Quantitative research and the importance of generalization
The quantitative approach within scientific research highlights the importance of the generalization or universalization of the results of an investigation. In other words. In a nutshell quantitative research generates numerical data or information that can be converted into numbers. In order for the quantitative methodology to be applied, it is necessary that among the elements of the problem to be investigated there is a linear nature relationship. Indicating clarity between the elements of the problem, that it is possible to define it, limit them and know exactly where the problem begins, in which direction it goes and what type of incidence exists between its elements.
Among the most important elements that lead to a linear research problem are: the variables, the relationship between variables and the observation unit. So that there is clarity between the elements of the quantitative methodology, the investigation must begin from the beginning to where it ends. Static and statistical data must be taken, assign numerical meanings and demonstrations with the separate aspects of their whole.
Quantitative research design constitutes the common experimental method of most scientific disciplines. Quantitative research seeks to quantify a phenomenon, is more structured, objective and helps reduce research bias. Quantitative data are numerical, think of measurable amounts such as length, size, quantity, price and duration, data can be used to confirm or discard a hypothesis or predict relationships which can be analyzed using statistical methods that canbe represented in tables, graphics, percentages or other statistical representations
Different quantitative limitations can be given at levels of various distortion risks. Among which can mention.
- Take a part of the system as an independent variable and all of the quantitative data what can be seen in traditional investigations.
- The quantitative conjugation of groups to study social systems.
The objective of any science is to acquire knowledge and the choice of the appropriate method that allows us to know the reality is therefore fundamental. The problem arises by accepting certain erroneous knowledge.
The deductive method is frequently associated with quantitative research. Since several researchers are useful to use this method to collect and analyze quantitative data on variables.
Among the most frequent quantitative research methods can be mentioned:
Poll. An efficient way to collect points of view of many people. Surveys can be carried out online, by phone, and even in person through structured interview questionnaires. They can have very specific questions or cover a wide content.
Observation. This is a structured way to measure certain actions or behaviors.
Experiment. This is the way to identify the cause and effect.
Quantitative research tries to determine the force of association or correlation between variables, the generalization and objectification of the results through a sample to make inferences to a population of which every sample proceeds. After the study of the association or correlation, in turn intends, to make a causal inference that explains why the causes occur in a certain way.
Quantitative research design is an excellent way to finish the results and test or refute a hypothesis. After the statistical analysis of the results, a comprehensive response is reached and the results can be discussed and legitimately published. Quantitative experiments also filter external factors, if properly designed, and thus the results obtained can be seen as real and impartial.
Quantitative experiments are useful for checking the results obtained by a series of qualitative experiments, leads to a final response and reduces the possible directions that the investigation can take.
Among the disadvantages of quantitative studies, it can be mentioned that there is an extensive statistical analysis, which can be complicated because most researchers are not statistical. The field of statistical study is a scientific discipline itself and can be difficult for those who are not mathematical, as well as the requirements for successful statistical confirmation of the results are very strict.
Quantitative research design also tends to generate results only proven or not tested, with little space for gray areas and uncertainties for social sciences is much more complex than a simple affirmative or negative response.
Bibliography
- Latorre, a. (2005). The research-action . Spain: Grao Editorial.
- Sampieri, r. h. (2014). Investigation methodology . Mexico: Inter -American Editors.
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