At the outset, it must be noted that when we talk about the “strengths” of quantitative research, we do not necessarily mean that it is better than qualitative research; nor we say that it is inferior to qualitative research if we talk about its weaknesses. Hence, these strengths and weaknesses depend only on a specific purpose they serve, such as in terms of the problems or gaps that it aims to address or in terms of the time needed to complete the research. This means, therefore, that quantitative research is better than qualitative research only in some respects, and vice versa.
So, what are some of the major strengths of quantitative research?
First, in terms of objectivity and accuracy. If the issue is about objectivity and accuracy, then quantitative research is strong and more preferrable because, as we may already know, quantitative research explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics. In this way, biases are reduced to the minimum and analysis and interpretations are more objective and accurate. In fact, another important point to remember in quantitative research is that it is informed by objectivist epistemology. This means that quantitative research seeks to develop explanatory universal laws, for example, in social behaviors, by statistically measuring what it assumes to be a static reality. In relative vein, a quantitative approach endorses the view that psychological and social phenomena have an objective reality that is independent of the subject, that is, the knower or the researcher and the known or subjects are viewed as relatively separate and independent. Hence, in quantitative research, reality should be studied objectively by the researchers who should put a distance between themselves and what is being studied. In other words, in quantitative research, the researcher lets the “object” speaks for itself by objectively describing rather than giving opinions about it. This explains why quantitative researchers are supposed to play a neutral role in the research process. Hence, the meaning participants ascribe to the phenomenon studied is largely ignored in quantitative studies.
Second, in terms of sample size. It must be noted that a broader study can be made with quantitative approach, which involves more subjects and enabling more generalizations of results. In fact, scholars and researchers argue that one major advantage of quantitative research is that it allows researchers to measure the responses of a large number of participants to a limited set of questions. Also, quantitative methods and procedures allow the researchers to obtain a broad and generalizable set of findings from huge sample size and present them succinctly and parsimoniously.
Third, in terms of efficiency in data gathering. In terms of data gathering, quantitative research allows researchers to use a pre-constructed standardized instrument or pre-determined response categories into which the participants’ varying perspectives and experiences are expected to fit. Hence, data gathering in quantitative research is faster and easier. In fact, data gathering in quantitative research can be automated via digital or mobile surveys which, for example, allows thousands of interviews to take place at the same time across multiple countries. As we can see, data gathering in quantitative research is efficient and requires less effort.
And fourth, in terms of cost efficiency. Since data gathering in quantitative research is efficient and requires less effort, then obviously, the cost of someone conducting quantitative research is typically far less than in qualitative research.
So much for the major strengths of quantitative research. Let me now discuss very briefly its major weaknesses.
First is that results in quantitative research are less detailed. Since results are based on numerical responses, then there is a big possibility that most results will not offer much insight into thoughts and behaviors of the respondents or participants. In this way too, results may lack proper context.
Second, because quantitative research puts too much emphasis on objectivity and accuracy, it does not consider meaning behind phenomena. Needles to say, in every phenomenon, there are always important points that cannot be fully captured by statistics or mathematical measurements. Indeed, not all phenomena can be explained by numbers alone.
Third is on the issue of artificiality. Quantitative research can be carried out in an unnatural environment so that controls can be applied. This means that results in quantitative research may differ from “real world” findings.
Fourth is that in quantitative research, there is a possibility of an improper representation of the target population. Improper representation of the target population might hinder the researcher from achieving its desired aims and objectives. Despite the application of an appropriate sampling plan, still representation of the subjects is dependent on the probability distribution of observed data. As we can see, this may lead to miscalculation of probability distribution and falsity in proposition.
Fifth, quantitative research is limiting. Quantitative research employs pre-set answers which might ask how people really behave or think, urging them to select an answer that may not reflect their true feelings. Also, quantitative research method involves structured questionnaire with close-ended questions which leads to limited outcomes outlined in the research proposal. In this way, the results, expressed in a generalized form, cannot always represent the actual occurrence or phenomenon.
And sixth is the difficulty in data analysis. Quantitative studies require extensive statistical analysis, which can be difficult to perform for researchers from non-statistical backgrounds. Statistical analysis is based on scientific discipline and, hence, difficult for non-mathematicians to perform. Also, quantitative research is a lot more complex for social sciences, education, sociology, and psychology. Effective response should depend on the research problem rather than just a simple yes or no response. For example, to understand the level of motivation perceived by Grade 12 students from the teaching approach taken by their class teachers, mere “yes” and “no” might lead to ambiguity in data collection and, hence, improper results. Instead, a detailed interview or focus group technique might develop in-depth views and perspectives of both the teachers and children.