Three Hypothetical Designs Design: Reasons For Use
Today, there are three research design paradigms used in different cases depending on several factors. Some of the major factors that determine the use of certain research are expected data format and the experience of the researcher. The three design methods have different characteristics and a researcher must review the characteristics of each before choosing what research methodology design to adopt (Donnelly and Trochim, 2006).
Quantitative research primarily relies on a collection of quantitative data obtained from precise measurements or structured data collection instruments such as rating scales or behavioral responses. Qualitative research, on the other hand, relies on a collection of data that is qualitative such as data obtained from interviews, open-ended questions, field notes, or participant observations. Lastly, mixed-method design collects and analyses both the qualitative and the quantitative data. This paper will be concerned with the design of three hypothetical designs for the three design paradigms. A discussion will be provided on the reasons supporting the use and the appropriateness for each design method.
There are three major types of quantitative design types. The three designs are non-experimental (Descriptive), quasi-experimental, and experimental. Usually, quasi-experimental and experimental designs are used to examine the effect and cause of a phenomenon. They study the effects of independent variables on the dependent variables. On the other hand, nonexperimental designs examine variables in natural occurrences without any artificial treatments (Lincoln and Egon, 2005).
The difference between experimental and quasi-experimental is that in quasi-experimental design, the researcher develops alternative means of examining causality instead of exposing the dependent variables to randomization and manipulation (Trochim and Douglas, 2006). In the example, the hypothetical design below, a bank branch is randomly chosen and different arrangements for the customer service desk are tested to evaluate the best arrangement for eliminating long customer queues.
This is an example of an experimental quantitative design where the outcome is controlled by making changes to the dependent variables. In a bank, for instance, the dependent variables would be the number of customer care representatives, the number of services offered by the customer care representatives, or the queuing system in use. The effects of these variables are tested against the results to obtain a solution to the queuing problem.
Qualitative designs usually use words and phrases instead of numbers like in the quantitative designs. Many designs incorporate data analysis and data collection stages to ensure effective research into the topic. An example of a qualitative design is an interview. To be able to collect as much information as possible, the researcher has to be actively involved and must analyze the response at the same time he/she is collecting the data. In contrast to quantitative design, the researcher can easily influence the results and hence the need for extra care when dealing with this type of design.
There are five approaches in qualitative research: Ethnography, grounded theory, historical, case study, and phenomenology. The different approaches have distinct characteristics and are chosen depending on the nature of the research problem (Patton, 2000). Using a similar example, a qualitative design in a bank queuing problem would be the use of an interview with randomly selected customers. The interview can be used to gauge the level of customer dissatisfaction as well as get suggestions on how to improve the services. For instance, the following interview questions may be used in a well-structured interview.
- Are there any problems you encounter as you receive services from our customer care desks?
- What suggestions would you make that can help us improve our services?
The use of this and other similar questions represent a qualitative design since the response is given in words or phrases. Additionally, the design will involve data analysis being done simultaneously with data collection since the type of response obtained varies from one customer to the next.
Mixed design methods hold the qualities of both qualitative and quantitative designs at least to some extent. This is one of the ways of coming up with a more creative alternative to the two traditional methods to enhance data collection and evaluation. The method produces a hybrid source where some underlying issues which would not have been possible to reveal using either qualitative or quantitative designs are revealed (Caracelli and Greene, 2007).
The method is more inclusive, critical, and accountable when compiling detailed research. A good hypothetical design example similar to the ones given above would be the use of questions that require a response in form of numbers and words. For instance, the following questions may represent a mixed design method:
- What is the average time that you spend as you seek information from our customer care desks?
- Do you think this can be improved?
- What are some of the ways you think we can use to improve our customer care services?
Since the response to these and other similar questions involve numbers and words, this model gives an example of a mixed-method design.
The three designs can be said to be systematic since they use a certain process of extracting information from research phenomena. Since qualitative designs use subjective elements, it is appropriate to use quantitative designs when the data can be treated objectively. Quantitative designs are deductive and are often used to test theories. In contrast, qualitative designs are inductive and are usually used to generate theories. In the above bank case, quantitative design can be used to estimate the average waiting time in a bank customer care department, while qualitative design can be used to generate a solution to the problem by receiving customer suggestions on how the service can be improved.
Qualitative designs are appropriate when dealing with subjective data in which case the researcher cannot remain detached from the research like in quantitative designs. For example, in the example about the waiting time in a customer care department in a bank, it is possible to quantify data in terms of the average waiting time using data collection instruments without directly being involved. This is possible to accomplish by using microfilming methods to obtain this quantitative data. However, any attempts to know exactly how the customers feel about the waiting time is quantitative and the researcher has to be actively involved in data collection.
Caracelli, V. & Greene, J. (2007). Crafting mixed-method evaluation design. New Directions for Program Evaluation, 74(1): 19-32.
Donnelly, J. & Trochim, W. (2006). The Research Methods Knowledge Base. NY: Atomic Dog.
Lincoln, Y. & Egon, G. (2005). Naturalistic inquiry. Beverly Hills, CA: Sage.
Patton, M. (2000). Qualitative evaluation and research methods. Newbury Park, CA: Sage.
Trochim, W. & Douglas, A. (2006). Designing designs for research. The Researcher, 1(1):1-6.