Quantitative Research and Its Four Designs

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The quantitative research approach focuses on quantifiable data, such as numbers and figures, in both collection and analysis (Aspers & Corte, 2019). It is scientific in nature, with the use of statistical data reducing the time and efforts researchers need to describe the results (Eyisi, 2016). The types of quantitative research include descriptive, correlational, quasi-experimental, and experimental studies that vary based on the treatment of variables as well as their control (Drummond & Murphy-Reyes, 2017). Descriptive quantitative research is concerned with the identification of relevant variables, which means that scholars aim to offer systematic information about a phenomenon. In this type of research, a hypothesis is not formed initially but is instead developed after collecting data, with data analysis and synthesis helping to test the hypothesis. Besides, it is notable that in descriptive research, none of the variables is being influenced in any way, which means that their nature and behavior cannot be controlled by a researcher.

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Correlational research, in contrast to the descriptive method, aims to identify the degree of relationships among two or more variables with the help of statistical data (Gravetter & Forzano, 2016). Within the correlational design, researchers aim to find relationships between a number of variables as well as interpret them. Although, variables are not manipulated but rather identified and studied as they occur within a natural setting. In 1981, Kanner and colleagues examined the effect of “daily hassles” on physical and psychological symptoms (Stensvehagen, Bronken, Lien, & Larsson, 2020). However, since the number of “daily hassles” could not be manipulated, they had to measure their number and the number of symptoms, which led to the development of correlational research.

Quasi-experimental or causal-comparative research aims to establish cause-effect relationships among variables. This quantitative design type has similarities to true experiments, while there are some differences. In research, an independent variable is identified but cannot be changed by researchers, while the impact of the independent on the dependent variable is being measured. The term quasi-experiment was coined by Campbell and Stanley in 1963 to denote research that would be similar to experimental design but would not allow scholars to manipulate variables in the identified control groups (Sung, Lee, Yang, & Chang, 2019). Similar to correlational research, quasi-experimental studies test causal relationships between variables.

Experimental research also referred to as true experimentation, implies the use of the scientific method for establishing cause-effect relationships between a group of variables that are included in a study. A widespread misconception about experiments is that they are only conducted in a laboratory setting, but this has no real link to experimental research. A true experiment refers to any research in which scholars aim to identify and manipulate all relevant variables except one (Little, 2016). It is expected that an independent one is adjusted for determining its impact on dependent variables. The invention of experimental design is attributed to R. A. Fisher who in 1935 developed the concepts and procedures of ANOVA as well as experiments, with his books becoming the basis for research reference in multiple disciplines (Langkjær‐Bain, 2018).

While the four quantitative designs have their distinct differences, it is notable that all of them are based on the scientific method. They use deductive reasoning in which a researcher develops a hypothesis, engages in data collection within problem investigation, and uses data for analysis and making conclusions. Depending on the findings, a researcher will prove that the hypothesis is either false or true.

References

Aspers, P., & Corte, U. (2019). What is qualitative in qualitative research. Qualitative Sociology, 42, 139-160.

Drummond, K., & Murphy-Reyes, A. (2017). Nutrition research: Concepts & Applications. Burlington: Jones & Bartlett Learning.

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Eyisi, D. (2016). The usefulness of qualitative and quantitative approaches and methods in researching problem-solving ability in science education curriculum. Journal of Education and Practice, 7(15), 91-100.

Gravetter, F., & Forzano, L-A. (2016). Research methods for the behavioral sciences. Stamford: Cengage Learning.

Langkjær‐Bain, R. (2018). Where the seeds of modern statistics were sown. Significance, 15(3), 14-19.

Little, W. (2016). Introduction to sociology – 2nd Canadian edition. BCcampus. Web.

Stensvehagen, M., Bronken, B., Lien, L., & Larsson, G. (2020). Interrelationship of posttraumatic stress, hassles, uplifts, and coping in women with a history of severe sexual abuse: A cross-sectional study. Journal of Interpersonal Violence, 2020, 1-21.

Sung, Y-T., Lee, H-Y., Yang, J-M., & Chang, K-E. (2019). The quality of experimental designs in mobile learning research: A systematic review and self-improvement tool. Educational Research Review, 28, 100279.