# Strengths and limitations

"Discuss the strengths and limitations of quantitative and qualitative data in supporting knowledge claims in the human sciences and at least one other area of knowledge?"

"Nearly 43,000 people died in car accidents in the United States in 2002, and 59% of the fatalities were not wearing seat belts[1] ."

Following the result from the research pertaining to the above matter, The US National Highway Traffic Safety Administration had introduced the law that made it compulsory for motorists to wear seat belt due to the issued claim that seat belt decreases the number of accidents among motorists.

Such claim on the effect of human preference on seat belt has been made responsible by the research conducted with much reference on the data of each type collected; quantitative and qualitative data. Quantitative data is a measurable and verifiable data amenable to statistical manipulation. Qualitative data is associated with the subjective quality of data and does not require measurement or cannot be measured because the reality they represent can only be approximated. Quantitative data is often automatically perceived as objective because of the availability of the figures or percentages which indicates the thorough critical examination done, and this allow people to trust the results.

"There's no such thing as qualitative data. Everything is either 1 or 0"

as said by Fred Kerlinger[2] his belief that "quantitative data is absolutely reliable than qualitative data" is only an objectivist's perspective of truth. Knowledge claim in human science differs of that with other areas of knowledge, thus, the degree of which type of data, quantitative or qualitative, is better for supporting certain knowledge claims vary independently. The question of which is "better" itself can be influenced by the emotion, language and even perception used to verify the claim. The assessment for strengths and limitations is subjected to subjectivity since what could be strength to some, could be insignificant to others. In this essay, I shall attempt to analyse the use of quantitative and qualitative data in supporting knowledge claims in various areas of knowledge and under what circumstances they are reliable in supporting the knowledge claims or facts deduced in these areas of knowledge.

Knowledge claim is open to both quantitative and qualitative aspects to verify it, yet which claims require figures and/or qualities? Statistics, a form of quantitative data, enables correlations to be drawn between things that are observed in the world (accident) and the possible factors, e.g. negligence of seat belts that contribute to the occurrence. Thus, academic fields related to the area of social science, for instance, economics and sociology rely on quantitative data for future verdict and reasoning of certain occurrences. Take this instance, to check for the efficiency of factory workers under different wage payments in different factories, the numbers of output produced would be the suitable parameter. Thus, from the quantitative data obtained, the assumption of workers with higher payment work more efficiently compared to those paid poorly can be supported. Employers can deduce that higher wage can motivate the workers. However, the experiment carried out may suffer from The Hawthorne Effect, the problem of the see-ers being seen by the seen.

Thus, the figures on the output could only have changed due to the realization by the workers over the experiment, so knowledge claims, which result from experiments where the respondents are aware of their purposes, cannot be simply justified by quantitative data. Qualitative data, which can be in the form of attributes and characteristics, is gained through observation combined with interpretative understanding of the underlying thing or phenomenon. To support an experimental claim like the one aforementioned, the workers' working attitudes should be included by the researchers in the measure of efficiency.

We may even attempt to use the statistics, a branch of reasoning, as a way of knowing the implications of the human behaviour, yet in reality, we only make decisions and control our actions in order to see the figures through. Economists observe the patterns of the rise and falls of prices on the stock exchange; the numerical pattern, and theorise by creating certain hypothesis for the occurrence. Next, they deduce on when the hypothesis will be realized, before predicting the exact time for the hypothesis to be effective. The verification of the prediction takes place on certain day, whether the hypothesis about the pattern is accurate or not. Thus, quantitative data actually assist us in predicting an occurrence, albeit not proven true until the day comes, but at least provides us with possibility and hope.

Mark Twain famously quoted the remark,

"there are three kinds of lies; lies, damned lies and statistics". [3]

When the statistics is misused, distorted and misinterpreted, it becomes lies that deviates us from the true elucidation on the claim. The limitation of quantitative data is that it is prone to misinterpretation and be generalized in defining certain claim. The numbers, figures can be interpreted into whatever possibility and understanding over the data, and often the knowledge about the claim is averaged out. The strength of statistics nonetheless, it does not attempt to generalize beyond what the data permits, hence it still offers us a measure of that uncertainty.

Yet, the question of limitation itself is dubious; does the term mean how a data is restricted from providing support for the claim or its weaknesses? We ought to eliminate the latter for weakness is subjective to each one's view.

Qualitative research's aim is complete, detailed prescription whereas quantitative research is obviously objective - seeks for precise measurement - contradicts with the highly subjective qualitative data. It is almost impossible to use numerical approach for investigation in human science, e.g. respect for the elderly. Qualitative data in the other hand, depends on the interpretation of events, by using the participant observation have an overview on the events. A clear cut example, to support the claim that social harmony can only be achieved through cooperation among the society, a thorough observation on the characteristics of the society, or even interviews can help to describe and further justify the claim. Qualitative data is the perfect collection of results in this area of human science since what quantitative data lose on reliability, it gains in terms of validity as it provides richer description.

Nevertheless, the reliability of both types of data may vary greatly in mathematics as they do in human science. Generally speaking, mathematics is the study of patterns and relationship between numbers and shapes. The pursuit for truth in mathematical claim hence, involves in quantitative measurement based on the language interpretation on the word mathematics itself. Yet, under Donald Campbell's belief that,

"all research ultimately has a qualitative grounding,"

the verification of knowledge claim in this objective-sound area of knowledge is achieved through qualitative analysis. A research done in mathematics would definitely prioritize on the quantitative data, or numbers to be exact. This is because qualitative data is usually associated to the attributes of human, and human qualities are not necessarily needed in mathematical research.

For example, in the calculation to find the average of a certain data, the claim is that it comprises of the summation of the occurrences divided by the number of data will give us the average. In mathematics, we always know in advance what we are looking for therefore numerical data usually confirms the targeted hypothesis, through the model constructed (model of average calculation). Quantitative data is in fact, highly specific than qualitative data, which suits the nature of mathematics that is less ambiguous and clearly defined and less open to random interpretation. Maths problem may not be easy to resolve, but compared to any other area of knowledge, there is right answer in mathematics, hence in-depth justification or even debates are often considered nonsensical to prove claims in mathematics. In fact, the truth of any mathematical theories, albeit axiom or Phytagoreas' are not definite, and the right answer in mathematics is actually the right answer in the theories.

Even so, the significance of the knowledge claim in mathematics cannot be verified by quantitative data. What is the meaning of the true meaning of average here? Is it only a numerical calculation without any significance in life? The attributes and characteristics of average are not even known, hence the claim is one-sidedly backed up by only quantitative data. What quantitative data is lacking of in the field of mathematics may be the contextual details missed. The story behind the ground and development of the mathematical models are not discussed. For instance, the famously claimed 1 + 1 = 2, underwent numbers of premises until implicit conclusion is obtained to prove the claim. Yet, the qualitative aspect of what the numbers represent that leads to the claim could not be perceived.

In modern research, most psychologists tend to adopt a combination of qualitative and quantitative approaches, which allow statistically reliable information obtained from numerical measurement to be backed up by and enriched by information about the research participants' explanations. Unfortunately in mathematics, the knowledge claims do not require word explanations from the researchers or founders nor have they to be supported the background information. As quoted by Hermann Weyl,

"You cannot apply mathematics as long as words still becloud reality."

There is a scope where qualitative data is essential in mathematics, the application part. Without the qualitative information obtained from the people, for example in building residences, the data collected can help the mathematicians to seek for the best mathematical method for the perfect dimension of the residences. Thus, in objective area of knowledge, qualitative data always come at the later part in supporting the knowledge claims and quantitative data is the main source for the justification.

Knowledge claims in different areas of knowledge should be supported by different approach of data, qualitative as well as quantitative. The strengths and limitations of each are dependent on the varying knowledge claims, thus as a knower, each individual should be able to evaluate the reliability of data in justifying the claims. If we can carefully analyse the right approach to support the claim, it is possible that it the either-or situation could be vanquished.

- Auto Accident Statistics - Online Lawyer Source
- Foundation of Behavioural Research 1973
- British Broadcasting Corporation: How to understand statistics. http://www.bbc.co.uk/dna/h2g2