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What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

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

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing 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.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, 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 generalized 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).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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what is quantitative approach of research

Once data is collected, you may need to process it before it can be analyzed. 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 visualize your data and check for any trends or outliers.

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

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

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 standardize data collection and generalize findings . Strengths of this approach include:

Replication

Repeating the study is possible because of standardized 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 analyzed using reliable and consistent procedures through quantitative data analysis.

Hypothesis testing

Using formalized 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 standardized 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.

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

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

Research bias

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

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

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

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

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

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize 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 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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Home » Quantitative Research – Methods, Types and Analysis

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.

What is the demographic makeup of Singapore in 2020?
How has the average temperature changed globally over the last century?
Does environmental pollution affect the prevalence of honey bees?
Does working from home increase productivity for people with long commutes?

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.

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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 .

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

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 .

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.

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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.

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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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USC Libraries
Research Guides

Organizing Your Social Sciences Research Paper

Quantitative Methods
Purpose of Guide
Design Flaws to Avoid
Independent and Dependent Variables
Glossary of Research Terms
Reading Research Effectively
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Theoretical Framework
Citation Tracking
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Primary Sources
Secondary Sources
Tiertiary Sources
Scholarly vs. Popular Publications
Qualitative Methods
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Limitations of the Study
Common Grammar Mistakes
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Avoiding Plagiarism
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Generative AI and Writing
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Bibliography

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.

Need Help Locating Statistics?

Resources for locating data and statistics can be found here:

Statistics & Data Research Guide

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.

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Handbook of Research Methods in Health Social Sciences pp 27–49 Cite as

Quantitative Research

Leigh A. Wilson 2 , 3
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First Online: 13 January 2019

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

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Quantitative research methods

a method of research that relies on measuring variables using a numerical system, analyzing these measurements using any of a variety of statistical models, and reporting relationships and associations among the studied variables. For example, these variables may be test scores or measurements of reaction time. The goal of gathering this quantitative data is to understand, describe, and predict the nature of a phenomenon, particularly through the development of models and theories. Quantitative research techniques include experiments and surveys.

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What are the strengths of quantitative research.

Professor Norma T. Mertz briefly discusses qualitative research and how it has changed since she entered the field. She emphasizes the importance of defining a research question before choosing a theoretical approach to research.

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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 .

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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.

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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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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:

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.

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!

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What is quantitative research?

Last updated

20 February 2023

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Quantitative methods and data are used by some business owners, for example, to evaluate their business, diagnose issues, and identify opportunities.

Quantitative research is used throughout the natural and social sciences, including economics, sociology, chemistry, biology, psychology, and marketing.

Researchers use quantitative research to get objective, robust, and representative answers from individuals. Researchers gather quantitative data from sample groups of people and generalize it to a larger population. This is to, in some instances, explain a given phenomenon and answer questions about the population, such as product preferences, political persuasion, or demography.

For example, a hotel owner in the US can conduct quantitative research, perhaps via a questionnaire, on a small sample of their customers to understand their opinions about their products and services. The analyzed quantitative data from this questionnaire can be generalized to the larger population of their customers. The hotel can use these opinions to maintain or improve its service provision.

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Quantitative research methods

Researchers employ various quantitative research methods to determine certain phenomena.

Observation

This method involves gathering information by simply observing behaviors or counting subjects relevant to a study. For example, a researcher could sit in a classroom and observe students when a teacher is teaching, recording those who are and are not paying attention.

Survey is one of the most popular and well-known quantitative methods. It involves asking individuals questions either physically or, most typically nowadays, online. These questions are usually in the form of a questionnaire that individuals can respond to, using a mix of single, multichoice, ranking, rating, and occasionally open-ended questions .

For example, a researcher could administer a questionnaire to first-year international college students about their college experiences using various question formats.

Experimental

This scientific approach is conducted with two sets of data, i.e., independent and dependent variables . Usually, researchers approach experimental studies with specific hypotheses to test. They may use two groups of participants: one who would receive the “treatment” and one who would not.

For example, a researcher might wish to test a short-term mindfulness treatment for individuals with depression. In this case, the independent or manipulated variable would be the mindfulness treatment group. One group would receive the mindfulness treatment, and another would not. In this case, the “experiment” would be to see if the individuals who received the mindfulness treatment experienced fewer depressive symptoms than those who did not.

What is quantitative analysis?

Quantitative analysis is a process that involves manipulating and evaluating collected, measurable data. The goal is to understand the behavior of a given phenomenon and answer a research question (and, in a scientific setting, prove or disprove a hypothesis).

A business owner, for example, may analyze quantitative sales data and consumer quantitative data using a questionnaire. By doing this, the owner can figure out if their business is doing well or if they need to make changes to improve.

If you are a business owner, you could consider quantitative analysis to better understand your business's past, present, and potential future.

What do quantitative analysts do?

A quantitative analyst is an expert in designing, developing, and implementing algorithms to answer research questions. They use quantitative research methods to help companies make appropriate business and financial decisions.

The primary responsibility of a quantitative analyst is to apply quantitative methods to identify opportunities and evaluate risks.

Quantitative analysts are important to staff in any business because:

They manage portfolio risks

They test a new trading strategy

They program and implement a new trading strategy

They improve signals used to evaluate trade ideas

Understanding quantitative analysis

Analysts use quantitative analysis to analyze a business's past, present, and future. You can also use quantitative analysis to determine the progress of your business.

State governments also use quantitative analysis to make monetary and other economic policy decisions. It is used in the financial services industry to analyze investment opportunities. For example, a business owner can use quantitative analysis to determine when to sell or purchase securities based on macroeconomic conditions.

Quantitative analysis versus qualitative analysis

If you are pursuing a career in research or business analysis, it is essential to understand the two concepts—quantitative and qualitative analysis.

Quantitative analysis, at a very basic level, relies on using numbers and discrete values collected from the research. In contrast, qualitative analysis relies on content (e.g., language or text data) that either can’t be expressed in numbers or doesn’t have sufficient scale to be counted or coded.

A business owner wanting to better understand their business might use a representative quantitative sample of customers to generate insight by completing a questionnaire. A website owner could analyze quantitative metrics associated with their website to understand which aspects of the site are working well and which elements need to be optimized. These include the length of visit, number of links clicked, and areas of the site visited.

Various measures could be correlated by sales (or other outcomes) to determine the UX and marketing strategy linked to the site.

Businesses might use qualitative analysis to get a greater depth of understanding or look at the ‘why’ behind the ‘what.’ For example, they might ask customers, who gave a low quantitative score for a provided product, why they gave that rating and how they might improve the said product.

Advantages of quantitative research

Quantitative research, done right, can help drive a business's success and generate a general understanding of key business metrics and customer behavior, wants, and needs. Quantitative research should be considered for the following reasons:

It is efficient and fast

An experienced quantitative researcher can complete the reporting and analysis phase efficiently and quickly with a defined reporting structure and outputs while taking some time to define and structure questions (versus unstructured qualitative data ).

It is objective and requires limited interpretation

Quantitative research relies on standardized statistical processes and rules to answer research questions. If performed correctly, data generated from small sample groups can be extrapolated to represent the views of larger populations.

It is focused

Owing to its structure, the goals of quantitative research are determined at the beginning of the study, forcing researchers to clearly understand and define the objectives of their studies.

Disadvantages of quantitative research

It’s only appropriate in certain cases

This method is only relevant when data can be captured and reflected in numbers. It cannot be used in situations where data is non-numerical, e.g., long-form verbal or textual responses that are not easily coded down into numerical responses.

It’s challenging to analyze the data collected

When quantitative research is collected, it can be difficult to make sense of the numbers without knowing statistical methods. Knowledge of research methods and data analytic techniques is essential for drawing conclusions about the study questions. These programs and methods take time to learn and can be time-consuming and complicated.

What are the limitations of quantitative research?

Requires vast resources

This method requires a considerable investment of time, energy, and finance. One needs to prepare and structure questions, test their understanding and relevance, and determine how to distribute them to the respondents. Some respondents may expect payment or incentives to respond to the questions (this may be in the form of entry into a prize draw.)

Requires many respondents

Quantitative research generally requires access to (relative to other methods) large samples to ensure inferences made from the research are robust and reliable. Finding this audience, especially where the incidence is low can be both time-consuming and expensive.

Research is limited in its scope

What quantitative research can explore is limited due to the need to agree on the specific questions to be asked and analyzed versus qualitative research. The latter doesn’t define specific numbers and forms of questions in advance.

Why is it called quantitative research?

It is called quantitative research because it involves the use of ‘quantities’ of things—things that can be expressed in numbers or measured.

What does quantitative research answer?

Quantitative research answers questions measuring value or size, which can be expressed in numbers. It answers questions such as how many, how much, and how often.

For example, you can study the number of individuals who wish to study at American universities and their traits. Questions can include how many come from low, medium, or high socio-economic brackets, how many want to study law versus humanities, and what proportion feel excited versus anxious about the prospect of undertaking higher education.

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Qualitative vs Quantitative Research Methods & Data Analysis

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Editor-in-Chief for Simply Psychology

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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

Agile & Development
Prioritization
Product Management
Product Marketing & Growth
Product Metrics
Product Strategy

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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Quantitative research

Affiliation.

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

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

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

Biomedical Research / methods*
Double-Blind Method
Evaluation Studies as Topic
Longitudinal Studies
Randomized Controlled Trials as Topic
United Kingdom

Psychological Research

Quantitative and qualitative approaches to research.

Three researchers review data while talking around a microscope.

When designing a study, typically, researchers choose a quantitative or qualitative research design. In some cases, a mixed-method approach may be appropriate. Which approach used will develop on the research question and the type of information sought. Quantitative methods may be better for understanding what is happening, while qualitative methods may be better for understanding the hows and why of a phenomenon.

Video 2.3.1. Types of Research explains the difference between qualitative and quantitative research. A closed-captioned version of this video is available here .

Quantitative Research

Quantitative research typically starts with a focused research question or hypothesis, collects a small amount of data from each of a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior; however, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation. But the relative weaknesses of quantitative research are the relative strengths of qualitative research.

Qualitative Research

Although this is by far the most common approach to conducting empirical research in psychology, there is a vital alternative called qualitative research . Qualitative research can help researchers to generate new and interesting research questions and hypotheses. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the ‘lived experience’ of the research participants.

Mixed-Methods

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize that qualitative methods lack objectivity, are challenging to evaluate, and do not allow generalization to other people or situations. At the same time, some qualitative researchers criticize that quantitative methods overlook the richness of behavior and experience, and instead answer simple questions about easily quantifiable variables. However, many researchers from both camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge, and how can they be reconciled?

Video 2.3.2. What are Qualitative and Quantitative Variables explains the difference between quantitative and qualitative variables that may be used in research.

Becoming Familiar with Research

An excellent way to become more familiar with these research approaches, both quantitative and qualitative, is to look at journal articles, which are written in sections that follow these steps in the scientific process. Most psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract (summary of the article), introduction or literature review, methods explaining how the study was conducted, results of the study, discussion and interpretation of findings, and references.

The aftermath of teenage suicide: a qualitative study of the psychosocial consequences for the supervising family

Per Lindqvist and his colleagues (2008), wanted to learn how the families of teenage suicide victims cope with their loss. They did not have a specific research question or hypothesis, such as, what percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience.

Quantitative and Qualitative Approaches to Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Located at : https://courses.lumenlearning.com/adolescent/chapter/quantitative-and-qualitative-approaches-to-research/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Research Methods in Psychology. Provided by : University of Minnesota . Located at : https://open.lib.umn.edu/psychologyresearchmethods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
Psychology. Provided by : Open Stax. License : CC BY: Attribution
Types of Research: Qualitative & Quantitative Designs. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Located at : https://hvcc.techsmithrelay.com/Vu4x . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

A Systems Approach for More Effective and Inclusive Agricultural Innovation for Sustainable Transformation

A call to action, reflection, and five research commentaries

By Erin McGuire , Maha Al-Zu'bi , Arwen Bailey , Hanna Ewell , Michel Kabirigi , Janelle Larson , Millicent Liani , Thi Thu Giang Luu , Janelle Sylvester , Eva Marina Valencia Leñero

Our Call to Action: A Fresh Perspective

According to the UN Global Stocktake Report 2023 , we are not on track to achieve the Sustainable Development Goals by 2030. The report highlights that current food systems perpetuate inequalities and fail to tackle rising food insecurity. To address the world's multiple crises, including climate and food crises, as well as escalating conflicts over politics and natural resources, there is a pressing need for effective innovations and urgent action at scale. These efforts should aim to contribute to improved and more sustainable outcomes for both people and the planet, achieved through equitable and resilient agricultural innovation systems. Agricultural Research for Development (A4RD) has a key role to play in providing evidence-based and end-user driven solutions for positive social outcomes in low and middle income countries.

Within AR4D there is an imperative for a fresh perspective—one that prioritizes inclusivity and scalable innovations from the very outset of problem identification and ideation. An outcome-based approach that prioritizes socially vulnerable populations, such as women, is vital to actually making progress on the largely social nature of many of the SDGs. Traditional and even dynamic approaches to innovation systems inadvertently obscure the pivotal roles played by decision-makers at every stage. Decision-makers within AR4D wield significant influence over our perception of challenges, our strategies to address them, and, consequently, the innovations that emerge and who ultimately benefits. While more innovation systems take into account the interconnected nature of innovations and acknowledge the complexity, diversity, and nonlinearity of innovation processes as shaped by multiple social, institutional, environmental, and economic complexities; there is less attention to the positionality of AR4D actors operationalizing these systems within AR4D work. Thus, whether AR4D actors correctly identify challenge-solution sets hinge on processes that precede many of our current representations of agricultural innovation systems and lack of reflexivity in our AR4D work. As noted, systemic innovation “concerns how people engage in a process to support systemic thinking and action” and can be considered ‘a game-changer’ for sustainability. Thus, we aim in this blog, and our accompanying five research commentaries, to present how AR4D actors can be more effective in their work — sustainably innovating for the world's vulnerable populations — through a self-reflective, systems approach.

Overcoming Historical Biases and Embracing an Outcome-Based Approach

Historically, we have grappled with the challenge of shedding disciplinary or personal biases when identifying sustainable solutions. Scientists tend to view Sustainable Development Goals ( SDGs ) through the lens of their own disciplinary disposition — e.g. breeders champion more breeding, and soil scientists advocate for conservation agriculture. It is evident that no single discipline can steer us toward SDG success, and we must caution ourselves against path dependency. Ideally, a systems approach adopts an integrative and interdisciplinary perspective in identifying multifactorial drivers, constraints and incompatibilities, incorporates feedback and highlights the dynamics of the system thus producing transformative coordinated inclusive actions for positive societal change. ‘Systems’ can be seen from multiple perspectives, which may not even agree on the nature of the ‘problem’ (much less the solution) and so a systems approach provides tools and models to surface and compare perspectives, and generate acceptable interventions. Therefore, we must reimagine our approach to innovation systems as being outcome-based and context-based, rather than innovation-based alone. This entails aligning unique perspectives, encompassing scale (local and global), discipline (social and natural sciences), and expertise (user, private sector, public sector) to assemble the necessary components for genuine change. This shift calls for not only more appropriate user-driven “innovation packages” but also upstream AR4D systems transformation.

AR4D actors should exhibit reflexivity in their work, acknowledging that the processes they follow and ideas they incorporate influence downstream outcomes. Factors such as funding sources, team composition, and inclusivity at the decision-making table impact the results of their efforts both positively and negatively. Given our imperative to meet the SDGs, many of which are social and environmental in nature, it is time we not only broadened who is at the table to define the challenge, innovation, and curate innovation, but also in how to and who defines success. For many years, A4RD has focused on productivity while overlooking externalities such as food loss and waste, and disregarding social conditions, environmental degradation, and equitable distribution of benefits — yet another system issue, even when the SDGs, CGIAR , and major bilateral organizations emphasize these goals.

Trade-offs, synergies and opportunities for inclusive approaches should be examined at the onset, as well as re-visited during project implementation. This means scaling responsibly and effectively, taking into account women, men and marginalized groups’ differentiated risk profiles, needs and priorities, and examining context-specific structural dynamics through time. Taking a systems perspective means that researchers recognize that problems are unbounded, interconnected, and often defined differently depending on the individual stakeholder's perspective. This entails taking accountability for the definition of a given 'system,' and what is included and excluded by that defining, and being responsible for those judgments. Moreover, it is about acknowledging that systems are in constant change, uncontrollable, and perhaps the only way to derive intended outcomes is to increase the structure and diversity within the system.

The Path to Robust AR4D and Increased Impact

In light of a growing body of literature and knowledge products exposing the pitfalls and unintended consequences of research in development efforts, this commentary series aims to explore how upstream AR4D actors, for and together with those downstream, can from a systems perspective, harness agricultural innovation to drive positive social change more effectively. This endeavor involves navigating complex terrain—uncovering structural barriers within the AR4D landscape that hinder the inclusion of fresh perspectives. For example, research demonstrates that non-white and female innovators generate ideas at the same rate as white men, yet their innovations often struggle to gain traction . By marginalizing the ideas of a significant portion of the world's population, we miss out on opportunities to create more effective solutions — and risk developing solutions that have harmful unintended consequences or do not meet the unique needs of a major contingency of stakeholders.

The Research Portfolio: Shaping the Future of AR4D with Systems Thinking

A systems approach means research must be focused on understanding the complexity within a context and problem-solving using integrated network approaches that do not envision the scaling of singular innovations but rather bundles or packages of socio-technical innovations that critically are available and accessible to women and marginalized groups. Scientists, agri-food actors, and farmers should be seen as research partners, with participatory methods used at all stages of the research cycle, and acknowledging farmers as innovators themselves, and not just “adopters”.

So, how can we, and those who have been structurally excluded, become more robust AR4D actors? How can we educate ourselves to excel in interdisciplinary work and recognize biases within our project teams? What are the consequences of failing to address these systemic issues to our effectiveness? Our new research portfolio delves into these questions, recognizing that this endeavor is not only a moral imperative but also crucial for enhancing our capabilities and success in the field. This research commentary series — click on the links below — explores five research themes shaping the future of A4RD with systems thinking, using qualitative and quantitative methods:

Commentary 1 - Investigating upstream agriculture research for development dynamics for downstream impacts
Commentary 2 - Integrating social and natural sciences in agricultural innovation systems
Commentary 3 - Obstacles to measuring and quantifying systems change
Commentary 4 - Outcome-based innovating and scaling, impact with systems framework
Commentary 5 - Considering social differentiation in innovation and scaling

Call to Action — Collective Impact and Future Progress

This portfolio encompasses numerous unanswered questions, guided by an underlying hypothesis that suggests we can enhance our effectiveness in downstream outcomes by applying systems approaches and situating ourselves more effectively through self-reflection, understanding leverage points, implementing structural changes, and strengthening capacities within our AR4D compositions and institutional systems. Deep understanding of these critical points will require a diverse team of researchers and scientists from different backgrounds, employing both qualitative and quantitative methods to support AR4D actors to better understand how they can align themselves to effectively innovate and scale for positive social outcomes.

Moreover, it must extend beyond theoretical considerations, encompassing practical applications that can be directly implemented within the realms of both the natural and social sciences. As such, this portfolio offers an opportunity for interdisciplinary collaboration, combining social scientists with bio-physical scientists in field studies with the intention of facilitating learning as trials progress. This means that already existing efforts can engage with this portfolio, helping to contribute to our greater body of knowledge on how we collectively operate as AR4D actors.

To this end, we invite others to join this effort or share current work being done to advance a similar portfolio and knowledge base. In each of these research commentaries we see the opportunity for advanced tools to be developed, capacity strengthening of system actors, and intentional collaboration with a wider set of partners. Together, we have the opportunity to reshape the landscape of AR4D, making it more inclusive, effective, and impactful through the lens of systems thinking. Through deepening our knowledge and wading through complex challenges, we can more effectively pursue SDGs by 2030. We invite you to be a part of this transformative journey, where innovation and collaboration lead the way to a brighter and more sustainable future for agriculture and development.

A Call for Collaborative Co-Creation: This research portfolio emerged from continuous dialogues among scientists from various backgrounds, including CGIAR organizations, USAID, and international academic institutions. These discussions reached their culmination at a workshop convened in New Delhi in October 2023 (See Figure 1).

Notably, this collaborative group predominantly comprises women, with a significant portion of early-career professionals, reflecting a diverse blend of nationalities from both the Global North and Global South. We anticipate that this portfolio will thrive through a multitude of collaborations and partnerships within the AR4D community. If you share our interest in this work and wish to get involved, please do not hesitate to reach out to us (see our linked profiles above, or email [email protected] ). Your contributions and engagement are highly valued as we collectively embark on this research journey.

Contributors of this blog highlighted (left to right starting with back row): Erin McGuire , Maria Boa-Alvarado, Michel Kabirigi , Elizabeth Bryan, Janelle Larson , Arwen Bailey , Millicent Liani , (bottom row): Prapti Barooah, Rita Kumari Gurung, Eva Marina Valencia Leñero , Hanna Ewell , 2023. Not shown: Maha Al-Zubi , Janelle Sylvester , Thi Thu Giang Luu

This is a 5-part research commentary blog series, offering reflections and a shared vision for more effective and inclusive agricultural innovation for sustainable transformation. These blogs were produced as part of a collaborative effort between colleagues coming from the Feed the Future Innovation Lab for Horticulture , University of Bonn , Pennsylvania State University , International Center for Maize and Wheat Research , The Alliance of Bioversity International and CIAT , the International Institute for Tropical Agriculture, and the International Water Management Institute .

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Research News

Scientists studied how cicadas pee. their insights could shed light on fluid dynamics.

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way. Ben Hasty / MediaNews Group/Reading Eagle via Getty Images hide caption

A cicada perches on a picnic table in front of Nolde Mansion in Cumru Township, PA in May 2021. New research shows that these insects urinate in a surprising way.

This spring and summer, across the Midwest and Southeast United States, cicadas will crawl out of their underground burrows by the trillions to mate — due to two different broods of these wingèd insects emerging at about the same time, one on a 13-year cycle and one on a 17-year cycle.

In their brief several weeks aboveground, their mission will be to reproduce. Each male will attempt to attract females by producing a buzzing noise as loud as a lawnmower.

But beyond their prodigious numbers and raucous noise, new research published in PNAS reveals that cicadas are special in yet another way — their urination. Based on their size and diet, scientists suspected they'd urinate in droplets, but it turns out that these insects produce jets of pee.

This often-overlooked sea creature may be quietly protecting the planet's coral reefs

The surprising results have numerous applications when it comes to manipulating fluids at small scales — including 3D printing, drug delivery, disease diagnostics, and even testing compounds in outer space.

It's a striking discovery in a realm we know relatively little about, scientists say.

"Excretion in general is not very well understood," says lead author Elio Challita , a bio-inspired roboticist at Harvard University. "Cicadas are some of the smallest insects, to the best of our knowledge, that can form such jets at this small scale."

"Insects are just the perfect laboratory for exploring handling fluids at the micro-scale," says Anne Staples , a fluid dynamicist at Virginia Tech who studies the mechanics of insect respiration and wasn't involved in the research. "They fly through air, they drink water, they handle nectar. As this paper shows, they urinate — they excrete."

Be that as it may, ask Challita what motivated the study, and he says it was simple curiosity. "I think people should understand science doesn't have to be very serious," he says. "It can be fun, too."

A spectrum of pee

Not all animals pee in the same way. On the one hand, there are larger animals like humans and elephants. "They rely on the forces of inertia and gravity to pull down the fluids from their bladder," says Challita.

This results in a stream or jet of urine, like the one that might hit your toilet bowl on a regular basis. In fact, a 2014 Georgia Tech study entitled " Duration of urination does not change with body size " found that all mammals more than six or seven pounds take an average of 21 seconds (plus or minus 13 seconds) to empty their bladders. The researchers referred to this as the Law of Urination .

But when an organism is small (like the size of an insect), then fluids care less about gravity. Instead, surface tension and friction dominate — forces that are negligible for larger organisms like us.

"Surface tension is an invisible kind of force that is very significant for small insects," says Challita. "Just pushing a fluid at a small scale is challenging."

The result is that most insects and most small mammals like mice and bats urinate in droplets through smaller orifices. In fact, while in grad school at Georgia Tech, Challita studied a kind of insect called a sharpshooter, which sucks low-nutrient sap from plants (sap that's 95% water). "And then we calculated what is the energy required to form a jet versus a droplet," Challita says.

California sea otters nearly went extinct. Now they're rescuing their coastal habitat

It wasn't even a contest. Droplet urination used way less energy. So that seemed to be the general rule: if you're big, you pee in a jet. If you're small — and especially if you're feeding on nutrient-poor sap — you pee in droplets.

But Challita knew that the world rarely divides so cleanly. "We try to create theories that can explain things in nature," he says, "but nature is always finding surprises and exceptions for us."

A splash of insight

Based on a handful of YouTube videos he'd seen, Challita had a hunch that cicadas might just prove to be the exception to the rule. The only trouble is that they're hard to observe.

"They're usually very high up on trees," he says. "And even if you find them, it's hard to not disturb them and then they would fly away."

But later, on a different project in the Peruvian Amazon, a stroke of luck: Challita and a couple colleagues had wrapped up their field work and were taking the six-hour boat ride back to town when their driver made an early pit stop for lunch.

"So we started walking around," Challita recalls, "and then one of our colleagues, he felt this little sprinkle on his head. And then we looked up — and then we saw a lot of cicadas."

Challita and his colleagues couldn't believe it. There were 20 or 30 cicadas low down in the trees, feeding and peeing with abandon. The team leapt into action, rushing to collect data before their driver's lunch break ended. They climbed one of the trees. They grabbed a table to stand on. And they filmed the cicadas using the high-speed video setting on their phones.

"All the villagers over there, they were just staring at us, like, 'What the hell's wrong with these guys?'" says Challita with a chuckle.

It was a rush.

And the experiment turned out well, too.

The researchers saw cicadas defying expectations. They are insects feeding on low-nutrient sap, but there they were — peeing in jets . It was one of those streams that had splashed against Challita's colleague that had made them all look up in the first place.

Here's what Challita thinks is going on: Cicadas are big insects with a wider gut, so they're not under the exact same size constraint as, say, a sharpshooter. Plus, they have to process a huge quantity of sap to extract enough energy to power their bodies.

"Peeing one droplet at a time takes too long and it's not very efficient," says Challita. "So they have to get rid of that fluid in jets."

This means that in addition to large animals that pee in jets, and small animals that pee in droplets, Challita's found a third category: small organisms that also pee in jets.

Staples says that while the research would have benefited from studying a larger number of cicadas, it still pushes the limits of our understanding.

"They've extended the scale into the lower reaches of the animal kingdom and showed some surprising results that are counterintuitive," says Staples. "You wouldn't think that this would be the most efficient exploitation of fundamental physics to urinate at that size."

And yet it is. It's the latest leap in the development of what Challita hopes could become a kind of Grand Urinating Theory.

surface tension
insect biology
microfluidics
fluid dynamics

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How teens and parents approach screen time, most teens at least sometimes feel happy and peaceful when they don’t have their phone, but 44% say this makes them anxious. half of parents say they have looked through their teen’s phone.

An image of a father and teen daughter in discussion while using a smartphone.

Pew Research Center conducted this study to better understand teens’ and parents’ experiences with screen time.

The Center conducted an online survey of 1,453 U.S. teens and parents from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos invited one parent from each of a representative set of households with parents of teens in the desired age range from its KnowledgePanel . The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. Parents were asked to think about one teen in their household (if there were multiple teens ages 13 to 17 in the household, one was randomly chosen). At the conclusion of the parent’s section, the parent was asked to have this chosen teen come to the computer and complete the survey in private.

The survey is weighted to be representative of two different populations: 1) parents with teens ages 13 to 17 and 2) teens ages 13 to 17 who live with parents. For each of these populations, they survey is weighted to be representative by age, gender, race and ethnicity, household income and other categories.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions among parents and among teens used for this report, along with responses, and its methodology .

Today’s teenagers are more digitally connected than ever. Most have access to smartphones and use social media , and nearly half say they are online almost constantly. But how are young people navigating this “always on” environment?

To better understand their experiences, we surveyed both teens and parents on a range of screen time-related topics. Our questions explored the emotions teens tie to their devices, the impact of smartphones on youth, and the challenges parents face when raising children in the digital age.

Key findings from the survey:

Phone-less: 72% of U.S. teens say they often or sometimes feel peaceful when they don’t have their smartphone; 44% say it makes them feel anxious.
Good for hobbies, less so for socialization: 69% of teens say smartphones make it easier for youth to pursue hobbies and interests; fewer (30%) say it helps people their age learn good social skills.
Parental snooping: Half of parents say they have looked through their teen’s phone.
Smartphone standoffs: About four-in-ten parents and teens report regularly arguing with one another about time spent on their phone.
Distracted parenting: Nearly half of teens (46%) say their parent is at least sometimes distracted by their phone when they’re trying to talk to them.

This Pew Research Center survey of 1,453 U.S. teens ages 13 to 17 and their parents was conducted Sept. 26-Oct. 23, 2023. 1

Jump to read about views among teens on: Screen time | Feelings when disconnected from phones | Thoughts on smartphones’ impact

Jump to read about views among parents on: Parenting in the smartphone age | Their own screen time struggles

Teens’ views on screen time and efforts to cut back

Fully 95% of teens have access to a smartphone, and about six-in-ten say they use TikTok, Snapchat or Instagram . But do teens think they spend too much time in front of screens?

A bar chart showing that About 4 in 10 teens say they spend too much time on their phone

More teens say they spend too much time on their phone or social media than say they don’t spend enough time on them. We found that 38% of teens say they spend too much time on their smartphone. About a quarter say the same regarding their social media use. 2

A dot plot chart showing that Teen girls are more likely than boys to say they spend too much time on their phone and social media

But the largest shares say the amount of time they spend on their phone (51%) or on social media (64%) is about right. Relatively few teens say they don’t spend enough time with these technologies.

Views on this differ by gender. Teen girls are more likely than boys to say they spend too much time on their smartphone (44% vs. 33%) or social media (32% vs. 22%).

Teens’ efforts to curb their screen time

A minority of teens have taken steps to reduce their screen time. Roughly four-in-ten teens (39%) say they have cut back on their time on social media. A similar share says the same about their phone (36%).

Still, most teens have not limited their smartphone (63%) or social media (60%) use.

A chart showing that Most teens haven’t cut back on their phone or social media use, but girls are more likely than boys to do so

How teens’ behaviors vary by gender

About four-in-ten or more girls say they have cut back on their smartphone or social media use. For boys, those figures drop to roughly one-third.

How teens’ behaviors vary based on their screen time

Teens who report spending too much time on social media and smartphones are especially likely to report cutting back on each. For instance, roughly six-in-ten teens who say they are on social media too much say they have cut back (57%). This is far higher than the 32% among those who say they are on social media too little or the right amount.

How teens feel when they don’t have their phone

A bar chart showing that Roughly three-quarters of teens at least sometimes feel happy or peaceful when they don’t have their phone; 44% feel anxious

Teens encounter a range of emotions when they don’t have their phones, but we asked them about five specific ones. Roughly three-quarters of teens say it often or sometimes makes them feel happy (74%) or peaceful (72%) when they don’t have their smartphone.

Smaller but notable shares of teens equate not having their phone with more negative emotions. Teens say not having their phone at least sometimes makes them feel anxious (44%), upset (40%) and lonely (39%).

It is worth noting that only a minority of teens – ranging from 7% to 32% – say they often feel these emotions when they’re phone-less.

Teens’ feelings on this differ by some demographic factors:

Age and gender: Older girls ages 15 to 17 (55%) are more likely than younger girls (41%) and teen boys who are younger (41%) and older (40%) to say they feel anxious at least sometimes when they don’t have their smartphone.
Gender: 45% of teen girls say not having their phone makes them feel lonely regularly, compared with 34% of teen boys.

Do teens think smartphones are negatively impacting young people?

As smartphones have become a universal part of teen life, many have asked what impact, if any, phones are having on today’s youth.

Teens shared their perspectives on smartphones’ impact on people their age and whether these devices have made certain aspects of growing up more or less challenging.

A bar chart showing that Most teens say the benefits of smartphones outweigh the harms for people their age

Most teens think the benefits of smartphones outweigh the harms for people their age. Seven-in-ten teens say smartphones provide more benefits than harms for people their age, while a smaller share (30%) take the opposing view, saying there are more harms than benefits.

Teens’ views, by gender and age

Younger girls ages 13 and 14 (39%) are more likely than older teen girls (29%) and teen boys who are younger (29%) and older (25%) to say that the harms of people their age using smartphones outweigh the benefits.

The survey also shows that teens see these devices’ impacts on specific aspects of life differently.

More teens believe smartphones make it easier, rather than harder, to be creative, pursue hobbies and do well in school. Majorities of teens say smartphones make it a little or a lot easier for people their age to pursue hobbies and interests (69%) and be creative (65%). Close to half (45%) say these devices have made it easier for youth to do well in school.

About two-thirds of teens say phones make it easier for youth to pursue interests, be creative; fewer think it helps peers learn good social skills

Views are more mixed when it comes to developing healthy friendships. Roughly four-in-ten teens say smartphones make it easier for teens to develop healthy friendships, while 31% each say they make it harder or neither easier nor harder.

But they think smartphones have a more negative than positive impact on teens’ social skills. A larger percentage of teens say smartphones make learning good social skills harder (42%) rather than easier (30%). About three-in-ten say it neither helps nor hurts.

How parents navigate raising teens in the smartphone age

With the rise of smartphones, today’s parents must tackle many questions that previous generations did not. How closely should you monitor their phone use? How much screen time is too much? And how often do phones lead to disagreements?

We developed a set of parallel questions to understand the perspectives of both parents and teens. Here’s what we found:

A bar chart Half of parents look through their teen’s phone; 43% of teens think their parent checks their phone

It’s common for parents to look through their teen’s phone – and many of their teens know it. Half of parents of teens say they look through their teen’s phone. When we asked teens if they thought their parents ever look through their phones, 43% believed this had happened.

Whether parents report looking through their child’s smartphone depends on their kid’s age. While 64% of parents of 13- to 14-year-olds say they look through their teen’s smartphone, this share drops to 41% among parents of 15- to 17-year-olds.

Teens’ accounts of this also vary depending on their age: 56% of 13- to 14-year-olds say their parent checks their smartphone, compared with 35% of teens ages 15 to 17.

How often do parents and teens argue about phone time?

A bar chart showing that About 4 in 10 parents and teens say the time teens spend on their phone regularly leads to arguments

Parents and teens are equally likely to say they argue about phone use. Roughly four-in-ten parents and teens (38% each) say they at least sometimes argue with each other about how much time their teen spends on the phone. This includes 10% in each group who say this happens often .

Still, others say they never have these types of disagreements. One-quarter of parents say they never argue with their teen about this, while 31% of teens say the same.

Teens’ and parents’ views, by race and ethnicity

Hispanic Americans stand out for reporting having these disagreements often. While 16% of Hispanic teens say they often argue with their parent about how much time they’re spending on their phone, that share drops to 9% for White teens and 6% for Black teens. 3

A similar pattern is present among parents. Hispanic parents (19%) are more likely than White (6%) or Black (7%) parents to say they often argue with their teen about this.

Teens’ views, by frequency of internet use

The amount of time teens report being online is also a factor. About half (47%) of teens who report being online almost constantly say they at least sometimes argue with their parent about the amount of time they spend on their phone, compared with those who are online less often (30%).

How much do parents prioritize tracking their teen’s phone use?

A bar chart showing that Most parents say managing how much time their teen is on the phone is a priority

Most parents prioritize managing the amount of time their teen spends on the phone. Roughly three-quarters of parents (76%) say managing how much time their teen spends on the phone is an important or a top priority. Still, 19% of parents don’t consider this a priority.

Parents’ views, by race and ethnicity

Majorities of parents across racial and ethnic groups think of this as a priority. But some groups stand out for how much they prioritize this. For example, Hispanic (25%) or Black (24%) parents are more likely to say managing how much time their teen is on the phone is a top priority. That share drops to 10% among White parents.

Parents’ views, by household income

We also see differences between the lowest and highest income households: 22% of parents whose annual household income is less than $30,000 consider managing the amount of time their teen is on the phone a top priority, compared with 14% of those whose household income is $75,000 or more a year. Those whose household income is $30,000 to $74,999 a year do not meaningfully differ from either group.

Do parents set time limits on their teen’s phone use?

A split bar chart showing that Parents with younger teens are more likely to set time limits on phone use

There’s a nearly even split between parents who restrict their teen’s time on their phone and those who don’t. About half of parents (47%) say they limit the amount of time their teen can be on their phone, while a similar share (48%) don’t do this.

Parents’ views, by teen’s age

Parents of younger teens are far more likely to regulate their child’s screen time. While 62% of parents of 13- to 14-year-olds say they limit how much time their teen can be on their phone, that share drops to 37% among those with a 15- to 17-year-old.

How difficult is it for parents to keep up with their teen’s phone use?

A chart showing that Higher-income parents are more likely to say it’s hard to manage how much time their teen is on the phone

Managing screen time can feel like an uphill battle for some parents. About four-in-ten say it’s hard to manage how much time their teen spends on their phone. A smaller share (26%) says this is easy to do.

Another 26% of parents fall in the middle – saying it’s neither easy nor hard.

Higher-income parents are more likely to find it difficult to manage their teen’s phone time. Roughly half (47%) of parents living in households earning $75,000 or more a year say managing the amount of time their teen is on their phone is hard. These shares are smaller among parents whose annual household income falls below $30,000 (38%) or is between $30,000 and $74,999 (32%).

Parents’ own struggles with device distractions, screen time

Teens aren’t the only ones who can be glued to their phones. Parents, too, can find themselves in an endless cycle of checking emails , text messages and social media.

With that in mind, we asked parents to think about their own screen time – both the time they spend on their phone, and if it ever gets in the way of connecting with their teen.

Do parents think they spend too much time on their phone?

A bar chart Roughly half of parents say they spend too much time on their phone, but this varies by income

Like teens, parents are far more likely to say they spend too much rather than not enough time on their phone. About half of parents (47%) say they spend too much time on their smartphone. Just 5% think they spend too little time on it. And 45% believe they spend the right amount of time on their phone.

Parents’ views differ by:

Household Income: 50% of parents with annual household incomes of $75,000 or more say they spend too much time on their phone. This share drops to 41% among those living in households earning $30,000 to $74,999 a year and 38% among those earning under $30,000.
Race and ethnicity: 57% of White parents believe they spend too much time on their phone, compared with 38% of Black parents and 34% of Hispanic parents.

How often are parents distracted by their phone when talking with their teen?

A bar chart showing that Nearly half of teens say their parent at least sometimes gets distracted by their phone in conversations; fewer parents see it this way

When it comes to distracted parenting, parents paint a rosier picture than teens. Nearly half of teens (46%) say their parent is at least sometimes distracted by their phone when they’re trying to talk to them, including 8% who say this happens often.

But when parents were asked to assess their own behavior, fewer – 31% – say this happens regularly.

Throughout this report, “teens” refers to those ages 13 to 17 and “parents” refers to those with a child ages 13 to 17. ↩
A 2018 Center survey also asked U.S teens some of the same questions about experiences and views related to smartphone and social media. Direct comparisons cannot be made across the two surveys due to mode, sampling and recruitment differences. Please read the Methodology section to learn more about how the current survey was conducted. ↩
There were not enough Asian respondents in the sample to be broken out into a separate analysis. As always, their responses are incorporated into the general population figures throughout the report. ↩

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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Going back to normal: The Bank of Canada’s balance sheet after quantitative tightening

Introduction.

Good morning. And thank you, Fred, for the kind introduction.

I last spoke here, virtually , almost three years ago. At that time, I updated you on the actions the Bank of Canada had taken to help financial markets and the economy get through the COVID-19 crisis. 1

I expect you all remember all too vividly the overwhelming panic that engulfed financial markets when the pandemic hit. The resulting dash-for-cash mindset caused widespread dysfunction across key debt markets, including the market for Government of Canada (GoC) bonds. So we stepped in quickly and forcefully with several programs to make sure these markets could work properly.

Our actions did the job. 2 Market functioning was restored. But in restoring market functioning—and, later, in supporting the economic recovery—we ended up substantially growing our balance sheet.

A lot has happened since then. As pandemic restrictions eased, inflation increased. In response, we rapidly raised our policy rate—our main tool for controlling inflation. And because of the progress that we have made on inflation, our monetary policy discussions have shifted from whether our policy rate is restrictive enough to how long it needs to stay at the current level.

We have also been shrinking our balance sheet through quantitative tightening (QT) since April 2022. Under QT , we let the bonds we hold roll off our balance sheet as they mature, without replacing them. So far, our asset holdings have declined by roughly $180 billion, or almost 40%, to about $300 billion.

After two years, we are getting closer to the end of that balance sheet normalization. So today I’m going to talk about how we plan to manage things after QT has ended. Though there will be some modest changes in the details, the main message is we will be back to normal balance sheet management. That is, we will buy assets simply to match the amount of liabilities on our balance sheet, the main liability being cash in circulation.

Before I explain the changes, I’ll remind you how our balance sheet evolves in normal times. And then I’ll talk about how we’ll know that balance sheet normalization has run its course.

What normal balance sheet management looks like

Let’s start with a refresher on how we manage our balance sheet in normal times. By normal I mean when there isn’t something exceptional going on, such as significant stress in the financial system or an economic crisis.

The first thing to understand is that the size and composition of our balance sheet reflect the unique role we play as Canada’s central bank. Our assets and liabilities are designed to support our core policy functions. That separates us from commercial banks and other financial institutions.

In periods of stress, we can use our balance sheet to support financial system stability and the economy, as we did during the pandemic. But in normal times, our asset purchases and the size of the asset side of the balance sheet simply reflect what is happening on the liability side. That is, our holdings of financial assets grow passively to reflect the growth of our main balance sheet liability—the cash in your wallets. When households and businesses want more bank notes for their day-to-day activities, we accommodate this by creating and circulating more notes via the banking sector. That increases the size of the bank note liability on our balance sheet.

The amount of cash in circulation tends to track the trend rate of nominal economic growth. So we can usually forecast how much cash will grow, even if we cannot control that growth.

When the balance sheet is normal, cash in circulation is our biggest liability (see the pre-pandemic period in Chart 1 , panel b). Deposits by the Government of Canada are typically our second biggest. The other important liability is settlement balances, which I’ll talk more about shortly.

We must hold a like amount of assets on our balance sheet as liabilities so that the balance sheet, well, balances ( Chart 1 , panels a and b).

Chart 1: Bank of Canada assets and liabilities (month end)

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To offset the ongoing growth of cash in circulation in normal times, we routinely buy assets and hold them to maturity. These are mainly GoC bonds, which are essentially risk-free. But remember: the key difference between buying assets in normal versus exceptional times is that in normal times, the aim is to hold just enough assets to correspond with our liabilities.

So what does that mean for balance sheet normalization? As QT ends, we will start buying GoC bonds and other assets again as part of our normal balance sheet management. Those purchases will not be quantitative easing (QE) , just as our asset purchases before the pandemic were not QE .

That’s the broad overview of how our balance sheet normally evolves.

Settlement balances and the floor system

Now let’s turn to our settlement balances, also known as central bank reserves. Banks and other members of Canada’s high-value payment system known as Lynx have accounts at the Bank of Canada in which settlement balances reside. They use these accounts to settle payments with each other. For example, suppose one bank owes another bank $1 million. To settle this transaction, the first bank will transfer $1 million of settlement balances from its account to the second bank’s account.

I mentioned that our balance sheet supports our policy functions. The interest rate we pay on settlement balances is equal to our target policy rate. Paying our target rate on settlement balances helps to ensure that the one-day or “overnight” market rate used by financial institutions is at or close to our target rate. This is because competitive and arbitrage pressures drive the market rate toward our target. Changes in the level of the overnight rate, in turn, influence longer-term interest rates. This is how we implement monetary policy.

There are different frameworks the Bank can use to get overnight market rates to trade at or close to our policy rate. Before the pandemic, we used an approach called a corridor system, which minimizes the amount of settlement balances held by banks. Since the pandemic, we've shifted to operating in a floor system, which uses a larger amount of settlement balances. Let me explain why. 3

In exceptional times, such as during the pandemic, our balance sheet grows as we use settlement balances to fund our active, policy-driven asset purchases. Think of this as the Bank of Canada buying assets from financial institutions and paying for them by adding funds to their accounts with us. In the early days of the pandemic, we shifted to a floor system because we were buying a lot of financial assets to restore market functioning. Banks were left with large amounts of settlement balances in their accounts, so the rate of interest we paid on these balances served as the “floor” for the overnight interest rate.

In April 2022, when we started normalizing our balance sheet, we said we’d be sticking with a floor system on a permanent basis. 4 Once normalization of our balance sheet is done—and outside periods of crisis—we will ensure we supply just enough settlement balances to fully satisfy the demand for these from the financial system.

We switched to a floor system because Canada’s financial system was growing and evolving, making a corridor system less tenable as a framework for effectively implementing our monetary policy. Specifically, we felt it would become increasingly difficult in the corridor system to fine-tune the aggregate amount of settlement balances in the Lynx system at the end of each day to maintain tight control over the benchmark overnight repo rate observed in markets. A floor system makes it easier to keep that market rate close to our target rate. 5 Moreover, a floor system provides the necessary flexibility to implement monetary policy effectively in both normal and exceptional circumstances.

So how has the financial system evolved? Changes across the payment landscape have meant that payment flows are larger and more volatile than in the past. As a result, there is more demand for settlement balances from the financial system to support payment-related activities. 6 There is also greater volatility in settlement balances, driven by our other liabilities such as GoC cash deposits and deposits by systemic financial market infrastructures and financial Crown corporations. 7 All of that makes it harder to keep the overnight rate on target in a corridor system.

It is also likely that there is more precautionary demand across the financial system for risk-free assets, including settlement balances. Precautionary demand is where risk-free assets are held as a buffer against the unexpected, like cash stashed under a mattress for a rainy day.

New regulatory requirements have increased demand for liquid assets. At the same time, recent financial-system stress events have made risk managers at major banks more likely to use settlement balances as part of their pool of liquid assets. Two events that come to mind are the market functioning breakdown related to the dash for cash early in the pandemic, and the liability-driven investment fund issues that disrupted the UK government bond market in 2022. Both events showed that even debt instruments that are typically expected to be easy to convert to cash, such as government bonds, may not be as liquid if markets aren’t working well. Settlement balances, on the other hand, are basically wholesale digital cash holdings and thus are perfectly liquid. So demand for them from Lynx participants has likely gone up.

Going forward, our aim is to supply enough settlement balances to satisfy the needs of Lynx participants. But we don’t want to supply more than is needed to operate in a floor system. Chart 2 shows conceptually where settlement balances are now and what we are aiming for, which is our lowest comfortable level of settlement balances.

Chart 2: Conceptual view of where we expect settlement balances to land

Why? Because we want to maintain market discipline and encourage participants to manage their liquidity risks appropriately.

For the most part, the opportunity cost associated with holding settlement balances instead of similar assets should limit precautionary demand—particularly when other liquid and safe assets earn a better return. Demand may also be limited if financial institutions feel confident in their ability to access central bank liquidity facilities, both in normal times and in times of stress. If that access is certain and easy, financial institutions will have less need for settlement balances.

Nevertheless, the right amount of settlement balances to supply the system is uncertain. The bottom line is we will lean toward holding them at the minimum level needed to effectively implement monetary policy in a floor system.

How we will know we are back to normal

Let’s turn to how we will know when our balance sheet is back to normal.

As our pandemic-related asset purchases continue to roll off our balance sheet and settlement balances continue to fall, we expect settlement balances to land in a range of $20 billion to $60 billion. 8 Based on the maturity profile of our assets, we should get to this range sometime in 2025. 9 Right now, we have roughly $100 billion in settlement balances.

Lately, though, we’ve all seen a lot of speculation about whether QT may need to end before we get to those levels. What’s driving that speculation is the upward pressure we saw in overnight repurchase agreement (repo) markets over an extended period, starting late in 2023 and continuing into the early part of this year. We don’t think the decline in settlement balances linked to our normalization process has been much of a factor behind that tightness in overnight markets. Nor do we see any signs of stress in the financial system that can be tied to those pressures.

Our assessment is that the surge in demand for repo funding in Canada came from growing market expectations that interest rates are going to fall. 10 Late in 2023, market participants around the world became increasingly convinced that major central banks would pivot to aggressive policy rate cuts this year. Because of this conviction, many participants in Canada and elsewhere took leveraged long positions in government bonds to get ahead of the expected shift in policy.

The more policy rates fall, the more those bonds will be worth. And the promise of big gains led many to borrow in repo markets to fund the trades. Starting in December 2023, high demand for repo funding caused a wider-than-usual spread between our target overnight rate and the benchmark overnight interest rate, which is calculated from transacted overnight repo rates ( Chart 3 ).

In addition to leveraged long positions, the “basis trade” that is common in the US Treasury market has recently become more popular in Canada.

This is an arbitrage strategy used to exploit gaps between prices for government bonds and the futures that are tied to them. 11 The basis trade also boosted demand for repo funding, particularly from the growing presence of active hedge funds in Canadian fixed-income markets ( Chart 4 ).

When the pressures in repo markets became evident earlier this year, we used our routine policy implementation operations to reinforce our policy rate. This is something we’ve done for decades, whenever it was needed. Let me explain.

When temporary periods of higher demand for overnight funding put upward pressure on the overnight interest rate, we supply additional liquidity to offset that pressure. In doing so, we are acting to keep the rate close to our target for the overnight rate. Our main tool for this is our overnight repo operations. 12 We will continue to use these routine operations as needed to make sure the market rate doesn’t drift too far from our target rate. If it did, other interest rates could drift away from it too. That could mean that borrowing costs on a range of loans that matter to businesses and households don’t reflect our monetary policy stance.

The pressures have already waned. That’s partly because upward pressure on overnight funding rates tends to attract new cash providers to supply repo funding to the market. Indeed, the recent launch of daily auctions to lend out a portion of excess cash that the government holds in deposits with us has helped push the overnight market rate closer to our target rate. The appeal of leveraged long bond positions that gave rise to upward pressure also appears to have waned, as market participants no longer expect that central banks will ease policy aggressively.

Nevertheless, we will be monitoring short-term funding conditions and speaking with market participants to assess whether anything is going on that could be more significant or longer lasting than what we have seen so far.

Could QT end before we get to the $20 billion to $60 billion range?

The recent pressures didn’t change our view of the level of settlement balances needed in a floor system. But we are aware there is a risk that we could be wrong about the $20 billion to $60 billion range. There is uncertainty because it is fundamentally difficult to assess the demand for settlement balances.

While we have confidence that daily auctions of excess government cash and our overnight repo operations can counteract temporary funding pressures, you may be wondering what would drive us to decide to end QT early.

We would base such a decision on multiple indicators and a thorough assessment of system-wide funding pressures, focused on:

persistent upward pressure on the overnight repo rate plus widespread take-up (across many counterparties) in overnight repo operations, or the need for multiple rounds per day
broad-based funding pressures that go beyond the overnight repo market
Lynx participants needing advances from our Standing Liquidity Facility for end-of-day funding
market intelligence from market and payment system participants that indicates concern about a lack of liquidity that could be due to longer-lasting factors instead of temporary ones

I want to emphasize that we would announce any action involving the normalization of our balance sheet very clearly and ahead of time.

We have also said that balance sheet normalization will likely continue once inflation is on a sustainable path to 2% and we have started cutting our policy rate. This, of course, applies only while our policy rate remains in what we view to be restrictive territory.

On the other hand, if the economy slowed sharply, we would likely need to be adding monetary stimulus. In that situation, we would probably be cutting rates quickly, likely into stimulative territory and, as such, we’d stop QT , at least temporarily.

The bottom line is the balance sheet normalization process is continuing as we laid out last year, and we have tools to manage any temporary funding pressures that might come up along the way.

Managing a new normal for our balance sheet

Before I conclude, I want to explain how we will manage the balance sheet once we’re back to normal and routinely buying assets to offset the autonomous growth of our liabilities.

Going into the pandemic, our balance sheet was almost totally Government of Canada securities: roughly 70% was GoC bonds and a little over 20% was GoC treasury bills (t-bills). Another roughly 10% was term repos, and less than 1% was Canada Mortgage Bonds (CMBs) .

Currently, our asset portfolio is entirely bonds—a legacy of our pandemic response—and the maturity profile of these bonds skews longer than it did before the pandemic.

After QT ends, we want to restore a more balanced mix of assets with a broader range of maturities, including more short-term assets than we hold now. GoC bonds will still be our biggest asset type and we will also hold GoC t-bills and term repos, just like before the pandemic. We will no longer buy CMBs .

While our term repos will still be auction-based operations, we are considering two purchase options for our other assets. Do we buy GoC t-bills and bonds in the secondary market via reverse auctions like we did during the pandemic for GoC bonds? Or purchase these in primary GoC debt auctions like we did before the pandemic? There are pros and cons to each approach.

Regardless of where we land, we will be guided by the core principles that govern how we manage the balance sheet in both normal and exceptional times. We will acquire assets in a way that doesn’t harm market functioning, but rather—whenever possible—improves it. We will also ensure we manage the Bank’s financial risks. Finally, we are committed to transparency. We’ll announce our intentions ahead of time so that market participants understand our decisions and are prepared for our operations.

Before we get to our steady-state balance sheet, there will be a multi-year transition period as we replenish our stock of money market instruments—t-bills and term repos. So we expect that our purchases will tilt toward shorter-term assets for some time. Once QT ends and we start acquiring assets again, the sequence will likely be term repos at first, then after some time we will add t-bills, with bond purchases coming later still.

After this transition period, our plan is to roughly match floating-rate liabilities with floating-rate assets, so term repos and t-bills. Cash in circulation is assumed to be a permanent liability, so we will continue to roughly match our bond holdings to the size of this liability. As long as our bond holdings are equal to or smaller than our bank note liabilities, the Bank will be protected against losses. 13

A key question is whether it makes sense for the composition of our asset holdings in steady state to tilt shorter-term relative to our pre-pandemic portfolio. We will consider this question as we near our steady-state portfolio of assets.

It’s time for me to wrap up and recap my key messages.

We have come a long way in normalizing the Bank of Canada’s balance sheet. We are getting close to the new normal. But we’re not there yet.

Normalization of our balance sheet will carry on as we work toward the $20 billion to $60 billion range for settlement balances.

We are also confident that QT was not a main driver of the pressures we saw in overnight repo markets earlier this year. But we recognize there is a risk that QT may need to end earlier than expected.

Meanwhile, the Bank has standard operating procedures for any temporary funding pressures that come up. And we will be watching funding markets closely for any pressures that could be longer lasting and that, as such, could warrant a rethink of our balance sheet normalization.

Once QT is done and we have resumed our routine balance sheet management, including asset purchases, there will be a few small differences compared with how we did things before the pandemic. And, during the transition for the first number of years, we will work to replenish our money market instruments and restore a greater mix of assets and maturities on our balance sheet.

Market participants can count on us to clearly communicate any changes in QT or in our balance sheet management policy more broadly. And all Canadians can count on us to manage our balance sheet in a way that lets us achieve our policy mandate as intended and promote a stable and efficient financial system.

I would like to thank Philippe Muller and André Usche for their help in preparing this speech.

Getting to a new normal

Deputy Governor Toni Gravelle speaks about how the Bank of Canada will manage its balance sheet once quantitative tightening ends.

Speech: CFA Society Toronto

Bank of Canada: Normalization of the Balance Sheet — Deputy Governor Toni Gravelle speaks before the CFA Society Toronto (9:50 (ET) approx.) .

1. See T. Gravelle, “ Market stress relief: The role of the Bank of Canada’s balance sheet ,” (speech delivered virtually to CFA Society Toronto, Toronto, Ontario, March 23, 2021).[ ← ]
2. See, for example, G. Johnson, “ A Review of the Bank of Canada’s Market Operations Related to COVID-19 ,” Bank of Canada Staff Discussion Paper No. 2023-6 (March 2023).[ ← ]
3. For more information about the differences between the two systems, see the Bank’s website. [ ← ]
4. See Bank of Canada, “ Bank of Canada provides operational details for quantitative tightening and announces that it will continue to implement monetary policy using a floor system ,” (market notice, April 13, 2022).[ ← ]
5. The Bank’s policy interest rate is our target for the overnight collateralized repurchase agreement (repo) rate at which financial institutions lend each other funds for a day. Such trades are used to calculate the reference rate called CORRA—the Canadian Overnight Repo Rate Average—which measures the cost of overnight general collateral funding in Canadian dollars using Government of Canada treasury bills and bonds as collateral for repurchase transactions. Making sure overnight rates remain at or close to the target for the overnight rate that we set for monetary policy is how we make sure borrowing and lending conditions in the economy reflect our policy stance.[ ← ]
6. See T. Gravelle, R. Morrow and J. Witmer, “ Reviewing Canada’s Monetary Policy Implementation System: Does the Evolving Environment Support Maintaining a Floor System? ” Bank of Canada Staff Discussion Paper No. 2023-10 (May 2023).[ ← ]
7. Financial market infrastructures, such as the Canadian Depository for Securities and the Canadian Derivatives Clearing Corporation, and financial Crown corporations often place deposits at the Bank of Canada to limit their exposure to private banks.[ ← ]
8. See T. Gravelle, “ The Bank of Canada’s market liquidity programs: Lessons from a pandemic ,” (speech delivered to the National Bank Financial Services Conference, Montréal, Quebec, March 29, 2023).[ ← ]
9. The timing of when this happens has shifted slightly because of changes to our assumption for the level of future GoC deposits.[ ← ]
10. See B. Plong and N. Maru, “ What has been putting upward pressure on CORRA? ” Bank of Canada Staff Analytical Note No. 2024-4 (March 2024).[ ← ]
11. For example, see F. Avalos and V. Sushko, “ Margin leverage and vulnerabilities in US Treasury futures ,” ( BIS Quarterly Review , Bank for International Settlements, September 2023).[ ← ]
12. Our market operations are symmetric in nature. If the overnight interest rate starts to move too far below our target policy rate, we do an overnight reverse repo, which takes liquidity out of the system for a day which puts upward pressure on the overnight rate, pushing it up toward the target. We were doing these regularly during the pandemic, right up until the autumn of 2023. [ ← ]
13. See “ About the Bank’s interest earnings ” for an explanation of seigniorage.[ ← ]

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Introduction. Good morning. And thank you, Fred, for the kind introduction. I last spoke here, virtually, almost three years ago.At that time, I updated you on the actions the Bank of Canada had taken to help financial markets and the economy get through the COVID-19 crisis.1 I expect you all remember all too vividly the overwhelming panic that engulfed financial markets when the pandemic hit.

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Pritha Bhandari

Quantitative and Qualitative Research

What is Quantitative Research?

Quantitative Research – Methods, Types and Analysis

Quantitative Research

Quantitative Research Methods

Descriptive Research Design

Correlational Research Design

Quasi-experimental Research Design

Experimental Research Design

Survey Research

Quantitative Research Analysis Methods

Statistical Analysis

Regression Analysis

Factor Analysis

Structural Equation Modeling

Time Series Analysis

Multilevel Modeling

Applications of Quantitative Research

Characteristics of Quantitative Research

Examples of Quantitative Research

How to Conduct Quantitative Research

When to use Quantitative Research

Purpose of Quantitative Research

Advantages of Quantitative Research

Limitations of Quantitative Research

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Secondary Quantitative Research Methods

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