Quantitative Research | Definition, Methods & Examples
Quantitative research uses numerical data, such as measurements, survey responses, and experiment results, to explore a research question. Quantitative data are analyzed with statistics to test a theory or hypothesis.
Quantitative research methods can be applied to find patterns, identify causal relationships, and model real-world processes. Many fields, including biology, chemistry, economics, physics, and psychology, use quantitative methods.
Not all phenomena can be expressed using numbers—in such cases, qualitative research methods should be used instead. Qualitative and quantitative methods can also be combined in mixed methods research.
Table of contents
What is quantitative research?
Quantitative research uses numerical data to answer “where,” “when,” and “what” questions. It is the opposite of qualitative research, which uses nonnumerical data (e.g., text, audio and video recordings, and photographs).
Quantitative methods are objective. Although no research is free from bias, the aim of quantitative research is to produce results that reflect reality and are not influenced by a researcher’s feelings or beliefs.
- When researching a new topic with limited existing theories
- When data cannot be expressed using numbers
- When trying to gain deeper insight into nuanced topics like individual experiences, emotions, and social relationships
Types of quantitative research
Different types of quantitative research exist that can be applied to different research questions. Some common types of quantitative research designs are described in the following table.
| Research design | Description | Example research question |
|---|---|---|
| Descriptive research | Quantifies the qualities of a group or phenomenon. When characteristics of two groups are compared, this is called comparative descriptive research. | What are the most frequently reported symptoms of long COVID? |
| Correlational research | Explores whether and how two or more variables are related using statistics. Two variables can be positively correlated, negatively correlated, or uncorrelated. | What is the relationship between screen time and social anxiety in teenagers? |
| Experimental research | Characterizes cause-and-effect relationships by randomly assigning participants to groups, manipulating an independent variable, and measuring its impact on a dependent variable. | Does stretching improve muscle soreness in athletes? |
| Quasi-experimental research | Characterizes a cause-and-effect relationship but without randomly assigning participants into groups. Lacks the control of experimental research and is susceptible to bias. | Does college performance differ between students who completed advanced placement (AP) courses and those who did not? |
Quantitative data collection methods
There are several factors to consider when collecting quantitative data.
Operationalization
It can be challenging to find an accurate and reliable way to capture phenomena numerically. The process of defining how a variable will be measured is called operationalization, or creating an operational definition.
Types of quantitative data
Researchers can use various tools to quantify the phenomenon they are studying. The best tool to use will depend on your specific research question and resources. Some common techniques to capture quantitative data are described below:
- Surveys include questions that help you understand certain characteristics of a population. They may include multiple choice questions, rating scales, Likert scales, and demographics information.
- Observations are collected by watching participants in a natural setting and recording behaviors. Observational data may be quantitative or qualitative.
- Measurement instruments can be used to capture physical quantities (e.g., length, time) or physiological signals (e.g., heart rate, pupil diameter).
Quantitative research analysis
Quantitative data are analyzed using statistics. The proper approach to analyze your data will depend on your research question. However, virtually any analysis of quantitative data will include some common steps:
- Data cleaning may involve removing incorrect or unnecessary data, handling missing data, and addressing outliers.
- Data transformation can be necessary to get your data in the format required for certain analyses. For example, you may want to convert exam scores (0–100) to a 0–1 range.
- Statistical analysis is when you apply statistical tests to your data to answer your research question.
The options for quantitative data analysis are vast—it would be impossible to cover them all in a single article or even textbook. However, many approaches can be categorized as descriptive statistics or inferential statistics.
Descriptive statistics describe a dataset. They provide summary information like proportions, central tendency (mean, median, and mode), or variability (the range of values or standard deviation of a variable).
Inferential statistics allow you to test hypotheses, make predictions about your data, and generalize to a wider population. Because inferential statistics are conducted on a sample that represents a larger population, they involve a degree of uncertainty.
Most studies will include both descriptive and inferential statistics.
Descriptive statistics related to this study may include the age range of participants or the average quiz score of each group.
Inferential statistics could include the results of a t test that compares the quiz scores of each group to determine if there is truly a difference between the two.
Advantages of quantitative research
Quantitative research offers the following benefits:
- High reliability: Because quantitative data are collected and analyzed systematically, someone else should be able to follow the same approach and obtain the same results
- High generalizability: Quantitative studies have relatively high sample sizes compared to qualitative research, increasing the likelihood that results reflect characteristics of the general population
- Objective: Though no research is totally free from bias, quantitative methods leave little room for personal interpretations and subjectivity.
Disadvantages of quantitative research
- Low validity: Because quantitative research is generally conducted in a tightly controlled setting, the results may not hold in the real world.
- Limited to phenomena that can be captured numerically: Quantitative data are, by definition, numeric. Quantitative methods can only be applied to constructs that can be captured as numbers.
- Relies on established theories: Quantitative research generally involves testing hypotheses based on existing theories. If there are no existing theories, qualitative methods may be more appropriate.
Mixed methods research, which combines quantitative and qualitative research, leverages the complementary advantages of each method. The strengths of one compensate for the weaknesses of the other.
Quantitative research examples
Quantitative research can be used to tackle research questions across a wide range of fields. Consider the following examples that leverage quantitative methods.
Research design: Quasi-experimental
Type of data: Physiological measurements
Statistical analysis: Descriptive statistics (e.g., demographic information), inferential statistics (e.g., comparisons of brain activity across groups).
Quantitative research can also be used outside of an academic setting.
Research design: Descriptive research
Type of data: Survey
Statistical analysis: Descriptive statistics (e.g., demographic information, average spent per month, proportion of money spent on different types of products).
Frequently asked questions about quantitative research
- How do I decide between qualitative and quantitative research?
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Qualitative and quantitative research methods are used to investigate different types of research questions.
Quantitative methods are best if:
- The topic you’re studying can easily be expressed using numbers
- You want to use statistics to test a hypothesis
Qualitative methods are best if:
- The topic you’re studying cannot be expressed numerically
- You’re studying a new research area with limited existing theories
- You want to gain deep insight into a nuanced issue
- Why is validity so important in psychology research?
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Psychology and other social sciences often involve the study of constructs—phenomena that cannot be directly measured—such as happiness or stress.
Because we cannot directly measure a construct, we must instead operationalize it, or define how we will approximate it using observable variables. These variables could include behaviors, survey responses, or physiological measures.
Validity is the extent to which a test or instrument actually captures the construct it’s been designed to measure. Researchers must demonstrate that their operationalization properly captures a construct by providing evidence of multiple types of validity, such as face validity, content validity, criterion validity, convergent validity, and discriminant validity.
When you find evidence of different types of validity for an instrument, you’re proving its construct validity—you can be fairly confident it’s measuring the thing it’s supposed to.
In short, validity helps researchers ensure that they’re measuring what they intended to, which is especially important when studying constructs that cannot be directly measured and instead must be operationally defined.
- What is operationalization?
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Operationalization is when you define how a variable will be measured. Operationalization is especially important in fields like psychology that involve the study of more abstract ideas (e.g., “fear”).
Because fear is a construct that cannot be directly measured, a researcher must define how they will represent it. For example, in studies involving mice, fear is often operationalized as “”how often a mouse freezes (i.e., stops moving) during an experiment.”
Operationalization can be used to turn an abstract concept into a numerical form for use in quantitative research.
Some operationalizations are better than others. It is important to consider both reliability and validity (how consistent and accurate a measurement is, respectively) when operationalizing a construct.
- What is data collection?
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Data collection is the process of gathering data (measurements, observations, and other information) to answer a research question. Though many different methods of data collection exist, all are systemic and follow a procedure defined before data collection begins. Data can be qualitative or quantitative.
