Many people believe that talking to your plants will nurture their growth. If you wanted to prove this hypothesis, it would be best to conduct quantitative research to gather observable data instead of relying on anecdotes.

You could set up a few plants in identical conditions (same temperature, sunlight, and amount of water) and manipulate the independent variable of the experiment by speaking to some plants and not others. Over the next few weeks, you measure the growth of each plant—the dependent variable—and see how they differ. The measurements over time will provide you with valuable quantitative data to see if there really is a correlation between speaking to plants and their growth. 

This experiment is an example of the power quantitative research has to transform an urban legend into a data-backed claim through collecting objective data that describes the relationship between speaking to a plant and its growth. Quantitative research can turn abstract concepts into measures that can be objectively assessed.¹ Common methods of collecting quantitative data include conducting an experiment in a controlled environment, distributing surveys with rating scales, or analyzing historical records.¹

Through the collection and analysis of numerical data using structured tools, quantitative research helps us test hypotheses, identify patterns, and predict outcomes. The reliance on numerical data means that quantitative research tends to be more objective than qualitative research, aiming to find correlations that can be applied outside of the context of the study.² 

Quantitative research is often concerned with the “what”—showing the relationship between a variable and outcome—rather than the “why.” Once quantitative research reveals a correlation between two variables, qualitative data can supplement the research and dive deeper into what the numbers indicate. The most robust studies will leverage both quantitative and qualitative research, producing comprehensive results that tackle the phenomenon from different angles. 

Qualitative Research: A methodology focused on collecting and analyzing descriptive, non-numerical data to understand complex human behavior, experiences, and social phenomena.

Structured Tools: Instruments or methods to collect in-depth numerical data for quantitative research. Common tools include surveys, polls, or questionnaires.⁴

Variables: Characteristics or conditions that can be described in measurable terms within the context of a study. The independent variable is changed or controlled in various conditions, while dependent variables are measured to gauge the result of these changes.⁵

Hypothesis: An assumption or theory that predicts the relationship between variables.⁶ It is common for researchers to postulate a hypothesis before conducting quantitative research to guide the experimental design. 

Descriptive Statistics: A process of summarizing numerical quantitative data in a concise and informative manner. Descriptive statistics represent the data through statistics like the mean, mode, or median, giving an overview rather than an in-depth analysis.⁷

Inferential Statistics: Using findings from a sample group to make predictions about a larger population. In quantitative research, the results from an experiment are generalized to a wider group.

Statistical Significance: The claim that observed correlation between variables can be attributed to a specific characteristic or condition rather than chance. Results of quantitative research can be said to be statistically significant when they meet a particular standard of statistical analysis, such as a p-value of lower than 0.05.⁸

Before the 16th century, knowledge about the world came primarily from philosophy, which largely focused on description and theoretical discourse. People acquired knowledge through studying ancient Greek texts and religious doctrines to make sense of the natural world and human behavior.⁹ A paradigm shift occurred between the 16th and 17th centuries, referred to as the Scientific Revolution, as great thinkers of the time championed the belief that scientific research would improve our collective understanding of the world.¹⁰

During the Scientific Revolution, researchers began to take a greater interest in the physical world and focus on what could be observed and verified. Mathematical models and geometry were used to dispel long-held beliefs about the cosmos, such as the idea that the Earth was at the center of the universe. Polish astronomer Nicolas Copernicus recorded the position of planets—quantitative data—to prove that the Earth actually revolved around the sun and not the other way around.¹⁰ While Copernicus’ groundbreaking work was challenged by scholars and religious leaders alike, over time, it became clear that his methodical assessment had been correct. Such discoveries caused people to question long-standing beliefs and put increasing faith in math and science to find observable evidence to support or dispel existing theories.

Shortly after, the development of practical technologies such as telescopes, microscopes, and thermometers further enhanced the ability of empiricists to generate and test theories through observation and measurement of the natural world.¹¹ It was during this time that quantitative research began to be seen as the gold standard for explaining natural phenomena. Francis Bacon, Lord Chancellor of England, advocated for a more systematic approach that prioritized observable outcomes to make claims about the natural world, known as inductive reasoning.¹¹ Bacon believed that by controlling variable factors of nature through experiments, natural phenomena could be better understood.¹²  In 1660, the Royal Society was founded to institutionalize science, bringing scientific leaders together to share and disseminate their wealth of information.¹³

When the social sciences emerged in the 17th and 18th centuries, researchers applied the same quantitative approach to studying human behavior as had been used in the natural sciences. Researchers took a positivist approach, a doctrine introduced by French philosopher Auguste Comte that suggested that society operated according to scientific laws and therefore could be observed and measured quantitatively. Quantitative research became a cornerstone of modern scientific inquiry across various fields, including the social sciences, economics, and psychology.¹⁴

In 1920, Daniel Starch, an American mathematician and psychologist, applied quantitative methods to market research, professionalizing the field. He conducted the first questionnaire survey, asking people on the street which advertisements resonated with them. Starch showed that quantitative research could provide insight into consumer behavior, pioneering market research and advocating for its importance.¹⁵

By the late 19th and early 20th centuries, quantitative methods were further refined through the contributions of Karl Pearson, a British mathematician known as the founder of modern statistics. Pearson introduced concepts such as the correlation coefficient and the p-value, providing a mathematical foundation for analyzing patterns and relationships in data and tools to determine the validity and generalizability of quantitative research.¹⁶

As the 20th century progressed, social scientists started to criticize the positivist approach that had long been the standard, claiming that it was too reductive and failed to explain the complexity of human behavior. People began to supplement experiments with qualitative research methods, such as interviews and focus groups, to understand the social, cultural, and historical contexts that affect behavior.¹⁴  

Today, technological advancements allow us to process vast amounts of data, enabling quantitative researchers to gather and analyze data in real-time and at unprecedented scales. Often, quantitative research is conducted alongside qualitative research for a holistic approach to understanding human behavior. This mixed-methods approach, referred to as triangulation, allows researchers to combine numerical data with deeper, more nuanced qualitative insights.

Francis Bacon

A leading figure in natural philosophy in the 16th and 17th centuries and Lord Chancellor of England, Bacon argued that theoretical discourse dominating knowledge at the time lacked a systematic methodology appropriate for studying the physical world. Bacon developed the Baconian method, which sets expectations for standard, quantitative reporting of experiments based on evidence. The Baconian method is divided into three steps: collection of data (or facts), organization into categories, and then drawing conclusions through inductive reasoning. Bacon’s approach laid the foundation for modern scientific methodology.¹⁷

Auguste Comte

Known as the founder of modern social science and the father of sociology, Comte was a French philosopher who was a central figure in the development of positivist theory. Positivism emphasizes relying on empirical evidence and objectivity, key tenets of quantitative research, to draw conclusions about human behavior. Comte coined the term ‘sociology’ in 1838, and his contributions were significant to the establishment of sociology as an academic field.¹⁸

Karl Pearson

Widely believed to be the founder of modern statistics, Pearson was a British mathematician who argued that scientific methods are descriptive and require quantitative research as a result.¹⁶ Pearson introduced concepts like probability and correlation and developed tools like the histogram to help visualize quantitative data.¹⁹

Daniel Starch

An American mathematician and psychologist, Starch was the first to professionalize market research by applying quantitative methods to understanding consumer behavior.²⁰ Starch conducted the first questionnaire survey for market research to see what advertisements resonated with consumers, which led to quantitative research to be seen as a tool to better understand consumer decision–making.²¹

Quantitative research has a profound impact on how we understand data and use it to make decisions. It lays the foundation for establishing causal relationships, reduces bias, and supports informed, data-driven choices in fields like healthcare, business, and policy. 

Demonstrating Causal Relationships

Quantitative research relies on observable, empirical data to support hypotheses. Through controlled environments and rigorous methodologies, quantitative research minimizes the impact of external variables by focusing on how changes in one variable (the independent variable) correspond to changes in others (dependable variables).²²  By isolating variables, researchers can more clearly see the impact of a specific change or difference.

For example, in medical research, researchers can conduct an experiment with a control group who does not receive a drug and a treatment group that does to test the effectiveness of the drug. By making the drug the only variable that changes between the two groups, researchers can be more confident that it is, in fact, the drug causing the change in patient outcomes.  

Minimizing Bias

Ever heard the quote “Numbers don’t lie, people do”? This belief highlights the objectivity and reliability of quantitative research. Quantitative research relies on numerical data from representative samples to draw conclusions about a given population while minimizing the impact of various biases. Alternatively, qualitative research that uses methods like focus groups or interviews to better understand human behavior can be undermined by biases like response bias, which refers to our tendency to provide inaccurate answers that we believe are more socially desirable. This is especially problematic in subjective methods, where human behavior and emotions are central to the research.

Quantitative research also reduces researcher bias in the interpretation of results. In qualitative research, a researcher’s personal experiences and beliefs can impact how they evaluate the results. However, as quantitative data is rooted in numerical values and can be better compared across studies to validate findings, there is less room for individual beliefs to influence conclusions.²³ 

The objectivity and reliability of quantitative research also increase its generalizability. Since it applies standardized methodologies and often leverages large sample sizes, the findings are more likely to apply to a broader group. Conclusions drawn from quantitative research with sufficient sample sizes are more applicable across different contexts, industries, and demographics, further reinforcing the value of quantitative research in policy-making, business strategies, and scientific inquiry.

Data-Driven Decision Making

In the digital age, data has become one of our most valuable resources. Quantitative research frameworks enable us to gather and interpret data effectively, making it a valuable research method. 

Quantitative research allows businesses and policy-makers to make decisions rooted in insights from data rather than relying on intuition or hearsay. By identifying patterns in large datasets and using that data to predict outcomes, quantitative research can lead to decisions that have greater chances of success.²⁴

The emphasis on objectivity in quantitative research comes with its limitations when trying to explain complex phenomena like human behavior. Many people argue that quantitative research is too reductive, restrictive, and lacking in depth. While quantitative research is a great starting point when looking for causal relationships, it can often be strengthened by additionally conducting qualitative research to understand the nuances behind the data. It may also draw inspiration from potential patterns in quantitative research, testing their generalizability.

Simplification of Complex Human Behavior

While numbers provide a more objective and measurable way to understand the relationship between variables, when it comes to human behavior, quantitative research can be reductive. Humans are very complex, and numerical data cannot always accurately capture the nuances and diversity of our behavior. 

For example, if a government conducted a quantitative study to understand the issues that contribute to homelessness, they may gather data on variables like employment status, income level, and average rental prices. The data may indicate that low-income level correlates to homelessness, suggesting financial assistance could diminish the problem. However, that would be an oversimplification of the issue. A multitude of variables contribute to homelessness, such as mental health challenges, substance abuse, or traumatic upbringings. These variables would not be captured by numerical results if the researchers had not set out to quantify them at the outset, which could lead to misguided policies.²⁵

Even if some of these variables were discovered during research, because quantitative research aims to be structured in its methodology, it is limited in its ability to explore unanticipated outcomes that may arise.²⁶ 

Limited Understanding of the “Why”

Quantitative research suggests that there is an objective reality—a “truth”—that can be uncovered through experiments and analysis. However, many people believe that reality is socially constructed and can be understood subjectively.²⁶ Instead of focusing on the “what,” they argue, we should focus on the “why.” 

For example, let’s imagine a business that’s interested in researching employee satisfaction. The company may circulate a survey that asks employees to rate their satisfaction on a scale of 1-5. The results would show the “what”—whether or not people were satisfied with their jobs—but would not provide meaningful insight into why. There are a myriad of variables that can impact employee satisfaction, such as work-life balance, income, company culture, career growth opportunities, etc. Only knowing the average rating would make it difficult for the business to enact meaningful change to improve employee satisfaction and engagement. 

Overlooking Uniqueness

Quantitative research does not capture the underlying meanings, emotions, or personal experiences that contribute to the decisions we each make. It reduces all of these complex motivators into mere numbers, missing out on much of the picture. It is focused on finding trends and patterns instead of comprehensive qualitative research that seeks to understand the specific characteristics and experiences of a person. While this quality lends itself to the generalizability of quantitative research, it can also lead to conclusions that sweep unique variables and the importance of context under the rug.

Supporting Efficient & Effective Governance of Organ Donation Across Canada

Organ donation has the potential to save hundreds if not thousands of lives in Canada every year. However, this can only be achieved if the regulations around organ donation are effective. Every year, there are over 4,000 Canadians on the waiting list to receive a transplant, and unfortunately, over 250 patients die before they are scheduled for transplant surgery. 

There are many stakeholders to consider when determining best practices for governing organ donation effectively, each with diverse perspectives on how it should be done. Who should decide who an organ goes to? What variables need to be considered when making the decision? 

Quantitative research provides a powerful method for aligning stakeholders with varied preferences. In 2018, The Decision Lab collaborated with the Organ Donation and Transplantation (ODT) Steering Committee to create an effective governance framework for organ donation. To account for and propose solutions that align the different perspectives and stakeholders, The Decision Lab conducted a discrete choice experiment, a quantitative technique where people decide between two options at a time. This forced individuals to make a decision when only two options were present, which was instrumental in quantifying their priorities. 

The discrete choice experiment provided objective conclusions on how to build an effective governance framework. While this provided a great foundation, The Decision Lab supplemented the quantitative research with qualitative methods to ensure all stakeholders had the opportunity to voice their opinions through a dotmocracy workshop. This exemplifies the value of triangulation, combining quantitative and qualitative methods to arrive at an efficient and practical governance framework that satisfies the priorities of multiple stakeholders. 

Informing Medical Education Policy Through Quantitative Research

The COVID-19 pandemic led to many unprecedented outcomes. The unique circumstances caused by the pandemic provide an opportunity to explore the impact of various decisions that were made to determine if they should be reversed or reinforced.

During the pandemic, all student clinical rotations with in-person patient care were paused in the United States. Medical researchers from the University of California San Francisco School of Medicine wanted to investigate the educational and psychological effects that this decision had on medical students to understand the importance of clinical rotations in their medical journey and whether they should be resumed. 

The researchers sent surveys to students from six medical schools across the country during the initial peak phase of the pandemic. They received 741 responses and found that 61.3% of medical students believed that they should continue clinical rotations during the pandemic and were willing to accept the risks associated with in-person patient careers. The survey revealed that adequate personal protective equipment was the most important factor to feel safe returning to clinical rotations.²⁷

This quantitative research demonstrated that medical students were eager and willing to return to in-person patient care and informed researchers of what was most important to them to feel safe and comfortable returning.

Unpacking the Stats: Digital Mental Health Interventions

In recent years, we have seen an increase in digital mental health interventions. In the interest of making interventions more accessible, governments have introduced mental health supports that leverage technology like websites, mobile apps, and video conferencing. In this article, our editor, Charlotte Sparkes reviews a quantitative research study that we conducted in 2023 to understand the effectiveness of digital mental health interventions.

Survey Design

A common method in quantitative research is the use of surveys to gather numerical data to better understand people’s opinions and experiences. In order to produce accurate and relevant information, surveys must be carefully designed. In this article, we provide tips on designing surveys and discuss the advantages and disadvantages of this method. 

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