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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• What Is Quantitative Research? | Definition & Methods

What Is Quantitative Research? | Definition & Methods

Published on 4 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

Quantitative research is the process of collecting and analysing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalise results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analysing non-numerical data (e.g. text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalised to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

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Once data is collected, you may need to process it before it can be analysed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualise your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalisations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardise data collection and generalise findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardised data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analysed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalised and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardised procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

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

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research , you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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Quantitative research

Affiliation.

• 1 Faculty of Health and Social Care, University of Hull, Hull, England.
• PMID: 25828021
• DOI: 10.7748/ns.29.31.44.e8681

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Keywords: Experiments; measurement; nursing research; quantitative research; reliability; surveys; validity.

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Home » Quantitative Research – Methods, Types and Analysis

Quantitative Research – Methods, Types and Analysis

Table of Contents

Quantitative Research

Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions . This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection methods to gather quantitative data.

Quantitative Research Methods

Quantitative Research Methods are as follows:

Descriptive Research Design

Descriptive research design is used to describe the characteristics of a population or phenomenon being studied. This research method is used to answer the questions of what, where, when, and how. Descriptive research designs use a variety of methods such as observation, case studies, and surveys to collect data. The data is then analyzed using statistical tools to identify patterns and relationships.

Correlational Research Design

Correlational research design is used to investigate the relationship between two or more variables. Researchers use correlational research to determine whether a relationship exists between variables and to what extent they are related. This research method involves collecting data from a sample and analyzing it using statistical tools such as correlation coefficients.

Quasi-experimental Research Design

Quasi-experimental research design is used to investigate cause-and-effect relationships between variables. This research method is similar to experimental research design, but it lacks full control over the independent variable. Researchers use quasi-experimental research designs when it is not feasible or ethical to manipulate the independent variable.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This research method involves manipulating the independent variable and observing the effects on the dependent variable. Researchers use experimental research designs to test hypotheses and establish cause-and-effect relationships.

Survey Research

Survey research involves collecting data from a sample of individuals using a standardized questionnaire. This research method is used to gather information on attitudes, beliefs, and behaviors of individuals. Researchers use survey research to collect data quickly and efficiently from a large sample size. Survey research can be conducted through various methods such as online, phone, mail, or in-person interviews.

Quantitative Research Analysis Methods

Here are some commonly used quantitative research analysis methods:

Statistical Analysis

Statistical analysis is the most common quantitative research analysis method. It involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis can be used to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.

Regression Analysis

Regression analysis is a statistical technique used to analyze the relationship between one dependent variable and one or more independent variables. Researchers use regression analysis to identify and quantify the impact of independent variables on the dependent variable.

Factor Analysis

Factor analysis is a statistical technique used to identify underlying factors that explain the correlations among a set of variables. Researchers use factor analysis to reduce a large number of variables to a smaller set of factors that capture the most important information.

Structural Equation Modeling

Structural equation modeling is a statistical technique used to test complex relationships between variables. It involves specifying a model that includes both observed and unobserved variables, and then using statistical methods to test the fit of the model to the data.

Time Series Analysis

Time series analysis is a statistical technique used to analyze data that is collected over time. It involves identifying patterns and trends in the data, as well as any seasonal or cyclical variations.

Multilevel Modeling

Multilevel modeling is a statistical technique used to analyze data that is nested within multiple levels. For example, researchers might use multilevel modeling to analyze data that is collected from individuals who are nested within groups, such as students nested within schools.

Applications of Quantitative Research

Quantitative research has many applications across a wide range of fields. Here are some common examples:

• Market Research : Quantitative research is used extensively in market research to understand consumer behavior, preferences, and trends. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform marketing strategies, product development, and pricing decisions.
• Health Research: Quantitative research is used in health research to study the effectiveness of medical treatments, identify risk factors for diseases, and track health outcomes over time. Researchers use statistical methods to analyze data from clinical trials, surveys, and other sources to inform medical practice and policy.
• Social Science Research: Quantitative research is used in social science research to study human behavior, attitudes, and social structures. Researchers use surveys, experiments, and other quantitative methods to collect data that can inform social policies, educational programs, and community interventions.
• Education Research: Quantitative research is used in education research to study the effectiveness of teaching methods, assess student learning outcomes, and identify factors that influence student success. Researchers use experimental and quasi-experimental designs, as well as surveys and other quantitative methods, to collect and analyze data.
• Environmental Research: Quantitative research is used in environmental research to study the impact of human activities on the environment, assess the effectiveness of conservation strategies, and identify ways to reduce environmental risks. Researchers use statistical methods to analyze data from field studies, experiments, and other sources.

Characteristics of Quantitative Research

Here are some key characteristics of quantitative research:

• Numerical data : Quantitative research involves collecting numerical data through standardized methods such as surveys, experiments, and observational studies. This data is analyzed using statistical methods to identify patterns and relationships.
• Large sample size: Quantitative research often involves collecting data from a large sample of individuals or groups in order to increase the reliability and generalizability of the findings.
• Objective approach: Quantitative research aims to be objective and impartial in its approach, focusing on the collection and analysis of data rather than personal beliefs, opinions, or experiences.
• Control over variables: Quantitative research often involves manipulating variables to test hypotheses and establish cause-and-effect relationships. Researchers aim to control for extraneous variables that may impact the results.
• Replicable : Quantitative research aims to be replicable, meaning that other researchers should be able to conduct similar studies and obtain similar results using the same methods.
• Statistical analysis: Quantitative research involves using statistical tools and techniques to analyze the numerical data collected during the research process. Statistical analysis allows researchers to identify patterns, trends, and relationships between variables, and to test hypotheses and theories.
• Generalizability: Quantitative research aims to produce findings that can be generalized to larger populations beyond the specific sample studied. This is achieved through the use of random sampling methods and statistical inference.

Examples of Quantitative Research

Here are some examples of quantitative research in different fields:

• Market Research: A company conducts a survey of 1000 consumers to determine their brand awareness and preferences. The data is analyzed using statistical methods to identify trends and patterns that can inform marketing strategies.
• Health Research : A researcher conducts a randomized controlled trial to test the effectiveness of a new drug for treating a particular medical condition. The study involves collecting data from a large sample of patients and analyzing the results using statistical methods.
• Social Science Research : A sociologist conducts a survey of 500 people to study attitudes toward immigration in a particular country. The data is analyzed using statistical methods to identify factors that influence these attitudes.
• Education Research: A researcher conducts an experiment to compare the effectiveness of two different teaching methods for improving student learning outcomes. The study involves randomly assigning students to different groups and collecting data on their performance on standardized tests.
• Environmental Research : A team of researchers conduct a study to investigate the impact of climate change on the distribution and abundance of a particular species of plant or animal. The study involves collecting data on environmental factors and population sizes over time and analyzing the results using statistical methods.
• Psychology : A researcher conducts a survey of 500 college students to investigate the relationship between social media use and mental health. The data is analyzed using statistical methods to identify correlations and potential causal relationships.
• Political Science: A team of researchers conducts a study to investigate voter behavior during an election. They use survey methods to collect data on voting patterns, demographics, and political attitudes, and analyze the results using statistical methods.

How to Conduct Quantitative Research

Here is a general overview of how to conduct quantitative research:

• Develop a research question: The first step in conducting quantitative research is to develop a clear and specific research question. This question should be based on a gap in existing knowledge, and should be answerable using quantitative methods.
• Develop a research design: Once you have a research question, you will need to develop a research design. This involves deciding on the appropriate methods to collect data, such as surveys, experiments, or observational studies. You will also need to determine the appropriate sample size, data collection instruments, and data analysis techniques.
• Collect data: The next step is to collect data. This may involve administering surveys or questionnaires, conducting experiments, or gathering data from existing sources. It is important to use standardized methods to ensure that the data is reliable and valid.
• Analyze data : Once the data has been collected, it is time to analyze it. This involves using statistical methods to identify patterns, trends, and relationships between variables. Common statistical techniques include correlation analysis, regression analysis, and hypothesis testing.
• Interpret results: After analyzing the data, you will need to interpret the results. This involves identifying the key findings, determining their significance, and drawing conclusions based on the data.
• Communicate findings: Finally, you will need to communicate your findings. This may involve writing a research report, presenting at a conference, or publishing in a peer-reviewed journal. It is important to clearly communicate the research question, methods, results, and conclusions to ensure that others can understand and replicate your research.

When to use Quantitative Research

Here are some situations when quantitative research can be appropriate:

• To test a hypothesis: Quantitative research is often used to test a hypothesis or a theory. It involves collecting numerical data and using statistical analysis to determine if the data supports or refutes the hypothesis.
• To generalize findings: If you want to generalize the findings of your study to a larger population, quantitative research can be useful. This is because it allows you to collect numerical data from a representative sample of the population and use statistical analysis to make inferences about the population as a whole.
• To measure relationships between variables: If you want to measure the relationship between two or more variables, such as the relationship between age and income, or between education level and job satisfaction, quantitative research can be useful. It allows you to collect numerical data on both variables and use statistical analysis to determine the strength and direction of the relationship.
• To identify patterns or trends: Quantitative research can be useful for identifying patterns or trends in data. For example, you can use quantitative research to identify trends in consumer behavior or to identify patterns in stock market data.
• To quantify attitudes or opinions : If you want to measure attitudes or opinions on a particular topic, quantitative research can be useful. It allows you to collect numerical data using surveys or questionnaires and analyze the data using statistical methods to determine the prevalence of certain attitudes or opinions.

Purpose of Quantitative Research

The purpose of quantitative research is to systematically investigate and measure the relationships between variables or phenomena using numerical data and statistical analysis. The main objectives of quantitative research include:

• Description : To provide a detailed and accurate description of a particular phenomenon or population.
• Explanation : To explain the reasons for the occurrence of a particular phenomenon, such as identifying the factors that influence a behavior or attitude.
• Prediction : To predict future trends or behaviors based on past patterns and relationships between variables.
• Control : To identify the best strategies for controlling or influencing a particular outcome or behavior.

Quantitative research is used in many different fields, including social sciences, business, engineering, and health sciences. It can be used to investigate a wide range of phenomena, from human behavior and attitudes to physical and biological processes. The purpose of quantitative research is to provide reliable and valid data that can be used to inform decision-making and improve understanding of the world around us.

Advantages of Quantitative Research

There are several advantages of quantitative research, including:

• Objectivity : Quantitative research is based on objective data and statistical analysis, which reduces the potential for bias or subjectivity in the research process.
• Reproducibility : Because quantitative research involves standardized methods and measurements, it is more likely to be reproducible and reliable.
• Generalizability : Quantitative research allows for generalizations to be made about a population based on a representative sample, which can inform decision-making and policy development.
• Precision : Quantitative research allows for precise measurement and analysis of data, which can provide a more accurate understanding of phenomena and relationships between variables.
• Efficiency : Quantitative research can be conducted relatively quickly and efficiently, especially when compared to qualitative research, which may involve lengthy data collection and analysis.
• Large sample sizes : Quantitative research can accommodate large sample sizes, which can increase the representativeness and generalizability of the results.

Limitations of Quantitative Research

There are several limitations of quantitative research, including:

• Limited understanding of context: Quantitative research typically focuses on numerical data and statistical analysis, which may not provide a comprehensive understanding of the context or underlying factors that influence a phenomenon.
• Simplification of complex phenomena: Quantitative research often involves simplifying complex phenomena into measurable variables, which may not capture the full complexity of the phenomenon being studied.
• Potential for researcher bias: Although quantitative research aims to be objective, there is still the potential for researcher bias in areas such as sampling, data collection, and data analysis.
• Limited ability to explore new ideas: Quantitative research is often based on pre-determined research questions and hypotheses, which may limit the ability to explore new ideas or unexpected findings.
• Limited ability to capture subjective experiences : Quantitative research is typically focused on objective data and may not capture the subjective experiences of individuals or groups being studied.
• Ethical concerns : Quantitative research may raise ethical concerns, such as invasion of privacy or the potential for harm to participants.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Methods – Types, Examples and Guide

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Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
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 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
• Popular quantitative software options
• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

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Quantitative research: Understanding the approaches and key elements

Quantitative research has many benefits and challenges but understanding how to properly conduct it can lead to a successful marketing research project.

Choosing the right quantitative approach

Editor’s note: Allison Von Borstel is the associate director of creative analytics at The Sound. This is an edited version of an article that originally appeared under the title “ Understanding Quantitative Research Approaches .”

What is quantitative research?

The systematic approaches that ground quantitative research involve hundreds or thousands of data points for one research project. The wonder of quantitative research is that each data point, or row in a spreadsheet, is a person and has a human story to tell. 

Quantitative research aggregates voices and distills them into numbers that uncover trends, illuminates relationships and correlations that inform decision-making with solid evidence and clarity.

The benefits of quantitative approach es

Why choose a quantitative   approach? Because you want a very clear story grounded in statistical rigor as a guide to making smart, data-backed decisions. 

Quantitative approaches shine because they:

Involve a lot of people

Large sample sizes (think hundreds or thousands) enable researchers to generalize findings because the sample is representative of the total population.  

They are grounded in statistical rigor

Allowing for precise measurement and analysis of data, providing statistically significant results that bolster confidence in research.

Reduce bias

Structured data collection and analysis methods enhance the reliability of findings. 

Boost efficiency

Quantitative methods often follow a qualitative phase, allowing researchers to validate findings by reporting the perspective of hundreds of people in a fraction of the time. 

Widen the analysis’ scope

The copious data collected in just a 20-minute (max) survey positions researchers to evaluate a broad spectrum of variables within the data. This thorough comprehension is instrumental when dealing with complex questions that require in-depth analysis. 

Quantitative approaches have hurdles, which include:

Limited flexibility

Once a survey is fielded, or data is gathered, there’s no opportunity to ask a live follow-up question. While it is possible to follow-up with the same people for two surveys, the likelihood of sufficient responses is small. 

Battling bots

One of the biggest concerns in data quality is making sure data represents people and not bots. 

Missing body language cues

Numbers, words and even images lack the cues that a researcher could pick up on during an interview. Unlike in a qualitative focus group, where one might deduce that a person is uncertain of an answer, in quantitative research, a static response is what the researcher works with.

Understanding quantitative research methods 

Quantitative approaches approach research from the same starting point as qualitative approaches – grounded in business objectives with a specific group of people to study. 

Once research has kicked off, the business objective thoroughly explored and the approach selected, research follows a general outline:  

Consider what data is needed

Think about what type of information needs to be gathered, with an approach in mind. While most quantitative research involves numbers, words and images also count.

• Numbers: Yes, the stereotypical rows of numbers in spreadsheets. Rows that capture people’s opinions and attitudes and are coded to numbers for comparative analytics. Numerical analysis is used for everything from descriptive statistics to regression/predictive analysis. 
• Words:  Text analysis employs a machine learning model to identify sentiment, emotion and meaning of text. Often used for sentiment analysis or content classification, it can be applied to single-word responses, elaborate open-ends, reviews or even social media posts.
• Images: Image analysis extracts meaningful information from images. A computer vision model that takes images as inputs and outputs numerical information (e.g., having a sample upload their favorite bag of chips and yielding the top three brands).

Design a survey

Create a survey to capture the data needed to address the objective. During this process, different pathways could be written to get a dynamic data set (capturing opinions that derive from various lived experiences). Survey logic is also written to provide a smooth UX experience for respondents.    

Prepare the data

The quality of quantitative research rests heavily on the quality of data. After data is collected (typically by fielding a survey or collecting already-existing data, more on that in a bit), it’s time to clean the data. 

Begin the analysis process

Now that you have a robust database (including numbers, words or images), it’s time to listen to the story that the data tells. Depending on the research approach used, advanced analytics come into play to tease out insights and nuances for the business objective. 

Tell the story

Strip the quantitative jargon and convey the insights from the research. Just because it’s quantitative research does not mean the results have to be told in a monotone drone with a monochrome chart. Answer business objectives dynamically, knowing that research is grounded in statistically sound information. 

The two options: Primary vs. secondary research

The two methods that encompass quantitative approaches are primary (collecting data oneself) and secondary (relying on already existing data).

Primary  research  is primarily used  

Most research involves primary data collection – where the researcher collects data directly. The main approach in primary research is survey data collection.  

The types of survey questions

Span various measurement scales (nominal, ordinal, interval and ratio) using a mix of question types (single and multi-choice, scales, matrix or open-ends).  

Analysis methods

Custom surveys yield great data for a variety of methods in market analysis. Here are a couple favorites: 

• Crosstabulation : Used to uncover insights that might not be obvious at first glance. This analysis organizes data into categories, revealing trends or patterns between variables. 
• Sentiment analysis: Used to sift through text to gauge emotions, opinions and attitudes. This method helps understand perception, fine-tune strategies and effectively respond to feedback.
• Market sizing: Used to map out the dimensions of a market. By calculating the total potential demand for a product or service in a specific market, this method reveals the scope of opportunities needed to make informed decisions about investment and growth strategies. 
• Conjoint analysis : Used to uncover what people value most in products or services. It breaks down features into bits and pieces and asks people to choose their ideal combo. By analyzing these preferences, this analysis reveals the hidden recipe for customer satisfaction.
• Job-To-Be-Done : Used to understand the underlying human motivations that drive people to act. People are multifaceted and experience a myriad of situations each day – meaning that a brand’s competition isn’t limited to in-category. 
• Segmentation: Used to identify specific cohorts within a greater population. It groups people with similar characteristics, behaviors or needs together. This method helps tailor products or services to specific groups, boosting satisfaction and sales.

Statistical rigor

Regardless of method, a quantitative approach then enables researchers to draw inferences and make predictions based upon the confidence in the data (looking at confidence intervals, margin of error, etc.)

Let’s not forget secondary research

By accessing a wide range of existing information, this research can be a cost-effective way to gain insights or can supplement primary research findings. 

Here are popular options: 

Government sources

Government sources can be extremely in-depth, can range across multiple industries and markets and reflect millions of people. This type of data is often instrumental for longitudinal or cultural trends analysis. 

Educational institutions

Research universities conduct in-depth studies on a variety of topics, often aggregating government data, nonprofit data and primary data.  

Client data

This includes any research that was conducted for or by companies before the   present research project. Whether it’s data gathered from customer reviews or prior quantitative work, these secondary resources can help extend findings and detect trends by connecting past data to future data.

Quantitative research enhances research projects

Quantitative research approaches are so much more than “how much” or “how many,” they reveal the   why   behind people’s actions, emotions and behaviors. By using standardized collection methods, like surveys, quant instills confidence and rigor in findings.

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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Answer a Short Quiz to Earn a Gift

What is the primary goal of quantitative research in design?

• To analyze numerical data and identify patterns
• To explore abstract design concepts for implementation
• To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

• Conduct a focus groups to collect detailed user feedback
• Participate in open-ended interviews to explore user experiences
• Run usability tests and measure task completion times

What is one key advantage of quantitative research?

• It allows participants to express their opinions in a flexible manner.
• It provides researchers with detailed narratives of user experiences and perspectives.
• It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

• It lacks objectivity which makes its results difficult to reproduce.
• It may oversimplify complex user behaviors into numbers and miss contextual insights.
• It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

• They can collect quantitative data first, followed by qualitative insights to explain the findings.
• They can completely replace quantitative methods with qualitative approaches.
• They can treat them as interchangeable methods to gather similar data.

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What is Quantitative Research?

"Quantitative research is a systematic process used to gather and statistically analyze information that has been measured by an instrument. Instruments are used to convert information into numbers. It studies only quantifiable concepts (concepts that can be measured and turned into numbers)." It examines phenomenon through the numerical representation of observations and statistical analysis.

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Qualitative vs Quantitative Research Methods & Data Analysis

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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

Qualitative vs. Quantitative Research | Differences, Examples & Methods

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

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

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

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

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

Table of contents

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

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

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

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

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

Qualitative research approach

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

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

Mixed methods approach

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

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

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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How do I find quantitative research articles?

Quantitative research focuses on gathering numerical data.

To locate qualitative research articles, use a  subject-specific database  or a general library database like  Academic Search Ultimate  or  Google Scholar .

Finding this types of research takes a bit of investigation. Try this method.

Begin by entering your keywords and conducting a search.     Example:      gardening AND mental health AND students

Since quantitative research is based on the collection and analysis of data (like numbers or statistics), you will need to look at article titles and abstracts for clues.   If a title or abstract contains terms like these, it's probably a quantitative research article.

• Data Analysis
• Longitudinal Studies
• Statistical Analysis
• Statistical Studies
• Statistical Surveys

You could also experiment with using one of those terms in your search query.     Example:      gardening AND mental health AND data analysis

See this guide from the University of Texas. Quantitative and Qualitative Research

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Quantitative Data Analysis: Everything You Need to Know

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Does the thought of quantitative data analysis bring back the horrors of math classes? We get it.

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In this article, we explore what quantitative data analysis is, the difference between quantitative and qualitative data analysis, and statistical methods you can apply to your data. We also walk you through the steps you can follow to analyze quantitative information, and how Userpilot can help you streamline the product analytics process. Let’s get started.

• Quantitative data analysis is the process of using statistical methods to define, summarize, and contextualize numerical data.
• Quantitative analysis is different from a qualitative one. The first deals with numerical data and focuses on answering “what,” “when,” and “where.” However, a qualitative analysis relies on text, graphics, or videos and explores “why” and “how” events occur.
• Pros of quantitative data analysis include objectivity, reliability, ease of comparison, and scalability.
• Cons of quantitative metrics include the data’s limited context and inflexibility, and the need for large sample sizes to get statistical significance.
• The methods for analyzing quantitative data are descriptive and inferential statistics.
• Choosing the right analysis method depends on the type of data collected and the specific research questions or hypotheses.
• These are the steps to conduct quantitative data analysis: 1. Defining goals and KPIs . 2. Collecting and cleaning data. 3. Visualizing the data. 4. Identifying patterns . 5. Sharing insights. 6. Acting on findings to improve decision-making.
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What is quantitative data analysis?

Quantitative data analysis is about applying statistical analysis methods to define, summarize, and contextualize numerical data. In short, it’s about turning raw numbers and data into actionable insights.

The analysis will vary depending on the research questions and the collected data (more on this below).

Quantitative vs qualitative data analysis

The main difference between these forms of analysis lies in the collected data. Quantitative data is numerical or easily quantifiable. For example, the answers to a customer satisfaction score (CSAT) survey are quantitative since you can count the number of people who answered “very satisfied”.

Qualitative feedback , on the other hand, analyzes information that requires interpretation. For instance, evaluating graphics, videos, text-based answers, or impressions.

Another difference between quantitative and qualitative analysis is the questions each seeks to answer. For instance, quantitative data analysis primarily answers what happened, when it happened, and where it happened. However, qualitative data analysis answers why and how an event occurred.

Quantitative data analysis also looks into identifying patterns , drivers, and metrics for different groups. However, qualitative analysis digs deeper into the sample dataset to understand underlying motivations and thinking processes.

Pros of quantitative data analysis

Quantitative or data-driven analysis has advantages such as:

• Objectivity and reliability. Since quantitative analysis is based on numerical data, this reduces biases and allows for more objective conclusions. Also, by relying on statistics, this method ensures the results are consistent and can be replicated by others, making the findings more reliable.
• Easy comparison. Quantitative data is easily comparable because you can identify trends , patterns, correlations, and differences within the same group and KPIs over time. But also, you can compare metrics in different scales by normalizing the data, e.g., bringing ratios and percentages into the same scale for comparison.
• Scalability. Quantitative analysis can handle large volumes of data efficiently, making it suitable for studies involving large populations or datasets. This makes this data analysis method scalable. Plus, researchers can use quantitative analysis to generalize their findings to broader populations.

Cons of quantitative data analysis

These are common disadvantages of data-driven analytics :

• Limited context. Since quantitative data looks at the numbers, it often strips away the data from the context, which can show the underlying reasons behind certain trends. This limitation can lead to a superficial understanding of complex issues, as you often miss the nuances and user motivations behind the data points.
• Inflexibility. When conducting quantitative research, you don’t have room to improvise based on the findings. You need to have predefined hypotheses, follow scientific methods, and select data collection instruments. This makes the process less adaptable to new or unexpected findings.
• Large sample sizes necessary. You need to use large sample sizes to achieve statistical significance and reliable results when doing quantitative analysis. Depending on the type of study you’re conducting, gathering such extensive data can be resource-intensive, time-consuming, and costly.

Quantitative data analysis methods

There are two statistical methods for reviewing quantitative data and user analytics . However, before exploring these in-depth, let’s refresh these key concepts:

• Population. This is the entire group of individuals or entities that are relevant to the research.
• Sample. The sample is a subset of the population that is actually selected for the research since it is often impractical or impossible to study the entire population.
• Statistical significance. The chances that the results gathered after your analysis are realistic and not due to random chance.

Here are methods for analyzing quantitative data:

Descriptive statistics

Descriptive statistics, as the name implies, describe your data and help you understand your sample in more depth. It doesn’t make inferences about the entire population but only focuses on the details of your specific sample.

Descriptive statistics usually include measures like the mean, median, percentage, frequency, skewness, and mode.

Inferential statistics

Inferential statistics aim to make predictions and test hypotheses about the real-world population based on your sample data.

Here, you can use methods such as a T-test, ANOVA, regression analysis, and correlation analysis.

Let’s take a look at this example. Through descriptive statistics, you identify that users under the age of 25 are more likely to skip your onboarding. You’ll need to apply inferential statistics to determine if the result is statistically significant and applicable to your entire ’25 or younger’ population.

How to choose the right method for your quantitative data analysis

The type of data that you collect and the research questions that you want to answer will impact which quantitative data analysis method you choose. Here’s how to choose the right method:

Determine your data type

Before choosing the quantitative data analysis method, you need to identify which group your data belongs to:

• Nominal —categories with no specific order, e.g., gender, age, or preferred device.
• Ordinal —categories with a specific order, but the intervals between them aren’t equal, e.g., customer satisfaction ratings .
• Interval —categories with an order and equal intervals, but no true zero point, e.g., temperature (where zero doesn’t mean “no temperature”).
• Ratio —categories with a specific order, equal intervals, and a true zero point, e.g., number of sessions per user .

Applying any statistical method to all data types can lead to meaningless results. Instead, identify which statistical analysis method supports your collected data types.

Consider your research questions

The specific research questions you want to answer, and your hypothesis (if you have one) impact the analysis method you choose. This is because they define the type of data you’ll collect and the relationships you’re investigating.

For instance, if you want to understand sample specifics, descriptive statistics—such as tracking NPS —will work. However, if you want to determine if other variables affect the NPS, you’ll need to conduct an inferential analysis.

The overarching questions vary in both of the previous examples. For calculating the NPS, your internal research question might be, “Where do we stand in customer loyalty ?” However, if you’re doing inferential analysis, you may ask, “How do various factors, such as demographics, affect NPS?”

6 steps to do quantitative data analysis and extract meaningful insights

Here’s how to conduct quantitative analysis and extract customer insights :

1. Set goals for your analysis

Before diving into data collection, you need to define clear goals for your analysis as these will guide the process. This is because your objectives determine what to look for and where to find data. These goals should also come with key performance indicators (KPIs) to determine how you’ll measure success.

For example, imagine your goal is to increase user engagement. So, relevant KPIs include product engagement score , feature usage rate , user retention rate, or other relevant product engagement metrics .

2. Collect quantitative data

Once you’ve defined your goals, you need to gather the data you’ll analyze. Quantitative data can come from multiple sources, including user surveys such as NPS, CSAT, and CES, website and application analytics , transaction records, and studies or whitepapers.

Remember: This data should help you reach your goals. So, if you want to increase user engagement , you may need to gather data from a mix of sources.

For instance, product analytics tools can provide insights into how users interact with your tool, click on buttons, or change text. Surveys, on the other hand, can capture user satisfaction levels . Collecting a broad range of data makes your analysis more robust and comprehensive.

3. Clean and visualize your data

Raw data is often messy and contains duplicates, outliers, or missing values that can skew your analysis. Before making any calculations, clean the data by removing these anomalies or outliers to ensure accurate results.

Once cleaned, turn it into visual data by using different types of charts , graphs, or heatmaps . Visualizations and data analytics charts make it easier to spot trends, patterns, and anomalies. If you’re using Userpilot, you can choose your preferred visualizations and organize your dashboard to your liking.

4. Identify patterns and trends

When looking at your dashboards, identify recurring themes, unusual spikes, or consistent declines that might indicate data analytics trends or potential issues.

Picture this: You notice a consistent increase in feature usage whenever you run seasonal marketing campaigns . So, you segment the data based on different promotional strategies. There, you discover that users exposed to email marketing campaigns have a 30% higher engagement rate than those reached through social media ads.

In this example, the pattern suggests that email promotions are more effective in driving feature usage.

If you’re a Userpilot user, you can conduct a trend analysis by tracking how your users perform certain events.

5. Share valuable insights with key stakeholders

Once you’ve discovered meaningful insights, you have to communicate them to your organization’s key stakeholders. Do this by turning your data into a shareable analysis report , one-pager, presentation, or email with clear and actionable next steps.

Your goal at this stage is for others to view and understand the data easily so they can use the insights to make data-led decisions.

Following the previous example, let’s say you’ve found that email campaigns significantly boost feature usage. Your email to other stakeholders should strongly recommend increasing the frequency of these campaigns and adding the supporting data points.

Take a look at how easy it is to share custom dashboards you built in Userpilot with others via email:

6. Act on the insights

Data analysis is only valuable if it leads to actionable steps that improve your product or service. So, make sure to act upon insights by assigning tasks to the right persons.

For example, after analyzing user onboarding data, you may find that users who completed the onboarding checklist were 3x more likely to become paying customers ( like Sked Social did! ).

Now that you have actual data on the checklist’s impact on conversions, you can work on improving it, such as simplifying its steps, adding interactive features, and launching an A/B test to experiment with different versions.

How can Userpilot help with analyzing quantitative data

As you’ve seen throughout this article, using a product analytics tool can simplify your data analysis and help you get insights faster. Here are different ways in which Userpilot can help:

Automatically capture quantitative data

Thanks to Userpilot’s new auto-capture feature, you can automatically track every time your users click, write a text, or fill out a form in your app—no engineers or manual tagging required!

Our customer analytics platform lets you use this data to build segments, trigger personalized in-app events and experiences, or launch surveys.

If you don’t want to auto-capture raw data, you can turn this functionality off in your settings, as seen below:

Monitor key metrics with customizable dashboards for real-time insights

Userpilot comes with template analytics dashboards , such as new user activation dashboards or customer engagement dashboards . However, you can create custom dashboards and reports to keep track of metrics that are relevant to your business in real time.

For instance, you could build a customer retention analytics dashboard and include all metrics that you find relevant, such as customer stickiness , NPS, or last accessed date.

Analyze experiment data with A/B and multivariate tests

Userpilot lets you conduct A/B and multivariate tests , either by following a controlled or a head-to-head approach. You can track the results on a dashboard.

For example, let’s say you want to test a variation of your onboarding flow to determine which leads to higher user activation .

You can go to Userpilot’s Flows tab and click on Experiments. There, you’ll be able to select the type of test you want to run, for instance, a controlled A/B test , build a new flow, test it, and get the results.

Use quantitative funnel analysis to increase conversion rates

With Userpilot, you can track your customers’ journey as they complete actions and move through the funnel. Funnel analytics give you insights into your conversion rates and conversion times between two events, helping you identify areas for improvement.

Imagine you want to analyze your free-to-paid conversions and the differences between devices. Just by looking at the graphic, you can draw some insights:

• There’s a significant drop-off between steps one and two, and two and three, indicating potential user friction .
• Users on desktops convert at higher rates than those on mobile or unspecified devices.
• Your average freemium conversion time is almost three days.

Leverage cohort analysis to optimize retention

Another Userpilot functionality that can help you analyze quantitative data is cohort analysis . This powerful tool lets you group users based on shared characteristics or experiences, allowing you to analyze their behavior over time and identify trends, patterns, and the long-term impact of changes on user behavior.

For example, let’s say you recently released a feature and want to measure its impact on user retention. Via a cohort analysis, you can group users who started using your product after the update and compare their retention rates to previous cohorts.

You can do this in Userpilot by creating segments and then tracking user segments ‘ retention rates over time.

Check how many users adopted a feature with a retention table

In Userpilot, you can use retention tables to stay on top of feature adoption . This means you can track how many users continue to use a feature over time and which features are most valuable to your users. The video below shows how to choose the features or events you want to analyze in Userpilot.

As you’ve seen, to conduct quantitative analysis, you first need to identify your business and research goals. Then, collect, clean, and visualize the data to spot trends and patterns. Lastly, analyze the data, share it with stakeholders, and act upon insights to build better products and drive customer satisfaction.

To stay on top of your KPIs, you need a product analytics tool. With Userpilot, you can automate data capture, analyze product analytics, and view results in shareable dashboards. Want to try it for yourself? Get a demo .

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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Embracing cultural humility in mixed methods research

Mixed methods research.

The increasing use of mixed methods research in public health and medicine presents new opportunities for investigators to obtain a more inclusive and holistic understanding of the complex factors underlying today’s pressing health challenges. Yet, navigating the mixed methods research process can be a challenge. The methodology not only involves the integration of quantitative and qualitative data – something many researchers are not trained to do – it also requires a carefully crafted approach that encourages diverse participants and perspectives and recognizes cultural differences.

In a recent paper , Yale School of Public Health Associate Research Scientist Sinem Toraman Turk and Professor Leslie Curry – both experts in mixed methods research methodology – introduce the concept of ‘cultural humility’ as a way of strengthening mixed methods research and ensuring optimal outcomes. Toraman Turk recently took a moment to discuss the advantages of mixed methods research – particularly as it pertains to addressing health inequities – and how cultural humility can help drive the field forward.

What is mixed methods research and how can it be used to address racial and ethnic health disparities?

Sinem Toraman Turk (STT): Mixed methods research is a type of research that collects, analyzes, and intentionally integrates qualitative and quantitative data. The approach takes advantage of the strength of each method in order to understand complex problems that cannot be addressed by one of the methods alone. Mixed methods can corroborate findings, generate more complete data, and enhance insights gained from each complementary method. We can use mixed methods to examine racial and ethnic disparities in health care and health outcomes in illuminating underlying drivers of inequity. We can uncover “causes of causes” of health outcomes through various combinations of qualitative and quantitative approaches and apply health justice theories to explain why we witness differences across groups.

What benefits does mixed methods research provide in strengthening quantitative research and clinical studies?

STT: Mixed methods research can strengthen quantitative research and clinical studies in a few ways. First, this approach enriches diversity in practice, data, and applications. Second, it can reduce biases introduced by big data or AI. Some examples include (1) capturing the social, cultural, and psychological complexities of individual beliefs and experiences; (2) generating holistic and integrated descriptions of a wide range of structural and social determinants across individual, community, organizational, and structural levels in great depth; and (3) supporting development of the models on which AI runs through identifying novel variables to counteract the data mining that can spin out. Third, mixed methods research plays an important role in the growth of authentic team science as appropriate given the focal topic as a potentially high-impact innovation. Lastly, mixed methods research can help implement more system-wide changes that are required to enable translation and implementation (and deimplementation) of evidence.

What is the concept of “cultural humility” and how is it applied in mixed methods research?

STT: Cultural humility is a dynamic process in which individuals actively practice self-awareness in social interactions within and across their surroundings. In research, we can think of cultural humility as a reflection to gain a deeper understanding of differences among various scientific disciplines to improve the way researchers interact with each other, study participants, and their respective fields. Practicing cultural humility includes openness, cultural and self-awareness, supportive interactions, self-reflection, recognition of power/privilege differential, and critique. We can engage with differences as cultural phenomena as opposed to personal traits. So, we define cultural humility for mixed methods research as an ongoing process of critical reflection embracing diversity undertaken by mixed methods researchers to 1) generate awareness of one’s own culture at the intrapersonal level; 2) recognize and respect cultural differences among mixed methods research team members, research participants, trainees, and community members at the interpersonal level; and 3) explore and advocate for a respectful and open culture within the field of mixed methods research at the system level. The goal for this ongoing process is to celebrate, navigate, and promote diverse perspectives, approaches, methods, and rhetoric in mixed methods research.

• Racial Disparities

Featured in this article

• Sinem Toraman Turk, PhD Associate Research Scientist in Public Health (Health Policy)
• Leslie Curry, PhD, MPH Professor of Public Health (Health Policy) and Professor of Management; Affiliated Faculty, Yale Institute for Global Health; Lecturer, Yale College; Associate Director Yale Scholars in Implementation Science Training Program

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• v.4(Suppl 1); 2019

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

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

Andrew Booth

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

Graham Moore

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

Kate Flemming

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

Özge Tunçalp

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

Elham Shakibazadeh

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

Associated Data

bmjgh-2018-000893supp001.pdf

bmjgh-2018-000893supp002.pdf

bmjgh-2018-000893supp003.pdf

bmjgh-2018-000893supp005.pdf

bmjgh-2018-000893supp004.pdf

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

Summary box

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

Introduction

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

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

Defining mixed-method research and reviews

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

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

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

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

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

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

Supplementary data

Taking a complexity perspective.

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

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

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

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

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

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

Compound questions requiring both quantitative and qualitative evidence?

Questions requiring mixed-methods studies?

Separate quantitative and qualitative questions?

Separate quantitative and qualitative research studies?

Related quantitative and qualitative research studies?

Mixed-methods studies?

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

Throughout the review?

Following separate reviews?

At the question point?

At the synthesis point?

At the evidence to recommendations stage?

Or a combination?

Narrative synthesis or summary?

Quantitising approach, eg, frequency analysis?

Qualitising approach, eg, thematic synthesis?

Tabulation?

Logic model?

Conceptual model/framework?

Graphical approach?

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

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

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

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

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

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

Questions as anchors or compasses

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

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

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

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

Types of integration and synthesis designs in mixed-method reviews

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

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

Segregated, integrated and contingent designs 32 33

Segregated design.

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

Integrated design

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

Contingent design

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

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

Convergent and sequential synthesis designs 34

Convergent synthesis design.

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

a. Data-based convergent synthesis design

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

b. Results-based convergent synthesis design

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

c. Parallel-results convergent synthesis design

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

Sequential synthesis design

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

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

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

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

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

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

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

Review team level

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

Question formulation

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

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

Screening and selecting studies

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

Assessment of methodological limitations in primary studies

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

Data extraction

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

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

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

Synthesis and integration

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

Types of findings produced by specific methods

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

Different levels of findings

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

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

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

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

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

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

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

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

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

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

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

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

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

Handling editor: Soumyadeep Bhaumik

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

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

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

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

Patient consent: Not required.

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

Data sharing statement: No additional data are available.

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

• Open access
• Published: 26 August 2024

Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples

• Aneta Pluta 1 , 13 ,
• Juan Pablo Jaworski 2 ,
• Casey Droscha 3 ,
• Sophie VanderWeele 3 ,
• Tasia M. Taxis 4 ,
• Stephen Valas 5 ,
• Dragan Brnić 6 ,
• Andreja Jungić 6 ,
• María José Ruano 7 ,
• Azucena Sánchez 7 ,
• Kenji Murakami 8 ,
• Kurumi Nakamura 8 ,
• Rodrigo Puentes 9 ,
• MLaureana De Brun 9 ,
• Vanesa Ruiz 2 ,
• Marla Eliana Ladera Gómez 10 ,
• Pamela Lendez 10 ,
• Guillermina Dolcini 10 ,
• Marcelo Fernandes Camargos 11 ,
• Antônio Fonseca 11 ,
• Subarna Barua 12 ,
• Chengming Wang 12 ,
• Aleksandra Giza 13 &
• Jacek Kuźmak 1  

BMC Veterinary Research volume  20 , Article number:  381 ( 2024 ) Cite this article

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Bovine leukemia virus (BLV) is the etiological agent of enzootic bovine leukosis and causes a persistent infection that can leave cattle with no symptoms. Many countries have been able to successfully eradicate BLV through improved detection and management methods. However, with the increasing novel molecular detection methods there have been few efforts to standardize these results at global scale. This study aimed to determine the interlaboratory accuracy and agreement of 11 molecular tests in detecting BLV. Each qPCR/ddPCR method varied by target gene, primer design, DNA input and chemistries. DNA samples were extracted from blood of BLV-seropositive cattle and lyophilized to grant a better preservation during shipping to all participants around the globe. Twenty nine out of 44 samples were correctly identified by the 11 labs and all methods exhibited a diagnostic sensitivity between 74 and 100%. Agreement amongst different assays was linked to BLV copy numbers present in samples and the characteristics of each assay (i.e., BLV target sequence). Finally, the mean correlation value for all assays was within the range of strong correlation. This study highlights the importance of continuous need for standardization and harmonization amongst assays and the different participants. The results underscore the need of an international calibrator to estimate the efficiency (standard curve) of the different assays and improve quantitation accuracy. Additionally, this will inform future participants about the variability associated with emerging chemistries, methods, and technologies used to study BLV. Altogether, by improving tests performance worldwide it will positively aid in the eradication efforts.

Peer Review reports

Introduction

Bovine leukemia virus (BLV) is a deltaretrovirus from the Orthoretrovirinae subfamily of the Retroviridae family. An essential step in the BLV replication cycle is the integration of DNA copy of its RNA genome into the DNA of a host cell [ 1 ]. Once integrated, the proviral DNA is replicated along with the host’s DNA during cellular divisions, as for any cellular gene. The BLV is the etiologic agent of enzootic bovine leukosis (EBL). BLV causes a persistent infection in cattle, and in most cases this infection is asymptomatic [ 2 ]. In one-third of infected animals the infection progresses to a state of persistent lymphocytosis, and in 1 to 10% of infected cattle it develops into lymphosarcoma [ 2 ]. BLV induces high economic losses due to trade restrictions, replacement cost, reduced milk production, immunosuppression, and increased susceptibility to pneumonia, diarrhea, mastitis, and so on [ 3 , 4 , 5 , 6 ]. BLV is globally distributed with a high prevalence, except for Western Europe and Oceania, where the virus has been successfully eradicated through detection and elimination of BLV-infected animals [ 7 , 8 ]. The agar gel immunodiffusion and ELISA for the detection of BLV-specific antibodies in sera and milk are the World Organization for Animal Health (WOAH, founded as OIE) prescribed tests for serological diagnosis but ELISA, due to its high sensitivity and ability to test many samples at a very low cost, is highly recommended [ 9 ]. Despite the advantages of serologic testing, there are some scenarios in which direct detection of the BLV genomic fragment was important to improve BLV detection. The most frequent cases is the screening of calves with maternal antibodies, acute infection, animals without persistent antibody response and animal subproducts (i.e., semen). In this regard, nucleic acid amplification tests such as real-time quantitative PCR (qPCR) allows for a rapid and highly sensitive detection of BLV proviral DNA (BLV DNA) that can be used to test infected and asymptomatic animals, before the elicitation of anti-BLV specific antibodies and when proviral load (PVL) are still low [ 10 ]. Furthermore, qPCR assays can serve as confirmatory tests for the clarification of inconclusive and discordant serological test results usually associated with these cases [ 11 ]. For these reasons, the inclusion of qPCR in combination with other screening tests might increase control programs efficiency. Additionally, qPCR allows the estimation of BLV PVL which is important for studying the dynamics of BLV infection (i.e., basic research). Further, considering that BLV PVL correlates with the risk of BLV transmission, this feature of qPCR can be exploited for developing rational segregation programs [ 12 , 13 ]. The results of Kobayashi et al. suggest that high PVL is also a significant risk factor for progression to EBL and should therefore be used as a parameter to identify cattle for culling from the herd well before EBL progression [ 14 ]. Several qPCRs have been developed globally for the quantitation of BLV DNA. Although most assays have been properly validated by each developer, a proper standardization and harmonization of such tests is currently lacking. Considering that standardization and harmonization of qPCR methods and results are essential for comparisons of data from BLV laboratories around the world, this could directly impact international surveillance programs and collaborative research. We built a global collaborative network of BLV reference laboratories to evaluate the interlaboratory variability of different qPCRs and sponsored a harmonization of assays to hopefully impact international surveillance programs and research going forward.

In 2018 we conducted the first global trial of this kind to assess the interlaboratory variability of six qPCRs for the detection of BLV DNA [ 15 ]. Since this complex process is a continuous rather than a one-time effort, we now started a second study of this type. In this follow up study, we built a more comprehensive sample panel, accounting for a broader geographical diversification. Additionally, we increased the number of participants to ten collaborating laboratories plus one WOAH reference lab and tested novel methodologies including digital PCR (ddPCR) and FRET-qPCR. Finally, we established the next steps towards the international standardization of molecular assays for the detection of BLV DNA.

Materials and methods

Participants.

The eleven laboratories that took part in the study were:(i) the Auburn University College of Veterinary Medicine (Auburn, Alabama, United States): (ii) AntelBio, a division of CentralStar Cooperative (Michigan, United States); (iii) Laboratórios Federais de Defesa Agropecuária de Minas Gerais (LFDA-MG, Pedro Leopoldo, Brasil); (iv) Centro de Investigación Veterinaria de Tandil (CIVETAN, Buenos Aires, Argentina); (v) the Faculty of Agriculture Iwate University (Iwate, Japan); (vi) Universidad de la República de Uruguay (UdelaR, Montevideo, Uruguay); (vii) the Croatian Veterinary Institute (Zagreb, Croatia); (viii) Instituto Nacional de Tecnología Agropecuaria (INTA, Buenos Aires, Argentina); (ix) Laboratorio Central de Veterinaria (LCV, Madrid, Spain); (x) the National Veterinary Research Institute (NVRI, Puławy, Poland) and (xi) the French Agency for Food, Environmental and Occupational Health and Safety (Anses, Niort, France). All European laboratories participating in this study are acting as national reference laboratories for EBL, NVRI acts as WOAH reference laboratory for EBL, while the remaining laboratories are nationally renowned entities for BLV diagnostics. The eleven participating methods are referred to below as qPCR1 – qPCR5, ddPCR6, qPCR7 – qPCR11, respectively.

Sample collection and DNA extraction

A total of 42 DNA samples obtained from blood of naturally BLV-infected dairy cattle from Poland, Moldova, Pakistan, Ukraine, Canada and United States were used for this study. Thirty-six of them were archival DNA samples obtained between 2012–2018 as described in our previous studies on samples from Poland ( n  = 21) [ 16 , 17 ], Moldova ( n  = 4) [ 18 ], Pakistan ( n  = 5) [ 19 ] and Ukraine ( n  = 6) [ 15 , 20 ]. Between 2020–2021 6 peripheral blood and serum samples from naturally BLV-infected cattle were obtained from three dairy farms of Alberta, Canada and two dairy farms of Michigan, US. Serological testing and sample processing were conducted by the laboratories from which the samples originated. The genomic DNA from Canadian and US samples was extracted from whole blood using a Quick DNA Miniprep Plus kit (Zymo Research) and a DNeasy Blood & Tissue Kit (Qiagen), respectively in University of Calgary and Michigan State University and sent to the NVRI in the form of DNA solutions. Additionally, one plasmid DNA sample (pBLV344) was kindly supplied by Luc Willems (University of Liège, Belgium) and DNA extracted from FLK-BLV cells were included as positive controls. Finally, DNA extracted from PBL of a serologically negative cattle was included as negative control. At the NVRI, the DNA concentration in all samples was estimated by spectrophotometry using a NanoPhotometer (Implen). Each sample was divided into eleven identical aliquots containing between 800 and 4,000 ng of lyophilised genomic DNA. Eleven identical sets of these samples were lyophilized (Alpha 1–4 LSC basic, Martin Christ Gefriertrocknungsanlagen GmbH) and distributed to participating laboratories. At the NVRI, all samples were coded (identification [ 21 ] run numbers 1 to 44) to perform a blinded testing. The samples, together with instructions for their preparation (Additional file 1), were shipped by air at room temperature (RT).

Examination of DNA quality/stability

Since different extraction methods and lyophilization process were employed for the preparation of the DNA samples, it was necessary to test the quality of the DNA at the NVRI laboratory. For that purpose, one complete set of samples ( n  = 44) was tested by Fragment Analyzer (Agilent Technologies), before and after freeze-drying, to assess DNA quality by calculating a Genomic Quality Number (GQN) for every sample. Low GQN value (< 2.5) represents sheared or degraded DNA. A high GQN (> 9) represents undegraded DNA. In addition, quality of DNA was assessed by determination of copy number of the histone H3 family 3A ( H3F3A ) housekeeping gene using quantitative real-time PCR (qPCR) [ 22 ]. The qPCR results were expressed as the number of H3F3A gene copies per 300 ng of DNA in each sample. Grubbs´ test was performed to determine outliers. To test the stability of DNA, samples were stored for 20 days at RT (10 days) and at + 4 °C (10 days) and were retested by Fragment Analyzer and qPCR 21 days later. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1), respectively.

Description of BLV qPCR protocols used by participating laboratories

All participating laboratories performed their qPCR or ddPCR using a variety of different equipment, reagents, and reaction conditions, which had been set up, validated, and evaluated previously and are currently used as working protocols. The specific features of each of these protocols are described below and summarized in Table  1 .

All laboratories applied standard procedures for avoiding false-positive results indicative of DNA contamination, such as the use of separate rooms for preparing reaction mixtures, adding the samples, and performing the amplification reaction. One of the ten BLV qPCRs used LTR region and the remaining nine qPCRs used the pol gene as the target sequence for amplification, while the ddPCR amplified the env gene.

Method qPCR1

The BLV qPCR amplifying a 187-bp pol gene was performed according to a previously published methods [ 23 , 24 ]. A real-time fluorescence resonance energy transfer (FRET) PCR was carried out in a 20-μl PCR mixture containing 10 μl handmade reaction master mix and 10 μl genomic DNA. The PCR buffer was 4.5 mM MgCl2, 50 mM KCl, 20 mM Tris–HCl, pH 8.4, supplemented with 0.05% each Tween20 and Non-idet P-40, and 0.03% acetylated BSA (Roche Applied Science). For each 20 μl total reaction volume, the nucleotides were used at 0.2 mM each and 1.5 U Platinum Taq DNA polymerase (Invitrogen, Carlsbad, CA, USA) was used. Primers were used at 1 μM, LCRed640 probe was used at 0.2 μM, and 6-FAM probe was used at 0.1 μM. Amplification was performed in the Roche Light Cycler 480 II (Roche Molecular Biochemicals) using 10 min denaturation step at 95 °C, followed by 18 high-stringency step-down thermal cycles and 30 low-stringency fluorescence acquisition cycles.

A plasmid containing the BLV-PCR amplicon region was diluted ten-fold from 1 × 10 5 copies to 10 copies per 10 µl and was used as a standard to measure the BLV copy numbers.

Method qPCR2

A BLV proviral load qPCR assay developed by AntelBio, a division of CentralStar Cooperative Inc. on Applied Biosystems 7500 Real-Time PCR system [ 25 , 33 ]. This multiplex assay amplifies the BLV pol gene along with the bovine β-actin gene and an internal amplification control, “Spike”. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 µl of 2X InhibiTaq Multiplex HotStart qPCR MasterMix (Empirical Bioscience), 16 nM each BLV primer, 16 nM each β-actin primer, 8 nM each spike primer, 8 nM BLV FAM-probe, 8 nM β-actin Cy5-probe, 4 nM spike JOE-probe, 1 µl of an internal spike-in control (10,000 copies per µl), 7.25 µl of nuclease-free water and 4 µl of DNA sample for each qPCR reaction. The thermal PCR protocol was as follows: 95 °C for 10 min, 40 × (95 °C for 15 s, 60 °C for 1 min). Copy numbers of both the BLV pol gene and bovine β-Actin were derived using a plasmid containing target sequences, quantified by ddPCR, diluted 1 × 10 6 copies per µl to 10 copies per µl in tenfold dilutions. DNA concentrations of each sample were measured using a Qubit 4 Fluorometer and used in combination with the qPCR copy numbers to calculate BLV copies per 100 ng.

Method qPCR3

The qPCR assays for the BLV LTR gene were performed according to a previously published methods [ 26 ]. Genomic DNA was amplified by TaqMan PCR with 10 μl of GoTaq Probe qPCR Master Mix × 2 (Promega), 0.6 pmol/μl each primer, 0.3 pmol/µl double-quenched probe and 100 ng genomic DNA. Amplification was performed in the CFX96 cycler (BioRad) according to the protocol: 5 min denaturation at 95°C followed by 45 cycles (60 s at 94°C and 60 s at 60°C). The efficiency of each reaction was calculated from the serial dilution of DNA extracted from BLV persistently infected fetal lamb kidney (FLK) cells, starting at a concentration of 100 ng/µl [ 21 ]. The detection limit was tested using a plasmid containing the target of the qPCRs, starting at 10 3 ng/µl.

Method qPCR4

The quantitative real-time PCR was done with the primers for the BLV pol gene as previously described [ 34 ]. The qPCR reaction mix contained 1 × PCR Master Mix with SYBR Green (FastStart Universal SYBR Green Master Rox, Roche), 0.3 μM each primer and 30 ng of extracted genomic DNA. Amplification was performed in QuantStudio 5 Real-Time PCR System (Applied Biosystems) under the following conditions: 2 min at 50 °C, 10 min at 95 °C, 40 cycles of 15 s at 95 °C and 60 s at 60 °C. A standard curve of six tenfold serial dilutions of pBLV, containing 1 × 10 6 to 10 BLV copies, was built and run 3 times for validation of the method. The number of provirus copies per reaction (100 ng) was calculated.

Method qPCR5

BLV PVLs were determined by using qPCR kit, RC202 (Takara Bio, Shiga, Japan) [ 28 , 35 ]. This qPCR assay amplifies the BLV pol gene along with the bovine RPPH1 gene as an internal control. Briefly, 100 ng genomic DNA was amplified by TaqMan PCR with four primers for pol gene and RPPH1 gene according to the manufacturer’s instructions: 30 s denaturation at 95 °C followed by 45 cycles (5 s at 95 °C and 30 s at 60 °C). The qPCR was performed on a QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific K.K., Tokyo, Japan). Standard curve was generated by creating tenfold serial dilutions of the standard plasmid included in the kit. The standards for calibration ranged from 1 to 10 6 copies/reaction and were run in duplicate. The number of provirus copies per 100 ng was calculated.

Method ddPCR6

The digital droplet PCR (ddPCR) assay for the env gene of the BLV was performed using the protocol previously described by [ 28 , 29 ]. An absolute quantification by TaqMan ddPCR was performed in a typical 20-μl assay, 1 μl of DNA sample was mixed with 1 μl of each primer (10 μM), 0.5 μl of probe (10 μM), and 2 × Supermix emulsified with oil (Bio-Rad). The droplets were transferred to a 96-well plate (Eppendorf). The PCR assay was performed in a thermocycler (C1000 touch cycler; Bio-Rad) with the following parameters: initial denaturation of 10 min at 95 °C, then 40 cycles of 30 s at 94 °C, and 1 min at 58 °C, with final deactivation of the enzyme for 10 min at 98 °C. The presence of fluorescent droplets determined the number of resulting positive events that were analyzed in the software (QuantaSoft v.1.7.4; Bio-Rad), using dot charts. The number of provirus copies per 100 ng were calculated. Each sample was run in duplicate, and results were averaged.

Method qPCR7

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. A quantitative TaqMan PCR was performed in a 20 μl PCR mix containing 10 μl of 2 × ORA qPCR Probe ROX L Mix (highQu, Kraichtal, Germany), 2 μl primer/probe mix (final concentration 400 nM of each of the primers, 200 nM of BLV probe), and 3 μl extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) with an initial denaturation step and polymerase activation at 95 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 10 copies to 1 × 10 1 copies per reaction and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR8

Proviral load quantification was assessed by SYBR Green real-time quantitative PCR (qPCR) using the pol gene as the target sequence [ 36 ]. Briefly, 12-μl PCR mixture contained Fast Start Universal SYBR Green Master Mix (Roche), 800 nM each BLV pol primers and 1 µl DNA as template. The reactions were incubated at 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles at 95 °C for 15 s, 55 °C for 15 s and 60 °C for 1 min. All samples were tested in duplicate on a StepOne Plus machine (Applied Biosystems). A positive and negative control, as well as a no-template control, were included in each plate. After the reaction was completed, the specificity of the amplicons was checked by analyzing the individual dissociation curves. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 6 to 10 copies per µl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR9

This qPCR method is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The detection of BLV genome was combined with an endogenous control system (Toussaint 2007) in a duplex assay. Briefly, 20-µl qPCR reaction contained AhPath ID™ One-Step RT-PCR Reagents with ROX (Applied Biosystems, CA, USA) – 10 µl of 2 × RT-PCR buffer and 0.8 µl of 25 × RT-PCR enzyme mix, 400 nM each primer for pol gene, 100 nM BLV specific probe, 40 nM each β-actin primer, 40 nM β-actin specific probe and 2 µl DNA sample. All samples were tested in ABI7500 Real-Time PCR System (Applied Biosystems) according to the following protocol: 10 min at 48 °C (reverse transcription), 10 min at 95 °C (inactivation reverse transcriptase / activation Taq polymerase) followed by 45 cycles (15 s at 95 °C and 60 s at 60 °C). As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were made from 1 × 10 4 copies to 0.1 copies per μl and used to generate the standard curve and estimate BLV copy number per 100 ng.

Method qPCR10

The BLV qPCR was performed as published previously [ 11 ]. A quantitative TaqMan PCR was carried out in a 25-μl PCR mixture containing 12.5 μl of 2 × QuantiTect Multiplex PCR NoROX master mix (Qiagen), 0.4 μM each primer, 0.2 μM specific BLV probe, and 500 ng of extracted genomic DNA. Amplification was performed in the Rotor-Gene Q system (Qiagen) using an initial denaturation step and polymerase activation at 95 °C for 15 min, followed by 50 cycles of 94 °C for 60 s and 60 °C for 60 s. All samples were amplified in duplicate. As a standard, the pBLV1 plasmid (NVRI, Pulawy, PL), containing a 120-bp BLV pol fragment, was used. Tenfold dilutions of this standard were made from 1 × 10 6 copies per μl to 100 copies per μl and were used to estimate the BLV copy numbers per 100 ng.

Method qPCR11

This qPCR method for the BLV pol gene is a modified option of widely available quantitative TaqMan qPCR described by Rola-Łuszczak et al. [ 11 ], using the same primers and standards. The reaction mixture contained 400 nM of each primer, 200 nM of probe, 10 µl of 2 × SsoFast probes supermix (Bio-Rad), 5 µl of DNA sample and H 2 O up to 20 µl of the final volume. PCR assays were carried out on a CFX96 thermocycler (Bio-Rad) under the following amplification profile: 98 °C for 3 min, followed by 45 cycles of 95 °C for 5 s and 60 °C for 30 s. As a standard, plasmid pBLV1 (NVRI, Pulawy, PL) containing a BLV pol fragment was used. Tenfold dilutions of plasmid DNA were used to generate the standard curve and estimate BLV copy number per 100 ng.

Analysis of BLV pol, env and LTR sequences targeted by particular qPCR/ddPCR assays

In order to assess full-length pol , env and LTR sequence variability among BLV genotypes, all BLV sequences ( n  = 2191) available on 30 September 2023 in GenBank ( https://www.ncbi.nlm.nih.gov/GenBank/ ) repository were retrieved. From the collected sequences, 100 pol , env and LTR sequences, which were characterized by the highest level of sequence variability and divergence, were selected for the further analysis. A pol -based, env -based and LTR-based maximum likelihood (ML) phylogenetic trees (see Additional file 6) was constructed to assign genotypes to the unassigned BLV genomes [ 37 , 38 , 39 ]. For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied. In this analysis, pol sequences were assigned to 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10), while env and LTR sequences were assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10). Phylogeny of the same isolates assigned to particular genotypes by ML method was confirmed by Mr. Bayes analysis [ 40 , 41 , 42 ] (data not shown). From this analysis, a total of 100 full-length pol, env and LTR sequences were used for multiple-sequence alignment (MSA) using ClustalW algorithm, implemented in MEGA X. For all sequences, nucleotide diversity (π), defined as the average number of nucleotide differences per site between two DNA sequences in all possible pairs in the sample population, was estimated using MEGA X. To measure the relative variation in different positions of aligned genes and LTR region the Shannon’s entropy (a quantitative measure of diversity in the alignment, where H = 0 indicates complete conservation) was estimated using BioEdit v. 7.2.5 software 64. The statistical analyses were performed using DATAtab e.U. Graz, Austria and GraphPad Software by Dotmatics, Boston.

Examination of the quality and stability of DNA samples

To test the quality of DNA samples, the H3F3A copy number of each individual sample was assessed by qPCR at the NVRI. Copy numbers were normalized to DNA mass input and results were expressed as copy numbers per 300 ng of total DNA. The respective values were tested by Grubbs' test. The results for 43 DNA samples (sample ID: 42 with BLV genome plasmid was excluded) followed a normal distribution (Shapiro–Wilk 0.97; P  = 0.286), with a mean value of 35,626 copies (95% confidence interval [ 43 ] 33,843 to 37,408 copies), a minimum value of 19,848 copies and a maximum value of 46,951 copies (see Additional file 2). Despite a low value for sample ID: 40 no significant outlier was detected in the dataset ( P  > 0.05). Therefore, it can be assumed that the DNA quality was acceptable for all samples present in the panel. Next, DNA stability was assessed by retesting the H3F3A copy numbers in each sample ( n  = 43) after a combined storage consisting in 10 days at RT and 10 days at + 4°C. A Mann–Whitney U-test was used to compare the median values between fresh and stored samples (time 0 and time 1, respectively), and no significant difference was observed at the 5% level ( P  = 0.187) (Fig.  1 A).

Assessment of the stability of DNA samples. A Shown are copy numbers of the H3F3A housekeeping gene in 43 DNA samples that were stored in 10 days at RT and 10 days at + 4°C and tested twice with a 21-day interval. A Mann–Whitney U-test was used to compare the median values between two groups ( P  = 0.187); B Shown are GQN values ( n  = 43) tested twice with a 21-day interval: `before freeze-drying` and `after freeze-drying`. A Mann–Whitney U-test results between two groups ( P  = 0.236)

In addition, the quality of DNA samples after lyophilization was analyzed. DNA from individual samples ( n  = 43) was assessed with the genomic DNA quality number on the Fragment Analyzer system. The GQN from all lyophilized samples ranged from 4.0 to 9.7—that represented undegraded DNA. There was no significant difference in GQN values between `before freeze-drying` and `after freeze-drying` groups with respect to the corresponding DNA samples ( P  = 0.236) (Fig.  1 B). Altogether, these results suggested that sample storage, lyophilization and shipping has a minimal impact in DNA stability and further testing during the interlaboratory trial.

Detection of BLV proviral DNA by different qPCR assays

A total of 44 DNA samples, including two positive (ID: 42 and 43) and one negative (ID: 32) controls, were blinded and independently tested by eleven laboratories using their own qPCR methods (Table  2 ). All laboratories measured the concentration of DNA in samples (Additional file 3). BLV provirus copy number was normalized to DNA concentration and expressed per 100 ng of genomic DNA for each test.

Except for the positive (pBLV344 and FLK cell line) and the negative controls, all samples had previously shown detectable levels of BLV-specific antibodies (BLV-Abs) by enzyme-linked immunosorbent assays (ELISA). During the current interlaboratory study, both the positive and negative controls were assessed adequately by all eleven PCR tests. Of all 43 positive samples, 43, 35, 37, 36, 40, 32, 40, 42, 42, 42 and 41 samples were detected as positive by the qPCR1, qPCR2, qPCR3, qPCR4, qPCR5, ddPCR6, qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 methods, respectively. Based on these observations, the most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. Comparison of qualitative results (positive versus negative) from all eleven methods revealed 87.33% overall agreement and a kappa value of 0.396 (Cohen's kappa method adapted by Fleiss) [ 44 , 45 ]. The levels of agreement among the results from the eleven methods are represented in Table  3 . The maximum agreement was seen between two methods (qPCR9 and qPCR10 [100% agreement and a Cohen's kappa value of 1.000]) that used similar protocols and targeted the same region of BLV pol .

Analysis of BLV pol, env and LTR sequences targeted by particular PCR assays

Due to differences in performance observed among the pol -based qPCR assays (the qPCR1, qPCR2, qPCR4, qPCR5 and qPCR7- qPCR11 methods), and considering that the env -based ddPCR6 and LTR-based qPCR3 assay showed the lowest sensitivity and the poorest agreement with the other assays, the degree of sequence variability between the pol , env and LTR genes was addressed. From the MSAs for pol , env and LTR, the nucleotide diversity (π) was calculated. The π value for pol gene was lower than that for LTR and env gene (π pol , 0.023 [standard deviation {SD}, 0.018]; π LTR , 0.024 [SD, 0.011]; π env , 0.037 [SD, 0.013]). From this analysis, pol sequences appeared to be less variable than env and LTR sequences. In addition, we performed a Shannon entropy-based per-site variability profile of the pol , env and LTR sequences used in this study (Fig.  2 A-C).

Sequence variability measured as per-site entropy. A Multiple alignment of the pol gene showing the locations of qPCR fragments in regions of the pol gene for the qPCR1 (highlighted in pink), qPCR4 (highlighted in yellow) and for the qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assays (highlighted in orange). B Multiple alignment of the env gene targeted by ddPCR6 (highlighted by blue rectangle). C Multiple alignment of the LTR region by qPCR3 (highlighted in mint)

The all-observed entropy plots were homogeneous along the whole sequences. Considering the three regions of pol gene, the highest entropy (4.67) occurred in the region targeted by the qPCR1 primers, whereas the entropy for qPCR7—qPCR11 and qPCR4 primers were 1.57 and 0.38, respectively. For the LTR region targeted by qPCR3 primers and for env gene targeted by ddPCR6, the total entropy was equal to 4.46 and 7.85, respectively. This analysis showed a marked region of variability for LTR and env fragments. Interestingly, we noted that the qPCR7—qPCR11 targeted the most conserved regions of reverse transcriptase and qPCR4 primers targeted the most-conserved region of virus integrase (Fig.  2 A-C; see also Additional file 7).

Quantitation of BLV proviral DNA by different qPCR/ddPCR assays

To analyze whether the range of copy numbers detected by each qPCR was comparable to those of the others, Kruskal–Wallis one-way analysis of variance (ANOVA) was used. The violin plots were used to visualize the ANOVA results (Fig.  3 A-B).

Comparison of detection of BLV proviral DNA copy numbers by eleven testing methods. Shown is a box plot of data from Kruskal–Wallis ANOVA, a rank test. The DNA copy numbers for 41 samples, determined independently by each of the 11 qPCRs, were used for the variance analysis. In this analysis, the positive controls (sample ID 42 and ID 43) and negative control (sample ID 32) were excluded. A Violin plot for graphical presentation of the ANOVA of proviral copy number values. B Violin plot for ANOVA analysis of variance, copy number values are presented on a logarithmic scale (Log1.2) for better illustration of copy number differences between PCR methods

The grouping variable revealed significant differences among the distributions of proviral DNA copy numbers with the various qPCRs ( P  < 0.001). These results showed that the abilities of qPCRs/ddPCR to determine the proviral DNA copy number differed. A Dunn-Bonferroni test was used to compare the groups in pairs to find out which was significantly different. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2—qPCR4, qPCR3—ddPCR6, qPCR4—qPCR5, qPCR4—ddPCR6, qPCR4—qPCR9, qPCR4—qPCR10, qPCR5—qPCR11, ddPCR6—qPCR11 and qPCR9—qPCR11 have an adjusted P value less than 0.05 and thus, it can be assumed that these groups were significantly different in each pair (see Additional file 4). The Pareto chart was used to show the average copy number values of all methods in descending order. These Pareto charts were prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes [ 46 ]. The methods that generated the highest copy numbers was qPCR3 and qPCR4, on the other hand the lowest copy numbers and/or highest negative results were generated by ddPCR6 (Fig.  4 ).

A Pareto chart with the proviral BLV copy mean values for eleven PCR assay arranged in descending order. Pareto charts was prepared based on 80–20 rule, which states that 80% of effects come from 20% of the various causes

The correlations between copy numbers detected by different qPCRs and ddPCR assays were calculated. The Kendall's Tau correlation coefficient measured between each pair of the assays was shown in the Additional file 5 and in Fig.  5 as a correlation heatmap. The average correlation for all qPCRs and ddPCR assays was strong (Kendall's tau = 0.748; P  < 0.001).

The heatmap of Kendall’s tau correlation coefficients between copy numbers detected by ten qPCRs and one ddPCR. Statistically significant differences in the distribution of copy numbers, a moderate, strong and very strong correlation between particular qPCRs/ddPCR was observed. The strength of the association, for absolute values of r, 0–0.19 is regarded as very weak, 0.2–0.39 as weak, 0.40–0.59 as moderate, 0.6–0.79 as strong and 0.8–1 as very strong correlation

Since the differences between PCR tests may be influenced by the number of BLV proviral copies present in each sample, we compared the average number of BLV copies between a group of genomic DNA samples that gave concordant results (group I [ n  = 28]) and a group that gave discordant results (group II [ n  = 15]). The mean number of copies was 73,907 (minimum, 0; maximum, 4,286,730) in group I, and 3,479 (minimum, 0; maximum, 218,583) in group II, and this difference was statistically significant ( P  < 0.001 by a Mann–Whitney U- test) (Fig.  6 ).

Impact of BLV proviral copy numbers on the level of agreement. Violin plot for graphical presentation of Mann–Whitney U test. The test was performed to compare BLV provirus copy number in two groups of samples: 28 samples with fully concordant results from all eleven qPCR/ddPCR assays (left) and 15 samples with discordant results from different qPCR/ddPCR assays (right) ( P  < 0.001). Sample ID 42 was excluded from the statistical analysis

The results show that the concordant results group had considerably higher copy numbers (median, 5,549.0) than the discordant results group (median, 6.3).

BLV control and eradication programs consist of correct identification and subsequent segregation/elimination of BLV-infected animals [ 47 ]. Detection of BLV- infected cows by testing for BLV-specific antibodies in serum by agar gel immunodiffusion and ELISA is the key step and standard to be implemented of EBL eradication programs according to WOAH ( https://www.woah.org/en/disease/enzootic-bovine-leukosis/) [ 9 ]. Despite the low cost and high throughput of serological tests, there are several scenarios where highly specific and sensitive molecular assays for the detection of BLV DNA might improve detection and program efficiency.

In this perspective, qPCR assays can detect small quantities of proviral DNA during acute infection, in which animals show very low levels of anti-BLV antibodies [ 43 , 48 , 49 , 50 ]. qPCR methods can also work as confirmatory tests to clarify ambiguous and inconsistent serological test results [ 11 ]. Such quantitative features of qPCRs are crucial when eradication programs progress and prevalence decreases. Moreover, qPCR allows not only the detection of BLV infection but also estimation of the BLV PVL, which directly correlates with the risk of disease transmission [ 51 , 52 ]. This feature of qPCR allows for a rational segregation of animals based on the stratified risk of transmission. These considerations allow for greater precision in the management of BLV within large herds with a high prevalence of BLV ELISA-positive animals to effectively reduce herd prevalence [ 13 , 53 ]. BLV is a global burden and the lack of technical standardization of molecular detection systems remains a huge obstacle to compare surveillance data globally based on the first interlaboratory trial performed in 2018 [ 15 ]. In the 2018 study we observed an adjusted level of agreement of 70% comparing qualitative qPCR results; however, inconsistencies amongst methods were larger when low number of copies of BLV DNA were compared. Samples with low copies of BLV DNA (< 20 copies per 100 ng) accounted for the higher variability and discrepancies amongst tests. We concluded from the first interlaboratory trial that standardizing protocols to improve sensitivity of assays with lower detection rates was necessary.

In this follow up study, we re-tested the TaqMan BLV qPCR developed and validated by NVRI (acting as reference WOAH laboratory) and the one adapted from this original protocol to be used with SYBR Green dye, allowing a significant reduction in costs [ 11 ]. Another 3 laboratories also performed NVRI´s qPCR with slight modifications (i.e., Spain performed a multiplex assay for internal normalization). The remaining 6 labs introduced novel methodologies to the trial including one ddPCR (UY).

To compare different qPCR methods, a more comprehensive sample panel, accounting for a more geographical diversification was used in this trial. The amounts of BLV DNA in these samples were representative of the different BLV proviral loads found in field samples (from 1 to > 10,000 copies of BLV proviral DNA). Of note, 34% of reference samples had less than 100 copies of BLV DNA per 100 ng; samples were lyophilized to grant better preservation and reduced variability during distribution to participants around the globe.

The panel included a single negative control and two positive controls. Diagnostic sensitivity (DxSn) was estimated for each qPCR. Considering the 43 positive samples, the DxSn for the different qPCRs were: qPCR1 = 100%, qPCR2 = 82%, qPCR3 = 86%, qPCR4 = 84%, qPCR5 = 93%, ddPCR6 = 74%, qPCR7 = 93%, qPCR8 = 98%, qPCR9 = 98%, qPCR10 = 98% and qPCR11 = 95%. The most sensitive method was the qPCR1, and the method with the lowest sensitivity was the ddPCR6 method. Twenty-nine out of 44 samples were identified correctly by all qPCRs. The remaining 15 samples gave discordant results. The comparison of qualitative qPCR results among all raters revealed an overall observed agreement of 87%, indicating strong interrater reliability (Cohen´s kappa = 0.396) [ 54 , 55 ].

There are several factors that contribute to variability in qPCR results (i.e., number of copies of target input, sample acquisition, processing, storage and shipping, DNA purification, target selection, assay design, calibrator, data analysis, etc.). For that reason and as expected, the level of agreement among sister qPCRs (qPCR7, qPCR9-11) sharing similar protocols was higher compared to the rest of assays; this was also true for qPCR8 which targets the same region of BLV pol gene (shares same primers) but has a particular set-up to be used with SYBR Green chemistry. Oppositely, lower sensitivity and larger discrepancy against other tests was observed for the ddPCR6 and qPCR2-4.

Based on these observations we investigated which factors might have accounted for larger assessment variability amongst tests. In the first place, we observed that the use of different chemistries was not detrimental for the sensitivity and agreement among tests; similar DxSn and comparable level of agreement were obtained comparing TaqMan (qPCR7, 10, 11) vs SYBR Green (qPCR8) chemistries while targeting identical BLV sequence and using same standards. Also, when a multiplex qPCR (TaqMan) targeting the same BLV sequence and using the same standard was compared to previous ones, agreement was kept high, indicating that the lower sensitivity described for some multiplex qPCRs did not take place in this comparison. The use of an international calibrator and the efficiency estimation (standard curve) might inform variability associated with different chemistries. In contrast, another multiplex assay targeting another region of BLV pol (qPCR2) showed much lower sensitivity and agreement. As qPCR2 is performed as service by private company and oligonucleotide sequences were not available, we were not able to investigate in which proportion each of these two variables contributed to the lower performance of this assay, but we note the addition of 4 µl genomic DNA to this assay that would have an impact the DxSn. In this regard, there is substantial evidence showing that the variability of target sequence among strains from different geographical areas, might affect the sensitivity of BLV qPCRs. Previous studies comparing the pol , gag , tax and env genes reported that the pol gene was the most suitable region to target for diagnostic purposes, since it provided the most-sensitive assays [ 11 , 15 , 56 , 57 , 58 , 59 ]. This might be due in part to higher sequence conservation of pol among strains from different geographical areas. Supporting this observation, it is noticeable how JPN qPCR improved their performance in the current trial, by targeting pol in place of tax , as it did in the previous interlaboratory trial. Since it is a commercial test, we cannot exclude other factors contributing for the performance upgrade observed for this qPCR. In the current study, qPCR3 and ddPCR6 targeting LTR and env sequences, showed lower performances than other assays. Standardization of DNA input into each qPCR would have likely resulted in higher concordance in results. For instance, qPCR1 added 10 µl of genomic DNA per reaction and ddPCR6 added 1 µl of genomic DNA, impacting the resulting sensitivity differences.

Since the sensitivity of each assay and, consequently, the level of agreement among assays might also be influenced by the number of BLV DNA copies present in each sample [ 48 ], we compared the average number of BLV DNA copies between a group of genomic DNA samples that gave concordant results and a group that gave discordant results, and observed that samples that gave discordant results had significantly lower numbers of BLV DNA copies than samples that gave concordant results. Related to this point, the degradation of target DNA during lyophilization, shipment and resuspension, could have been more significant in low-copy compared to high-copy samples. Consequently, the degradation of target DNA in samples with low copies of BLV DNA might have accounted for the greater level of discrepancy within this subset of samples. The rational of adding a large proportion of such samples (34% samples with less than 100 BLV copies per 100 ng of total DNA) was to mimic what is frequently observed in surveillance programs (i.e., hyperacute infection, chronic asymptomatic infection, etc.).

Quantitative methods for the detection of BLV DNA copies are important for segregation programs based on animal level of BLV PVL, as well as for scientific research and the study of BLV dynamics. When the numbers of copies of BLV DNA detected by different assays were compared, in the present study, we observed that although the ability to quantify BLV DNA differed among qPCRs/ddPCR and there were statistically significant differences in the distribution of copy numbers among assays, a strong average correlation was found for the eleven qPCRs/ddPCR. In this regard, the lack of an international calibrator (standard curve) could be a major contributor to the increment of quantitative variation amongst laboratories. For that reason, plasmid pBLV1 containing pol 120 bp sequence was originally constructed for use as standard for quantification and shared with some collaborators (i.e., qPCR7, qPCR8, qPCR 9, qPCR10 and qPCR11). Remarkably, the laboratories used pBLV1 standard in the current trial obtained the most comparable results, indicating that the use of an international standard may have significant impact on the convergence of results; such standard reference material should be prepared under identical conditions. To avoid further variability a detailed protocol for lyophilized DNA sample resuspension, quantitation and template input into each qPCR should be shared with all participants.

Conclusions

BLV DNA was detected with different level of sensitivity in serologically positive samples from different origin and classified into different BLV genotypes. Overall agreement was high; however, we found significant differences in results for the samples with low BLV DNA copy numbers. This second interlaboratory study demonstrated that differences in target sequence, DNA input and calibration curve standards can increase interlaboratory variability considerably. Next steps should focus on (i) standard unification (international gold standard) to estimate individual test efficiency and improve quantitative accuracy amongst tests; (ii) building a new panel of samples with low BLV DNA copy numbers to re-evaluate sensitivity and quantitation of molecular methods. Since no variation was observed in samples from different genotypes, all samples will be collected in Poland to standardize the collection, purification, lyophilization and shipping steps with precise instructions for suspension and constant input volume for the PCR reaction. Finally, we believe that following this standardization approach we will be able to improve overall agreement amongst tests, improving the diagnostic of BLV around the world.

Availability of data and materials

Not applicable.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

One-way analysis of variance

Bovine leukemia virus

BLV-specific antibodies

Digital PCR

Diagnostic sensitivity

Enzootic bovine leukosis

Enzyme-linked immunosorbent assays

Real-time fluorescence resonance energy transfer PCR

Genomic quality number

Histone H3 family 3A housekeeping gene

Maximum likelihood phylogenetic tree

Multiple-sequence alignment

Peripheral blood leukocytes

Phosphate-buffered saline

Proviral load

Quantitative real-time PCR

Room temperature

World Organisation for Animal Health

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Acknowledgements

The authors thank Luc Willems (University of Liège, Belgium) for plasmid DNA sample pBLV344; Marlena Smagacz and Eliza Czarnecka (National Veterinary Research Institute, Poland) for lyophilizing DNA samples and DNA analysis, respectively; Ali Sakhawat (Animal Quarantine Department, Pakistan), Vitaliy Bolotin (National Scientific Center IECVM, Ukraine), Frank van der Meer and Sulav Shrestha (University of Calgary, Canada) for sharing material.

The APC was funded by the National Veterinary Research Institute, Puławy, Poland.

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Department of Biochemistry, National Veterinary Research Institute, Puławy, 24-100, Poland

Aneta Pluta & Jacek Kuźmak

Instituto de Virología E Innovaciones Tecnológicas (IVIT), Centro de Investigaciones en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA) - CONICET, Buenos Aires, Argentina

Juan Pablo Jaworski & Vanesa Ruiz

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Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, Michigan, 48824, USA

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Contributions

Proposed the conception and design of the study, A.P.; data curation, A.P., J.P.J., C.D., S.V., D.B., A.S., K.M., R.P., G.D., M.F.C. and CH.W.; investigation, A.P., V.R., S.VW., S.V., A.J., M.J.R., K.N., M.L.B., M.L.G., P.L., A.F., A.G. and S.B., formal analysis, A.P.; statistical analysis, A.P.; database analysis, A.P., visualization of the results, A.P.; resources, A.P., T.M.T. and J.K; writing—original draft preparation, A.P., J.P.J.; writing—review and editing, A.P., J.P.J., C.D., S.VW., T.M.T. and J.K; project administration, A.P. All authors read and approved the submitted version.

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Ethics approval and consent to participate.

The study was approved by the Veterinary Sciences Animal Care Committee No. AC21-0210, Canada; the Institutional Animal Care and Use Committee No. PROTO202000096 from 4/13/2020 to 4/14/2023, Michigan State University, United States and the Ethics Review Board, COMSATS Institute of Information Technology, Islamabad, Pakistan, no. CIIT/Bio/ERB/17/26. Blood samples from Polish, Moldovan and Ukrainian cattle, naturally infected with BLV, were selected from collections at local diagnostic laboratories as part of the Enzootic bovine leukosis (EBL) monitoring program between 2012 and 2018 and sent to the National Veterinary Research Institute (NVRI) in Pulawy for confirmation study. The approval for collection of these samples from ethics committee was not required according to Polish regulation (“Act on the Protection of Animals Used for Scientific or Educational Purposes”, Journal of Laws of 2015). All methods were carried out in accordance with relevant guidelines and regulations. The owners of the cattle herds from which the DNA samples originated, the district veterinarians caring for these farms and the ministries of agriculture were informed and consented to the collection of blood from the animals for scientific purposes and the sending of samples to NVRI.

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Supplementary Information

12917_2024_4228_moesm1_esm.pdf.

Additional file 1. Copy of the instruction included with the panel of 44 DNA samples sent to participating laboratories for dilution of the lyophilisates

12917_2024_4228_MOESM2_ESM.png

Additional file 2. Detection of the H3F3A gene copy number in 43 DNA samples; no outlier was found for any samples ( P <0.05) (two-sided).

12917_2024_4228_MOESM3_ESM.docx

Additional file 3. Concentration values of 44 DNA samples measured by the 11 participating laboratories (given in ng per µl)

12917_2024_4228_MOESM4_ESM.pdf

Additional file 4. Post hoc - Dunn-Bonferroni-Tests. The Dunn-Bonferroni test revealed that the pairwise group comparisons of qPCR2 - qPCR4, qPCR3 - ddPCR6, qPCR4 - qPCR5, qPCR4 - ddPCR6, qPCR4 - qPCR9, qPCR4 - qPCR10, qPCR5 - qPCR11, ddPCR6 - qPCR11 and qPCR9 - qPCR11 have an adjusted p-value less than 0,05

12917_2024_4228_MOESM5_ESM.docx

Additional file 5. Kendall's Tau correlation coefficient values measured between each pair of assays. The numbers 1 to 11 in the first column and last row of the table indicate the names of the assays qPCR1-qPCR5, ddPCR6, qPCR7-qPCR11 respectively

12917_2024_4228_MOESM6_ESM.png

Additional file 6. Maximum-likelihood phylogenetic analysis of full-length BLV-pol gene sequences representing 7 BLV genotypes (G1, G2, G3, G4, G6, G9, and G10) (A); (B) env-based sequences assigned to 10 BLV genotypes (G1, G2, G3, G4, G5, G6, G7, G8, G9, and G10); (C) LTR-based sequences representing 10 BLV genotypes (G1-G10). For all genes and LTR region the Tamura-Nei model and Bootstrap replications (1,000) were applied in MEGA X

12917_2024_4228_MOESM7_ESM.pdf

Additional file 7. Multiple sequence alignment of reverse transcriptase, integrase, envelope and LTR sequences in the context of the specific primers used by different qPCR assays. (A) Multiple sequence alignment of reverse transcriptase (pol gene) sequences in the context of qPCR7, qPCR8, qPCR9, qPCR10 and qPCR11 assay primers. (B) Multiple sequence alignment of integrase (pol gene) sequences in the context of qPCR4 assay primers. (C) Multiple sequence alignment of env gene sequences in the context of ddPCR6. (D) Sequence alignment of LTR region sequences in the context of qPCR3 method primers

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Pluta, A., Jaworski, J.P., Droscha, C. et al. Inter-laboratory comparison of eleven quantitative or digital PCR assays for detection of proviral bovine leukemia virus in blood samples. BMC Vet Res 20 , 381 (2024). https://doi.org/10.1186/s12917-024-04228-z

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DOI : https://doi.org/10.1186/s12917-024-04228-z

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• Bovine leukemia virus ( BLV)
• Quantitative real-time PCR (qPCR)
• Proviral DNA
• BLV international network
• Update on the efforts in harmonization qPCR

BMC Veterinary Research

ISSN: 1746-6148

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