because they have a rare characteristic. Therefore, the individuals who serve as the focus of case studies are not like most other people. If scientists ultimately want to explain all behavior, focusing attention on such a special group of people can make it difficult to generalize any observations to the larger population as a whole. Generalizing refers to the ability to apply the findings of a particular research project to larger segments of society. Again, case studies provide enormous amounts of information, but since the cases are so specific, the potential to apply what’s learned to the average person may be very limited.

Naturalistic Observation

If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances are that almost everyone in the classroom will raise their hand, but do you think hand washing after every trip to the restroom is really that universal?

This is very similar to the phenomenon mentioned earlier in this module: many individuals do not feel comfortable answering a question honestly. But if we are committed to finding out the facts about hand washing, we have other options available to us.

Suppose we send a classmate into the restroom to actually watch whether everyone washes their hands after using the restroom. Will our observer blend into the restroom environment by wearing a white lab coat, sitting with a clipboard, and staring at the sinks? We want our researcher to be inconspicuous—perhaps standing at one of the sinks pretending to put in contact lenses while secretly recording the relevant information. This type of observational study is called naturalistic observation : observing behavior in its natural setting. To better understand peer exclusion, Suzanne Fanger collaborated with colleagues at the University of Texas to observe the behavior of preschool children on a playground. How did the observers remain inconspicuous over the duration of the study? They equipped a few of the children with wireless microphones (which the children quickly forgot about) and observed while taking notes from a distance. Also, the children in that particular preschool (a “laboratory preschool”) were accustomed to having observers on the playground (Fanger, Frankel, & Hazen, 2012).

It is critical that the observer be as unobtrusive and as inconspicuous as possible: when people know they are being watched, they are less likely to behave naturally. If you have any doubt about this, ask yourself how your driving behavior might differ in two situations: In the first situation, you are driving down a deserted highway during the middle of the day; in the second situation, you are being followed by a police car down the same deserted highway (Figure 1).

It should be pointed out that naturalistic observation is not limited to research involving humans. Indeed, some of the best-known examples of naturalistic observation involve researchers going into the field to observe various kinds of animals in their own environments. As with human studies, the researchers maintain their distance and avoid interfering with the animal subjects so as not to influence their natural behaviors. Scientists have used this technique to study social hierarchies and interactions among animals ranging from ground squirrels to gorillas. The information provided by these studies is invaluable in understanding how those animals organize socially and communicate with one another. The anthropologist Jane Goodall, for example, spent nearly five decades observing the behavior of chimpanzees in Africa (Figure 2). As an illustration of the types of concerns that a researcher might encounter in naturalistic observation, some scientists criticized Goodall for giving the chimps names instead of referring to them by numbers—using names was thought to undermine the emotional detachment required for the objectivity of the study (McKie, 2010).

The greatest benefit of naturalistic observation is the validity, or accuracy, of information collected unobtrusively in a natural setting. Having individuals behave as they normally would in a given situation means that we have a higher degree of ecological validity, or realism, than we might achieve with other research approaches. Therefore, our ability to generalize the findings of the research to real-world situations is enhanced. If done correctly, we need not worry about people or animals modifying their behavior simply because they are being observed. Sometimes, people may assume that reality programs give us a glimpse into authentic human behavior. However, the principle of inconspicuous observation is violated as reality stars are followed by camera crews and are interviewed on camera for personal confessionals. Given that environment, we must doubt how natural and realistic their behaviors are.

The major downside of naturalistic observation is that they are often difficult to set up and control. In our restroom study, what if you stood in the restroom all day prepared to record people’s hand washing behavior and no one came in? Or, what if you have been closely observing a troop of gorillas for weeks only to find that they migrated to a new place while you were sleeping in your tent? The benefit of realistic data comes at a cost. As a researcher you have no control of when (or if) you have behavior to observe. In addition, this type of observational research often requires significant investments of time, money, and a good dose of luck.

Sometimes studies involve structured observation. In these cases, people are observed while engaging in set, specific tasks. An excellent example of structured observation comes from Strange Situation by Mary Ainsworth (you will read more about this in the module on lifespan development). The Strange Situation is a procedure used to evaluate attachment styles that exist between an infant and caregiver. In this scenario, caregivers bring their infants into a room filled with toys. The Strange Situation involves a number of phases, including a stranger coming into the room, the caregiver leaving the room, and the caregiver’s return to the room. The infant’s behavior is closely monitored at each phase, but it is the behavior of the infant upon being reunited with the caregiver that is most telling in terms of characterizing the infant’s attachment style with the caregiver.

Another potential problem in observational research is observer bias . Generally, people who act as observers are closely involved in the research project and may unconsciously skew their observations to fit their research goals or expectations. To protect against this type of bias, researchers should have clear criteria established for the types of behaviors recorded and how those behaviors should be classified. In addition, researchers often compare observations of the same event by multiple observers, in order to test inter-rater reliability : a measure of reliability that assesses the consistency of observations by different observers.

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally (Figure 3). Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

Surveys allow researchers to gather data from larger samples than may be afforded by other research methods . A sample is a subset of individuals selected from a population , which is the overall group of individuals that the researchers are interested in. Researchers study the sample and seek to generalize their findings to the population.

There is both strength and weakness of the survey in comparison to case studies. By using surveys, we can collect information from a larger sample of people. A larger sample is better able to reflect the actual diversity of the population, thus allowing better generalizability. Therefore, if our sample is sufficiently large and diverse, we can assume that the data we collect from the survey can be generalized to the larger population with more certainty than the information collected through a case study. However, given the greater number of people involved, we are not able to collect the same depth of information on each person that would be collected in a case study.

Another potential weakness of surveys is something we touched on earlier in this module: people don’t always give accurate responses. They may lie, misremember, or answer questions in a way that they think makes them look good. For example, people may report drinking less alcohol than is actually the case.

Any number of research questions can be answered through the use of surveys. One real-world example is the research conducted by Jenkins, Ruppel, Kizer, Yehl, and Griffin (2012) about the backlash against the US Arab-American community following the terrorist attacks of September 11, 2001. Jenkins and colleagues wanted to determine to what extent these negative attitudes toward Arab-Americans still existed nearly a decade after the attacks occurred. In one study, 140 research participants filled out a survey with 10 questions, including questions asking directly about the participant’s overt prejudicial attitudes toward people of various ethnicities. The survey also asked indirect questions about how likely the participant would be to interact with a person of a given ethnicity in a variety of settings (such as, “How likely do you think it is that you would introduce yourself to a person of Arab-American descent?”). The results of the research suggested that participants were unwilling to report prejudicial attitudes toward any ethnic group. However, there were significant differences between their pattern of responses to questions about social interaction with Arab-Americans compared to other ethnic groups: they indicated less willingness for social interaction with Arab-Americans compared to the other ethnic groups. This suggested that the participants harbored subtle forms of prejudice against Arab-Americans, despite their assertions that this was not the case (Jenkins et al., 2012).

Think It Over

A friend of yours is working part-time in a local pet store. Your friend has become increasingly interested in how dogs normally communicate and interact with each other, and is thinking of visiting a local veterinary clinic to see how dogs interact in the waiting room. After reading this section, do you think this is the best way to better understand such interactions? Do you have any suggestions that might result in more valid data?

CC licensed content, Original

• Modification and adaptation. Provided by : Lumen Learning. License : CC BY-SA: Attribution-ShareAlike

CC licensed content, Shared previously

• Approaches to Research. Authored by : OpenStax College. Located at : https://openstax.org/books/psychology-2e/pages/2-2-approaches-to-research . License : CC BY: Attribution . License Terms : Download for free at https://openstax.org/books/psychology-2e/pages/1-introduction.
• Descriptive Research. Provided by : Boundless. Located at : https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/researching-psychology-2/types-of-research-studies-27/descriptive-research-124-12659/ . License : CC BY-SA: Attribution-ShareAlike

research studies that do not test specific relationships between variables; they are used to describe general or specific behaviors and attributes that are observed and measured

tests whether a relationship exists between two or more variables

tests a hypothesis to determine cause and effect relationships

observational research study focusing on one or a few people

observation of behavior in its natural setting

inferring that the results for a sample apply to the larger population

when observations may be skewed to align with observer expectations

measure of agreement among observers on how they record and classify a particular event

list of questions to be answered by research participants—given as paper-and-pencil questionnaires, administered electronically, or conducted verbally—allowing researchers to collect data from a large number of people

the collection of individuals on which we collect data.

a larger collection of individuals that we would like to generalize our results to.

General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

Using Science to Inform Educational Practices

Descriptive Research

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior. Some methods rely on observational techniques. Other approaches involve interactions between the researcher and the individuals who are being studied—ranging from a series of simple questions to extensive, in-depth interviews—to well-controlled experiments. The main categories of psychological research are descriptive, correlational, and experimental research. Each of these research methods has unique strengths and weaknesses, and each method may only be appropriate for certain types of research questions.

Research studies that do not test specific relationships between variables are called  descriptive studies . For this method, the research question or hypothesis can be about a single variable (e.g., How accurate are people’s first impressions?) or can be a broad and exploratory question (e.g., What is it like to be a working mother diagnosed with depression?). The variable of the study is measured and reported without any further relationship analysis. A researcher might choose this method if they only needed to report information, such as a tally, an average, or a list of responses. Descriptive research can answer interesting and important questions, but what it cannot do is answer questions about relationships between variables.

Video 2.4.1.  Descriptive Research Design  provides explanation and examples for quantitative descriptive research. A closed-captioned version of this video is available here .

Descriptive research is distinct from  correlational research , in which researchers formally test whether a relationship exists between two or more variables.  Experimental research  goes a step further beyond descriptive and correlational research and randomly assigns people to different conditions, using hypothesis testing to make inferences about causal relationships between variables. We will discuss each of these methods more in-depth later.

Table 2.4.1. Comparison of research design methods

Candela Citations

• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/edpsy/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Descriptive Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Retrieved from : https://courses.lumenlearning.com/adolescent/chapter/descriptive-research/. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Educational Psychology Copyright © 2020 by Nicole Arduini-Van Hoose is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Characteristics of Qualitative Descriptive Studies: A Systematic Review

MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing

Justine S. Sefcik

MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing

Christine Bradway

PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing

Qualitative description (QD) is a term that is widely used to describe qualitative studies of health care and nursing-related phenomena. However, limited discussions regarding QD are found in the existing literature. In this systematic review, we identified characteristics of methods and findings reported in research articles published in 2014 whose authors identified the work as QD. After searching and screening, data were extracted from the sample of 55 QD articles and examined to characterize research objectives, design justification, theoretical/philosophical frameworks, sampling and sample size, data collection and sources, data analysis, and presentation of findings. In this review, three primary findings were identified. First, despite inconsistencies, most articles included characteristics consistent with limited, available QD definitions and descriptions. Next, flexibility or variability of methods was common and desirable for obtaining rich data and achieving understanding of a phenomenon. Finally, justification for how a QD approach was chosen and why it would be an appropriate fit for a particular study was limited in the sample and, therefore, in need of increased attention. Based on these findings, recommendations include encouragement to researchers to provide as many details as possible regarding the methods of their QD study so that readers can determine whether the methods used were reasonable and effective in producing useful findings.

Qualitative description (QD) is a label used in qualitative research for studies which are descriptive in nature, particularly for examining health care and nursing-related phenomena ( Polit & Beck, 2009 , 2014 ). QD is a widely cited research tradition and has been identified as important and appropriate for research questions focused on discovering the who, what, and where of events or experiences and gaining insights from informants regarding a poorly understood phenomenon. It is also the label of choice when a straight description of a phenomenon is desired or information is sought to develop and refine questionnaires or interventions ( Neergaard et al., 2009 ; Sullivan-Bolyai et al., 2005 ).

Despite many strengths and frequent citations of its use, limited discussions regarding QD are found in qualitative research textbooks and publications. To the best of our knowledge, only seven articles include specific guidance on how to design, implement, analyze, or report the results of a QD study ( Milne & Oberle, 2005 ; Neergaard, Olesen, Andersen, & Sondergaard, 2009 ; Sandelowski, 2000 , 2010 ; Sullivan-Bolyai, Bova, & Harper, 2005 ; Vaismoradi, Turunen, & Bondas, 2013 ; Willis, Sullivan-Bolyai, Knafl, & Zichi-Cohen, 2016 ). Furthermore, little is known about characteristics of QD as reported in journal-published, nursing-related, qualitative studies. Therefore, the purpose of this systematic review was to describe specific characteristics of methods and findings of studies reported in journal articles (published in 2014) self-labeled as QD. In this review, we did not have a goal to judge whether QD was done correctly but rather to report on the features of the methods and findings.

Features of QD

Several QD design features and techniques have been described in the literature. First, researchers generally draw from a naturalistic perspective and examine a phenomenon in its natural state ( Sandelowski, 2000 ). Second, QD has been described as less theoretical compared to other qualitative approaches ( Neergaard et al., 2009 ), facilitating flexibility in commitment to a theory or framework when designing and conducting a study ( Sandelowski, 2000 , 2010 ). For example, researchers may or may not decide to begin with a theory of the targeted phenomenon and do not need to stay committed to a theory or framework if their investigations take them down another path ( Sandelowski, 2010 ). Third, data collection strategies typically involve individual and/or focus group interviews with minimal to semi-structured interview guides ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fourth, researchers commonly employ purposeful sampling techniques such as maximum variation sampling which has been described as being useful for obtaining broad insights and rich information ( Neergaard et al., 2009 ; Sandelowski, 2000 ). Fifth, content analysis (and in many cases, supplemented by descriptive quantitative data to describe the study sample) is considered a primary strategy for data analysis ( Neergaard et al., 2009 ; Sandelowski, 2000 ). In some instances thematic analysis may also be used to analyze data; however, experts suggest care should be taken that this type of analysis is not confused with content analysis ( Vaismoradi et al., 2013 ). These data analysis approaches allow researchers to stay close to the data and as such, interpretation is of low-inference ( Neergaard et al., 2009 ), meaning that different researchers will agree more readily on the same findings even if they do not choose to present the findings in the same way ( Sandelowski, 2000 ). Finally, representation of study findings in published reports is expected to be straightforward, including comprehensive descriptive summaries and accurate details of the data collected, and presented in a way that makes sense to the reader ( Neergaard et al., 2009 ; Sandelowski, 2000 ).

It is also important to acknowledge that variations in methods or techniques may be appropriate across QD studies ( Sandelowski, 2010 ). For example, when consistent with the study goals, decisions may be made to use techniques from other qualitative traditions, such as employing a constant comparative analytic approach typically associated with grounded theory ( Sandelowski, 2000 ).

Search Strategy and Study Screening

The PubMed electronic database was searched for articles written in English and published from January 1, 2014 to December 31, 2014, using the terms, “qualitative descriptive study,” “qualitative descriptive design,” and “qualitative description,” combined with “nursing.” This specific publication year, “2014,” was chosen because it was the most recent full year at the time of beginning this systematic review. As we did not intend to identify trends in QD approaches over time, it seemed reasonable to focus on the nursing QD studies published in a certain year. The inclusion criterion for this review was data-based, nursing-related, research articles in which authors used the terms QD, qualitative descriptive study, or qualitative descriptive design in their titles or abstracts as well as in the main texts of the publication.

All articles yielded through an initial search in PubMed were exported into EndNote X7 ( Thomson Reuters, 2014 ), a reference management software, and duplicates were removed. Next, titles and abstracts were reviewed to determine if the publication met inclusion criteria; all articles meeting inclusion criteria were then read independently in full by two authors (HK and JS) to determine if the terms – QD or qualitative descriptive study/design – were clearly stated in the main texts. Any articles in which researchers did not specifically state these key terms in the main text were then excluded, even if the terms had been used in the study title or abstract. In one article, for example, although “qualitative descriptive study” was reported in the published abstract, the researchers reported a “qualitative exploratory design” in the main text of the article ( Sundqvist & Carlsson, 2014 ); therefore, this article was excluded from our review. Despite the possibility that there may be other QD studies published in 2014 that were not labeled as such, to facilitate our screening process we only included articles where the researchers clearly used our search terms for their approach. Finally, the two authors compared, discussed, and reconciled their lists of articles with a third author (CB).

Study Selection

Initially, although the year 2014 was specifically requested, 95 articles were identified (due to ahead of print/Epub) and exported into the EndNote program. Three duplicate publications were removed and the 20 articles with final publication dates of 2015 were also excluded. The remaining 72 articles were then screened by examining titles, abstracts, and full-texts. Based on our inclusion criteria, 15 (of 72) were then excluded because QD or QD design/study was not identified in the main text. We then re-examined the remaining 57 articles and excluded two additional articles that did not meet inclusion criteria (e.g., QD was only reported as an analytic approach in the data analysis section). The remaining 55 publications met inclusion criteria and comprised the sample for our systematic review (see Figure 1 ).

Flow Diagram of Study Selection

Of the 55 publications, 23 originated from North America (17 in the United States; 6 in Canada), 12 from Asia, 11 from Europe, 7 from Australia and New Zealand, and 2 from South America. Eleven studies were part of larger research projects and two of them were reported as part of larger mixed-methods studies. Four were described as a secondary analysis.

Quality Appraisal Process

Following the identification of the 55 publications, two authors (HK and JS) independently examined each article using the Critical Appraisal Skills Programme (CASP) qualitative checklist ( CASP, 2013 ). The CASP was chosen to determine the general adequacy (or rigor) of the qualitative studies included in this review as the CASP criteria are generic and intend to be applied to qualitative studies in general. In addition, the CASP was useful because we were able to examine the internal consistency between study aims and methods and between study aims and findings as well as the usefulness of findings ( CASP, 2013 ). The CASP consists of 10 main questions with several sub-questions to consider when making a decision about the main question ( CASP, 2013 ). The first two questions have reviewers examine the clarity of study aims and appropriateness of using qualitative research to achieve the aims. With the next eight questions, reviewers assess study design, sampling, data collection, and analysis as well as the clarity of the study’s results statement and the value of the research. We used the seven questions and 17 sub-questions related to methods and statement of findings to evaluate the articles. The results of this process are presented in Table 1 .

CASP Questions and Quality Appraisal Results (N = 55)

Note . The CASP questions are adapted from “10 questions to help you make sense of qualitative research,” by Critical Appraisal Skills Programme, 2013, retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . Its license can be found at http://creativecommons.org/licenses/by-nc-sa/3.0/

Once articles were assessed by the two authors independently, all three authors discussed and reconciled our assessment. No articles were excluded based on CASP results; rather, results were used to depict the general adequacy (or rigor) of all 55 articles meeting inclusion criteria for our systematic review. In addition, the CASP was included to enhance our examination of the relationship between the methods and the usefulness of the findings documented in each of the QD articles included in this review.

Process for Data Extraction and Analysis

To further assess each of the 55 articles, data were extracted on: (a) research objectives, (b) design justification, (c) theoretical or philosophical framework, (d) sampling and sample size, (e) data collection and data sources, (f) data analysis, and (g) presentation of findings (see Table 2 ). We discussed extracted data and identified common and unique features in the articles included in our systematic review. Findings are described in detail below and in Table 3 .

Elements for Data Extraction

Data Extraction and Analysis Results

Note . NR = not reported

Quality Appraisal Results

Justification for use of a QD design was evident in close to half (47.3%) of the 55 publications. While most researchers clearly described recruitment strategies (80%) and data collection methods (100%), justification for how the study setting was selected was only identified in 38.2% of the articles and almost 75% of the articles did not include any reason for the choice of data collection methods (e.g., focus-group interviews). In the vast majority (90.9%) of the articles, researchers did not explain their involvement and positionality during the process of recruitment and data collection or during data analysis (63.6%). Ethical standards were reported in greater than 89% of all articles and most articles included an in-depth description of data analysis (83.6%) and development of categories or themes (92.7%). Finally, all researchers clearly stated their findings in relation to research questions/objectives. Researchers of 83.3% of the articles discussed the credibility of their findings (see Table 1 ).

Research Objectives

In statements of study objectives and/or questions, the most frequently used verbs were “explore” ( n = 22) and “describe” ( n = 17). Researchers also used “identify” ( n = 3), “understand” ( n = 4), or “investigate” ( n = 2). Most articles focused on participants’ experiences related to certain phenomena ( n = 18), facilitators/challenges/factors/reasons ( n = 14), perceptions about specific care/nursing practice/interventions ( n = 11), and knowledge/attitudes/beliefs ( n = 3).

Design Justification

A total of 30 articles included references for QD. The most frequently cited references ( n = 23) were “Whatever happened to qualitative description?” ( Sandelowski, 2000 ) and “What’s in a name? Qualitative description revisited” ( Sandelowski, 2010 ). Other references cited included “Qualitative description – the poor cousin of health research?” ( Neergaard et al., 2009 ), “Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research” ( Pope & Mays, 1995 ), and general research textbooks ( Polit & Beck, 2004 , 2012 ).

In 26 articles (and not necessarily the same as those citing specific references to QD), researchers provided a rationale for selecting QD. Most researchers chose QD because this approach aims to produce a straight description and comprehensive summary of the phenomenon of interest using participants’ language and staying close to the data (or using low inference).

Authors of two articles distinctly stated a QD design, yet also acknowledged grounded-theory or phenomenological overtones by adopting some techniques from these qualitative traditions ( Michael, O'Callaghan, Baird, Hiscock, & Clayton, 2014 ; Peacock, Hammond-Collins, & Forbes, 2014 ). For example, Michael et al. (2014 , p. 1066) reported:

The research used a qualitative descriptive design with grounded theory overtones ( Sandelowski, 2000 ). We sought to provide a comprehensive summary of participants’ views through theoretical sampling; multiple data sources (focus groups [FGs] and interviews); inductive, cyclic, and constant comparative analysis; and condensation of data into thematic representations ( Corbin & Strauss, 1990 , 2008 ).

Authors of four additional articles included language suggestive of a grounded-theory or phenomenological tradition, e.g., by employing a constant comparison technique or translating themes stated in participants’ language into the primary language of the researchers during data analysis ( Asemani et al., 2014 ; Li, Lee, Chen, Jeng, & Chen, 2014 ; Ma, 2014 ; Soule, 2014 ). Additionally, Li et al. (2014) specifically reported use of a grounded-theory approach.

Theoretical or Philosophical Framework

In most (n = 48) articles, researchers did not specify any theoretical or philosophical framework. Of those articles in which a framework or philosophical stance was included, the authors of five articles described the framework as guiding the development of an interview guide ( Al-Zadjali, Keller, Larkey, & Evans, 2014 ; DeBruyn, Ochoa-Marin, & Semenic, 2014 ; Fantasia, Sutherland, Fontenot, & Ierardi, 2014 ; Ma, 2014 ; Wiens, Babenko-Mould, & Iwasiw, 2014 ). In two articles, data analysis was described as including key concepts of a framework being used as pre-determined codes or categories ( Al-Zadjali et al., 2014 ; Wiens et al., 2014 ). Oosterveld-Vlug et al. (2014) and Zhang, Shan, and Jiang (2014) discussed a conceptual model and underlying philosophy in detail in the background or discussion section, although the model and philosophy were not described as being used in developing interview questions or analyzing data.

Sampling and Sample Size

In 38 of the 55 articles, researchers reported ‘purposeful sampling’ or some derivation of purposeful sampling such as convenience ( n = 10), maximum variation ( n = 8), snowball ( n = 3), and theoretical sampling ( n = 1). In three instances ( Asemani et al., 2014 ; Chan & Lopez, 2014 ; Soule, 2014 ), multiple sampling strategies were described, for example, a combination of snowball, convenience, and maximum variation sampling. In articles where maximum variation sampling was employed, “variation” referred to seeking diversity in participants’ demographics ( n = 7; e.g., age, gender, and education level), while one article did not include details regarding how their maximum variation sampling strategy was operationalized ( Marcinowicz, Abramowicz, Zarzycka, Abramowicz, & Konstantynowicz, 2014 ). Authors of 17 articles did not specify their sampling techniques.

Sample sizes ranged from 8 to 1,932 with nine studies in the 8–10 participant range and 24 studies in the 11–20 participant range. The participant range of 21–30 and 31–50 was reported in eight articles each. Six studies included more than 50 participants. Two of these articles depicted quite large sample sizes (N=253, Hart & Mareno, 2014 ; N=1,932, Lyndon et al., 2014 ) and the authors of these articles described the use of survey instruments and analysis of responses to open-ended questions. This was in contrast to studies with smaller sample sizes where individual interviews and focus groups were more commonly employed.

Data Collection and Data Sources

In a majority of studies, researchers collected data through individual ( n = 39) and/or focus-group ( n = 14) interviews that were semistructured. Most researchers reported that interviews were audiotaped ( n = 51) and interview guides were described as the primary data collection tool in 29 of the 51 studies. In some cases, researchers also described additional data sources, for example, taking memos or field notes during participant observation sessions or as a way to reflect their thoughts about interviews ( n = 10). Written responses to open-ended questions in survey questionnaires were another type of data source in a small number of studies ( n = 4).

Data Analysis

The analysis strategy most commonly used in the QD studies included in this review was qualitative content analysis ( n = 30). Among the studies where this technique was used, most researchers described an inductive approach; researchers of two studies analyzed data both inductively and deductively. Thematic analysis was adopted in 14 studies and the constant comparison technique in 10 studies. In nine studies, researchers employed multiple techniques to analyze data including qualitative content analysis with constant comparison ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland, Christensen, Shone, Kearney, & Kitzman, 2014 ; Li et al., 2014 ) and thematic analysis with constant comparison ( Johansson, Hildingsson, & Fenwick, 2014 ; Oosterveld-Vlug et al., 2014 ). In addition, five teams conducted descriptive statistical analysis using both quantitative and qualitative data and counting the frequencies of codes/themes ( Ewens, Chapman, Tulloch, & Hendricks, 2014 ; Miller, 2014 ; Santos, Sandelowski, & Gualda, 2014 ; Villar, Celdran, Faba, & Serrat, 2014 ) or targeted events through video monitoring ( Martorella, Boitor, Michaud, & Gelinas, 2014 ). Tseng, Chen, and Wang (2014) cited Thorne, Reimer Kirkham, and O’Flynn-Magee (2004)’s interpretive description as the inductive analytic approach. In five out of 55 articles, researchers did not specifically name their analysis strategies, despite including descriptions about procedural aspects of data analysis. Researchers of 20 studies reported that data saturation for their themes was achieved.

Presentation of Findings

Researchers described participants’ experiences of health care, interventions, or illnesses in 18 articles and presented straightforward, focused, detailed descriptions of facilitators, challenges, factors, reasons, and causes in 15 articles. Participants’ perceptions of specific care, interventions, or programs were described in detail in 11 articles. All researchers presented their findings with extensive descriptions including themes or categories. In 25 of 55 articles, figures or tables were also presented to illustrate or summarize the findings. In addition, the authors of three articles summarized, organized, and described their data using key concepts of conceptual models ( Al-Zadjali et al., 2014 ; Oosterveld-Vlug et al., 2014 ; Wiens et al., 2014 ). Martorella et al. (2014) assessed acceptability and feasibility of hand massage therapy and arranged their findings in relation to pre-determined indicators of acceptability and feasibility. In one longitudinal QD study ( Kneck, Fagerberg, Eriksson, & Lundman, 2014 ), the researchers presented the findings as several key patterns of learning for persons living with diabetes; in another longitudinal QD study ( Stegenga & Macpherson, 2014 ), findings were presented as processes and themes regarding patients’ identity work across the cancer trajectory. In another two studies, the researchers described and compared themes or categories from two different perspectives, such as patients and nurses ( Canzan, Heilemann, Saiani, Mortari, & Ambrosi, 2014 ) or parents and children ( Marcinowicz et al., 2014 ). Additionally, Ma (2014) reported themes using both participants’ language and the researcher’s language.

In this systematic review, we examined and reported specific characteristics of methods and findings reported in journal articles self-identified as QD and published during one calendar year. To accomplish this we identified 55 articles that met inclusion criteria, performed a quality appraisal following CASP guidelines, and extracted and analyzed data focusing on QD features. In general, three primary findings emerged. First, despite inconsistencies, most QD publications had the characteristics that were originally observed by Sandelowski (2000) and summarized by other limited available QD literature. Next, there are no clear boundaries in methods used in the QD studies included in this review; in a number of studies, researchers adopted and combined techniques originating from other qualitative traditions to obtain rich data and increase their understanding of the phenomenon under investigation. Finally, justification for how QD was chosen and why it would be an appropriate fit for a particular study is an area in need of increased attention.

In general, the overall characteristics were consistent with design features of QD studies described in the literature ( Neergaard et al., 2009 ; Sandelowski, 2000 , 2010 ; Vaismoradi et al., 2013 ). For example, many authors reported that study objectives were to describe or explore participants’ experiences and factors related to certain phenomena, events, or interventions. In most cases, these authors cited Sandelowski (2000) as a reference for this particular characteristic. It was rare that theoretical or philosophical frameworks were identified, which also is consistent with descriptions of QD. In most studies, researchers used purposeful sampling and its derivative sampling techniques, collected data through interviews, and analyzed data using qualitative content analysis or thematic analysis. Moreover, all researchers presented focused or comprehensive, descriptive summaries of data including themes or categories answering their research questions. These characteristics do not indicate that there are correct ways to do QD studies; rather, they demonstrate how others designed and produced QD studies.

In several studies, researchers combined techniques that originated from other qualitative traditions for sampling, data collection, and analysis. This flexibility or variability, a key feature of recently published QD studies, may indicate that there are no clear boundaries in designing QD studies. Sandelowski (2010) articulated: “in the actual world of research practice, methods bleed into each other; they are so much messier than textbook depictions” (p. 81). Hammersley (2007) also observed:

“We are not so much faced with a set of clearly differentiated qualitative approaches as with a complex landscape of variable practice in which the inhabitants use a range of labels (‘ethnography’, ‘discourse analysis’, ‘life history work’, narrative study’, ……, and so on) in diverse and open-ended ways in order to characterize their orientation, and probably do this somewhat differently across audiences and occasions” (p. 293).

This concept of having no clear boundaries in methods when designing a QD study should enable researchers to obtain rich data and produce a comprehensive summary of data through various data collection and analysis approaches to answer their research questions. For example, using an ethnographical approach (e.g., participant observation) in data collection for a QD study may facilitate an in-depth description of participants’ nonverbal expressions and interactions with others and their environment as well as situations or events in which researchers are interested ( Kawulich, 2005 ). One example found in our review is that Adams et al. (2014) explored family members’ responses to nursing communication strategies for patients in intensive care units (ICUs). In this study, researchers conducted interviews with family members, observed interactions between healthcare providers, patients, and family members in ICUs, attended ICU rounds and family meetings, and took field notes about their observations and reflections. Accordingly, the variability in methods provided Adams and colleagues (2014) with many different aspects of data that were then used to complement participants’ interviews (i.e., data triangulation). Moreover, by using a constant comparison technique in addition to qualitative content analysis or thematic analysis in QD studies, researchers compare each case with others looking for similarities and differences as well as reasoning why differences exist, to generate more general understanding of phenomena of interest ( Thorne, 2000 ). In fact, this constant comparison analysis is compatible with qualitative content analysis and thematic analysis and we found several examples of using this approach in studies we reviewed ( Asemani et al., 2014 ; DeBruyn et al., 2014 ; Holland et al., 2014 ; Johansson et al., 2014 ; Li et al., 2014 ; Oosterveld-Vlug et al., 2014 ).

However, this flexibility or variability in methods of QD studies may cause readers’ as well as researchers’ confusion in designing and often labeling qualitative studies ( Neergaard et al., 2009 ). Especially, it could be difficult for scholars unfamiliar with qualitative studies to differentiate QD studies with “hues, tones, and textures” of qualitative traditions ( Sandelowski, 2000 , p. 337) from grounded theory, phenomenological, and ethnographical research. In fact, the major difference is in the presentation of the findings (or outcomes of qualitative research) ( Neergaard et al., 2009 ; Sandelowski, 2000 ). The final products of grounded theory, phenomenological, and ethnographical research are a generation of a theory, a description of the meaning or essence of people’s lived experience, and an in-depth, narrative description about certain culture, respectively, through researchers’ intensive/deep interpretations, reflections, and/or transformation of data ( Streubert & Carpenter, 2011 ). In contrast, QD studies result in “a rich, straight description” of experiences, perceptions, or events using language from the collected data ( Neergaard et al., 2009 ) through low-inference (or data-near) interpretations during data analysis ( Sandelowski, 2000 , 2010 ). This feature is consistent with our finding regarding presentation of findings: in all QD articles included in this systematic review, the researchers presented focused or comprehensive, descriptive summaries to their research questions.

Finally, an explanation or justification of why a QD approach was chosen or appropriate for the study aims was not found in more than half of studies in the sample. While other qualitative approaches, including grounded theory, phenomenology, ethnography, and narrative analysis, are used to better understand people’s thoughts, behaviors, and situations regarding certain phenomena ( Sullivan-Bolyai et al., 2005 ), as noted above, the results will likely read differently than those for a QD study ( Carter & Little, 2007 ). Therefore, it is important that researchers accurately label and justify their choices of approach, particularly for studies focused on participants’ experiences, which could be addressed with other qualitative traditions. Justifying one’s research epistemology, methodology, and methods allows readers to evaluate these choices for internal consistency, provides context to assist in understanding the findings, and contributes to the transparency of choices, all of which enhance the rigor of the study ( Carter & Little, 2007 ; Wu, Thompson, Aroian, McQuaid, & Deatrick, 2016 ).

Use of the CASP tool drew our attention to the credibility and usefulness of the findings of the QD studies included in this review. Although justification for study design and methods was lacking in many articles, most authors reported techniques of recruitment, data collection, and analysis that appeared. Internal consistencies among study objectives, methods, and findings were achieved in most studies, increasing readers’ confidence that the findings of these studies are credible and useful in understanding under-explored phenomenon of interest.

In summary, our findings support the notion that many scholars employ QD and include a variety of commonly observed characteristics in their study design and subsequent publications. Based on our review, we found that QD as a scholarly approach allows flexibility as research questions and study findings emerge. We encourage authors to provide as many details as possible regarding how QD was chosen for a particular study as well as details regarding methods to facilitate readers’ understanding and evaluation of the study design and rigor. We acknowledge the challenge of strict word limitation with submissions to print journals; potential solutions include collaboration with journal editors and staff to consider creative use of charts or tables, or using more citations and less text in background sections so that methods sections are robust.

Limitations

Several limitations of this review deserve mention. First, only articles where researchers explicitly stated in the main body of the article that a QD design was employed were included. In contrast, articles labeled as QD in only the title or abstract, or without their research design named were not examined due to the lack of certainty that the researchers actually carried out a QD study. As a result, we may have excluded some studies where a QD design was followed. Second, only one database was searched and therefore we did not identify or describe potential studies following a QD approach that were published in non-PubMed databases. Third, our review is limited by reliance on what was included in the published version of a study. In some cases, this may have been a result of word limits or specific styles imposed by journals, or inconsistent reporting preferences of authors and may have limited our ability to appraise the general adequacy with the CASP tool and examine specific characteristics of these studies.

Conclusions

A systematic review was conducted by examining QD research articles focused on nursing-related phenomena and published in one calendar year. Current patterns include some characteristics of QD studies consistent with the previous observations described in the literature, a focus on the flexibility or variability of methods in QD studies, and a need for increased explanations of why QD was an appropriate label for a particular study. Based on these findings, recommendations include encouragement to authors to provide as many details as possible regarding the methods of their QD study. In this way, readers can thoroughly consider and examine if the methods used were effective and reasonable in producing credible and useful findings.

Acknowledgments

This work was supported in part by the John A. Hartford Foundation’s National Hartford Centers of Gerontological Nursing Excellence Award Program.

Hyejin Kim is a Ruth L. Kirschstein NRSA Predoctoral Fellow (F31NR015702) and 2013–2015 National Hartford Centers of Gerontological Nursing Excellence Patricia G. Archbold Scholar. Justine Sefcik is a Ruth L. Kirschstein Predoctoral Fellow (F31NR015693) through the National Institutes of Health, National Institute of Nursing Research.

Conflict of Interest Statement

The Authors declare that there is no conflict of interest.

Contributor Information

Hyejin Kim, MSN, CRNP, Doctoral Candidate, University of Pennsylvania School of Nursing.

Justine S. Sefcik, MS, RN, Doctoral Candidate, University of Pennsylvania School of Nursing.

Christine Bradway, PhD, CRNP, FAAN, Associate Professor of Gerontological Nursing, University of Pennsylvania School of Nursing.

• Adams JA, Anderson RA, Docherty SL, Tulsky JA, Steinhauser KE, Bailey DE., Jr Nursing strategies to support family members of ICU patients at high risk of dying. Heart & Lung. 2014; 43 (5):406–415. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ahlin J, Ericson-Lidman E, Norberg A, Strandberg G. Care providers' experiences of guidelines in daily work at a municipal residential care facility for older people. Scandinavian Journal of Caring Sciences. 2014; 28 (2):355–363. [ PubMed ] [ Google Scholar ]
• Al-Zadjali M, Keller C, Larkey L, Evans B. GCC women: causes and processes of midlife weight gain. Health Care for Women International. 2014; 35 (11–12):1267–1286. [ PubMed ] [ Google Scholar ]
• Asemani O, Iman MT, Moattari M, Tabei SZ, Sharif F, Khayyer M. An exploratory study on the elements that might affect medical students' and residents' responsibility during clinical training. Journal of Medical Ethics and History of Medicine. 2014; 7 :8. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Atefi N, Abdullah KL, Wong LP, Mazlom R. Factors influencing registered nurses perception of their overall job satisfaction: a qualitative study. International Nursing Review. 2014; 61 (3):352–360. [ PubMed ] [ Google Scholar ]
• Ballangrud R, Hall-Lord ML, Persenius M, Hedelin B. Intensive care nurses' perceptions of simulation-based team training for building patient safety in intensive care: a descriptive qualitative study. Intensive and Critical Care Nursing. 2014; 30 (4):179–187. [ PubMed ] [ Google Scholar ]
• Benavides-Vaello S, Katz JR, Peterson JC, Allen CB, Paul R, Charette-Bluff AL, Morris P. Nursing and health sciences workforce diversity research using. PhotoVoice: a college and high school student participatory project. Journal of Nursing Education. 2014; 53 (4):217–222. [ PubMed ] [ Google Scholar ]
• Bernhard C, Zielinski R, Ackerson K, English J. Home birth after hospital birth: women's choices and reflections. Journal of Midwifery and Women's Health. 2014; 59 (2):160–166. [ PubMed ] [ Google Scholar ]
• Borbasi S, Jackson D, Langford RW. Navigating the maze of nursing research: An interactive learning adventure. 2nd. New South Wales, Australia: Mosby/Elsevier; 2008. [ Google Scholar ]
• Bradford B, Maude R. Fetal response to maternal hunger and satiation - novel finding from a qualitative descriptive study of maternal perception of fetal movements. BMC Pregnancy and Childbirth. 2014; 14 :288. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Burns N, Grove SK. The practice of nursing research: Conduct, critique, & utilization. 5th. Philadelphia, PA: Elsevier/Saunders; 2005. [ Google Scholar ]
• Canzan F, Heilemann MV, Saiani L, Mortari L, Ambrosi E. Visible and invisible caring in nursing from the perspectives of patients and nurses in the gerontological context. Scandinavian Journal of Caring Sciences. 2014; 28 (4):732–740. [ PubMed ] [ Google Scholar ]
• Carter SM, Littler M. Justifying knowledge, justifying methods, taking action: Epistemologies, methodologies, and methods in qualitative research. Qualitative Health Research. 2007; 17 (10):1316–1328. [ PubMed ] [ Google Scholar ]
• Critical Appraisal Skills Programme (CASP 2013) 10 questions to help you make sense of qualitative research. Oxford: CASP; 2013. Retrieved from http://media.wix.com/ugd/dded87_29c5b002d99342f788c6ac670e49f274.pdf . [ Google Scholar ]
• Chan CW, Lopez V. A qualitative descriptive study of risk reduction for coronary disease among the Hong Kong Chinese. Public Health Nursing. 2014; 31 (4):327–335. [ PubMed ] [ Google Scholar ]
• Chen YJ, Tsai YF, Lee SH, Lee HL. Protective factors against suicide among young-old Chinese outpatients. BMC Public Health. 2014; 14 :372. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Cleveland LM, Bonugli R. Experiences of mothers of infants with neonatal abstinence syndrome in the neonatal intensive care unit. Journal of Obstetric Gynecologic, & Neonatal Nursing. 2014; 43 (3):318–329. [ PubMed ] [ Google Scholar ]
• Corbin J, Strauss A. Basics of qualitative research: Techniques and procedures for developing grounded theory. 3rd. Thousand Oaks, CA: Sage Publications; 2008. [ Google Scholar ]
• Corbin JM, Strauss A. Grounded theory research: Procedures, canons and evaluation criteria. Qualitative Sociology. 1990; 13 (1):3–21. [ Google Scholar ]
• DeBruyn RR, Ochoa-Marin SC, Semenic S. Barriers and facilitators to evidence-based nursing in Colombia: perspectives of nurse educators, nurse researchers and graduate students. Investigación y Educación en Enfermería. 2014; 32 (1):9–21. [ PubMed ] [ Google Scholar ]
• Denzin NK, Lincoln YS. The Discipline and practice of qualitative research. In: Denzin NK, Lincoln YS, editors. Handbook of qualitative research. 2nd. Thousand Oaks, CA: Sage Publications; 2000. pp. 1–28. [ Google Scholar ]
• Ewens B, Chapman R, Tulloch A, Hendricks JM. ICU survivors' utilisation of diaries post discharge: a qualitative descriptive study. Australian Critical Care. 2014; 27 (1):28–35. [ PubMed ] [ Google Scholar ]
• Fantasia HC, Sutherland MA, Fontenot H, Ierardi JA. Knowledge, attitudes and beliefs about contraceptive and sexual consent negotiation among college women. Journal of Forensic Nursing. 2014; 10 (4):199–207. [ PubMed ] [ Google Scholar ]
• Friman A, Wahlberg AC, Mattiasson AC, Ebbeskog B. District nurses' knowledge development in wound management: ongoing learning without organizational support. Primary Health Care Research & Development. 2014; 15 (4):386–395. [ PubMed ] [ Google Scholar ]
• Gaughan V, Logan D, Sethna N, Mott S. Parents' perspective of their journey caring for a child with chronic neuropathic pain. Pain Management Nursing. 2014; 15 (1):246–257. [ PubMed ] [ Google Scholar ]
• Hammersley M. The issue of quality in qualitative research. International Journal of Research & Method in Education. 2007; 30 (3):287–305. [ Google Scholar ]
• Hart PL, Mareno N. Cultural challenges and barriers through the voices of nurses. Journal of Clinical Nursing. 2014; 23 (15–16):2223–2232. [ PubMed ] [ Google Scholar ]
• Hasman K, Kjaergaard H, Esbensen BA. Fathers' experience of childbirth when non-progressive labour occurs and augmentation is established. A qualitative study. Sexual & Reproductive HealthCare. 2014; 5 (2):69–73. [ PubMed ] [ Google Scholar ]
• Higgins I, van der Riet P, Sneesby L, Good P. Nutrition and hydration in dying patients: the perceptions of acute care nurses. Journal of Clinical Nursing. 2014; 23 (17–18):2609–2617. [ PubMed ] [ Google Scholar ]
• Holland ML, Christensen JJ, Shone LP, Kearney MH, Kitzman HJ. Women's reasons for attrition from a nurse home visiting program. Journal of Obstetric, Gynecologic, & Neonatal Nursing. 2014; 43 (1):61–70. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Johansson M, Hildingsson I, Fenwick J. 'As long as they are safe--birth mode does not matter' Swedish fathers' experiences of decision-making around caesarean section. Women and Birth. 2014; 27 (3):208–213. [ PubMed ] [ Google Scholar ]
• Kao MH, Tsai YF. Illness experiences in middle-aged adults with early-stage knee osteoarthritis: findings from a qualitative study. Journal of Advanced Nursing. 2014; 70 (7):1564–1572. [ PubMed ] [ Google Scholar ]
• Kawulich BB. Participant observation as a data collection method. Forum: Qualitative Social Research. 2005; 6 (2) Art. 43. Retrieved from http://www.qualitative-research.net/index.php/fqs/article/view/466/997 . [ Google Scholar ]
• Kerr D, McKay K, Klim S, Kelly AM, McCann T. Attitudes of emergency department patients about handover at the bedside. Journal of Clinical Nursing. 2014; 23 (11–12):1685–1693. [ PubMed ] [ Google Scholar ]
• Kneck A, Fagerberg I, Eriksson LE, Lundman B. Living with diabetes - development of learning patterns over a 3-year period. International Journal of Qualitative Studies on Health and Well-being. 2014; 9 :24375. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Krippendorf K. Content analysis: An introduction to its methodology. 2nd. Thousand Oaks, CA: Sage Publications; 2004. [ Google Scholar ]
• Larocque N, Schotsman C, Kaasalainen S, Crawshaw D, McAiney C, Brazil E. Using a book chat to improve attitudes and perceptions of long-term care staff about dementia. Journal of Gerontological Nursing. 2014; 40 (5):46–52. [ PubMed ] [ Google Scholar ]
• Li IC, Lee SY, Chen CY, Jeng YQ, Chen YC. Facilitators and barriers to effective smoking cessation: counselling services for inpatients from nurse-counsellors' perspectives--a qualitative study. International Journal of Environmental Research and Public Health. 2014; 11 (5):4782–4798. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Lux KM, Hutcheson JB, Peden AR. Ending disruptive behavior: staff nurse recommendations to nurse educators. Nurse Education in Practice. 2014; 14 (1):37–42. [ PubMed ] [ Google Scholar ]
• Lyndon A, Zlatnik MG, Maxfield DG, Lewis A, McMillan C, Kennedy HP. Contributions of clinical disconnections and unresolved conflict to failures in intrapartum safety. Journal of Obstetric, Gynecologic, & Neonatal Nursing. 2014; 43 (1):2–12. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ma F, Li J, Liang H, Bai Y, Song J. Baccalaureate nursing students' perspectives on learning about caring in China: a qualitative descriptive study. BMC Medical Education. 2014; 14 :42. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ma L. A humanbecoming qualitative descriptive study on quality of life with older adults. Nursing Science Quarterly. 2014; 27 (2):132–141. [ PubMed ] [ Google Scholar ]
• Marcinowicz L, Abramowicz P, Zarzycka D, Abramowicz M, Konstantynowicz J. How hospitalized children and parents perceive nurses and hospital amenities: A qualitative descriptive study in Poland. Journal of Child Health Care. 2014 [ PubMed ] [ Google Scholar ]
• Martorella G, Boitor M, Michaud C, Gelinas C. Feasibility and acceptability of hand massage therapy for pain management of postoperative cardiac surgery patients in the intensive care unit. Heart & Lung. 2014; 43 (5):437–444. [ PubMed ] [ Google Scholar ]
• McDonough A, Callans KM, Carroll DL. Understanding the challenges during transitions of care for children with critical airway conditions. ORL Head and Neck Nursing. 2014; 32 (4):12–17. [ PubMed ] [ Google Scholar ]
• McGilton KS, Boscart VM, Brown M, Bowers B. Making tradeoffs between the reasons to leave and reasons to stay employed in long-term care homes: perspectives of licensed nursing staff. International Journal of Nursing Studies. 2014; 51 (6):917–926. [ PubMed ] [ Google Scholar ]
• Michael N, O'Callaghan C, Baird A, Hiscock N, Clayton J. Cancer caregivers advocate a patient- and family-centered approach to advance care planning. Journal of Pain and Symptom Management. 2014; 47 (6):1064–1077. [ PubMed ] [ Google Scholar ]
• Miller WR. Patient-centered outcomes in older adults with epilepsy. Seizure. 2014; 23 (8):592–597. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Milne J, Oberle K. Enhancing rigor in qualitative description: a case study. Journal of Wound Ostomy & Continence Nursing. 2005; 32 (6):413–420. [ PubMed ] [ Google Scholar ]
• Neergaard MA, Olesen F, Andersen RS, Sondergaard J. Qualitative description - the poor cousin of health research? BMC Medical Research Methodology. 2009; 9 :52. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• O'Shea MF. Staff nurses' perceptions regarding palliative care for hospitalized older adults. The American Journal of Nursing. 2014; 114 (11):26–34. [ PubMed ] [ Google Scholar ]
• Oosterveld-Vlug MG, Pasman HR, van Gennip IE, Muller MT, Willems DL, Onwuteaka-Philipsen BD. Dignity and the factors that influence it according to nursing home residents: a qualitative interview study. Journal of Advanced Nursing. 2014; 70 (1):97–106. [ PubMed ] [ Google Scholar ]
• Oruche UM, Draucker C, Alkhattab H, Knopf A, Mazurcyk J. Interventions for family members of adolescents with disruptive behavior disorders. Journal of Child and Adolescent Psychiatric Nursing. 2014; 27 (3):99–108. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Parse RR. Qualitative inquiry: The path of sciencing. Sudbury, MA: Jones and Barlett; 2001. [ Google Scholar ]
• Peacock SC, Hammond-Collins K, Forbes DA. The journey with dementia from the perspective of bereaved family caregivers: a qualitative descriptive study. BMC Nursing. 2014; 13 (1):42. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Peterson WE, Sprague AE, Reszel J, Walker M, Fell DB, Perkins SL, Johnson M. Women's perspectives of the fetal fibronectin testing process: a qualitative descriptive study. BMC Pregnancy and Childbirth. 2014; 14 :190. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Polit DF, Beck CT. Nursing research: principles and methods. 7. Philadelphia, PA: Lippincott Williams & Wilkins; 2004. [ Google Scholar ]
• Polit DF, Beck CT. International differences in nursing research, 2005–2006. Journal of Nursing Scholarship. 2009; 41 (1):44–53. [ PubMed ] [ Google Scholar ]
• Polit DF, Beck CT. Nursing research: generating and assessing evidence for nursing practice. 9. Philadelphia, PA: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2012. [ Google Scholar ]
• Polit DF, Beck CT. Essentials of Nursing Research: Appraising Evidence for Nursing Practice. 8. Philadelphia, PA: Wolters Kluwer Health; Lippincott Willians & Wilkins; 2014. Supplement for Chapter 14: Qualitative Descriptive Studies. Retrieved from http://downloads.lww.com/wolterskluwer_vitalstream_com/sample-content/9781451176797_Polit/samples/CS_Chapter_14.pdf . [ Google Scholar ]
• Pope C, Mays N. Qualitative research in health care. 3rd. Victoria, Australia: Blackwell Publishing; 2006. [ Google Scholar ]
• Pope C, Mays N. Reaching the parts other methods cannot reach: an introduction to qualitative methods in health and health services research. BMJ. 1995; 311 (6996):42–45. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Raphael D, Waterworth S, Gott M. The role of practice nurses in providing palliative and end-of-life care to older patients with long-term conditions. International Journal of Palliative Nursing. 2014; 20 (8):373–379. [ PubMed ] [ Google Scholar ]
• Saldana J. Longitudinal qualitative research: Analyzing change through time. Walnut Creek, CA: AltaMira Press; 2003. [ Google Scholar ]
• Sandelowski M. Whatever happened to qualitative description? Research in Nursing & Health. 2000; 23 (4):334–340. [ PubMed ] [ Google Scholar ]
• Sandelowski M. What's in a name? Qualitative description revisited. Research in Nursing & Health. 2010; 33 (1):77–84. [ PubMed ] [ Google Scholar ]
• Santos HP, Jr, Sandelowski M, Gualda DM. Bad thoughts: Brazilian women's responses to mothering while experiencing postnatal depression. Midwifery. 2014; 30 (6):788–794. [ PubMed ] [ Google Scholar ]
• Sharp R, Grech C, Fielder A, Mikocka-Walus A, Cummings M, Esterman A. The patient experience of a peripherally inserted central catheter (PICC): A qualitative descriptive study. Contemporary Nurse. 2014; 48 (1):26–35. [ PubMed ] [ Google Scholar ]
• Soule I. Cultural competence in health care: an emerging theory. ANS Advances in Nursing Science. 2014; 37 (1):48–60. [ PubMed ] [ Google Scholar ]
• Stegenga K, Macpherson CF. "I'm a survivor, go study that word and you'll see my name": adolescent and cancer identity work over the first year after diagnosis. Cancer Nursing. 2014; 37 (6):418–428. [ PubMed ] [ Google Scholar ]
• Streubert HJ, Carpenter DR. Qualitative research in nursing: Advancing the humanistic imperative. 5th. Philadelphia, PA: Lippincott Williams & Wilkins; 2011. [ Google Scholar ]
• Sturesson A, Ziegert K. Prepare the patient for future challenges when facing hemodialysis: nurses' experiences. International Journal of Qualitative Studies on Health and Well-being. 2014; 9 :22952. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sullivan-Bolyai S, Bova C, Harper D. Developing and refining interventions in persons with health disparities: the use of qualitative description. Nursing Outlook. 2005; 53 (3):127–133. [ PubMed ] [ Google Scholar ]
• Sundqvist AS, Carlsson AA. Holding the patient's life in my hands: Swedish registered nurse anaesthetists' perspective of advocacy. Scandinavian Journal of Caring Sciences. 2014; 28 (2):281–288. [ PubMed ] [ Google Scholar ]
• Thomson Reuters. EndNote X7. 2014 Retrieved from http://endnote.com/product-details/x7 .
• Thorne S. Data analysis in qualitative research. Evidence Based Nursing. 2000; 3 :68–70. [ Google Scholar ]
• Thorne S, Reimer Kirkham S, O’Flynn-Magee K. The analytic challenge in interpretive description. International Journal of Qualitative Methods. 2004; 3 (1):1–11. [ Google Scholar ]
• Tseng YF, Chen CH, Wang HH. Taiwanese women's process of recovery from stillbirth: a qualitative descriptive study. Research in Nursing & Health. 2014; 37 (3):219–228. [ PubMed ] [ Google Scholar ]
• Vaismoradi M, Jordan S, Turunen H, Bondas T. Nursing students' perspectives of the cause of medication errors. Nurse Education Today. 2014; 34 (3):434–440. [ PubMed ] [ Google Scholar ]
• Vaismoradi M, Turunen H, Bondas T. Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences. 2013; 15 (3):398–405. [ PubMed ] [ Google Scholar ]
• Valizadeh L, Zamanzadeh V, Fooladi MM, Azadi A, Negarandeh R, Monadi M. The image of nursing, as perceived by Iranian male nurses. Nursing & Health Sciences. 2014; 16 (3):307–313. [ PubMed ] [ Google Scholar ]
• Villar F, Celdran M, Faba J, Serrat R. Barriers to sexual expression in residential aged care facilities (RACFs): comparison of staff and residents' views. Journal of Advanced Nursing. 2014; 70 (11):2518–2527. [ PubMed ] [ Google Scholar ]
• Wiens S, Babenko-Mould Y, Iwasiw C. Clinical instructors' perceptions of structural and psychological empowerment in academic nursing environments. Journal of Nursing Education. 2014; 53 (5):265–270. [ PubMed ] [ Google Scholar ]
• Willis DG, Sullivan-Bolyai S, Knafl K, Zichi-Cohen M. Distinguishing Features and Similarities Between Descriptive Phenomenological and Qualitative Description Research. West J Nurs Res. 2016 [ PubMed ] [ Google Scholar ]
• Wu YP, Thompson D, Aroian KJ, McQuaid EL, Deatrick JA. Commentary: Writing and Evaluating Qualitative Research Reports. J Pediatr Psychol. 2016; 41 (5):493–505. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Zhang H, Shan W, Jiang A. The meaning of life and health experience for the Chinese elderly with chronic illness: a qualitative study from positive health philosophy. International Journal of Nursing Practice. 2014; 20 (5):530–539. [ PubMed ] [ Google Scholar ]

• Introduction
• Article Information

RCT indicates randomized clinical trial.

CDC indicates Centers for Disease Control and Prevention; NICE, UK National Institute for Health and Care Excellence; WHO, World Health Organization.

eAppendix. Search Strategy

Data Sharing Statement

• Toward a Universal Definition of Post–COVID-19 Condition JAMA Network Open Invited Commentary April 5, 2023 Daniel Pan, MRCP; Manish Pareek, PhD, MRCP

See More About

Sign up for emails based on your interests, select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Download PDF
• X Facebook More LinkedIn

Chaichana U , Man KKC , Chen A, et al. Definition of Post–COVID-19 Condition Among Published Research Studies. JAMA Netw Open. 2023;6(4):e235856. doi:10.1001/jamanetworkopen.2023.5856

Manage citations:

© 2024

• Permissions

Definition of Post–COVID-19 Condition Among Published Research Studies

• 1 UCL School of Pharmacy, London, United Kingdom
• 2 Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region, China
• 3 Centre for Medicines Optimisation Research and Education, University College London Hospitals National Health Service (NHS) Foundation Trust, London, United Kingdom
• 4 Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
• 5 Ninewells Hospital, University of Dundee Medical School, Dundee, United Kingdom
• 6 University College London Hospitals NHS Foundation Trust, London, United Kingdom
• Invited Commentary Toward a Universal Definition of Post–COVID-19 Condition Daniel Pan, MRCP; Manish Pareek, PhD, MRCP JAMA Network Open

As of February 2023, there have been approximately 759 million confirmed cases of COVID-19 infections globally 1 and some individuals have experienced persistent symptoms, such as fatigue and shortness of breath, after recovering from the initial illness from COVID-19. The UK National Institute for Health and Care Excellence (NICE), 2 the World Health Organization (WHO), 3 and the US Centers for Disease Control and Prevention (CDC) 4 have published their definitions of post–COVID-19 condition (PCC) between December 2020 and October 2021, with some discrepancies between them. Despite the growing volume of research on lasting symptoms of COVID-19, the definition has not been universally agreed on. This study aimed to describe how post–COVID-19 condition has been defined to date in studies on this topic.

We conducted a descriptive study on PCC definition following the STROBE reporting guideline and performed the literature search using the PRISMA checklist in PubMed on October 26, 2022. A total of 7087 studies containing information on PCC were identified from February 1, 2020, to October 26, 2022. Definition of PCC (eAppendix in Supplement 1 ), study type, country where the study was conducted, and manuscript submission date were extracted from the publications and are presented chronologically (eAppendix in Supplement 1 ).

Two investigators (U.C. and A.C.) reviewed the studies and screened titles and abstracts independently and cross-checked a 10% sample of the data collected from the studies. When submission dates were not available, the publication dates were used to determine the study time. Exemption from ethical approval was indicated by the University College of London Ethics Committee. SPSS Statistics for Windows, version 28 (IBM Corp) was used for data analysis.

Among 7087 studies, we excluded 6792 that were not relevant to PCC (eg, SARS-CoV-2 vaccines, commentary, systematic review, and full articles in languages other than English). The remaining 295 studies were included, consisting of 2 randomized clinical trials (0.7%), 134 cohort studies (45.4%), 66 cross-sectional studies (22.4%), 13 case-control studies (4.4%), 45 case reports or case series (15.3%), and 35 studies using other designs (11.9%) ( Figure 1 ). Of these, 167 studies (56.6%) were conducted in European countries. We found that only 102 studies (34.6%) used 1 of the 3 organizational definitions for their studies (NICE: 56, WHO: 31, and CDC: 15). A total of 193 studies (65.4%) did not follow any of the 3 definitions for PCC and 6 studies were submitted for publication before NICE released their PCC definition (ie, before December 18, 2020) ( Figure 2 ).

Of 193 studies that did not follow any of 3 definitions, 129 studies (66.8%) used their own definitions for PCC (eg, presence of chronic symptoms that last >5 months or after 2 weeks of SARS-CoV-2 infection), while 64 studies (33.2%) did not define PCC.

We found substantial heterogeneity in defining PCC in the published studies, with almost two-thirds (65.4%) not complying with the definitions from the NICE, CDC, or WHO. This study highlights major issues in comparing interventions and outcomes between these reported studies in PCC due to differences in definition. The differences also result in considerable variation when translating findings into clinical management and cost-effectiveness assessments of interventions in patients with PCC. The clinical management of PCC must be evidence-based and include a personalized approach. A clearer definition of PCC is timely so that clinical trial evidence can reliably be applied to clinical management and the well-being of patients with PCC can be improved.

Our study has some limitations. We conducted the literature search only in PubMed. Furthermore, the NICE updated their PCC definition in November 2022 after we finished the study screening. However, the updated definition would not affect our study and would only apply to studies conducted after November 2022.

Accepted for Publication: February 8, 2023.

Published: April 5, 2023. doi:10.1001/jamanetworkopen.2023.5856

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Chaichana U et al. JAMA Network Open .

Corresponding Author: Li Wei, PhD, UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, United Kingdom ( [email protected] ).

Author Contributions: Ms Chaichana and Dr Wei had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Chaichana, Man, Wong, Wei.

Acquisition, analysis, or interpretation of data: Chaichana, Man, Chen, George, Wilson, Wei.

Drafting of the manuscript: Chaichana, Chen.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Chaichana, Chen.

Obtained funding: Wong.

Administrative, technical, or material support: Chen.

Supervision: Man, Wilson, Wei.

Conflict of Interest Disclosures: Ms Chaichana reported receiving a scholarship from the Royal Thai Government outside the submitted work. Dr Man reported receiving grants from the Hong Kong Research Grant Council during the conduct of the study; grants from CW Maplethorpe Fellowship, European Commission Horizon 2020, and the National Institute for Health and Care Research; and personal fees from IQVIA Ltd outside the submitted work. Dr Wong reported receiving grants from the Hong Kong Health and Medical Research Fund, Amgen, Bristol-Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, the UK National Institute for Health and Care Research, the European Commission, and the National Health and Medical Research Council in Australia outside the submitted work; receiving consulting fees from IQVIA outside the submitted work; and serving as a paid nonexecutive director of Jacobson Medical in Hong Kong and a paid consultant to the World Health Organization. Dr Wilson reported receiving personal fees from the Pfizer Advisory Board and the Roche Drug Safety Monitoring Board outside the submitted work. Dr Wei reported receiving grants from the National Institute Health Research Health Technology Assessment, Hong Kong Innovation and Technology Commission, Diabetes UK, The Cure Parkinson’s Trust, and BOPA-PRUK outside the submitted work. No other disclosures were reported.

Funding/Support: This work was partially supported by grant C7154-20G from the Research Grants Council of Hong Kong under the Collaborative Research Fund Scheme.

Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

This paper is in the following e-collection/theme issue:

Published on 8.4.2024 in Vol 8 (2024)

Development and Implementation of an eHealth Oncohematonootric Program: Descriptive, Observational, Prospective Cohort Pilot Study

Authors of this article:

Original Paper

• Beatriz Sánchez-Quiñones 1, 2 * , MD   ; 
• Cristina Antón-Maldonado 1, 2 * , MD   ; 
• Nataly Ibarra Vega 1, 2 * , MD   ; 
• Isabel Martorell Mariné 3 * , PhD   ; 
• Amparo Santamaria 1, 2 * , MD, PhD  

1 Hybrid Hematology Department, University Hospital Vinalopó, Alicante, Elche, Spain

2 Hematoinnova Unit, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, Valencia, Spain

3 Nutrition Department, Nootric Inc, Barcelona, Spain

*all authors contributed equally

Corresponding Author:

Amparo Santamaria, MD, PhD

Hybrid Hematology Department

University Hospital Vinalopó

Calle Tonico Sansano Mora, 14

Alicante, Elche, 03293

Phone: 34 658981769

Fax:34 965781564

Email: [email protected]

Background: In oncohematology, both the development of the disease and the side effects of antineoplastic treatment often take a toll on patients’ physical and nutritional well-being. In this era of digital transformation, we launched a pioneering project for oncohematologic patients to promote adherence to a healthy lifestyle and improve their physical and nutritional well-being. We aim to achieve this goal by involving doctors and nutritionists through the Nootric app.

Objective: This study aims to assess the impact of the use of eHealth tools to facilitate nutrition and well-being in oncohematologic patients. We also aim to determine the usefulness of physical-nutritional management in improving tolerance to chemotherapy treatments within routine clinical practice.

Methods: We designed a descriptive, observational, longitudinal, prospective cohort pilot study that included a total of 22 patients from March to May 2022 in the Vinalopó University Hospital. The inclusion criteria were adults over 18 years of age diagnosed with oncohematological pathology in active chemotherapy treatment. An action plan was created to generate alerts between the doctor and the nutritionist. In the beginning, the patients were trained to use the app and received education highlighting the importance of nutrition and physical exercise. Sociodemographic, clinical-biological-analytical (eg, malnutrition index), health care impact, usability, and patient adherence data were collected. Tolerance to chemotherapy treatment and its health care impact were evaluated.

Results: We included 22 patients, 11 (50%) female and 11 (50%) male, ranging between 42 and 84 years of age. Among them, 13 (59%) were adherents to the program. The most frequent diseases were lymphoproliferative syndromes (13/22, 59%) and multiple myeloma (4/22, 18%). Moreover, 15 (68%) out of 22 patients received immunochemotherapy, while 7 (32%) out of 22 patients received biological treatment. No worsening of clinical-biological parameters was observed. Excluding dropouts and abandonments (n=9/22, 41%), the adherence rate was 81%, established by calculating the arithmetic mean of the adherence rates of 13 patients. No admission was observed due to gastrointestinal toxicity or discontinuation of treatment related to alterations in physical and nutritional well-being. In addition, only 5.5% of unscheduled consultations were increased due to incidents in well-being, mostly telematic (n=6/103 consultation are unscheduled). Additionally, 92% of patients reported an improvement in their nutritional habits (n=12/13), and up to 45% required adjustment of medical supportive treatment (n=5/11). There were no cases of grade 3 or greater gastrointestinal toxicity. All of this reflects improved tolerance to treatments. Patients reported a satisfaction score of 4.3 out of 5, while professionals rated their satisfaction at 4.8 out of 5.

Conclusions: We demonstrated the usefulness of integrating new technologies through a multidisciplinary approach. The Nootric app facilitated collaboration among the medical team, nutritionists, and patients. It enabled us to detect health issues related to physical-nutritional well-being, anticipate major complications, and mitigate potentially avoidable risks. Consequently, there was a decrease in unscheduled visits and admissions related to this condition.

Introduction

Hematological malignancies encompass a heterogeneous group of diseases that have different behaviors, evolution, treatments, and prognoses. However, all of them similarly compromise the patient’s nutritional and physical status. This is because both the development of the disease and antineoplastic treatment can lead to caloric-protein malnutrition, leading to a high prevalence of adverse effects in daily clinical practice [ 1 ]. New treatments and conventional chemotherapy lead to toxicity in the gastrointestinal tract, which has a direct impact on the patient's well-being and survival [ 2 - 9 ]. Medical management is often insufficient to carry out a comprehensive assessment of the patient’s physical and nutritional well-being. Therefore, professional support in this aspect through the application of information and communication technologies (ICTs) in patients’ everyday environment outside the hospital is a useful tool to improve well-being and reduce health care costs [ 6 , 10 - 12 ]. There is increasing evidence showing that lifestyle interventions can improve symptoms, quality of life, and even overall survival rates for patients with cancer. Digital interventions can help implement physical-nutritional behavior modifications and empower patients through healthy lifestyle education and support [ 13 ].

An effective system for patient physical-nutritional monitoring and treatment after nutritional risk assessment appears to be lacking. We identified the need for a standardized system to prevent and treat malnutrition related to these diseases. Currently, there are studies involving mobile apps for nutritional control and support to monitor dietary intake among patients who are hospitalized and face nutritional risks. These apps have demonstrated good acceptance among patients and have the potential to be useful dietary evaluation tools for use in clinical practice. These results suggest that such tools could be extrapolated to the field of oncohematology consultations [ 14 ].

The studies available so far confirm that the application of mobile apps, among other appropriately designed digital interventions, can be effective tools in nutritional interventions [ 2 , 3 , 15 ]. It is estimated that 59% or more of the currently available apps are health-related [ 2 ]. Some studies show that mobile app interventions can improve the quality of life of patients with malignant hemopathies by reducing symptoms [ 16 ].

However, many of these apps are not developed by nutrition experts, validated by official agencies, or part of routine use in the hospital setting. The use of mobile apps for nutritional interventions to improve dietary patterns, avoid or reduce side effects, and improve patient physical and nutritional well-being is a new challenge currently facing health care.

Therefore, we have launched a pioneering project with the aim of improving and mitigating malnutrition and side effects through proper nutritional and physical well-being monitoring. We aim to deliver this digital nutrition service via the Nootric app, thereby promoting patients’ adherence to a healthy lifestyle.

In this study, we included under the term “oncohematological patient” those who met the inclusion criteria: adults over 18 years of age with a diagnosis of oncohematologic pathology undergoing active treatment. The hematologic malignancies included were mostly lymphoproliferative syndromes and multiple myeloma. We analyzed only nutritional and physical parameters within this group.

We evaluated an intervention designed to support oncohematological patients in active treatment. The primary goal was to assess how eHealth tools in nutrition and well-being management impact patients in oncohematology. Additionally, we aimed to determine the usefulness of physical-nutritional management in improving tolerance to chemotherapy treatments within routine clinical practice.

As secondary objectives, on the one hand, we aimed to evaluate the usefulness of the application of a physical-nutritional intervention among oncohematological patients by observing serial cases. On the other hand, we wanted to evaluate the adequacy and acceptability of this app in this group of patients. We aimed to qualitatively evaluate how knowledge guides the reorientation of intervention strategies regarding physical-nutritional well-being in these patients. Finally, we aimed to understand the nutritional and physical requirements throughout each phase of these patients' treatment, considering the potential implications for their well-being. Measures of engagement with the intervention and semistructured interviews with intervention participants were used to evaluate the feasibility of the intervention.

Study Design

A descriptive, observational, prospective, longitudinal cohort pilot study was conducted among oncohematological patients in our hospital center ( Figure 1 ).

Recruitment took place between April and May 22. The exposure, follow-up, and data collection period lasted 3 months (from May 22 to August 22). The sample consisted of 22 patients and was not divided under any concept at the beginning of the study. During study development, patients who had good adherence were included in the exposed cohort. However, those who dropped out of the study and had a low adherence rate were included in the unexposed cohort. They were not taken into account in the analysis of the results, and no comparative study was performed. To ensure that older adults were not excluded due to the digital gap, we included patients aged above 80 years old, and during the first visit, we encouraged them to continue using the Nootric app.

Study Setting

This study was conducted in the province of Alicante, Spain, at Vialopó University Hospital, which tends to a culturally, linguistically, and socioeconomically diverse population. The participants were recruited from the hospital’s hematology department.

Participants

Patients were eligible to participate if they (1) were aged ≥18 years, (2) had a documented diagnosis of oncohematological pathology, and (3) were receiving medical treatment for cancer at the time of study initiation. Patients were excluded from the study if they (1) were children, adolescents, or pregnant women; (2) were due for surgery with hospital nutritional treatment; (3) were following nutritional care or hospital treatment; (4) had any acute or chronic condition that the practitioner believed limited their ability to participate in the study; (5) were unable to provide written consent; (6) were not literate; and (7) did not have a smartphone.

Intervention: OncohematoNootric Program

In recent years, there has been a growing use of eHealth tools, such as mobile apps, in nutritional interventions with good acceptance and results [ 2 , 3 ]. These technologies have been applied in different health care settings, such as mental health support and chronic disease management, to enable interaction with patients and promote engagement with health care interventions. Ultimately, they aim to increase the acceptability, use, and effectiveness of interventions [ 17 , 18 ]. However, to date, few studies have evaluated the effectiveness of these apps in routine clinical practice within the health care environment. There have been no studies in patients with hematological malignancies undergoing treatment [ 4 ].

The OncohematoNootric program involves a continuous approach and follow-up of patients by their physician and nutritionist through the Nootric app over 3 months via face-to-face/telematic consultations, voice calls, and direct chat through the app.

The intervention aims to provide nutritional-physical support tools to oncohematology patients receiving treatment who may develop adverse effects that put their physical and nutritional well-being at risk. The program provides training and information. It also facilitates risk assessment and clinical support, as needed, with the help of a physician-nutritionist alarm system.

Education Website

Nootric is a digital nutrition service that creates personalized nutrition plans tailored to the oncohematological patient, featuring recipes compatible with the potential side effects of treatment. It augments cognitive-behavioral therapy and provides guides and challenges that address aspects related to nutrition and its application in daily life. Physicians can monitor patients through the app using a dynamic panel that displays real-time actions. It also includes a chat feature to communicate with a dietitian-nutritionist.

In this study, all patients received dietary and exercise recommendations from professionals. The Nootric app aimed to help patients improve their health and well-being by facilitating behavioral changes.

Intervention Development and Patient Involvement

The intervention was designed in conjunction with patients, medical and nutritional health professionals, and professionals with expertise in eHealth and wellness management programs and technologists. No generative artificial intelligence (AI) was used in this study.

For program implementation, a training session on the use of the Nootric app was held with different medical teams. When the candidate patient was selected by the center, the team gave them a patient information sheet, an informed consent form, and an information leaflet. Once the patient agreed to participate in the pilot, they handed over the signed informed consent form and began to participate. The center registered the patient on the Nootric website by entering a code assigned to the patient. Once this registration was completed, the patient downloaded the app and logged in. Next, the patient completed a series of forms that served as a basis for the dietitian-nutritionist to establish their personalized plan. In addition, a clinical-biological test was performed via a blood test requested by the medical team and carried out at the hospital, after which the results were recorded, and the nutritional counseling intervention was initiated. At the beginning of the intervention, the health care professionals oriented the patient on the use of the Nootric application and emphasized the importance of good nutrition and physical exercise. Each patient received a weekly menu and shopping list, was able to upload photos of their meals during follow-up, and had direct access to an app-based chat with a nutritionist. During the study, the patient was able to contact their dietitian-nutritionist through the app’s chat function to solve nutritional doubts and receive motivational support to increase physical activities and food recording.

During the project, improvements were made to the app to provide better patient care, including adapting 70 menu prescriptions to be compatible with the potential side effects of the treatment, configuring menu items to ensure suitability for the most common side effects, and preparing and adapting informative guides and challenges. Other improvements included sending activity and hydration reminders, optimizing the internal messaging system for medical professionals to exchange information with nutritionists, and making functional changes to facilitate uploading files for medical professionals.

An action plan was created to generate alerts between the physician and nutritionist with all the possible adverse events that patients could present (eg, hyporexia, weight loss, skin and nail changes, diarrhea, dyspepsia, pain according to a visual analog scale, edema, fatigue, constipation, dysphagia, odynophagia, mucositis, canker sores, nausea, vomiting, diarrhea, insomnia, urinary and bladder problems, anuria, bleeding, flu-like symptoms, fever, xerostomia, rash, and pruritus), along with their severity criteria and the action plan to be followed by the doctor and nutritionist.

When any of these issues were detected, the nutritionist informed the doctor, who carried out an unscheduled telematic/presential consultation with the patient to resolve the issue with the nutritionist’s support. On the other hand, if the issue was detected by the doctor, the latter informed the nutritionist so that the patient could receive support from both. In this way, both professionals were always kept up to date on incidents and procedures. The communication channel between professionals was the Nootric web platform.

For patients who were observed to have low adherence to the program during the pilot, the professionals studied the potential causes and intensified their actions to avoid dropout.

During the follow-up period, a weekly evaluation of the variables under study was carried out by the professionals. Biweekly follow-up meetings were held with the team to track the program and make possible improvements. Patients completed weekly program evaluation forms. After the follow-up study, a satisfaction survey was conducted to qualitatively evaluate patient satisfaction and program usefulness.

A new clinical-biological test was performed to comparatively analyze the results obtained before and after the intervention ( Figure 2 ).

Study Outcomes

The primary outcome of the study was the health impact of the use of an eHealth tool related to nutrition and physical well-being on the oncohematological patient.This was measured by determining the following variables: nº alerts resolved, nº emergency visits, nº unscheduled consultations, nº treatments suspended, nº hospital admissions and improvement in nutritional habits according to the patient's perception, nº patients referred to the hospital’s Nutrition Unit, nº patients requiring adjustment of support treatment, and nº patients with gastrointestinal toxicity (which determines the impact on improving tolerance to treatments). The secondary outcomes included assessing the perceived improvement in the patients’ physical and nutritional well-being, determined through satisfaction and usefulness questionnaires at the end of the intervention. We also sought to assess the feasibility of the intervention, focusing on usability, acceptability, and adherence regarding different intervention components.

Data Collection and Study Procedures

The variables were collected on a form based on Microsoft Office Excel 2021 (Microsoft Corp), with each coded and subsequently exported to the SPSS statistical software (version 28.0; IBM Corp). To describe continuous variables with normal distribution, measures of central tendency were used, such as arithmetic mean. The qualitative variables were presented as frequency and proportion.

The variables collected at the beginning of the pilot study, during follow-up, and at the end were: (1) sociodemographic, including sex, age, level of education, main disease, comorbidities, and medication; (2) clinical-biological-analytical, with the following parameters included in the blood analysis to assess nutritional status and treatment toxicity and direct the professionals’ action plan: hemoglobin, creatinine clearance, calcium, total protein, albumin, vitamin B12, folic acid, ferritin, malnutrition index, total cholesterol, liver enzymes aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, and gamma-glutamyl transferase, cancer medical treatment scheme, food consumption patterns measured through the form provided by Nootric through the app, and BMI; (3) health care impact; (4) usability; and (5) adherence.

To weight the usability of the application, the following user interaction variables were considered: viewing, rating, comments on recipes, uploaded photos, access to the chat, review of a guide or completion of a challenge (framed in cognitive-behavioral training), points achieved (result of the gamification provided by Nootric), and exercises displayed. Adherence was determined by analyzing each patient’s use of the Nootric app during the program application period. For adherence, the percentage of access to the Nootric application was calculated, considering the 120-day access as 100% adherence.

The evaluations performed can be viewed in more detail in Tables 1 and 2 and Figures 3 - 5 . The data extracted from the patients' medical records included results of the blood analysis, main disease, comorbidities and their medication, cancer medical treatment scheme, and health care impact variables.

a Patients received medical treatment, physical-nutritional recommendations, and close monitoring by their multidisciplinary assistance program. Most of the study population consisted of patients without severe comorbidities (18/22, 80%).

b These include rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone, (R-CHOP); adriamycin, bleomycin, vinblastine, and dacarbazine (ABVD); rituximab, bortezomib, cyclophosphamide, adriamycin, and prednisone (VR-CAP); and methotrexate, regimens with melphalan, and 5-azacitidine.

c Therapeutic regimens included daratumumab, bortezomib, venetoclax, obinutuzumab, ibrutinib, imatinib, and nilotinib.

d These included 3 women and 3 men. Dropouts were considered as users with adherence rates below 7%, and they were not accounted for in the analytical results, body variables, and interactions with the app.

Ethical Considerations

This study was approved by the Ethics Committee of Vinalopó University Hospital on March 30, 2022. All research activities involving human patients in this study have been treated in accordance with the ethical guidelines established by The Organic Law on Data Protection. All necessary approvals were obtained, including for the analysis of the research data.

This study complies with the ethical provisions outlined in the informed consent, and any additional analysis has been conducted in accordance with the existing ethical approvals. Informed consent was obtained from all patients for the conduct of this study and publication of this article, and no compensation was provided. Patients were informed that the doctor would receive information about their progress.

To protect the privacy and confidentiality of the participants, all data collected in this study were anonymized before the analysis. Measures were taken to ensure that the participants’ identifiable details were not disclosed. We used an anonymous identification system that consisted of assigning an alphanumeric code to each patient registered on the Nootric web platform and in the Nootric app. No one else, apart from Ribera's medical team and Nootric's team of nutritionists, had access to the data in the Nootric app and website, which are confidential and encrypted.

The original informed consent allows for secondary analysis without additional consent; this includes data collected from the participants’ medical history.

Sociodemographic Variables

Of the 22 included patients, 11 (50%) were female and 11 (50%) were male, with an average age of 70 (range 42-84) years. Among them, 13 (60%) patients were under 65 years and 9 (40%) were over 65 years. The variables regarding sociodemographic characteristics, treatment received during the study, and program adherence are described in Table 1 .

Clinical-Biological-Analytical Variables

A clinical-biological test was conducted for all patients at the beginning of the study, but only 13 (59%) out of 22 patients completed it. On this population, we performed the analysis of the results. Table 2 presents all the analyzed laboratory parameters and the results at the beginning and end of the

intervention. Some of the relevant data are shown in Figure 3 . Some patients received corticosteroid therapy, which increases the risk of developing steroidal hyperglycemia. These patients benefited from an adapted physical-nutritional plan. There were no patients in the study who presented with alterations in blood glucose levels. Of the 2 (15%) patients who had dyslipidemia at baseline, 1 (8%) patient did not maintain dyslipidemia at the end of the study. Furthermore, 1 (8%) patient presented with iron overload secondary to a high transfusion requirement. Iron chelation therapy was initiated, which triggered a grade 3 hepatotoxicity. After discontinuing it, it improved clinically and analytically to grade 1. Of 6 (46%) patients with malnutrition at baseline, only 1 (8%) patient still had malnutrition at the end of the study. Of the 4 (30%) patients who presented with hypoproteinemia at baseline, 1 (23%) did not have it at the end. Moreover, 1 patient (8%) presented with a folic deficit at baseline and maintained it at baseline despite having received supportive treatment and dietary recommendations.

The clinical interview confirmed that treatment compliance was inadequate. Among 13 patients who had anemia at the beginning of the study, 3 (23%) did not maintain it at the end of the study. Conversely, 3 (23%) of the 13 patients who had anemia at the end of the study did not have it at baseline. None of the causes were caused by vitamin B12, folic acid, or iron deficiency but by myelotoxicity due to targeted cancer therapy.

Program Adherence and Usability

A total of 6 (27%) out of 22 patients abandoned the study due to a lack of adherence to the program, attributed to advanced age, insufficient socioeconomic level to ensure proper use of the app, lack of family support for improving adherence, and lack of initiative to establish a change in the physical-nutritional routine. They were not taken into account in the analysis of the results. In addition, users with less than 7% adherence were considered dropouts (3/22, 14%), and they were not taken into account in the analysis of the results. None of these patients were older adults or dropped out due to the digital gap.

Excluding dropouts and abandonments (n=9, 30%), the adherence rate was 81%, established by calculating the arithmetic mean of the adherence rates of 13 patients, much higher than the average rate in well-being. Of the 22 patients, between 7 and 11 (30%-50%) perceived an improvement in well-being, determined by the satisfaction survey conducted at the end of the study. To determine the adherence rate of each patient, the use of the app by the patients was evaluated and the parameters were nº interactions with the app, use of the chat with the nutritionist, nº interactions with the recipes, nº photos of recipes uploaded by the patients, and gamification points earned ( Figure 4 ). Regarding the impact of usability, we obtained an average of 655 impacts per app user ( Figure 4 ).

Impact on Health Care

Regarding the impact on health care quality, Table 2 and Figure 5 show data that demonstrates highly satisfactory results. None of the total emergency visits were related to physical and nutritional well-being.

No hospitalizations occurred during the study for any cause, including those related to physical and nutritional well-being. No patient had to be referred to the hospital’s Nutrition Unit. Of the total number of medical consultations carried out, only 5.5% (6/103) were unscheduled and none of them were carried out for physical or nutritional issues. A total of 7 patients presented toxicity, among which 5 (71%) were cases of digestive toxicity. Of the 11 patients who required adjustment of supportive or symptomatic treatment due to toxicity, 5 (45%) had digestive toxicity. No treatment was suspended due to physical or nutritional conditions.

The patients showed an improvement in tolerance to chemotherapy treatments since there were no cases of grade 3 or higher gastrointestinal toxicity, defined as complications requiring intravenous support treatment. There were also no hospitalizations, emergency visits, or chemotherapy treatment discontinuations for this reason. A high percentage of patients (12/13, 92%) perceived an improvement in their nutritional habits.

Impact on Patients’ Perceived Improvements in Physical and Nutritional Well-Being

At the end of the study, the Nootric team disseminated an anonymous survey to measure satisfaction and usefulness for the patients and medical professionals involved. This enabled us to evaluate the impact on the perceived improvement in the patients’ physical and nutritional well-being. We observed that the users who adhered adequately to the program showed an improvement in this aspect. A total of 12 questionnaires were filled out by patients.

The average satisfaction rating among professionals was 4.8 out of 5, while patients rated their satisfaction at 4.3 out of 5. Table 3 highlights these results.

Our study is based on a multidisciplinary nutritional and exercise support program for oncohematological patients undergoing active treatment using new technologies. This study provides initial data on the effectiveness of a novel physical and nutritional support program aimed at patients with malignant hemopathies (largely represented in our study as lymphoproliferative syndromes and multiple myeloma) receiving targeted cancer treatment. It also provides a detailed evaluation of the implementation, adoption, and overall acceptability of this digital care intervention through a mobile app. The evaluation design has been adapted to the study objectives to provide new data to enable a better estimation of such an intervention’s impact and inform further development of digital care interventions for malignant blood diseases under active treatment.

Principal Findings

We did not observe any hospital admissions or discontinuation of chemotherapy treatment related to the patients' physical-nutritional well-being, supporting the benefit of the program. Adequate nutritional support was provided to ensure patients' well-being and mitigate the need for referral to the hospital's Nutrition Unit. Regarding the impact on physical and nutritional well-being, we observed that users who adhered adequately to the program improved in this aspect.

We observed a reduction in the number of unplanned consultations related to physical and nutritional well-being. In terms of impact on health care quality, the results demonstrated highly satisfactory results. None of the total emergency visits were related to physical and nutritional well-being. Moreover, the intervention received a high satisfaction rating from both professionals and patients.

Regarding other works in the field, there is little information about the best nutritional support for patients with cancer [ 9 , 19 , 20 ]. Antineoplastic agents are known to be associated with gastrointestinal complications that lead to physical and nutritional repercussions, which can decrease well-being and result in death due to malnutrition [ 19 ]. Additionally, early nutritional intervention can improve prognosis and reduce the disease’s complication rate [ 12 , 19 ].

One study used a novel mobile app to assess and evaluate dietary behaviors in 39 oncologic patients. Although 5 patients dropped out prior to the study, the authors concluded that participants who tracked their daily dietary habits using a mobile phone app were more likely to reach their nutritional goals than the control patients. Other studies have used mobile apps to record nutritional status and activity levels in patients with breast cancer or other or other diseases, but none of them are similar to our study [ 20 ]. Our study was performed by a multidisciplinary team using both the app and the internet to maintain contact with the patients. Furthermore, the multidisciplinary team tailored each patient's diet to suit their individual needs throughout their cancer treatment journey, particularly addressing gastrointestinal toxicities associated with active chemotherapy. This underscores the effectiveness of such technologies for integration into clinical practice without compromising the human touch in health care delivery.

Limitations and Strengths

This study highlights the importance of eHealth programs in addressing nutrition and

well-being among oncohematology patients, offering significant value in multidisciplinary care management. The use of the Nootric app allowed for improved health care indicators and physical-nutritional well-being, promoting better patient outcomes.

Another notable strength of this study is the finding that over 50% (n=11) of the patients improved their physical-nutritional habits, leading to a considerable enhancement in their perception of well-being.

In terms of limitations, we must point out that this study has a small sample size of 22 patients, which may limit the generalizability of the results. Moreover, we experienced a 27% (6/22) dropout rate due to a lack of adherence to the program, which could have affected the overall results. In addition, not all of those who completed the study completed the clinical-biological tests. Finally, we acknowledge that the 12-week follow-up period might not adequately capture the program’s clinical impact on adherence to healthy habits and improved physical and nutritional well-being.

In conclusion, using targeted eHealth programs for nutrition and well-being among oncohematological patients undergoing active treatment offers significant value in multidisciplinary care management. This is achieved through enhanced interaction between physicians, dietitian-nutritionists, and patients via a digital nutrition service, such as the Nootric app. Supporting patients throughout their cancer journey, these technologies serve as valuable tools for integration into clinical practice without detracting from the human aspect of health care. Therefore, implementing projects that leverage new technologies in routine holistic clinical practice for oncohematological patients could prove cost-effective in both the short and long term. By facilitating the early detection of health issues related to physical-nutritional well-being and anticipating potential complications, these initiatives may help reduce unscheduled visits and admissions related to this condition.

Acknowledgments

We extend our gratitude to all the patients and families involved in this study.

Data Availability

Our data adheres to open science availability guidelines for broader research accessibility.

Conflicts of Interest

None declared.

• Martín Salces M, de Paz R, Hernández-Navarro F. Therapeutic recommendations in the oncohematological patient. Nutr Hosp. 2006;21(3):379-385. [ Medline ]
• Lee EY, Cha SA, Yun JS, Lim SY, Lee JH, Ahn YB, et al. Efficacy of personalized diabetes self-care using an electronic medical record-integrated mobile app in patients with type 2 diabetes: 6-month randomized controlled trial. J Med Internet Res. Jul 28, 2022;24(7):e37430. [ FREE Full text ] [ CrossRef ] [ Medline ]
• San Mauro Martín I, González Fernández M, Collado Yurrita L. Mobile applications for nutrition, dietetics and healthy habits; analysis and consequences of an increasing trend. Nutr Hosp. Jul 01, 2014;30(1):15-24. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Sandín-Vázquez M, Sarría-Santamera A. Health Impact Assessment: assessing the effectiveness of policies in population health. Rev Esp Salud Publica. Jun 2008;82(3):261-272. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, et al. Cancer statistics for the year 2020: An overview. Int J Cancer. Apr 05, 2021.:778-789. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Guest DD, Cox T, Voss AC, Kelley K, Ma X, Nguyen A, et al. Assessing impact of nutrition care by registered dietitian nutritionists on patient medical and treatment outcomes in outpatient cancer clinics: a cohort feasibility study. Nutr Cancer. 2023;75(3):923-936. [ CrossRef ] [ Medline ]
• Planas M, Fernández-Ortega JF, Abilés J, Metabolism Nutrition Working Group of the Spanish Society of Intensive Care MedicineCoronary units. Guidelines for specialized nutritional and metabolic support in the critically-ill patient: update. Consensus SEMICYUC-SENPE: oncohematological patient. Nutr Hosp. Nov 2011;26 Suppl 2:50-53. [ CrossRef ] [ Medline ]
• Saigí Ullastre I, Pérez Pérez A. Hiperglucemia inducida por glucocorticoides. Semin de la Fund. Espanola de Reumatol. Jul 2011;12(3):83-90. [ CrossRef ] [ Medline ]
• Alonso Domínguez T, Civera Andrés M, Santiago Crespo JA, García Malpartida K, Botella Romero F. Digestive toxicity in cancer treatments. Bibliographic review. Influence on nutritional status. Endocrinol Diabetes Nutr (Engl Ed). Feb 2023;70(2):136-150. [ CrossRef ] [ Medline ]
• Kuijpers W, Groen WG, Aaronson NK, van Harten WH. A systematic review of web-based interventions for patient empowerment and physical activity in chronic diseases: relevance for cancer survivors. J Med Internet Res. Feb 20, 2013;15(2):e37. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Singleton AC, Raeside R, Hyun KK, Partridge SR, Di Tanna GL, Hafiz N, et al. Electronic health interventions for patients with breast cancer: systematic review and meta-analyses. J Clin Oncol. Jul 10, 2022;40(20):2257-2270. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lazar DE, Postolica R, Hanganu B, Mocanu V, Ioan BG. Web-based nutrition: a useful resource for cancer patients? Front Nutr. 2023;10:1134793. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gudmundsson GH, Mészáros J, Björnsdóttir AE, Ámundadóttir ML, Thorvardardottir GE, Magnusdottir E, et al. Evaluating the feasibility of a digital therapeutic program for patients with cancer during active treatment: pre-post interventional study. JMIR Form Res. Oct 13, 2022;6(10):e39764. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Paulsen MM, Hagen MLL, Frøyen MH, Foss-Pedersen RJ, Bergsager D, Tangvik RJ, et al. A dietary assessment app for hospitalized patients at nutritional risk: development and evaluation of the MyFood app. JMIR Mhealth Uhealth. Sep 07, 2018;6(9):e175. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Karimi N, Opie R, Crawford D, O'Connell S, Ball K. Digitally delivered interventions to improve nutrition behaviors among resource-poor and ethnic minority groups with type 2 diabetes: systematic review. J Med Internet Res. Feb 01, 2024;26:e42595. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Win H, Russell S, Wertheim BC, Maizes V, Crocker R, Brooks AJ, et al. Mobile app intervention on reducing the myeloproliferative neoplasm symptom burden: pilot feasibility and acceptability study. JMIR Form Res. Mar 31, 2022;6(3):e33581. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Franco-Carrero JA, Dominguez Padilla M, Domínguez-Fernandez JM. El uso de aplicaciones móviles como herramienta para abordar el estrés en trabajadores: revisión bibliográfica. Med Segur Trab. Mar 30, 2023;69(270):49-60. [ CrossRef ]
• Quevedo Rodríguez A, Wägner AM. Mobile phone applications for diabetes management: A systematic review. Endocrinol Diabetes Nutr (Engl Ed). May 2019;66(5):330-337. [ CrossRef ] [ Medline ]
• Molina Villaverde R. El paciente oncológico del siglo xxi. Maridaje terapéutico Nutrición-Oncología. Nutr Hosp. Jun 03, 2016;33(Suppl 1):174. [ CrossRef ] [ Medline ]
• Orlemann T, Reljic D, Zenker B, Meyer J, Eskofier B, Thiemt J, et al. A novel mobile phone app (OncoFood) to record and optimize the dietary behavior of oncologic patients: pilot study. JMIR Cancer. Nov 20, 2018;4(2):e10703. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 03.06.23; peer-reviewed by C Herrero; comments to author 02.10.23; revised version received 13.10.23; accepted 14.02.24; published 08.04.24.

©Beatriz Sánchez-Quiñones, Cristina Antón-Maldonado, Nataly Ibarra Vega, Isabel Martorell Mariné, Amparo Santamaria. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.