Quantitative research design is an excellent way of finalizing results and proving or disproving a hypothesis. The structure has not changed for centuries, so is standard across many scientific fields and disciplines. After statistical analysis of the results, a comprehensive answer is reached, and the results can be legitimately discussed and published. Quantitative experiments also filter out external factors, if properly designed, and so the results gained can be seen as real and unbiased.
Quantitative experiments are useful for testing the results gained by a series of qualitative experiments, leading to a final answer, and a narrowing down of possible directions for follow up research to take.
Quantitative experiments can be difficult and expensive and require a lot of time to perform. They must be carefully planned to ensure that there is complete randomization and correct designation of control groups. Quantitative studies usually require extensive statistical analysis, which can be difficult, due to most scientists not being statisticians. The field of statistical study is a whole scientific discipline and can be difficult for non-mathematicians.
In addition, the requirements for the successful statistical confirmation of results are very stringent, with very few experiments comprehensively proving a hypothesis ; there is usually some ambiguity, which requires retesting and refinement to the design.
This means another investment of time and resources must be committed to fine-tune the results. Quantitative research design also tends to generate only proved or unproven results, with there being very little room for grey areas and uncertainty.
For the social sciences, education, anthropology and psychology, human nature is a lot more complex than just a simple yes or no response. Check out our quiz-page with tests about:. Martyn Shuttleworth Mar 7, Retrieved Sep 12, from Explorable.
When deciding on the appropriate approach, the Decision Tree from Ebling Library may be helpful. The following video, Quantitative Research Designs, further describes the differences between quantitative research approaches and offers tips on how to decide on methodology. Planning the Methodology — The Quantitative Pathway — The following link provides a description of the four types of quantitative approaches and examples of each. Quantitative Design — The following resource describes quantitative research approaches and exmaples.
Choosing an appropriate study design — Below is a link to a presentation that describes the factors to consider when choosing the appropriate approach for quantitative research. This pin will expire , on Change. This pin never expires. Select an expiration date. About Us Contact Us. Search Community Search Community. Quantitative Approaches In this module, the four approaches to quantitative research are described and examples are provided.
List and explain the four approaches to quantitative research. Provide an example of each method. Positivist scholars like Comte believed only scientific methods rather than previous spiritual explanations for human behavior could advance. Quantitative methods are an integral component of the five angles of analysis fostered by the data percolation methodology,  which also includes qualitative methods, reviews of the literature including scholarly , interviews with experts and computer simulation, and which forms an extension of data triangulation.
Quantitative methods have limitations. These studies do not provide reasoning behind participants' responses, they often do not reach underrepresented populations, and they may span long periods in order to collect the data. Statistics is the most widely used branch of mathematics in quantitative research outside of the physical sciences, and also finds applications within the physical sciences, such as in statistical mechanics.
Statistical methods are used extensively within fields such as economics, social sciences and biology. Quantitative research using statistical methods starts with the collection of data, based on the hypothesis or theory. Usually a big sample of data is collected — this would require verification, validation and recording before the analysis can take place. Causal relationships are studied by manipulating factors thought to influence the phenomena of interest while controlling other variables relevant to the experimental outcomes.
In the field of health, for example, researchers might measure and study the relationship between dietary intake and measurable physiological effects such as weight loss, controlling for other key variables such as exercise. Quantitatively based opinion surveys are widely used in the media, with statistics such as the proportion of respondents in favor of a position commonly reported.
In opinion surveys, respondents are asked a set of structured questions and their responses are tabulated. In the field of climate science, researchers compile and compare statistics such as temperature or atmospheric concentrations of carbon dioxide. Empirical relationships and associations are also frequently studied by using some form of general linear model , non-linear model, or by using factor analysis. A fundamental principle in quantitative research is that correlation does not imply causation , although some such as Clive Granger suggest that a series of correlations can imply a degree of causality.
This principle follows from the fact that it is always possible a spurious relationship exists for variables between which covariance is found in some degree. Associations may be examined between any combination of continuous and categorical variables using methods of statistics.
Views regarding the role of measurement in quantitative research are somewhat divergent. Measurement is often regarded as being only a means by which observations are expressed numerically in order to investigate causal relations or associations.
However, it has been argued that measurement often plays a more important role in quantitative research. This is because accepting a theory based on results of quantitative data could prove to be a natural phenomenon. He argued that such abnormalities are interesting when done during the process of obtaining data, as seen below:. In classical physics, the theory and definitions which underpin measurement are generally deterministic in nature. In contrast, probabilistic measurement models known as the Rasch model and Item response theory models are generally employed in the social sciences.
Psychometrics is the field of study concerned with the theory and technique for measuring social and psychological attributes and phenomena.
Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies .
Quantitative research design is an excellent way of finalizing results and proving or disproving a hypothesis. The structure has not changed for centuries, so is .
The following video, Quantitative Research Designs, further describes the differences between quantitative research approaches and offers tips on how to decide on methodology. Suggested Readings: Bernard, H. R., & Bernard, H. R. (). Quantitative Research Methods [Description]   [Common Uses] Description. Use quantitative research methods in market research when: You want to know “how many” and/or “how often”.
What’s the difference between qualitative and quantitative research? Susan E. DeFranzo September 16, Many times those that undertake a research project often find they are not aware of the differences between Qualitative Research and Quantitative Research methods. Therefore, as quantitative research is essentially about collecting numerical data to explain a particular phenomenon, particular questions seem imme- diately suited to being answered using quantitative methods.