An introduction to quantitative research

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.

Quantitative research question examples
  • 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

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

Correlational and experimental research can both be used to formally test hypotheses, or predictions, using statistics. In these two types of research, the results can 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).

Quantitative research methods
Research methodHow to useExample
ExperimentControl or manipulate an independent variable to measure its effect on a dependent variable.To test whether an intervention can reduce procrastination in college students, you give equal-sized groups either a procrastination intervention or a comparable task. You compare self-ratings of procrastination behaviors between the groups after the intervention.
SurveyAsk questions of a group of people in-person, over-the-phone or online.You distribute questionnaires with rating scales to first-year international college students to investigate their experiences of culture shock.
(Systematic) observationIdentify a behavior or occurrence of interest and monitor it in its natural setting.To study college classroom participation, you sit in on classes to observe them, counting and recording the prevalence of active and passive behaviors by students from different backgrounds.
Secondary researchCollect data that has been gathered for other purposes e.g., national surveys or historical records.To assess whether attitudes towards climate change have changed since the 1980s, you collect relevant questionnaire data from widely available longitudinal studies.

Quantitative data analysis

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, to analyze your data, you can use descriptive and/or inferential statistics 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.

Examples of descriptive and inferential statistics
You hypothesize that first-year college students procrastinate more than fourth-year college students. You collect data on procrastination levels of the two groups using 7-point self-rating scales.

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.

Next, you perform inferential statistics to test your hypothesis. Using a t-test to compare the mean ratings of the two groups, you find a significant difference and support for your hypothesis.

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.

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Advantages of quantitative research

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.

Disadvantages of quantitative research

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.

Frequently asked questions about quantitative research

What’s the difference between quantitative and qualitative methods?

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 analyzing data, while qualitative methods allow you to explore ideas and experiences in depth.

What is mixed-methods research?

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

What is data collection?

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.

What is operationalization?

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.

What’s the difference between reliability and validity?

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.

What is hypothesis testing?

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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Pritha Bhandari

Pritha has an academic background in English, psychology and cognitive neuroscience. As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics.

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