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Quantitative and Qualitative Research

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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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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Quantitative Research – Methods, Types and Analysis

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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.

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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

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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Literature on Quantitative Research

Here’s the entire UX literature on Quantitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Quantitative Research

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• 3 years ago

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• 11 mths ago

7 Simple Ways to Get Better Results From Ethnographic Research

Question Everything

Tree Testing

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• 8 years ago

How to Fit Quantitative Research into the Project Lifecycle

Why and When to Use Surveys

Correlation in User Experience

Rating Scales in UX Research: The Ultimate Guide

First-Click Testing

What to Test

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Quantitative Methods

• Living reference work entry
• First Online: 11 June 2021
• Cite this living reference work entry

• Juwel Rana 2 , 3 , 4 ,
• Patricia Luna Gutierrez 5 &
• John C. Oldroyd 6  

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Quantitative analysis ; Quantitative research methods ; Study design

Quantitative method is the collection and analysis of numerical data to answer scientific research questions. Quantitative method is used to summarize, average, find patterns, make predictions, and test causal associations as well as generalizing results to wider populations. It allows us to quantify effect sizes, determine the strength of associations, rank priorities, and weigh the strength of evidence of effectiveness.

Introduction

This entry aims to introduce the most common ways to use numbers and statistics to describe variables, establish relationships among variables, and build numerical understanding of a topic. In general, the quantitative research process uses a deductive approach (Neuman 2014 ; Leavy 2017 ), extrapolating from a particular case to the general situation (Babones 2016 ).

In practical ways, quantitative methods are an approach to studying a research topic. In research, the...

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Babones S (2016) Interpretive quantitative methods for the social sciences. Sociology. https://doi.org/10.1177/0038038515583637

Balnaves M, Caputi P (2001) Introduction to quantitative research methods: an investigative approach. Sage, London

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Brenner PS (2020) Understanding survey methodology: sociological theory and applications. Springer, Boston

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Creswell JW (2014) Research design: qualitative, quantitative, and mixed methods approaches. Sage, London

Leavy P (2017) Research design. The Gilford Press, New York

Mertens W, Pugliese A, Recker J (2018) Quantitative data analysis, research methods: information, systems, and contexts: second edition. https://doi.org/10.1016/B978-0-08-102220-7.00018-2

Neuman LW (2014) Social research methods: qualitative and quantitative approaches. Pearson Education Limited, Edinburgh

Treiman DJ (2009) Quantitative data analysis: doing social research to test ideas. Jossey-Bass, San Francisco

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Department of Public Health, School of Health and Life Sciences, North South University, Dhaka, Bangladesh

Department of Biostatistics and Epidemiology, School of Health and Health Sciences, University of Massachusetts Amherst, MA, USA

Department of Research and Innovation, South Asia Institute for Social Transformation (SAIST), Dhaka, Bangladesh

Independent Researcher, Masatepe, Nicaragua

Patricia Luna Gutierrez

School of Behavioral and Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia

John C. Oldroyd

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Rana, J., Gutierrez, P.L., Oldroyd, J.C. (2021). Quantitative Methods. In: Farazmand, A. (eds) Global Encyclopedia of Public Administration, Public Policy, and Governance. Springer, Cham. https://doi.org/10.1007/978-3-319-31816-5_460-1

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Quantitative and Empirical Research vs. Other Types of Research: Quantitative Research

• Quantitative Research
• Other Types of Research
• What are Scholarly Journals?

     P rofessors often want you to use scholarly journal articles for your assignments.

     Sometimes, they will require you to use scholarly journal articles that contain quantitative research .

DEFINITIONS

QUANTITATIVE

     Quantitative research looks at factors that can actually be measured in some way, in other words, quantified . It produces numerical results that can be analyzed statistically.

     Quantitative research commonly involves experimentation, surveys, or questionnaires in the context of a large, randomly selected group.

     The term  empirical research  is often used as a synonym for quantitative research, but strictly speaking, empirical research is simply any form of research based upon direct observation. It might also be quantitative, but it might not.

PLEASE NOTE: Some professors use these two terms interchangeably.  When this occurs, they are usually referring to articles that fit the quantitative description above.

HINT: Don't use the words "quantitative" or "empirical" in your keyword searches.  They usually do not appear in article titles, abstracts, or subject words.  Instead, check the articles you find to see if some sort of numerical measuring and statistical analysis is present along with the characteristics listed on the right.

CHARACTERISTICS OF QUANTITATIVE RESEARCH

      W atch for these features when determining if an article has quantitative research. They may appear in the abstract, or you may need to skim the text of the article to find them.

• Introduction : a statement of background or purpose (what was being studied and why). May review prior studies on the same topic.
• Description of the design and/or method of the study (the experimental group or sample , control, variables, number of test subjects, test conditions, etc.)
• Results , or report of the findings (in numeric form as tables, charts, or graphs, etc., often with statistical analysis)
• Conclusions that can be drawn from the results (may be labeled  discussion or significance )
• Footnotes and/or a bibliography
• Author credentials (degrees earned, where they work, etc.)  

SAMPLE QUANTITATIVE RESEARCH ARTICLES

• Relations Between Trait Impulsivity, Behavioral Impulsivity, Physiological Arousal, and Risky Sexual Behavior Among Young Men
• Nocturnal Heart Rate Variability in Patients Treated with Cognitive–Behavioral Therapy for Insomnia.
• Characterisation of Mainstream and Passive Vapors Emitted by Selected Electronic Cigarettes

• Next: Other Types of Research >>
• Last Updated: Apr 6, 2023 8:16 AM
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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Home » Quantitative Research: Definition, Methods, and Examples

Quantitative Research: Definition, Methods, and Examples

June 13, 2023 max 8min read.

This article covers:

What Is Quantitative Research?

Quantitative research methods .

• Data Collection and Analysis

Types of Quantitative Research

• Advantages and Disadvantages of Quantitative Research

Examples of Quantitative Research

Picture this: you’re a product or project manager and must make a crucial decision. You need data-driven insights to guide your choices, understand customer preferences, and predict market trends. That’s where quantitative research comes into play. It’s like having a secret weapon that empowers you to make informed decisions confidently.

Quantitative research is all about numbers, statistics, and measurable data. It’s a systematic approach that allows you to gather and analyze numerical information to uncover patterns, trends, and correlations. 

Quantitative research provides concrete, objective data to drive your strategies, whether conducting surveys, analyzing large datasets, or crunching numbers.

In this article, we’ll dive and learn all about quantitative research; get ready to uncover the power of numbers.

Quantitative Research Definition:

Quantitative research is a systematic and objective approach to collecting, analyzing, and interpreting numerical data. It measures and quantifies variables, employing statistical methods to uncover patterns, relationships, and trends.

Quantitative research gets utilized across a wide range of fields, including market research, social sciences, psychology, economics, and healthcare. It follows a structured methodology that uses standardized instruments, such as surveys, experiments, or polls, to collect data. This data is then analyzed using statistical techniques to uncover patterns and relationships.

The purpose of quantitative research is to measure and quantify variables, assess the connections between variables, and draw objective and generalizable conclusions. Its benefits are numerous:

• Rigorous and scientific approach : Quantitative research provides a comprehensive and scientific approach to studying phenomena. It enables researchers to gather empirical evidence and draw reliable conclusions based on solid data.
• Evidence-based decision-making : By utilizing quantitative research, researchers can make evidence-based decisions. It helps in developing informed strategies and evaluating the effectiveness of interventions or policies by relying on data-driven insights.
• Advancement of knowledge : Quantitative research contributes to the advancement of knowledge by building upon existing theories. It expands understanding in various fields and informs future research directions, allowing for continued growth and development.

Here are various quantitative research methods:

Survey research : This method involves collecting data from a sample of individuals through questionnaires, interviews, or online surveys. Surveys gather information about people’s attitudes, opinions, behaviors, and characteristics.

Experimentation: It is a research method that allows researchers to determine cause-and-effect relationships. In an experiment, participants randomly get assigned to different groups. While the other group does not receive treatment or intervention, one group does. The outcomes of the two groups then get measured to analyze the effects of the treatment or intervention.

Here are the steps involved in an experiment:

• Define the research question. What do you want to learn about?
• Develop a hypothesis. What do you think the answer to your research question is?
• Design the experiment. How will you manipulate the variables and measure the outcomes?
• Recruit participants. Who will you study?
• Randomly assign participants to groups. This ensures that the groups are as similar as possible.
• Apply the treatments or interventions. This is what the researcher is attempting to test the effects of.
• Measure the outcomes. This is how the researcher will determine whether the treatments or interventions had any effect.
• Analyze the data. This is how the researcher will determine whether the results support the hypothesis.
• Draw conclusions. What do the results mean?
• Content analysis : Content analysis is a systematic approach to analyzing written, verbal, or visual communication. Researchers identify and categorize specific content, themes, or patterns in various forms of media, such as books, articles, speeches, or social media posts.
• Secondary data analysis : It is a research method that involves analyzing data already collected by someone else. This data can be from various sources, such as government reports, previous research studies, or large datasets like surveys or medical records. 

Researchers use secondary data analysis to answer new research questions or gain additional insights into a topic.

Data Collection and Analysis for Quantitative Research

Quantitative research is research that uses numbers and statistics to answer questions. It often measures things like attitudes, behaviors, and opinions.

There are three main methods for collecting quantitative data:

• Surveys and questionnaires: These are structured instruments used to gather data from a sample of people.
• Experiments and controlled observations: These are conducted in a controlled setting to measure variables and determine cause-and-effect relationships.
• Existing data sources (secondary data): This data gets collected from databases, archives, or previous studies.

Data preprocessing and cleaning is the first step in data analysis. It involves identifying and correcting errors, removing outliers, and ensuring the data is consistent.

Descriptive statistics is a branch of statistics that deals with the description of the data. It summarizes and describes the data using central tendency, variability, and shape measures.

Inferential statistics again comes under statistics which deals with the inference of properties of a population from a sample. It tests hypotheses, estimates parameters, and makes predictions.

Here are some of the most common inferential statistical techniques:

• Hypothesis testing : This assesses the significance of relationships or differences between variables.
• Confidence intervals : This estimates the range within which population parameters likely fall.
• Correlation and regression analysis : This examines relationships and predicts outcomes based on variables.
• Analysis of variance (ANOVA) : This compare means across multiple groups or conditions.

Statistical software and tools for data analysis can perform complex statistical analyses efficiently. Some of the most popular statistical software packages include SPSS, SAS, and R.

Here are some of the main types of quantitative research methodology:

• Descriptive research describes a particular population’s characteristics, trends, or behaviors. For example, a descriptive study might look at the average height of students in a school, the number of people who voted in an election, or the types of food people eat.
• Correlational research checks the relationship between two or more variables. For example, a correlational study might examine the relationship between income and happiness or stress and weight gain. Correlational research can show that two variables are related but cannot show that one variable causes the other.
• Experimental research is a type of research that investigates cause-and-effect relationships. In an experiment, researchers manipulate one variable (the independent variable) and measure the impact on another variable (the dependent variable). This allows researchers to make inferences about the relationship between the two variables.
• Quasi-experimental research is similar to experimental research. However, it does not involve random assignment of participants to groups. This can be due to practical or ethical considerations, such as when assigning people to receive a new medication randomly is impossible. In quasi-experimental research, researchers try to control for other factors affecting the results, such as the participant’s age, gender, or health status.
• Longitudinal research studies change patterns over an extended time. For example, a longitudinal study might examine how children’s reading skills develop over a few years or how people’s attitudes change as they age. But longitudinal research can be expensive and time-consuming. Still, it can offer valuable insights into how people and things change over time.

 Advantages and Disadvantages of Quantitative Research

Here are the advantages and downsides of quantitative research:

Advantages of Quantitative Research:

• Objectivity: Quantitative research aims to be objective and unbiased. This is because it relies on numbers and statistical methods, which reduce the potential for researcher bias and subjective interpretation.
• Generalizability: Quantitative research often involves large sample sizes, which increases the likelihood of obtaining representative data. The study findings are more likely to apply to a wider population.
• Replicability: Using standardized procedures and measurement instruments in quantitative research enhances replicability. This means that other researchers can repeat the study using the same methods to test the reliability of the findings.
• Statistical analysis: Quantitative research employs various statistical techniques for data analysis. This allows researchers to identify data patterns, relationships, and associations. Additionally, statistical analysis can provide precision and help draw objective conclusions.
• Numerical precision: Quantitative research produces numerical data that can be analyzed using mathematical calculations. This numeric precision allows for clear comparisons and quantitative interpretations.

Disadvantages of Quantitative Research :

• Lack of Contextual Understanding : Quantitative research often focuses on measurable variables, which may limit the exploration of complex phenomena. It may overlook the social, cultural, and contextual factors that could influence the research findings.
• Limited Insight : While quantitative research can identify correlations and associations, it may not uncover underlying causes or explanations of these relationships. It may provide answers to “what” and “how much,” but not necessarily “why.”
• Potential for Simplification : The quantification of data can lead to oversimplification, as it may reduce complex phenomena into numerical values. This simplification may overlook nuances and intricacies important to understanding the research topic fully.
• Cost and Time-Intensive : Quantitative research requires significant resources. It includes time, funding, and specialized expertise. Researchers must collect and analyze large amounts of numerical data, which can be lengthy and expensive.
• Limited Flexibility : A systematic and planned strategy typically gets employed in quantitative research. It signifies the researcher’s use of a predetermined data collection and analysis approach. As a result, you may be more confident that your study gets conducted consistently and equitably. But it may also make it more difficult for the researcher to change the research plan or pose additional inquiries while gathering data. This could lead to missing valuable insights.

Here are some real-life examples of quantitative research:

• Market Research : Quantitative market research is a type of market research that uses numerical data to understand consumer preferences, buying behavior, and market trends. This data typically gets gathered through surveys and questionnaires, which are then analyzed to make informed business decisions.
• Health Studies : Quantitative research, such as clinical trials and epidemiological research, is vital in health studies. Researchers collect numerical data on treatment effectiveness, disease prevalence, risk factors, and patient outcomes. This data is then analyzed statistically to draw conclusions and make evidence-based recommendations for healthcare practices.
• Educational Research : Quantitative research is used extensively in educational studies to examine various aspects of learning, teaching methods, and academic achievement. Researchers collect data through standardized tests, surveys, or observations. The reason for this approach is to analyze factors influencing student performance, educational interventions, and educational policy effectiveness.
• Social Science Surveys : Social science researchers often employ quantitative research methods. The aim here is to study social phenomena and gather data on individuals’ or groups’ attitudes, beliefs, and behaviors. Large-scale surveys collect numerical data, then statistically analyze to identify patterns, trends, and associations within the population.
• Opinion Polls : Opinion polls and public opinion research rely heavily on quantitative research techniques. Polling organizations conduct surveys with representative samples of the population. The companies do this intending to gather numerical data on public opinions, political preferences, and social attitudes. The data then gets analyzed to gauge public sentiment and predict election outcomes or public opinion on specific issues.
• Economic Research : Quantitative research is widely used in economic studies to analyze economic indicators, trends, and patterns. Economists collect numerical data on GDP, inflation, employment, and consumer spending. Statistical analysis of this data helps understand economic phenomena, forecast future trends, and inform economic policy decisions.

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Qualitative research is about understanding and exploring something in depth. It uses non-numerical data, like interviews, observations, and open-ended survey responses, to gather rich, descriptive insights. Quantitative research is about measuring and analyzing relationships between variables using numerical data.

Quantitative research gets characterized by the following:

• The collection of numerical information
• The use of statistical analysis
• The goal of measuring and quantifying phenomena
• The purpose of examining relationships between variables
• The purpose of generalizing findings to a larger population
• The use of large sample sizes
• The use of structured surveys or experiments
• The usage of statistical techniques to analyze data objectively

The primary goal of quantitative research is to gather numerical data and analyze it statistically to uncover patterns, relationships, and trends. It aims to provide objective and generalizable insights using systematic data collection methods, standardized instruments, and statistical analysis techniques. Quantitative research seeks to test hypotheses, make predictions, and inform decision-making in various fields.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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Quantitative and Qualitative Research

• Quantitative vs. Qualitative Research
• Find quantitative or qualitative research in CINAHL
• Find quantitative or qualitative research in PsycINFO
• Relevant book titles

Mixed Methods Research

As its name suggests, mixed methods research involves using elements of both quantitative and qualitative research methods. Using mixed methods, a researcher can more fully explore a research question and provide greater insight. 

What is Empirical Research?

Empirical research is based on observed  and measured phenomena. Knowledge is extracted from real lived experience rather than from theory or belief. 

IMRaD: Scholarly journals sometimes use the "IMRaD" format to communicate empirical research findings.

Introduction:  explains why this research is important or necessary. Provides context ("literature review").

Methodology:  explains how the research was conducted ("research design").

Results: presents what was learned through the study ("findings").

Discussion:  explains or comments upon the findings including why the study is important and connecting to other research ("conclusion").

What is Quantitative Research?

Quantitative research gathers data that can be measured numerically and analyzed mathematically. Quantitative research attempts to answer research questions through the quantification of data. 

Indicators of quantitative research include:

contains statistical analysis 

large sample size 

objective - little room to argue with the numbers 

types of research: descriptive studies, exploratory studies, experimental studies, explanatory studies, predictive studies, clinical trials 

What is Qualitative Research?

Qualitative research is based upon data that is gathered by observation. Qualitative research articles will attempt to answer questions that cannot be measured by numbers but rather by perceived meaning. Qualitative research will likely include interviews, case studies, ethnography, or focus groups. 

Indicators of qualitative research include:

interviews or focus groups 

small sample size 

subjective - researchers are often interpreting meaning 

methods used: phenomenology, ethnography, grounded theory, historical method, case study 

Video: Empirical Studies: Qualitative vs. Quantitative

This video from usu libraries walks you through the differences between quantitative and qualitative research methods. (5:51 minutes) creative commons attribution license (reuse allowed)  https://youtu.be/rzcfma1l6ce.

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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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• Published: 13 May 2024

SCIPAC: quantitative estimation of cell-phenotype associations

• Dailin Gan 1 ,
• Yini Zhu 2 ,
• Xin Lu 2 , 3 &
• Jun Li   ORCID: orcid.org/0000-0003-4353-5761 1  

Genome Biology volume  25 , Article number:  119 ( 2024 ) Cite this article

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Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p -value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC’s accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.

Single-cell RNA sequencing (scRNA-seq) technologies are revolutionizing biomedical research by providing comprehensive characterizations of diverse cell populations in heterogeneous tissues [ 1 , 2 ]. Unlike bulk RNA sequencing (RNA-seq), which measures the average expression profile of the whole tissue, scRNA-seq gives the expression profiles of thousands of individual cells in the tissue [ 3 , 4 , 5 , 6 , 7 ]. Based on this rich data, cell types may be discovered/determined in an unsupervised (e.g., [ 8 , 9 ]), semi-supervised (e.g., [ 10 , 11 , 12 , 13 ]), or supervised manner (e.g., [ 14 , 15 , 16 ]). Despite the fast development, there are still limitations with scRNA-seq technologies. Notably, the cost for each scRNA-seq experiment is still high; as a result, most scRNA-seq data are from a single or a few biological samples/tissues. Very few datasets consist of large numbers of samples with different phenotypes, e.g., cancer vs. normal. This places great difficulties in determining how a cell type contributes to a phenotype based on single-cell studies (especially if the cell type is discovered in a completely unsupervised manner or if people have limited knowledge of this cell type). For example, without having single-cell data from multiple cancer patients and multiple normal controls, it could be hard to computationally infer whether a cell type may promote or inhibit cancer development. However, such association can be critical for cancer research [ 17 ], disease diagnosis [ 18 ], cell-type targeted therapy development [ 19 ], etc.

Fortunately, this difficulty may be overcome by borrowing information from bulk RNA-seq data. Over the past decade, a considerable amount of bulk RNA-seq data from a large number of samples with different phenotypes have been accumulated and made available through databases like The Cancer Genome Atlas (TCGA) [ 20 ] and cBioPortal [ 21 , 22 ]. Data in these databases often contain comprehensive patient phenotype information, such as cancer status, cancer stages, survival status and time, and tumor metastasis. Combining single-cell data from a single or a few individuals and bulk data from a relatively large number of individuals regarding a particular phenotype can be a cost-effective way to determine how a cell type contributes to the phenotype. A recent method Scissor [ 23 ] took an essential step in this direction. It uses single-cell and bulk RNA-seq data with phenotype information to classify the cells into three discrete categories: Scissor+, Scissor−, and null cells, corresponding to cells that are positively associated, negatively associated, and not associated with the phenotype.

Here, we present a method that takes another big step in this direction, which is called Single-Cell and bulk data-based Identifier for Phenotype Associated Cells or SCIPAC for short. SCIPAC enables quantitative estimation of the strength of association between each cell in a scRNA-seq data and a phenotype, with the help of bulk RNA-seq data with phenotype information. Moreover, SCIPAC also enables the estimation of the statistical significance of the association. That is, it gives a p -value for the association between each cell and the phenotype. Furthermore, SCIPAC enables the estimation of association between cells and an ordinal phenotype (e.g., different stages of cancer), which could be informative as people may not only be interested in the emergence/existence of cancer (cancer vs. healthy, a binary problem) but also in the progression of cancer (different stages of cancer, an ordinal problem).

To study the performance of SCIPAC, we first apply SCIPAC to simulated data under three schemes. SCIPAC shows high accuracy with low false positive rates. We further show the broad applicability of SCIPAC on real datasets across various diseases, including prostate cancer, breast cancer, lung cancer, and muscular dystrophy. The association inferred by SCIPAC is highly informative. In real datasets, some cell types have definite and well-studied functions, while others are less well-understood: their functions may be disease-dependent or tissue-dependent, and they may contain different sub-types with distinct functions. In the former case, SCIPAC’s results agree with current biological knowledge. In the latter case, SCIPAC’s discoveries inspire the generation of new hypotheses regarding the roles and functions of cells under different conditions.

An overview of the SCIPAC algorithm

SCIPAC is a computational method that identifies cells in single-cell data that are associated with a given phenotype. This phenotype can be binary (e.g., cancer vs. normal), ordinal (e.g., cancer stage), continuous (e.g., quantitative traits), or survival (i.e., survival time and status). SCIPAC uses input data consisting of three parts: single-cell RNA-seq data that measures the expression of p genes in m cells, bulk RNA-seq data that measures the expression of the same set of p genes in n samples/tissues, and the statuses/values of the phenotype of the n bulk samples/tissues. The output of SCIPAC is the strength and the p -value of the association between each cell and the phenotype.

SCIPAC proposes the following definition of “association” between a cell and a phenotype: A group of cells that are likely to play a similar role in the phenotype (such as cells of a specific cell type or sub-type, cells in a particular state, cells in a cluster, cells with similar expression profiles, or cells with similar functions) is considered to be positively/negatively associated with a phenotype if an increase in their proportion within the tissue likely indicates an increased/decreased probability of the phenotype’s presence. SCIPAC assigns the same association to all cells within such a group. Taking cancer as the phenotype as an example, if increasing the proportion of a cell type indicates a higher chance of having cancer (binary), having a higher cancer stage (ordinal), or a higher hazard rate (survival), all cells in this cell type is positively associated with cancer.

The algorithm of SCIPAC follows the following four steps. First, the cells in the single-cell data are grouped into clusters according to their expression profiles. The Louvain algorithm from the Seurat package [ 24 , 25 ] is used as the default clustering algorithm, but the user may choose any clustering algorithm they prefer. Or if information of the cell types or other groupings of cells is available a priori, it may be supplied to SCIPAC as the cell clusters, and this clustering step can be skipped. In the second step, a regression model is learned from bulk gene expression data with the phenotype. Depending on the type of the phenotype, this model can be logistic regression, ordinary linear regression, proportional odds model, or Cox proportional hazards model. To achieve a higher prediction power with less variance, by default, the elastic net (a blender of Lasso and ridge regression [ 26 ]) is used to fit the model. In the third step, SCIPAC computes the association strength \(\Lambda\) between each cell cluster and the phenotype based on a mathematical formula that we derive. Finally, the p -values are computed. The association strength and its p -value between a cell cluster and the phenotype are given to all cells in the cluster.

SCIPAC requires minimum tuning. When the cell-type information is given in step 1, SCIPAC does not have any (hyper)parameter. Otherwise, the Louvain algorithm used in step 1 has a “resolution” parameter that controls the number of cell clusters: a larger resolution results in more clusters. SCIPAC inherits this parameter as its only parameter. Since SCIPAC gives the same association strength and p -value to cells from the same cluster, this parameter also determines the resolution of results provided by SCIPAC. Thus, we still call it “resolution” in SCIPAC. Because of its meaning, we recommend setting it so that the number of cell clusters given by the clustering algorithm is comparable to, or reasonably larger than, the number of cell types (or sub-types) in the data. We will see that the performance of SCIPAC is insensitive to this resolution parameter, and the default value 2.0 typically works well.

The details of the SCIPAC algorithm are given in the “ Methods ” section.

Performance in simulated data

We assess the performance of SCIPAC in simulated data under three different schemes. The first scheme is simple and consists of only three cell types. The second scheme is more complicated and consists of seven cell types, which better imitates actual scRNA-seq data. In the third scheme, we simulate cells under different cell development stages to test the performance of SCIPAC under an ordinal phenotype. Details of the simulation are given in Additional file 1.

Simulation scheme I

Under this scheme, the single-cell data consists of three cell types: one is positively associated with the phenotype, one is negatively associated, and the third is not associated (we call it “null”). Figure 1 a gives the UMAP [ 27 ] plot of the three cell types, and Fig. 1 b gives the true associations of these three cell types with the phenotype, with red, blue, and light gray denoting positive, negative, and null associations.

UMAP visualization and numeric measures of the simulated data under scheme I. All the plots in a–e  are scatterplots of the two dimensional single-cell data given by UMAP. The x and y axes represent the two dimensions, and their scales are not shown as their specific values are not directly relevant. Points in the plots represents single cells, and they are colored differently in each subplot to reflect different information/results. a  Cell types. b  True associations. The association between cell types 1, 2, and 3 and the phenotype are positive, negative, and null, respectively. c  Association strengths \(\Lambda\) given by SCIPAC under different resolutions. Red/blue represents the sign of \(\Lambda\) , and the shade gives the absolute value of \(\Lambda\) . Every cell is colored red or blue since no \(\Lambda\) is exactly zero. Below each subplot, Res stands for resolution, and K stands for the number of cell clusters given by this resolution. d   p -values given by SCIPAC. Only cells with p -value \(< 0.05\) are colored red (positive association) or blue (negative association); others are colored white. e  Results given by Scissor under different \(\alpha\) values. Red, blue, and light gray stands for Scissor+, Scissor−, and background (i.e., null) cells. f  F1 scores and g  FSC for SCIPAC and Scissor under different parameter values. For SCIPAC, each bar is the value under a resolution/number of clusters. For Scissor, each bar is the value under an \(\alpha\)

We apply SCIPAC to the simulated data. For the resolution parameter (see the “ Methods ” section), values 0.5, 1.0, and 1.5 give 3, 4, and 4 clusters, respectively, close to the actual number of cell types. They are good choices based on the guidance for choosing this parameter. To show how SCIPAC behaves under parameter misspecification, we also set the resolution up to 4.0, which gives a whopping 61 clusters. Figure 1 c and d give the association strengths \(\Lambda\) and the p -values given by four different resolutions (results under other resolutions are provided in Additional file 1: Fig. S1 and S2). In Fig. 1 c, red and blue denote positive and negative associations, respectively, and the shade of the color represents the strength of the association, i.e., the absolute value of \(\Lambda\) . Every cell is colored blue or red, as none of \(\Lambda\) is exactly zero. In Fig. 1 d, red and blue denote positive and negative associations that are statistically significant ( p -value \(< 0.05\) ). Cells whose associations are not statistically significant ( p -value \(\ge 0.05\) ) are shown in white. To avoid confusion, it is worth repeating that cells that are colored in red/blue in Fig. 1 c are shown in red/blue in Fig. 1 d only if they are statistically significant; otherwise, they are colored white in Fig. 1 d.

From Fig. 1 c, d (as well as Additional file 1: Fig. S1 and S2), it is clear that the results of SCIPAC are highly consistent under different resolution values, including both the estimated association strengths and the p -values. It is also clear that SCIPAC is highly accurate: most truly associated cells are identified as significant, and most, if not all, truly null cells are identified as null.

As the first algorithm that quantitatively estimates the association strength and the first algorithm that gives the p -value of the association, SCIPAC does not have a real competitor. A previous algorithm, Scissor, is able to classify cells into three discrete categories according to their associations with the phenotype. So, we compare SCIPAC with Scissor in respect of the ability to differentiate positively associated, negatively associated, and null cells.

Running Scissor requires tuning a parameter called \(\alpha\) , which is a number between 0 and 1 that balances the amount of regularization for the smoothness and for the sparsity of the associations. The Scissor R package does not provide a default value for this \(\alpha\) or a function to help select this value. The Scissor paper suggests the following criterion: “the number of Scissor-selected cells should not exceed a certain percentage of total cells (default 20%) in the single-cell data. In each experiment, a search on the above searching list is performed from the smallest to the largest until a value of \(\alpha\) meets the above criteria.” In practice, we have found that this criterion does not often work properly, as the truly associated cells may not compose 20% of all cells in actual data. Therefore, instead of setting \(\alpha\) to any particular value, we set \(\alpha\) values that span the whole range of \(\alpha\) to see the best possible performance of Scissor.

The performance of Scissor in our simulation data under four different \(\alpha\) values are shown in Fig. 1 e, and results under more \(\alpha\) values are shown in Additional file 1: Fig. S3. In the figures, red, blue, and light gray denote Scissor+, Scissor−, and null (called “background” in Scissor) cells, respectively. The results of Scissor have several characteristics different from SCIPAC. First, Scissor does not give the strength or statistical significance of the association, and thus the colors of the cells in the figures do not have different shades. Second, different \(\alpha\) values give very different results. Greater \(\alpha\) values generally give fewer Scissor+ and Scissor− cells, but there are additional complexities. One complexity is that the Scissor+ (or Scissor−) cells under a greater \(\alpha\) value are not a strict subset of Scissor+ (or Scissor−) cells under a smaller \(\alpha\) value. For example, the number of truly negatively associated cells detected as Scissor− increases when \(\alpha\) increases from 0.01 to 0.30. Another complexity is that the direction of the association may flip as \(\alpha\) increases. For example, most cells of cell type 2 are identified as Scissor+ under \(\alpha =0.01\) , but many of them are identified as Scissor− under larger \(\alpha\) values. Third, Scissor does not achieve high power and low false-positive rate at the same time under any \(\alpha\) . No matter what the \(\alpha\) value is, there is only a small proportion of cells from cell type 2 that are correctly identified as negatively associated, and there is always a non-negligible proportion of null cells (i.e., cells from cell type 3) that are incorrectly identified as positively or negatively associated. Fourth, Scissor+ and Scissor− cells can be close to each other in the figure, even under a large \(\alpha\) value. This means that cells with nearly identical expression profiles are detected to be associated with the phenotype in opposite directions, which can place difficulties in interpreting the results.

SCIPAC overcomes the difficulties of Scissor and gives results that are more informative (quantitative strengths with p -values), more accurate (both high power and low false-positive rate), less sensitive to the tuning parameter, and easier to interpret (cells with similar expression typically have similar associations to the phenotype).

SCIPAC’s higher accuracy in differentiating positively associated, negatively associated, and null cells than Scissors can also be measured numerically using the F1 score and the fraction of sign correctness (FSC). F1, which is the harmonic mean of precision and recall, is a commonly used measure of calling accuracy. Note that precision and recall are only defined for two-class problems, which try to classify desired signals/discoveries (so-called “positives”) against noises/trivial results (so-called “negatives”). Our case, on the other hand, is a three-class problem: positive association, negative association, and null. To compute F1, we combine the positive and negative associations and treat them as “positives,” and treat null as “negatives.” This F1 score ignores the direction of the association; thus, it alone is not enough to describe the performance of an association-detection algorithm. For example, an algorithm may have a perfect F1 score even if it incorrectly calls all negative associations positive. To measure an algorithm’s ability to determine the direction of the association, we propose a statistic called FSC, defined as the fraction of true discoveries that also have the correct direction of the association. The F1 score and FSC are numbers between 0 and 1, and higher values are preferred. A mathematical definition of these two measures is given in Additional file 1.

Figure 1 f, g show the F1 score and FSC of SCIPAC and Scissor under different values of tuning parameters. The F1 score of Scissor is between 0.2 and 0.7 under different \(\alpha\) ’s. The FSC of Scissor increases from around 0.5 to nearly 1 as \(\alpha\) increases, but Scissor does not achieve high F1 and FSC scores at the same time under any \(\alpha\) . On the other hand, the F1 score of SCIPAC is close to perfection when the resolution parameter is properly set, and it is still above 0.90 even if the resolution parameter is set too large. The FSC of SCIPAC is always above 0.96 under different resolutions. That is, SCIPAC achieves high F1 and FSC scores simultaneously under a wide range of resolutions, representing a much higher accuracy than Scissor.

Simulation scheme II

This more complicated simulation scheme has seven cell types, which are shown in Fig. 2 a. As shown in Fig. 2 b, cell types 1 and 3 are negatively associated (colored blue), 2 and 4 are positively associated (colored red), and 5, 6, and 7 are not associated (colored light gray).

UMAP visualization of the simulated data under a–g  scheme II and h–k  scheme III. a  Cell types. b  True associations. c , d  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. e  Results given by Scissor under different \(\alpha\) values. f  F1 scores and g  FSC for SCIPAC and Scissor under different parameter values. h  Cell differentiation paths. The four paths have the same starting location, which is in the center, but different ending locations. This can be considered as a progenitor cell type differentiating into four specialized cell types. i  Cell differentiation steps. These steps are used to create four stages, each containing 500 steps. Thus, this plot of differentiation steps can also be viewed as the plot of true association strengths. j , k  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution

The association strengths and p -values given by SCIPAC under the default resolution are illustrated in Fig. 2 c, d, respectively. Results under several other resolutions are given in Additional file 1: Fig. S4 and S5. Again, we find that SCIPAC gives highly consistent results under different resolutions. SCIPAC successfully identifies three out of the four truly associated cell types. For the other truly associated cell type, cell type 1, SCIPAC correctly recognizes its association with the phenotype as negative, although the p -values are not significant enough. The F1 score is 0.85, and the FSC is greater than 0.99, as shown in Fig. 2 f, g.

The results of Scissor under four different \(\alpha\) values are given in Fig. 2 e. (More shown in Additional file 1: Fig. S6.) Under this highly challenging simulation scheme, Scissor can only identify one out of four truly associated cell types. Its F1 score is below 0.4.

Simulation scheme III

This simulation scheme is to assess the performance of SCIPAC for ordinal phenotypes. We simulate cells along four cell-differentiation paths with the same starting location but different ending locations, as shown in Fig. 2 h. These cells can be considered as a progenitor cell population differentiating into four specialized cell types. In Fig. 2 i, the “step” reflects their position in the differentiation path, with step 0 meaning the start and step 2000 meaning the end of the differentiation. Then, the “stage” is generated according to the step: cells in steps 0 \(\sim\) 500, 501 \(\sim\) 1000, 1001 \(\sim\) 1500, and 1501 \(\sim\) 2000 are assigned to stages I, II, III, and IV, respectively. This stage is treated as the ordinal phenotype. Under this simulation scheme, Fig. 2 i also gives the actual associations, and all cells are associated with the phenotype.

The results of SCIPAC under the default resolution are shown in Fig. 2 j, k. Clearly, the associations SCIPAC identifies are highly consistent with the truth. Particularly, it successfully identifies the cells in the center as early-stage cells and most cells at the end of branches as last-stage cells. The results of SCIPAC under other resolutions are given in Additional file 1: Fig. S7 and S8, which are highly consistent. Scissor does not work with ordinal phenotypes; thus, no results are reported here.

Performance in real data

We consider four real datasets: a prostate cancer dataset, a breast cancer dataset, a lung cancer dataset, and a muscular dystrophy dataset. The bulk RNA-seq data of the three cancer datasets are obtained from the TCGA database, and that of the muscular dystrophy dataset is obtained from a published paper [ 28 ]. A detailed description of these datasets is given in Additional file 1. We will use these datasets to assess the performance of SCIPAC on different types of phenotypes. The cell type information (i.e., which cell belongs to which cell type) is available for the first three datasets, but we ignore this information so that we can make a fair comparison with Scissor, which cannot utilize this information.

Prostate cancer data with a binary phenotype

We use the single-cell expression of 8,700 cells from prostate-cancer tumors sequenced by [ 29 ]. The cell types of these cells are known and given in Fig. 3 a. The bulk data comprises 550 TCGA-PRAD (prostate adenocarcinoma) samples with phenotype (cancer vs. normal) information. Here the phenotype is cancer, and it is binary: present or absent.

UMAP visualization of the prostate cancer data, with a zoom-in view for the red-circled region (cell type MNP). a  True cell types. BE, HE, and CE stand for basal, hillock, club epithelial cells, LE-KLK3 and LE-KLK4 stand for luminal epithelial cells with high levels of kallikrein related peptidase 3 and 4, and MNP stands for mononuclear phagocytes. In the zoom-in view, the sub-types of MNP cells are given. b  Association strengths \(\Lambda\) given by SCIPAC under the default resolution. The cyan-circled cells are B cells, which are estimated by SCIPAC as negatively associated with cancer but estimated by Scissor as Scissor+ or null. c   p -values given by SCIPAC. The MNP cell type, which is red-circled in the plot, is estimated by SCIPAC to be strongly negatively associated with cancer but estimated by Scissor to be positively associated with cancer. d  Results given by Scissor under different \(\alpha\) values

Results from SCIPAC with the default resolution are shown in Fig. 3 b, c (results with other resolutions, given in Additional file 1: Fig. S9 and S10, are highly consistent with results here.) Compared with results from Scissor, shown in Fig. 3 d, results from SCIPAC again show three advantages. First, results from SCIPAC are richer and more comprehensive. SCIPAC gives estimated associations and the corresponding p -values, and the estimated associations are quantitative (shown in Fig. 3 b as different shades to the red or blue color) instead of discrete (shown in Fig. 3 d as a uniform shade to the red, blue, or light gray color). Second, SCIPAC’s results can be easier to interpret as the red and blue colors are more block-wise instead of scattered. Third, unlike Scissor, which produces multiple sets of results varying based on the parameter \(\alpha\) —a parameter without a default value or tuning guidance—typically, a single set of results from SCIPAC under its default settings suffices.

Comparing the results from our SCIPAC method with those from Scissor is non-trivial, as the latter’s outcomes are scattered and include multiple sets. We propose the following solutions to summarize the inferred association of a known cell type with the phenotype using a specific method (Scissor under a specific \(\alpha\) value, or SCIPAC with the default setting). We first calculate the proportion of cells in this cell type identified as Scissor+ (by Scissor at a specific \(\alpha\) value) or as significantly positively associated (by SCIPAC), denoted by \(p_{+}\) . We also calculate the proportion of all cells, encompassing any cell type, which are identified as Scissor+ or significantly positively associated, serving as the average background strength, denoted by \(p_{a}\) . Then, we compute the log odds ratio for this cell type to be positively associated with the phenotype compared to the background, represented as:

Similarly, the log odds ratio for the cell type to be negatively associated with the phenotype, \(\rho _-\) , is computed in a parallel manner.

For SCIPAC, a cell type is summarized as positively associated with the phenotype if \(\rho _+ \ge 1\) and \(\rho _- < 1\)  and negatively associated if \(\rho _- \ge 1\) and \(\rho _+ < 1\) . If neither condition is met, the association is inconclusive. For Scissor, we apply it under six different \(\alpha\) values: 0.01, 0.05, 0.10, 0.15, 0.20, and 0.25. A cell type is summarized as positively associated with the phenotype if \(\rho _+ \ge 1\) and \(\rho _- < 1\) in at least four of these \(\alpha\) values and negatively associated if \(\rho _- \ge 1\) and \(\rho _+ < 1\) in at least four \(\alpha\) values. If these criteria are not met, the association is deemed inconclusive. The above computation of log odds ratios and the determination of associations are performed only on cell types that each compose at least 1% of the cell population, to ensure adequate power.

For the prostate cancer data, the log odds ratios for each cell type using each method are presented in Tables S1 and S2. The final associations determined for each cell type are summarized in Table S3. In the last column of this table, we also indicate whether the conclusions drawn from SCIPAC and Scissor are consistent or not.

We find that SCIPAC’s results agree with Scissor on most cell types. However, there are three exceptions: mononuclear phagocytes (MNPs), B cells, and LE-KLK4.

MNPs are red-circled and zoomed in in each sub-figure of Fig. 3 . Most cells in this cell type are colored red in Fig. 3 d but colored dark blue in Fig. 3 b. In other words, while Scissor determines that this cell type is Scissor+, SCIPAC makes the opposite inference. Moreover, SCIPAC is confident about its judgment by giving small p -values, as shown in Fig. 3 c. To see which inference is closer to the biological fact is not easy, as biologically MNPs contain a number of sub-types that each have different functions [ 30 , 31 ]. Fortunately, this cell population has been studied in detail in the original paper that generated this dataset [ 29 ], and the sub-type information of each cell is provided there: this MNP population contains six sub-types, which are dendritic cells (DC), M1 macrophages (Mac1), metallothionein-expressing macrophages (Mac-MT), M2 macrophages (Mac2), proliferating macrophages (Mac-cycling), and monocytes (Mono), as shown in the zoom-in view of Fig. 3 a. Among these six sub-types, DC, Mac1, and Mac-MT are believed to inhibit cancer development and can serve as targets in cancer immunotherapy [ 29 ]; they compose more than 60% of all MNP cells in this dataset. SCIPAC makes the correct inference on this majority of MNP cells. Another cell type, Mac2, is reported to promote tumor development [ 32 ], but it only composes less than \(15\%\) of the MNPs. How the other two cell types, Mac-cycling and Mono, are associated with cancer is less studied. Overall, the results given by SCIPAC are more consistent with the current biological knowledge.

B cells are cyan-circled in Fig. 3 b. B cells are generally believed to have anti-tumor activity by producing tumor-reactive antibodies and forming tertiary lymphoid structures [ 29 , 33 ]. This means that B cells are likely to be negatively associated with cancer. SCIPAC successfully identifies this negative association, while Scissor fails.

LE-KLK4, a subtype of cancer cells, is thought to be positively associated with the tumor phenotype [ 29 ]. SCIPAC successfully identified this positive association, in contrast to Scissor, which failed to do so (in the figure, a proportion of LE-KLK4 cells are identified as Scissor+, especially under the smallest \(\alpha\) value; however, this proportion is not significantly higher than the background Scissor+ level under the majority of \(\alpha\) values).

In summary, across all three cell types, the results from SCIPAC appear to be more consistent with current biological knowledge. For more discussions regarding this dataset, refer to Additional file 1.

Breast cancer data with an ordinal phenotype

The scRNA-seq data for breast cancer are from [ 34 ], and we use the 19,311 cells from the five HER2+ tumor tissues. The true cell types are shown in Fig. 4 a. The bulk data include 1215 TCGA-BRCA samples with information on the cancer stage (I, II, III, or IV), which is treated as an ordinal phenotype.

UMAP visualization of the breast cancer data. a  True cell types. CAFs stand for cancer-associated fibroblasts, PB stands for plasmablasts and PVL stands for perivascular-like cells. b , c  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. Cyan-circled are a group of T cells that are estimated by SCIPAC to be most significantly associated with the cancer stage in the negative direction, and orange-circled are a group of T cells that are estimated by SCIPAC to be significantly positively associated with the cancer stage. d  DE analysis of the cyan-circled T cells vs. all the other T cells. e  DE analysis of the cyan-circled T cells vs. all the other cells. f  Expression of CD8+ T cell marker genes in the cyan-circled cells and all the other cells. g  DE analysis of the orange-circled T cells vs. all the other cells. h  Expression of regulatory T cell marker genes in the orange-circled cells and all the other cells

Association strengths and p -values given by SCIPAC under the default resolution are shown in Fig. 4 b, c. Results under other resolutions are given in Additional file 1: Fig. S11 and S12, and again they are highly consistent with results under the default resolution. We do not present the results from Scissor, as Scissor does not take ordinal phenotypes.

In the SCIPAC results, cells that are most strongly and statistically significantly associated with the phenotype in the positive direction are the cancer-associated fibroblasts (CAFs). This finding agrees with the literature: CAFs contribute to therapy resistance and metastasis of cancer cells via the production of secreted factors and direct interaction with cancer cells [ 35 ], and they are also active players in breast cancer initiation and progression [ 36 , 37 , 38 , 39 ]. Another large group of cells identified as positively associated with the phenotype is the cancer epithelial cells. They are malignant cells in breast cancer tissues and are thus expected to be associated with severe cancer stages.

Of the cells identified as negatively associated with severe cancer stages, a large portion of T cells is the most noticeable. Biologically, T cells contain many sub-types, including CD4+, CD8+, regulatory T cells, and more, and their functions are diverse in the tumor microenvironment [ 40 ]. To explore SCIPAC’s discoveries, we compare T cells that are identified as most statistically significant, with p -values \(< 10^{-6}\) and circled in Fig. 4 d, with the other T cells. Differential expression (DE) analysis (details about DE analysis and other analyses are given in Additional file 1) identifies seven genes upregulated in these most significant T cells. Of these seven genes, at least five are supported by the literature: CCL4, XCL1, IFNG, and GZMB are associated with CD8+ T cell infiltration; they have been shown to have anti-tumor functions and are involved in cancer immunotherapy [ 41 , 42 , 43 ]. Also, IL2 has been shown to serve an important role in combination therapies for autoimmunity and cancer [ 44 ]. We also perform an enrichment analysis [ 45 ], in which a pathway called Myc stands out with a \(\textit{p}\text{-value}<10^{-7}\) , much smaller than all other pathways. Myc is downregulated in the T cells that are identified as most negatively associated with cancer stage progress. This agrees with current biological knowledge about this pathway: Myc is known to contribute to malignant cell transformation and tumor metastasis [ 46 , 47 , 48 ].

On the above, we have compared T cells that are most significantly associated with cancer stages in the negative direction with the other T cells using DE and pathway analysis, and the results could suggest that these cells are tumor-infiltrated CD8+ T cells with tumor-inhibition functions. To check this hypothesis, we perform DE analysis of these cells against all other cells (i.e., the other T cells and all the other cell types). The DE genes are shown in Fig. 4 e. It can be noted that CD8+ T cell marker genes such as CD8A, CD8B, and GZMK are upregulated. We further obtain CD8+ T cell marker genes from CellMarker [ 49 ] and check their expression, as illustrated in Fig. 4 f. Marker genes CD8A, CD8B, CD3D, GZMK, and CD7 show significantly higher expression in these T cells. This again supports our hypothesis that these cells are tumor-infiltrated CD8+ T cells that have anti-tumor functions.

Interestingly, not all T cells are identified as negatively associated with severe cancer stages; a group of T cells is identified as positively associated, as circled in Fig. 4 c. To explore the function of this group of T cells, we perform DE analysis of these T cells against the other T cells. The DE genes are shown in Fig. 4 g. Based on the literature, six out of eight over-expressed genes are associated with cancer development. The high expression of NUSAP1 gene is associated with poor patient overall survival, and this gene also serves as a prognostic factor in breast cancer [ 50 , 51 , 52 ]. Gene MKI67 has been treated as a candidate prognostic prediction for cancer proliferation [ 53 , 54 ]. The over-expression of RRM2 has been linked to higher proliferation and invasiveness of malignant cells [ 55 , 56 ], and the upregulation of RRM2 in breast cancer suggests it to be a possible prognostic indicator [ 57 , 58 , 59 , 60 , 61 , 62 ]. The high expression of UBE2C gene always occurs in cancers with a high degree of malignancy, low differentiation, and high metastatic tendency [ 63 ]. For gene TOP2A, it has been proposed that the HER2 amplification in HER2 breast cancers may be a direct result of the frequent co-amplification of TOP2A [ 64 , 65 , 66 ], and there is a high correlation between the high expressions of TOP2A and the oncogene HER2 [ 67 , 68 ]. Gene CENPF is a cell cycle-associated gene, and it has been identified as a marker of cell proliferation in breast cancers [ 69 ]. The over-expression of these genes strongly supports the correctness of the association identified by SCIPAC. To further validate this positive association, we perform DE analysis of these cells against all the other cells. We find that the top marker genes obtained from CellMarker [ 49 ] for the regulatory T cells, which are known to be immunosuppressive and promote cancer progression [ 70 ], are over-expressed with statistical significance, as shown in Fig. 4 h. This finding again provides strong evidence that the positive association identified by SCIPAC for this group of T cells is correct.

Lung cancer data with survival information

The scRNA-seq data for lung cancer are from [ 71 ], and we use two lung adenocarcinoma (LUAD) patients’ data with 29,888 cells. The true cell types are shown in Fig. 5 a. The bulk data consist of 576 TCGA-LUAD samples with survival status and time.

UMAP visualization of a–d  the lung cancer data and e–g  the muscular dystrophy data. a  True cell types. b , c  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. d  Results given by Scissor under different \(\alpha\) values. e , f  Association strengths \(\Lambda\) and p -values given by SCIPAC under the default resolution. Circled are a group of cells that are identified by SCIPAC as significantly positively associated with the disease but identified by Scissor as null. g  Results given by Scissor under different \(\alpha\) values

Association strengths and p -values given by SCIPAC are given in Fig. 5 b, c (results under other resolutions are given in Additional file 1: Fig. S13 and S14). In Fig. 5 c, most cells with statistically significant associations are CD4+ T cells or B cells. These associations are negative, meaning that the abundance of these cells is associated with a reduced death rate, i.e., longer survival time. This agrees with the literature: CD4+ T cells primarily mediate anti-tumor immunity and are associated with favorable prognosis in lung cancer patients [ 72 , 73 , 74 ]; B cells also show anti-tumor functions in all stages of human lung cancer development and play an essential role in anti-tumor responses [ 75 , 76 ].

The results by Scissor under different \(\alpha\) values are shown in Fig. 5 d. The highly scattered Scissor+ and Scissor− cells make identifying and interpreting meaningful phenotype-associated cell groups difficult.

Muscular dystrophy data with a binary phenotype

This dataset contains cells from four facioscapulohumeral muscular dystrophy (FSHD) samples and two control samples [ 77 ]. We pool all the 7047 cells from these six samples together. The true cell types of these cells are unknown. The bulk data consists of 27 FSHD patients and eight controls from [ 28 ]. Here the phenotype is FSHD, and it is binary: present or absent.

The results of SCIPAC with the default resolution are given in Fig. 5 e, f. Results under other resolutions are highly similar (shown in Additional file 1: Fig. S15 and S16). For comparison, results given by Scissor under different \(\alpha\) values are presented in Fig. 5 g. The agreements between the results of SCIPAC and Scissor are clear. For example, both methods identify cells located at the top and lower left part of UMAP plots to be negatively associated with FSHD, and cells located at the center and right parts of UMAP plots to be positively associated. However, the discrepancies in their results are also evident. The most pronounced one is a large group of cells (circled in Fig. 5 f) that are identified by SCIPAC as significantly positively associated but are completely ignored by Scissor. Checking into this group of cells, we find that over 90% (424 out of 469) come from the FSHD patients, and less than 10% come from the control samples. However, cells from FSHD patients only compose 73% (5133) of all the 7047 cells. This statistically significant ( p -value \(<10^{-15}\) , Fisher’s exact test) over-representation (odds ratio = 3.51) suggests that the positive association identified SCIPAC is likely to be correct.

SCIPAC is computationally highly efficient. On an 8-core machine with 2.50 GHz CPU and 16 GB RAM, SCIPAC takes 7, 24, and 2 s to finish all the computation and give the estimated association strengths and p -values on the prostate cancer, lung cancer, and muscular dystrophy datasets, respectively. As a reference, Scissor takes 314, 539, and 171 seconds, respectively.

SCIPAC works with various phenotype types, including binary, continuous, survival, and ordinal. It can easily accommodate other types by using a proper regression model with a systematic component in the form of Eq. 3 (see the “ Methods ” section). For example, a Poisson or negative binomial log-linear model can be used if the phenotype is a count (i.e., non-negative integer).

In SCIPAC’s definition of association, a cell type is associated with the phenotype if increasing the proportion of this cell type leads to a change of probability of the phenotype occurring. The strength of association represents the extent of the increase or decrease in this probability. In the case of binary-response, this change is measured by the log odds ratio. For example, if the association strength of cell type A is twice that of cell type B, increasing cell type A by a certain proportion leads to twice the amount of change in the log odds ratio of having the phenotype compared to increasing cell type B by the same proportion. The association strength under other types of phenotypes can be interpreted similarly, with the major difference lying in the measure of change in probability. For quantitative, ordinal, and survival outcomes, the difference in the quantitative outcome, log odds ratio of the right-tail probability, and log hazard ratio respectively are used. Despite the differences in the exact form of the association strength under different types of phenotypes, the underlying concept remains the same: a larger (absolute value of) association strength indicates that the same increase/decrease in a cell type leads to a larger change in the occurrence of the phenotype.

As SCIPAC utilizes both bulk RNA-seq data with phenotype and single-cell RNA-seq data, the estimated associations for the cells are influenced by the choice of the bulk data. Although different bulk data can yield varying estimations of the association for the same single cells, the estimated associations appear to be reasonably robust even when minor changes are made to the bulk data. See Additional file 1 for further discussions.

When using the Louvain algorithm in the Seurat package to cluster cells, SCIPAC’s default resolution is 2.0, larger than the default setting of Seurat. This allows for the identification of potential subtypes within the major cell type and enables the estimation of individual association strengths. Consequently, a more detailed and comprehensive description of the association between single cells and the phenotype can be obtained by SCIPAC.

When applying SCIPAC to real datasets, we made a deliberate choice to disregard the cell annotation provided by the original publications and instead relied on the inferred cell clusters produced by the Louvain algorithm. We made this decision for several reasons. Firstly, we aimed to ensure a fair comparison with Scissor, as it does not utilize cell-type annotations. Secondly, the original annotation might not be sufficiently comprehensive or detailed. Presumed cell types could potentially encompass multiple subtypes, each of which may exhibit distinct associations with the phenotype under investigation. In such cases, employing the Louvain algorithm with a relatively high resolution, which is the default setting in SCIPAC, enables us to differentiate between these subtypes and allows SCIPAC to assign varying association strengths to each subtype.

SCIPAC fits the regression model using the elastic net, a machine-learning algorithm that maximizes a penalized version of the likelihood. The elastic net can be replaced by other penalized estimates of regression models, such as SCAD [ 78 ], without altering the rest of the SCIPAC algorithm. The combination of a regression model and a penalized estimation algorithm such as the elastic net has shown comparable or higher prediction power than other sophisticated methods such as random forests, boosting, or neural networks in numerous applications, especially for gene expression data [ 79 ]. However, there can still be datasets where other models have higher prediction power. It will be future work to incorporate these models into SCIPAC.

The use of metacells is becoming an efficient way to handle large single-cell datasets [ 80 , 81 , 82 , 83 ]. Conceptually, SCIPAC can incorporate metacells and their representatives as an alternative to its default setting of using cell clusters/types and their centroids. We have explored this aspect using metacells provided by SEACells [ 81 ]. Details are given in Additional file 1. Our comparative analysis reveals that combining SCIPAC with SEACells results in significantly reduced performance compared to using SCIPAC directly on original single-cell data. The primary reason for this appears to be the subpar performance of SEACells in cell grouping, especially when contrasted with the Louvain algorithm. Given these findings, we do not suggest using metacells provided by SEACells for SCIPAC applications in the current stage.

Conclusions

SCIPAC is a novel algorithm for studying the associations between cells and phenotypes. Compared to the previous algorithm, SCIPAC gives a much more detailed and comprehensive description of the associations by enabling a quantitative estimation of the association strength and by providing a quality control—the p -value. Underlying SCIPAC are a general statistical model that accommodates virtually all types of phenotypes, including ordinal (and potentially count) phenotypes that have never been considered before, and a concise and closed-form mathematical formula that quantifies the association, which minimizes the computational load. The mathematical conciseness also largely frees SCIPAC from parameter tuning. The only parameter (i.e., the resolution) barely changes the results given by SCIPAC. Overall, compared with its predecessor, SCIPAC represents a substantially more capable software by being much more informative, versatile, robust, and user-friendly.

The improvement in accuracy is also remarkable. In simulated data, SCIPAC achieves high power and low false positives, which is evident from the UMAP plot, F1 score, and FSC score. In real data, SCIPAC gives results that are consistent with current biological knowledge for cell types whose functions are well understood. For cell types whose functions are less studied or more multifaceted, SCIPAC gives support to certain biological hypotheses or helps identify/discover cell sub-types.

SCIPAC’s identification of cell-phenotype associations closely follows its definition of association: when increasing the fraction of a cell type increases (or decreases) the probability for a phenotype to be present, this cell type is positively (or negatively) associated with the phenotype.

The increase of the fraction of a cell type

For a bulk sample, let vector \(\varvec{G} \in \mathbb {R}^p\) be its expression profile, that is, its expression on the p genes. Suppose there are K cell types in the tissue, and let \(\varvec{g}_{k}\) be the representative expression of the k ’th cell type. Usually, people assume that \(\varvec{G}\) can be decomposed by

where \(\gamma _{k}\) is the proportion of cell type k in the bulk tissue, with \(\sum _{k = 1}^{K}\gamma _{k} = 1\) . This equation links the bulk and single-cell expression data.

Now consider increasing cells from cell type k by \(\Delta \gamma\) proportion of the original number of cells. Then, the new proportion of cell type k becomes \(\frac{\gamma _{k} + \Delta \gamma }{1 + \Delta \gamma }\) , and the new proportion of cell type \(j \ne k\) becomes \(\frac{\gamma _{j}}{1 + \Delta \gamma }\)  (note that the new proportions of all cell types should still add up to 1). Thus, the bulk expression profile with the increase of cell type k becomes

Plugging Eq. 1 , we get

Interestingly, this expression of \(\varvec{G}^*\) does not include \(\gamma _{1}, \ldots , \gamma _{K}\) . This means that there is no need actually to compute \(\gamma _{1}, \ldots , \gamma _{K}\) in Eq. 1 , which could otherwise be done using a cell-type-decomposition software, but an accurate and robust decomposition is non-trivial [ 84 , 85 , 86 ]. See Additional file 1 for a more in-depth discussion on the connections of SCIPAC with decomposition/deconvolution.

The change in chance of a phenotype

In this section, we consider how the increase in the fraction of a cell type will change the chance for a binary phenotype such as cancer to occur. Other types of phenotypes will be considered in the next section.

Let \(\pi (\varvec{G})\) be the chance of an individual with gene expression profile \(\varvec{G}\) for this phenotype to occur. We assume a logistic regression model to describe the relationship between \(\pi (\varvec{G})\) and \(\varvec{G}\) :

here the left-hand side is the log odds of \(\pi (\varvec{G})\) , \(\beta _{0}\) is the intercept, and \(\varvec{\beta }\) is a length- p vector of coefficients. In the section after the next, we will describe how we obtain \(\beta _{0}\) and \(\varvec{\beta }\) from the data.

When increasing cells from cell type k by \(\Delta \gamma\) , \(\varvec{G}\) becomes \(\varvec{G}^*\) in Eq. 3 . Plugging Eq. 2 , we get

We further take the difference between Eqs. 4 and 3 and get

The left-hand side of this equation is the log odds ratio (i.e., the change of log odds). On the right-hand side, \(\frac{\Delta \gamma }{1 + \Delta \gamma }\) is an increasing function with respect to \(\Delta \gamma\) , and \(\varvec{\beta }^T(\varvec{g}_{k} - \varvec{G})\) is independent of \(\Delta \gamma\) . This indicates that given any specific \(\Delta \gamma\) , the log odds ratio under over-representation of cell type k is proportional to

\(\lambda _k\) describes the strength of the effect of increasing cell type k to a bulk sample with expression profile \(\varvec{G}\) . Given the presence of numerous bulk samples, employing multiple \(\lambda _k\) ’s could be cumbersome and obscure the overall effect of a particular cell type. To concisely summarize the association of cell type k , we propose averaging their effects. The average effect on all bulk samples can be obtained by

where \(\bar{\varvec{G}}\) is the average expression profile of all bulk samples.

\(\Lambda _k\) gives an overall impression of how strong the effect is when cell type k over-represents to the probability for the phenotype to be present. Its sign represents the direction of the change: a positive value means an increase in probability, and a negative value means a decrease in probability. Its absolute value represents the strength of the effect. In SCIPAC, we call \(\Lambda _k\) the association strength of cell type k and the phenotype.

Note that this derivation does not involve likelihood, although the computation of \(\varvec{\beta }\) does. Here, it serves more as a definitional approach.

Definition of the association strength for other types of phenotype

Our definition of \(\Lambda _k\) relies on vector \(\varvec{\beta }\) . In the case of a binary phenotype, \(\varvec{\beta }\) are the coefficients of a logistic regression that describes a linear relationship between the expression profile and the log odds of having the phenotype, as shown in Eq. 3 . For other types of phenotype, \(\varvec{\beta }\) can be defined/computed similarly.

For a quantitative (i.e., continuous) phenotype, an ordinary linear regression can be used, and the left-hand side of Eq. 3 is changed to the quantitative value of the phenotype.

For a survival phenotype, a Cox proportional hazards model can be used, and the left-hand side of Eq. 3 is changed to the log hazard ratio.

For an ordinal phenotype, we use a proportional odds model

where \(j \in \{1, 2, ..., (J - 1)\}\) and J is the number of ordinal levels. It should be noted that here we use the right-tail probability \(\Pr (Y_{i} \ge j + 1 | X)\) instead of the commonly used cumulative probability (left-tail probability) \(\Pr (Y_{i} \le j | X)\) . Such a change makes the interpretation consistent with other types of phenotypes: in our model, a larger value on the right-hand side indicates a larger chance for \(Y_{i}\) to have a higher level, which in turn guarantees that the sign of the association strength defined according to this \(\varvec{\beta }\) has the usual meaning: a positive \(\Lambda _k\) value means a positive association with the phenotype-using the cancer stage as an example. A positive \(\Lambda _k\) means the over-representation of cell type k increases the chance of a higher cancer stage. In contrast, using the commonly used cumulative probability leads to a counter-intuitive, reversed interpretation.

Computation of the association strength in practice

In practice, \(\varvec{\beta }\) in Eq. 3 needs to be learned from the bulk data. By default, SCIPAC uses the elastic net, a popular and powerful penalized regression method:

In this model, \(l(\beta _{0}, \varvec{\beta })\) is a log-likelihood of the linear model (i.e., logistic regression for a binary phenotype, ordinary linear regression for a quantitative phenotype, Cox proportional odds model for a survival phenotype, and proportional odds model for an ordinal phenotype). \(\alpha\) is a number between 0 and 1, denoting a combination of \(\ell _1\) and \(\ell _2\) penalties, and \(\lambda\) is the penalty strength. SCIPAC fixes \(\alpha\) to be 0.4 (see Additional file 1 for discussions on this choice) and uses 10-fold cross-validation to decide \(\lambda\) automatically. This way, they do not become hyperparameters.

In SCIPAC, the fitting and cross-validation of the elastic net are done by calling the ordinalNet [ 87 ] R package for the ordinal phenotype and by calling the glmnet R package [ 88 , 89 , 90 , 91 ] for other types of phenotypes.

The computation of the association strength, as defined by Eq. 7 , does not only require \(\varvec{\beta }\) , but also \(\varvec{g}_k\) and \(\bar{\varvec{G}}\) . \(\bar{\varvec{G}}\) is simply the average expression profile of all bulk samples. On the other hand, \(\varvec{g}_k\) requires knowing the cell type of each cell. By default, SCIPAC does not assume this information to be given, and it uses the Louvain clustering implemented in the Seurat [ 24 , 25 ] R package to infer it. This clustering algorithm has one tuning parameter called “resolution.” SCIPAC sets its default value as 2.0, and the user can use other values. With the inferred or given cell types, \(\varvec{g}_k\) is computed as the centroid (i.e., the mean expression profile) of cells in cluster k .

Given \(\varvec{\beta }\) , \(\bar{\varvec{G}}\) , and \(\varvec{g}_k\) , the association strength can be computed using Eq. 7 . Knowing the association strength for each cell type and the cell-type label for each cell, we also know the association strength for every single cell. In practice, we standardize the association strengths for all cells. That is, we compute the mean and standard deviation of the association strengths of all cells and use them to centralize and scale the association strength, respectively. We have found such standardization makes SCIPAC more robust to the possible unbalance in sample size of bulk data in different phenotype groups.

Computation of the p -value

SCIPAC uses non-parametric bootstrap [ 92 ] to compute the standard deviation and hence the p -value of the association. Fifty bootstrap samples, which are believed to be enough to compute the standard error of most statistics [ 93 ], are generated for the bulk expression data, and each is used to compute (standardized) \(\Lambda\) values for all the cells. For cell i , let its original \(\Lambda\) values be \(\Lambda _i\) , and the bootstrapped values be \(\Lambda _i^{(1)}, \ldots , \Lambda _i^{(50)}\) . A z -score is then computed using

and then the p -value is computed according to the cumulative distribution function of the standard Gaussian distribution. See Additional file 1 for more discussions on the calculation of p -value.

Availability of data and materials

The simulated datasets [ 94 ] under three schemes are available at Zenodo with DOI 10.5281/zenodo.11013320 [ 95 ]. The SCIPAC package is available at GitHub website https://github.com/RavenGan/SCIPAC under the MIT license [ 96 ]. The source code of SCIPAC is also deposited at Zenodo with DOI 10.5281/zenodo.11013696 [ 97 ]. A vignette of the R package is available on the GitHub page and in the Additional file 2. The prostate cancer scRNA-seq data is obtained from the Prostate Cell Atlas https://www.prostatecellatlas.org [ 29 ]; the scRNA-seq data for the breast cancer are from the Gene Expression Omnibus (GEO) under accession number GSE176078 [ 34 , 98 ]; the scRNA-seq data for the lung cancer are from E-MTAB-6149 [ 99 ] and E-MTAB-6653 [ 71 , 100 ]; the scRNA-seq data for facioscapulohumeral muscular dystrophy data are from the GEO under accession number GSE122873 [ 101 ]. The bulk RNA-seq data are obtained from the TCGA database via TCGAbiolinks (ver. 2.25.2) R package [ 102 ]. More details about the simulated and real scRNA-seq and bulk RNA-seq data can be found in the Additional file 1.

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Veronique van den Berghe was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

This work is supported by the National Institutes of Health (R01CA280097 to X.L. and J.L, R01CA252878 to J.L.) and the DOD BCRP Breakthrough Award, Level 2 (W81XWH2110432 to J.L.).

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Dailin Gan & Jun Li

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J.L. conceived and supervised the study. J.L. and D.G. proposed the methods. D.G. implemented the methods and analyzed the data. D.G. and J.L. drafted the paper. D.G., Y.Z., X.L., and J.L. interpreted the results and revised the paper.

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Gan, D., Zhu, Y., Lu, X. et al. SCIPAC: quantitative estimation of cell-phenotype associations. Genome Biol 25 , 119 (2024). https://doi.org/10.1186/s13059-024-03263-1

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Plants can communicate and respond to touch. Does that mean they're intelligent?

Tonya Mosley

"The primary way plants communicate with each other is through a language, so to speak, of chemical gasses," journalist Zoë Schlanger says. Mohd Rasfan/AFP via Getty Images hide caption

"The primary way plants communicate with each other is through a language, so to speak, of chemical gasses," journalist Zoë Schlanger says.

In the 1960s and '70s, a series of questionable experiments claimed to prove that plants could behave like humans, that they had feelings, responded to music and could even take a polygraph test .

Though most of those claims have since been debunked, climate journalist Zoë Schlanger says a new wave of research suggests that plants are indeed "intelligent" in complex ways that challenge our understanding of agency and consciousness.

"Agency is this effect of having ... an active stake in the outcome of your life," Schlanger says. "And when I was looking at plants and speaking to botanists, it became very clear to me that plants have this."

In her new book, The Light Eaters: How the Unseen World of Plant Intelligence Offers a New Understanding of Life on Earth , Schlanger, a staff reporter at The Atlantic, writes about how plants use information from the environment, and from the past, to make "choices" for the future.

Happy Arbor Day! These 20 books will change the way you think about trees

Schlanger notes that some tomato plants, when being eaten by caterpillars, fill their leaves with a chemical that makes them so unappetizing that the caterpillars start eating each other instead. Corn plants have been known to sample the saliva of predator caterpillars — and then use that information to emit a chemical to attract a parasitic wasp that will attack the caterpillar.

Stop overwatering your houseplants, and other things plant experts want you to know

Schlanger acknowledges that our understanding of plants is still developing — as are the definitions of "intelligence" and "consciousness." "Science is there [for] observation and to experiment, but it can't answer questions about this ineffable, squishy concept of intelligence and consciousness," she says.

But, she adds, "part of me feels like it almost doesn't matter, because what we see plants doing — what we now understand they can do — simply brings them into this realm of alert, active processing beings, which is a huge step from how many of us were raised to view them, which is more like ornaments in our world or this decorative backdrop for our our lives."

Interview highlights

On the concept of plant "intelligence"

Intelligence is this thing that's loaded with so much human meaning. It's too muddled up sometimes with academic notions of intelligence. ... Is this even something we want to layer on to plants? And that's something that I hear a lot of plant scientists talk about. They recognize more than anyone that plants are not little humans. They don't want their subjects to be reduced in a way to human tropes or human standards of either of those things.

On the debate over if plants have nervous systems

I was able to go to a lab in Wisconsin where there [were] plants that had ... been engineered to glow, but only to glow when they've been touched. So I used tweezers to pinch a plant on its vein, ... the kind of mid-rib of a leaf. And I got to watch this glowing green signal emanate from the point where I pinch the plant out to the whole rest of the plant. Within two minutes, the whole plant had received a signal of my touch, of my "assault," so to speak, with these tweezers. And research like that is leading people within the plant sciences, but also people who work on neurobiology in people to question whether or not it's time to expand the notion of a nervous system.

On if plants feel pain

TED Radio Hour

Plants don't have brains — but they sure act smart.

We have nothing at the moment to suggest that plants feel pain, but do they sense being touched, or sense being eaten, and respond with a flurry of defensive chemicals that suggest that they really want to prevent whatever's going on from continuing? Absolutely. So this is where we get into tricky territory. Do we ascribe human concepts like pain ... to a plant, even though it has no brain? And we can't ask it if it feels pain. We have not found pain receptors in a plant. But then again, I mean, the devil's advocate view here is that we only found the mechanoreceptors for pain in humans, like, fairly recently. But we do know plants are receiving inputs all the time. They know when a caterpillar is chewing on them, and they will respond with aggressive defensiveness. They will do wild things to keep that caterpillar from destroying them further.

On how plants communicate with each other

Zoë Schlanger is a staff writer at The Atlantic. Heather Sten/Harper Collins hide caption

Zoë Schlanger is a staff writer at The Atlantic.

The primary way plants communicate with each other is through a language, so to speak, of chemical gasses. ... And there's little pores on plants that are microscopic. And under the microscope, they look like little fish lips. ... And they open to release these gasses. And those gasses contain information. So when a plant is being eaten or knocked over by an animal or hit by wind too hard, it will release an alarm call that other plants in the area can pick up on. And this alarm call can travel pretty long distances, and the plants that receive it will prime their immune systems and their defense systems to be ready for this invasion, for this group of chewing animals before they even arrive. So it's a way of saving themselves, and it makes evolutionary sense. If you're a plant, you don't want to be standing out in a field alone, so to speak. It's not good for reproductive fitness. It's not good for attracting pollinators. It's often in the interest of plants to warn their neighbors of attacks like this.

On plant "memory"

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There's one concept that I think is very beautiful, called the "memory of winter." And that's this thing where many plants, most of our fruit trees, for example, have to have the "memory," so to speak, of a certain number of days of cold in the winter in order to bloom in the spring. It's not enough that the warm weather comes. They have to get this profound cold period as well, which means to some extent they're counting. They're counting the elapsed days of cold and then the elapsed days of warmth to make sure they're also not necessarily emerging in a freak warm spell in February. This does sometimes happen, of course. We hear stories about farmers losing their crops to freak warm spells. But there is evidence to suggest there's parts of plants physiology that helps them record this information. But much like in people, we don't quite know the substrate of that memory. We can't quite locate where or how it's possibly being recorded.

On not anthropomorphizing plants

What's interesting is that scientists and botany journals will do somersaults to avoid using human language for plants. And I totally get why. But when you go meet them in their labs, they are willing to anthropomorphize the heck out of their study subjects. They'll say things like, "Oh, the plants hate when I do that." Or, "They really like this when I do this or they like this treatment." I once heard a scientist talk about, "We're going to go torture the plant again." So they're perfectly willing to do that in private. And the reason for that is not because they're holding some secret about how plants are actually just little humans. It's that they've already resolved that complexity in their mind. They trust themselves to not be reducing their subjects to human, simplistic human tropes. And that's going to be a task for all of us to somehow come to that place.

It's a real challenge for me. So much of what I was learning while doing research for this book was super intangible. You can't see a plant communicating, you can't watch a plant priming its immune system or manipulating an insect. A lot of these things are happening in invisible ways. ... Now when I go into a park, I feel totally surrounded by little aliens. I know that there is immense plant drama happening all over the place around me.

Sam Briger and Susan Nyakundi produced and edited this interview for broadcast. Bridget Bentz and Molly Seavy-Nesper adapted it for the web.