In this article, you will learn about the pros and cons of quantitative research. Show Pros of Quantitative Research1. Upholds anonymity When carrying out research, you need to win the trust of the people you interview and engage. The quantitative research is anonymous in nature. This means that it preserves the identity of the researcher all the while of engagement. In this way, it gives the subjects the confidence they need to reveal the information that may be sought. Also, it ensures that the answers given forth are as authentic as can be. The deductions made from such kinds of data tend to reflect the reality on the ground. 2. Trustworthy and repeatable Even if numerous examinations are carried out over the same subject or sample, the validity of the data remains pretty much the same. This stems from the accurate and truthful nature of the responses that the research brings forth. The mix of these twin issues means that the subsequent courses of action taken in pursuance of the research findings also remain steadfast all the way. Hardly is there any room for ambiguity in making any further decisions in light of the responses evoked by the research strategy. 3. In-depth scrutiny In collecting the data from the field, the same is subjected to numerous tests that vouch for its reliability. The process of scrutinizing the data brings along many salient benefits and advantages. Chief among these is the elimination of errors. Further to the above is that it wards off subjectivity while maintaining the integrity of the results. The same ends, unfortunately, may not be achieved via the qualitative approach. With this scrutiny comes the ability to flag and eliminate any potential errors that may compromise the ends. 4. Random models Apart from merely being anonymous, this research mode also employs random models to capture the raw data. This kind of approach is neutral in the sense that it eliminates all kinds of bias or favoritism. The benefits of this kind of approach cannot in any way be gainsaid. It upholds the reliability of the data captured and staves off the possibility of favoritism arising in the course of making do with the available data. This, of course, has the ripple effect of preserving the integrity of the findings. 5. Collects data faster Its pace of collecting data is faster when matched against the prevailing alternatives. This is because it captures data in real-time instead of a situation in which a delay exists in between. It is hence great for employment in circumstances when time is scarce. Adding to this is that it suffers less from contaminations and other issues that may compromise the integrity of the findings. The fast speed definitely prevents an unscrupulous person from chipping in and interfering with the data so obtained. 6. Impacts a larger sample size This form of research is generally able to impact a larger sample size of the data. This manner allows the researcher to tap into broader subjects and deduce more accurate results than a circumstance in which smaller sample size is employed. Dealing with a larger sample size also ensures that the outcomes are not skewed adversely. That is because the remaining accurate data may easily neutralize such a variation. Yet again, it goes a long way in diminishing any form of corruption. 7. Allows for research to be carried out from afar off Most research methods demand that you get to the venue of the samples to carry out the research. This is not only confusing but also time-consuming, not to mention being resource-intensive. Thankfully, this quantitative approach does not require such direct contacts. Instead, it enables the researchers to draw the necessary data from afar off without necessarily being near the field. In this way, it cuts down the time you take while also making the entire process somewhat cheaper.  8. Can focus on specific pieces of information or facts Using this technique, it is also possible to emphasize specific pieces of information or facts. This grants the researchers the free hand to narrow to specific facts they wish to gain deeper insights into from a general population. Thus, it comes in handy when the population is huge and the time and resources, on the other hand, are both scanty. The same also goes for the situations in which you might have to obtain some clarifications or dispel certain ambiguities from the sample. 9. Allows for the duplication of outcomes In some instances, it may be that you have to make copies of the outcomes. This is largely impossible with the prevailing alternatives as they lack the symmetry necessary to make that happen. The quantitative approach eliminates this issue chiefly by dealing with actual data. Also, its work is validated because the outcomes often point to much the same data regardless of the random conditions. There could be some minute variations granted. However, the general conclusions deduced from the samples remain largely the same. 10. Suitable for complicated applications Of all the modes of data collection, research and analysis, the quantitative approach is the most suited for complicated applications. It finds wider usage across complex fields like data analysis, complex statistics, stock selection, computer modeling, and portfolio evaluation, to name but a few! Considering that these fields are too technical for the laymen, those who are professional or well-versed in them tend to command some level of prestige. Those professionals accrue some elevated respect and admiration in the societies in which they reside. 11. Multiple uses The quantitative form of research can serve multiple applications and areas of use. Below are some of the leading benefits or areas of applications it may be devoted to:
Cons of Quantitative Research1. Conclusion may be irrelevant to the overall question We have already noted above that this mode of research relies heavily on randomized data. Thus, there are chances that its conclusions may not always be relevant to the overall questions that are asked in the first place. Closely related to this is that the conclusions arrived at only apply to those who participated in the study or the survey. It does not in any way hold to the entire population. Of course, this is somewhat inconvenient on the part of those who may be in a hurry. 2. Zero follow-up This form of research stands in sharp contrast with the prevailing alternatives in that it does not have any room for follow-up. Immediate, the survey is carried out by the participants; there is absolutely no room for the researcher to return and seek further clarifications. Though it saves time, this approach makes it virtually impossible for any errors to be highlighted before the same take effect. Hence, it is not recommended for those places and circumstances that may demand that the data be verified first before proceeding to make meaning of the same. 3. Higher costs On the whole, this form of conducting research tends to be costlier than the alternatives that be. This is largely due to the need to use lots of cash to hire skilled data collectors and researchers. Also, the entire process is quite involving as it entails the employment of great expertise. What does this mean? The approach is largely unsuitable for those areas and persons who have less cash at their disposal. Only those institutions that have strong financial muscle power may leverage it to their advantage. 4. Prone to the biases of the researchers As stated above, this form of research makes great use of randomized samples. The sample size, character, and nature are all left to the discretion of the researcher. Thus, there is room for the personal bias of the researcher to come into play here. He may only pick samples that are likely to bring forth the outcomes he is desirous of. This form of bias affects quantitative research more than qualitative one when all factors are put into consideration. Needless to say, the outcomes or conclusions may never be reliable hence. 5. Difficulties in setting up a research model We have already stated severally that this approach requires skilled expertise and labor. That is due to its somewhat complicated nature. As such, there is the overall difficulty in setting up the research model that is to be used to guide the researcher in drawing the necessary conclusions. Making matters worse is the fact that the errors incurred in setting the model ultimately get reflected in the kinds of outcomes or deductions drawn from it. The mistakes might never be really easy to handle or reverse if they arise. 6. Inability to determine the accuracy of the answers This approach assumes that the answers obtained are all true. This might never really be the case due to human nature’s fallibility and the bias that arises when conducting research. What compounds the situation is the fact that there is no vetting mechanism for the answers given. For one, no direct contact exists between the researcher and the interviewees or population targeted. Then again, there is no follow-up or at least a mechanism for that. The overall reliability of the outcomes may never be guaranteed hence. 7. Too much emphasis on the numbers Going by its name, this form of research relies heavily on numbers and figures. While this is not really bad, it is inconclusive. Not everything you need to know about a given subject may be expressed in numbers. They may also involve explanations and descriptions. This approach, however, does not factor these equally vital elements in the pursuit of facts over a given subject. Thus, it misses the big picture and may end up in some erroneous conclusions. A lot more may be required hence for conclusive outcomes to be realized. 8. Potentially out of touch with reality For the quantitative approach to be used, the data has to be deployed in a laboratory setting. This means the prevailing environment has to be tightly controlled. This is only good for mathematical and scientific applications. It may not work well for social experiments. On the same note, it may not be easy for you to quantify some social phenomena like clients’ reactions to specific products and consumer behaviors over a given period of time. These no doubt require some alternative forms of interventions. 9. Limited applications Given that it largely uses the figures and takes place in a controlled setting, this approach, unfortunately, might not be useful in all circumstances everywhere. It is only limited to the complicated mathematical and scientific settings that demand the use of many numbers. Add this to the fact that data may never be sufficient to explain the complex issues, the difficulty of comprehending the contexts of phenomena, and unavailable secondary data that may be relevant to the issues at hand, and you find yourself in a state of confusion. References: https://www.thebalancesmb.com/quantitative-research-advantages-and-disadvantages-2296728 https://betterthesis.dk/research-methods/lesson-1different-approaches-to-research/strengths-and-limitations https://www.theclassroom.com/advantages-disadvantages-of-qualitative-quantitative-research-12082716.html We want to spread the word about energy and help make our world a better place for all people. We will never spam your email. Please Support Us if you like our content! I am a conservationist at heart and on a journey to provide the best information on energy in a format that is easy to understand. Those topics include but are not limited to: Renewable energy, Energy Statistics, Company reviews and facts/information about different energy sources. What is the key limitation of quantitative data?Limitations of Quantitative Research
Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
What are some of the limitations to quantitative management?Limitations of Quantitative Management:. Its application is restricted in areas that deal with human behaviour. ... . Amongst different functions of management, its use is limited in organising, staffing and directing. ... . Even where this theory is applicable, it does not eliminate risk but only attempts to reduce it.. |
