Assessing and Recommending Quantitative Research Design


A research design can be defined as a basic plan that directs the collection of data and analysis stages in the development of a project. It can be referred to as a framework that highlights the specific data to be gathered and the resources that will be required during the procedures of data collection. It is a strategy on how to carry out a study and a laid down plan on how the strategy will be executed. It points out the procedures and methods that will be used in collecting, measuring, and analyzing data (Pallant, n.d).

There are different types of research designs. Quantitative research designs make use of traditional mathematical and statistical procedures to make conclusive measurements of data. They use a standard format with minimal nuances used to generate hypotheses that are either proved or disapproved. The hypothesis must have a mathematical way of proving it and form the basis of the entire experiment. Quantitative research designs can be descriptive where measurements on subjects are done only once or experimental where measurements are done prior and subsequently after a certain action is done (Someck & Lewin, 2005).

Experiments as Quantitative Research Design


Many scholars have argued that information gained through experimentation as one of the quantitative research designs is devoid of human inconsistency. However, there is a weakness in this research design in that human beings are prone to making errors. The individual bias of the researcher may affect the results of the experiment. For example, researchers may possess specific preconceptions that may influence the course the research will take. This problem may be heightened when researchers have knowledge of their preconceptions which will force them to table results that will be accepted in their fields of study.

The other weakness of experiments as a form of quantitative research design is that a sample may not represent the entire population accurately. This often occurs because the researchers do not have the chance to ascertain the presence of a representative sample. For example, the subjects might be residents of one location instead of coming from different locations, small in number than expected or the study may have been conducted when there is no adequate time (Babbie, 2010).

Strengths of experiments

Although some inconsistencies arise from the use of experiments as quantitative research designs, they are advantageous in that the researcher has the capacity to control the experiment. This makes it possible for the researchers to determine accurately individual aspects of variables. It is also possible to establish interaction between the variables. The other strength of experiments is that manipulation of variables is possible so that the experiments meet the requirements of the researcher. The results, therefore, do not have a great effect on material reality (Creswell, 2009).

Cross-sectional Research Design


Cross-sectional research observes varying groups of people with different variable interests but with similarities in other characteristics such as social status or ethnicity. One of the weaknesses of this type of quantitative research design is that it may be difficult to get participants who are similar in most aspects. The possibility is that similarities will only be found in one variable. There is also another weakness that differences arising from specific experiences of particular people may cause effects. For instance, individuals who were born during the same time period may have similar experiences with regard to historical events. On the other hand, people from the same geographical location may be sharing experiences that are restricted to that particular location. There is also the possibility of cross-sectional research design being full of incidence bias (Ruane, 2004).


Despite the weaknesses associated with cross-sectional research design, it still has a number of strengths. The first strength of this research design is that it is inexpensive and consumes little time to accomplish. The other strength is that with this approach, it is possible to make estimates on prevalent interest outcomes since the samples are derived from the entire population. This makes it possible for many other factors that pose risks to be assessed.

Quasi-experimental Research Design


Quasi-experimental quantitative designs are applied where it is impossible to select groups in advance or randomize. This design can be used when compiling results to be used in general terms. For example, a study that is trying to establish the effect of alcohol on pregnant women will strictly employ an experimental design and randomly give pregnant women alcohol.

However, this is illegal because of the harm associated with alcohol during this period. Researchers will only enquire about the amount of alcohol use during pregnancy then attach women to groups. Quasi-experimental design can be incorporated with case studies conducted individually. The results obtained from case studies are used to back the findings of case studies hence making it possible for statistical analysis (Experiment-resources, 2011).


One of the weaknesses of quasi-experimental design is that for statistical tests to be meaningful, it is imperative to randomize properly. For example, the experimental designs never take into account any factors existing before conducting them. They also do not know the outcomes of the experiment may be affected by factors outside it. This makes it necessary for researchers to control other factors that are outside the experiment which is often difficult (Social research methods, 2006).

Research Design to be used in Research

The most suitable quantitative research design that I will use to conduct the research is the cross-sectional design. The topic is about studying the problem of poverty in Africa so it will be important to sample a cross-section of the African people affected by poverty. It is a suitable research design since most of the people affected by poverty have a similarity, that they share common socio-economic statuses and problems experienced in Africa. This research design will be an excellent one since it is not expensive and consumes little time. In conducting research, considerations of cost and time factors are critical hence a research design that will reduce the cost and save time is preferable.

The reason why I did not choose an experiment as a quantitative design for this research is that experiments may be full of research bias. This is research that intends to handle the problem of poverty in Africa and bias on the researcher may affect the results of the research. Another reason that makes experiments unsuitable is that experiments use samples and samples may not represent the whole population. Quasi-experimental research is also not suitable for this research because it is mostly used in situations where pre-selection or randomization is not possible.


Quantitative research designs are important aspects of researching different areas. They are categorized into experiments, cross-sectional and quasi-experimental designs. The three different types of quantitative research designs have their own weaknesses and strengths. This makes the research designs to be applicable in different situations. One research design may be suitable for certain research but not another. It is therefore upon researchers to identify the most suitable research design for their research topics.


  1. Babbie, E. (2010). The Practice of Social Research. Belmont: Cengage Learning.
  2. Creswell, J. (2009). Research Design:Qualitative, Quantitative, and mixed methods approaches. Thousand Oaks, CA: Sage Publications.
  3. Experiment-resources. (2011). Quasi Experimental Resources.
  4. Pallant, J. (n.d). Quantitative Research Designs. Web.
  5. Ruane, J. (2004). Essentials of research methods: a guide to social research. London: Wiley-Blackwell.
  6. Socialresearchmethods. (2006). Social research methods knowledge base.
  7. Someck, B., & Lewin, C. (2005). Research methods in the social sciences. California: Sage.

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