Quantitative research is a mode of inquiry that attempts to systematically measure and predict phenomena through the use of standardized tools. It is inherently a world of numbers, percentages, and therefore tangible analysis.
Quantitative researchers tend to approach the world as a computable place. Data is possible; they just need to go find it. They tend to follow the deductive method, which begins with general principles and moves towards specific conclusions. This approach contends that information (or knowledge) is everywhere, and we must purposefully corner off bits and pieces of it to ever know reality in its entirety. (1)
Since quantitative research is founded in a positivist school of thought, it is heavily reliant on standardized tools of inquiry. Positivism argues that research must be conducted and found in the “real” world through the human senses (and instruments), thus it is no surprise that the first form of quantitative research was modeled for the physical sciences. Gustav Fechner was the first to model a quantitative approach to psychology in his work in psychophysics. (1)
While we generally do not think of statistical data as “physical” evidence, the definition of data has evolved over time. Counting is thought to be the first of all quantitative efforts, and we have come a long way since the primary forms of quantitative assessment. Rather than measuring physical entities, assessment in the social sciences typically attempts to record phenomena.
EditAssumptions of Quantitative Research
Some of the key philosophical assumptions of quantitative researchers are as follows.
- Reality is single, fragmentable, and tangible.
- Social facts have an objective reality.
- Causality is traceable.
- Variables within an experiment can be isolated, identified, and controlled. (1)
Quantitative research aims to generalize and find a consensus or norm. Thus, variables, especially those found in the relationships between proposed causes, are controlled through research design. Quantitative researchers typically aim to minimize the statistical hindrances variances cause through the implementation of constants or controls within their experimentation. (3)
EditThe Quantitative Method
The quantitative method closely follows the process of the scientific method. Simply put, quantitative researchers typically:
- Form a hypothesis or theory
- Conduct their research using valid and formal instruments
- Generalize about their findings
Oftentimes, generalizations within an experiment are used as jumping-off points for further quantitative investigation.
EditQuantitative Results
Since quantitative researchers are interested in predicting and measuring phenomena, they are most interested in finding a consensus or norm. Likewise, they often have a large pool of results to help verify this norm.
Within this large pool of results, quantitative researchers can determine whether or not their hypothesis is correct. Since quantitative research is deductive in method, a well controlled experiment or survey will ideally glean results that prove or disprove the hypothesis, rather than offer completely unexpected data. This is quite different from qualitative results, which can evolve significantly before the experiment is complete.
Quantitative results are most often presented in numerical indices and the write-up is typically in abstract language. Because quantitative results are by nature mathematical, the results are often presented in graph form. (2)
One of the most fundamental laws in statistical quantitative analysis today is the notion that correlation does not necessarily indicate causation. It is quite easy to graph a variable that appears to be having direct effect on another variable, but it is always possible that their relationship is invalid (a spurious relationship), and that both are affected by an unknown variable (moderating variable). (9)
EditDaily applications
Most researchers do not have the time on their hands to grapple with the philosophical implications of their research methods. They have deadlines or presentations to prepare and at times must limit the extent of their data analysis. In a perfect world, researchers would begin with a hypothesis or theory, use a reliable research instrument, and take time to thoroughly analyze their results.