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Research Designs: Quantitative Reasoning and Analysis


The choice of the research design is extremely important. Before getting down to conducting a research, it is important to review the information about possible research designs and state which one is the most appropriate for a particular case. All the problems which exist in the society are considered via different kinds of the research designs. To make a decision, it is important to collect data. The process of data collection is called a research. The main idea of this paper is to review possible research designs, identify their strengths and weaknesses, recommend a research design for our planed research, highlight the validity threats, and make argumentative discussion of the reasons why the rest of the research designs are not used for our research plan.

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Strengths and limitations of each of the research designs

There are a number of different research designs used in the modern social science. The most spread types of quantitative designs used in social sciences are as follows, cross-sectional designs, quasi-experimental designs, and combined (mixed) designs. Each of these designs has its particular advantages and disadvantages which determine their appropriateness this or that research. It is important to be aware of the main idea of the problem and the peculiarities of different types of research designs to make a correct choice (Frankfort-Nachmias & Nachmias, 2000).

The main idea of a cross-sectional design is that people belonging to different groups (e.g. age, gender, etc.) are tested at one and the same period of time. This definition states the main strength of this research design. Using it, researchers are able to consider the differences and the similarities among different groups of people under similar circumstances (Somekh & Lewin, 2005). Yet, this research design has one specific weakness. People are tested within one specific period of time. There is no opportunity to check the effect of the experiment in the continuity of development. For example, when testing human preferences at different ages (e.g. 18 and 80), cross-sectional research design does not allow a researcher to check whether the preferences in 18 change or remain the same in 80 (Cavanaugh, & Blanchard-Fields, 2006).

Quasi-experimental designs are specific types of research designs used when it is impossible to “adhere to the tenets of experimental design” (Ryan, 2007, p. 23). Such designs are used for studies when time plays prominent role and it is impossible to randomize it due to longitudinal characteristic feature. Such research designs are usually considered in social and behavioral sciences (Ryan, 2007). The main advantage of this research design is that there are cases when this type of the research is the most appropriate. Additionally, researchers can “both maximize differences in independent variable(s) and minimize error variance to measurement issues” (Heppner, Wampold, & Kivlighan, 2008, p. 180).

The experimental design concerns with the analysis of information drawn from an experiment. In order to ensure the validity of the research, it is necessary to properly gather reliable data to answer the research questions objectively. Importantly, the question that an experiment should answer must be strictly defined before conducting the actual experiment (Nachmias and Nachmias, 1999). A classical experimental design is composed of groups to compare and contrast that where one is experimental and another is controlled by random sampling. The results of the experiment are traditionally based on pre-test and post-test procedures. In addition, the purpose of the experiment is to deduce causal relationships. For instance, if X, then Y or if not X, then not Y (Nachmias and Nachmias, 1999). In other words, if there is one point, then another point should occur as an outcome and vice versa. The major strength of the experimental design consists in a possibility to establish co-variation and allow to take control of time order (Nachmias and Nachmias, 1999). In this respect, the major weakness of this research method lies in the necessity to identify a control group as well as other components of the research, which are indispensible for the experimental procedure.

Combined designs are the designs which mix the features and components of different research designs. It is difficult to dwell upon the strengths and weaknesses of such type of research design in general, as each combined research should be considered in detail due to its specifics and uniqueness.

Quantitative design for personal research plan

To choose a research design for our research plan, it is important to remember the main idea of the experiment as it is a core condition for choosing a correct design. The hypothesis we state is as follows, low economical development and lack of population awareness about HIV/AIDS danger and the ways of infection transmission are the main reasons for the increase of newly infected people in African regions. I strongly recommend choosing a cross sectional research design in a number of reasons. One of the main reasons of choosing a cross-sectional research design is that it is the most appropriate for defining the characteristics of people who are at risk of getting HIV/AIDS in African countries. In addition, the design enables to choose an appropriate population sample necessary for meeting the research questions.

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Cross-sectional research design presupposes testing people in a particular time under particular conditions. Regarding the research hypothesis as well as the essence and nature of the research questions, this method seems to be the most appropriate for the given study due to several reasons. First, it is possible to use existing statistical information about demographics and education in African countries as well as the level of economical development. This information will help to infer the information supporting the hypothesis. Second, cross-sectional design allows to choose a random sample of people from an African country to gather knowledge about people who has tested positive. Finally, cross-sectional research design is used to measure a number of groups and make conclusion s about the level of their economical development, literacy and the rate of HIV/AIDS infected people. This type of the research design should be used in a year when a recurring research should be conducted within the same groups. The main principles of cross-sectional experiment should be followed.

Why each of non-chosen is not appropriate for research questions, hypotheses, and variables

Quasi-experimental research designs cannot be used for our planned research separately it does not fit our research questions, hypothesis, and variables appropriately. We have two research questions, (1) Can HIV/AIDS be controlled by means of population informing? And (2) Can HIV/AIDS rate be reduced by means of the improving country’s wellbeing? Quasi-experimental design cannot be used and we should measure the results with high certainty. This is the validity threat we want to avoid.

The same deals with hypotheses and variables, as they come out of the research questions. Having dependant (rate of the infected people with HIV/AIDS), independent (population in African countries) and controlled (the increase of the population information awareness and the improvement of the economical situation) variables, a research cannot be conducted with the help quasi-experimental research designs as in this case the validity threat is increased. Our research is going to be valid and the results will be accepted in case the research is unbiased with a low error and minimum limitations.

Experimental research design is also not appropriate for the research because it contradicts the scope of the research. Specifically, the goal of the given study is to identify how economic development and awareness influences the proliferation of disease, but not to provide causal relationships between the presented variables.

Applying to a cross-sectional design, we still experience a number of limitations which can become validity threat for the results. Thus, we cannot affect economical situation of the region to consider how this is going to influence the development of the situation with newly HIV/AIDS infected people. Furthermore, the absence of the appropriate social structure of the population in the African tribes increases validity threat of the results we are going to obtain. In addition, cross-sectional design also implies the analysis of post-test data only and has deficiency in random assignment to the identified groups. Lack of control because of non-spuriousness of the research design is a very serious threat to the research process, but it can be overcome if all stages of data analysis are carefully planned.


Therefore, it should be concluded that cross-sectional research design should be used for conducting a planned research. Specifically, the cross-sectional and offers an opportunity to contribute to the non-spuriousness of the research as well as to its generalizability. It means that the findings of the research can be applicable to other sample populations. To confirm the hypothesis that low economical development and lack of population awareness about HIV/AIDS danger and the ways of infection transmission are the main reasons for the increase of newly infected people in African regions, we should remember about research longitudinal nature and necessity to measure different groups of subjects in the particular periods of time.

Reference List

Berg, K. E., & Latin, R. W. (2007). Essentials of research methods in health, physical education, exercise science, and recreation. Philadelphia, PA: Lippincott Williams & Wilkins.

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Cavanaugh, J. C. & Blanchard-Fields, F. (2006). Adult development and aging. Stamford: Cengage Learning.

Frankfort-Nachmias, C., & Nachmias, D. (2000). Research Methods in the Social Sciences. New York: Worth Publishers.

Heppner, P. P., Wampold, B. E. & Kivlighan, D. M. (2008). Research design in counseling. Stamford: Cengage Learning.

Nachmias, D., and Nachmias C. F. (1999). Research Methods in the Social Sciences. US: World Publishers.

Ryan, T. P. (2007). Modern experimental design. New York: Wiley-Interscience.

Somekh, B., & Lewin, C. (2005). Research methods in the social sciences. New York: SAGE.

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