Quantitative research methods are empirical approaches to scholarly inquiry that involve the collection and analysis of numerical data. These methods are primarily informed by positivism, a philosophical stance that emphasizes objective, measurable observations and the use of scientific procedures to understand reality.

What are Key Practices and Epistemological Assumptions

1. Empirical research: Quantitative methods rely on observable, measurable evidence (empirical data) rather than opinion, theory or logic alone. Researchers collect data through experiments, surveys, or other systematic observations.
2. Positivism: This philosophical approach underpins much of quantitative research. Key aspects of positivism include:
   • Assumption of order: Positivism assumes there’s a non-random, orderly structure to the universe that can be discovered through systematic observation and measurement.
   • Cause and effect: It focuses on identifying causal relationships between variables, aiming to explain phenomena through these cause-and-effect connections.
   • Objectivity: Positivism emphasizes the importance of researcher objectivity, asserting that reality can be studied independently of the researcher’s beliefs or biases.
   • Empirical methods: It relies on observable, measurable evidence gathered through scientific methods.
   • Probabilistic nature of findings: While seeking to uncover universal laws, positivism recognizes that:
     • Scientific research can never definitively “prove” or conclusively answer a research question.
     • It relies on probabilistic statistics to support or refute hypotheses.
     • Findings are always provisional and subject to revision.
3. Incremental progress: Scientific knowledge grows incrementally through a process of constantly repeating and refining experiments, with each study building on or challenging previous work.
   • Falsifiability: Theories should be formulated in a way that allows them to be potentially proven false, rather than seeking absolute proof.
4. Numerical data: Quantitative research deals with data that can be counted or measured, such as scores, ratings, or frequencies.
5. Statistical analysis: Researchers use mathematical and statistical techniques to analyze data, test hypotheses, and draw conclusions. Results are often expressed in terms of statistical significance and confidence intervals, acknowledging inherent uncertainty.
6. Generalizability:
7. Scientific Laws and Generalizability: While scientific research indeed relies heavily on probability and statistics, there is a point at which certain findings become so well-established and consistently replicated that they are considered scientific laws. This is a key difference between scientific inquiry and some other forms of scholarship. Laws allow scientists to make highly reliable predictions about natural phenomena, forming the basis for technological advancements and further research.
   • Scientific Laws: These are descriptions of observed phenomena that have been repeatedly tested and confirmed to the point where they are considered universal and invariable under specific conditions. Examples include Newton’s laws of motion or the laws of thermodynamics.
   • Threshold for Laws: There’s no fixed number of studies required to establish a law, but rather a consensus in the scientific community based on overwhelming evidence and consistent replication across various contexts and conditions. Established laws provide a solid foundation upon which new theories and hypotheses can be built and tested. The reliability of scientific laws enables their application in engineering, medicine, and other fields with high confidence.
   • Probabilistic Foundation: Even laws are based on probabilistic reasoning, but the probability approaches certainty as evidence accumulates. This ability to establish widely applicable laws distinguishes natural sciences from social sciences and humanities, where context-dependent findings are more common. That said, even scientific laws remain open to revision if new evidence contradicts them, maintaining the self-correcting nature of science.
   • Generalizability: Scientific laws represent the highest level of generalizability, applying broadly across relevant contexts.
8. Hypothesis testing: Many quantitative studies start with a hypothesis (a predicted relationship between variables) which is then tested using statistical methods.
9. Replication: The importance of replicating studies to build confidence in results over time, rather than relying on a single study for definitive answers.
10. Limitations: Recognition that even well-designed quantitative studies have limitations and potential sources of error.

Mixed Methods Approach

Quantitative methods can also be used in conjunction with qualitative methods in what’s known as a mixed methods approach. This combination allows researchers to:

1. Use quantitative data to inform qualitative inquiry or vice versa
2. Triangulate findings from different methodological approaches
3. Provide a more comprehensive understanding of complex phenomena
4. Address research questions that cannot be fully answered by either quantitative or qualitative methods alone

Quantitative methods are widely used in natural and social sciences to examine relationships between variables, test theories, and make predictions. While they strive for objectivity and generalizability, they operate within a framework of probability and ongoing scientific inquiry rather than absolute truth.