Quantitative Research Design Is The Sta

“Quantitative research design is the standard experimental method of most scientific disciplines.
These experiments are sometimes referred to as true science, and use traditional mathematical and statistical means to measure results conclusively.
They are most commonly used by physical scientists, although social sciences, education and economics have been known to use this type of research. It is the opposite of qualitative research.
Quantitative experiments all use a standard format, with a few minor inter-disciplinary differences, of generating a hypothesis to be proved or disproved. This hypothesis must be provable by mathematical and statistical means, and is the basis around which the whole experiment is designed.
What is research hypothesis?
A research hypothesis is the statement created by researchers when they speculate upon the outcome of a research or experiment.
Every true experimental design must have this statement at the core of its structure, as the ultimate aim of any experiment.
The hypothesis is generated via a number of means, but is usually the result of a process of inductive reasoning where observations lead to the formation of a theory. Scientists then use a large battery of deductive methods to arrive at a hypothesis that is testable, falsifiable and realistic.
The research hypothesis is a paring down of the problem into something testable and falsifiable. In the aforementioned example, a researcher might speculate that the decline in the fish stocks is due to prolonged over fishing. Scientists must generate a realistic and testable hypothesis around which they can build the experiment.
A hypothesis must be testable, taking into account current knowledge and techniques, and be realistic. If the researcher does not have a multi-million dollar budget then there is no point in generating complicated hypotheses. A hypothesis must be verifiable by statistical and analytical means, to allow a verification or falsification.
In fact, a hypothesis is never proved, and it is better practice to use the terms ‘supported’ or ‘verified’. This means that the research showed that the evidence supported the hypothesis and further research is built upon that.

Randomization of any study groups is essential, and a control group should be included, wherever possible. A sound quantitative design should only manipulate one variable at a time, or statistical analysis becomes cumbersome and open to question.
Ideally, the research should be constructed in a manner that allows others to repeat the experiment and obtain similar results.”
(https://explorable.com/quantitative-research-design)
“Importance of Quantitative Research
More reliable and objective
Can use statistics to generalize a finding
Often reduces and restructures a complex problem to a limited number of variables
Looks at relationships between variables and can establish cause and effect in highly controlled circumstances
Tests theories or hypotheses
Assumes sample is representative of the population
Subjectivity of researcher in methodology is recognized less
Less detailed than qualitative data and may miss a desired response from the participant.
Quantitative Analysis
Laboratory experiments deliberate manipulation of independent variable, strict control of other variables test cause and effect relationship
Field experiments natural environment but independent variable still manipulated difficulty in controlling the situation so more likelihood of extraneous variables ethical problems of consent, deception, invasion of privacy
Quasi-or natural experiments examine effects of independent variable without control over independent variable itself which often occurs naturally unable to manipulate independent variable because of ethics or because it is impossible

Quantitative Observation
Observation can also be carried out in a quantitative context and may involve:
Counting the use of services
Number of people accessing services
Ascertain busy/quiet times “
(http://libweb.surrey.ac.uk/library/skills/Introduction%20to%20Research%20and%20Managing%20Information%20Leicester/page_45.htm)
“Advantages
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.
Disadvantages
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.”
(https://explorable.com/quantitative-research-design)