Research has played an integral role in addressing many problems in the global. It has been essential in the development of novel technology to help humankind in their daily endeavors. Research can be broadly categorized into two main namely qualitative and quantitative researches (Sofaer, 2002). Notably, these two types of researches have played a crucial role at bettering the lives of human beings in many ways. In business, for instance, quantitative research has been deployed to generate information regarding user preferences, market size, and demographics. Businesspersons often rely on such information when making important decisions. On the other hand, qualitative research has been used extensively in the medical sector to improve the quality of services being rendered to patients (Sofaer, 2002). Typically, the results from qualitative research are important when it comes to designing products and services. As such, qualitative research offers vital information that is essential when understanding clients’ needs and behaviors. However, these two types of researches have their strengths and weaknesses. Besides, using these research approaches in tandem has plenty of merits to offer. Herein, the discussions shall focus on the strengths and weaknesses of qualitative and quantitative types of researches.
Quantitative Research
Definition and Strengths
Qualitative research refers to a type of research that provides data that can be expressed in numerical terms as the name suggest (Sukamolson, 2010). Since the data generated from this type of research can be expressed in numeric forms, statistical tools of analysis can be used to interpret the data. The process of data interpretation is important because it helps the researcher to make justifiable statements regarding the subject under research. Notably, statistical analysis aids in creating an understanding of the facts gathered from the research. This may include demographics, taste, and preferences among others. The common statistical tools that are often used in data analysis include mean, median and standard deviation. Nonetheless, other statistical methods that are usually deployed to help in generating meaningful inferences from the quantitative research include Analysis of Variance (ANOVA), Multiple Regression Correlation (MRC), Fisher tests (F-Test), and the T-test among others (Maxwell, 2012).
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Statistical techniques that are multivariate in nature such as MRC and those that are correlation regression type like ANOVA help in providing a deeper insight into the collected data. For example, they can help in determining how particular factors (such as age) influence specific aspects (like preferences). Usually, statistical studies deploy automated mechanisms of gathering information (Matveev, 2002). This may include the use of surveys. Therefore, the greatest strength of qualitative research is the fact that it provides descriptive data. Such type of data is useful when collecting information to understand various aspects relating to the information about the user of specific products and services.
Weaknesses
Difficulties are likely to be encountered when interpreting quantitative data. For instance, a quantitative study can provide information regarding the approval of a product, but fail to give the information that can be used to interpret the provided information. Failure to provide such data implies that people will always find it hard to understand the reasons why the product was approved. Some people may have a perception that the product is superior over others for particular reasons while others may disapprove it for their own reasons. Nonetheless, lack of the necessary information to support the findings from qualitative research would make it difficult for people to understand the reasons for the approval or disapproval of the products in question. As such, quantitative research can only be conducted with persons who understand how to use and interpret the results from such studies (Creswel, 2009).
In an environment that focuses on product development, lack of supporting information would lead to significant errors that would significantly affect a product's design. For instance, a quantitative survey may indicate that many users fancy 3D screen displays. This may make a product team to opt to redesign their products so that they integrate 3D displays in them. However, the demand for such products will be dependent on the type of 3D display that people prefer. This may be either autostereoscopic that does not require them to have glasses on while watching 3D displays or the other type that requires them to put on glasses prior to visualizing date. Therefore, lack of comprehensive information from qualitative research would lead to misinterpretations that might turn out to be very costly to businesses.
As aforementioned, qualitative research is a very biased process. This is because it needs someone who is well versed in the use and interpretation of qualitative data to conduct the research. Apparently, many tests that are used to analyze the results from qualitative research often over rely on the p-value and the size of the sample. In statistics, the p-value is used to indicate whether the findings from a research were a product of chance. Therefore, the findings from research can significant or not significant depending on the p-value. For instance, p<0.05 indicates that the results from a study were statistically significant (Castellan, 2010). This shows that the results are <5% likely to be a product of chance.
The p-value can easily be manipulated using the size of the sample. However, to do so, a researcher would require a good size of the sample so that he/she can have enough of what is commonly referred to as statistical power. Statistical power helps in determining if the results are accurate or not. This is an implication that if the research was underpowered due to a small size of the sample; the researcher may not be able to attain the statistical significance despite the findings being accurate. On the other hand, there are instances where statistical significance can be achieved with a small study sample. In that case, the researcher will not be required to increase the sample size. Hence, the results will be true irrespective of the sample size. This is an implication that, if it is possible to make meaningful inferences from a small study population, the same ought to be true for large study samples. Scholars argue that increasing the size of the study population would increase the statistical power of the results. Nevertheless, the increase can reach a point where the results of the research are not statistically significant. In such instances, researchers need to check the size effect. This is a statistic tool that informs them how the variables will impact on the variance.
While statistical significance tells the researcher about the accuracy of their data, the effect size informs the researcher about how statistical significance matter. For instance, in a study to examine the impact that adding a feature would have on the value of a product, a researcher could reach statistically meaningful results. However, the magnitude of the product’s value increase might be minimal. On the contrary, significant effect size might lead to an enhanced value of about $20 per unit. Ideally, researchers can be able to gain statistical significance with a small study population and the effect size would be meaningful. As such researchers need to take into consideration both the effect size and statistical significance when they are interpreting their data.
Qualitative Research
Definition and Strengths
Qualitative research is a type of research that gathers data through observation and direct interviews (Jacobs, 2013). It focuses on aspects that cannot be represented numerically. This may include information about the behavior of humans, their emotions, and personal characteristics. Normally, qualitative research cannot provide such details. In most cases, the data or rather the information gathered from qualitative studies provides the information about the characteristics of certain things. Such descriptions cannot be reduced to numbers with ease as it is done in quantitative research. However, through an encoding process, such data can be manipulated such that it becomes represented in numerical terms. Qualitative research has been extensively used to provide an insight into the human behaviors. The qualitative information collected from such studies may revolve around various human aspects such as desires, use cases, and norms. This may also include other information that might be integral when designing products that will meet the ever-changing human desires.
While qualitative studies need the process of data collection to be standardized so that statistical comparisons can be made, qualitative investigations need flexibility in order to allow the researcher to respond to the user data as it is provided in a particular session (Hsieh, 2004). Therefore, qualitative research may involve a natural observation process like structured interviews or ethnography. As such, the researcher needs to monitor and record the opinions, needs, behaviors, patterns, emotions and any other relevant information from the respondent without having the notion that the gathered data will be significant.
After the process of data collection, the researcher observes the trends that the data show. This is different from qualitative research where the results need to be subjected to statistical analysis. When observing the trends, the researcher checks for statements or rather information within the gathered data that are identical. During the analysis process, certain terms are usually used to make meaningful inferences. For instance, if a statement is heard from just one respondent it is deemed as an anecdote. If two respondents provide the same statement, then that particular information is said to be a coincidence. However, if three or more people provide the same information, then the information is said to be a trend. The identified trend is important in guiding businesses when making decisions, creating marketing strategies, and developing products. This is the notable strength of qualitative research.
Weaknesses
The trends that are identified following a qualitative research cannot be subjected to statistical analysis. In addition, they cannot be validated statistically by determining the p-value and effect size. Therefore, the researcher ought to be very cautious when applying them. Importantly, the researcher needs to conduct regular studies in order to verify the noted trend. Qualitative research is a time and resource consuming process. For instance, in order to make meaningful conclusions from the collected data, a researcher might need to engage in an activity referred to as behavioral coding (Castellan, 2010). This involves assigning qualitative behavior with numeric identifiers. Through this process, qualitative data will be transformed to quantitative data which can further be subjected to statistical tools. In addition, qualitative research requires a lag sequential analysis to be performed. This refers to a statistical inquiry that studies the sequence of behavior. Nevertheless, the use of behavioral coding is not only time consuming but also makes the entire process to be very expensive.
Additionally, deploying behavioral coding technique requires a researcher who is highly trained (Creswell, 2009). Besides, the process of data collection in qualitative research cannot be automated as in quantitative research. As a result, the process is time-consuming and costly, especially where large amounts of data are required. This implies that qualitative research can be conducted effectively within a small study population. Therefore, most qualitative research has less statistical power.
Conclusion
In conclusion, when conducting research, researchers need to understand the strengths and weaknesses of both the qualitative and quantitative research. This understanding will help the researcher to pick the appropriate method for his/her research. Use of appropriate research would ensure that the research findings are significant hence they would help to address public concerns.
References
Castellan, C. M. (2010). Quantitative and qualitative research: A view for clarity. International journal of education , 2 (2), 1.
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods . Thousand Oaks, CA: Sage.
Hsieh, C. E. (2004). Strengths and weaknesses of qualitative case study research. University of Leicester Publishing .
Jacobs, S. K. (2013). Research Guides: Locating Qualitative Research: General Strategies.
Matveev, A. V. (2002). The advantages of employing quantitative and qualitative methods in intercultural research: Practical implications from the study of the perceptions of intercultural communication competence by American and Russian managers. Theory of communication and applied communication , 1 (6), 59-67.
Maxwell, J. A. (2012). Qualitative research design: An interactive approach: An interactive approach . Sage.
Sofaer, S. (2002). Qualitative research methods. International Journal for Quality in Health Care , 14 (4), 329-336.
Sukamolson, S. (2010). Fundamentals of quantitative research. Language Institute, Chulalongkorn University .
