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The methodology employed by a researcher can significantly affect the outcome and accuracy of their study. The research design encompasses the framework, structure, and strategy researchers use to address a research question. By carefully considering these elements, researchers can establish their hypothesis, conduct their study, and interpret their data (Leavy, 2022). In quantitative research, it is pivotal to maintain control and mitigate any factors that could sway or skew the findings. Reviewing the key characteristics and assumptions that underpin quantitative research is vital to appreciate how control plays a crucial role in quantitative research design (Asenahabi, 2019; Bloomfield & Fisher, 2019).
Quantitative research is a formal and systematic process for describing variables, testing their relationships, and examining cause-and-effect associations between variables. Quantitative research generates numerical data, predominantly informed by positivist or post-positivist paradigms and underpinned by various assumptions, such as the belief in a single truth or reality, objectivity, and deduction (Hair et al., 2021; Sukamolson, 2007).
Quantitative research is a scientific method that employs objective and impartial techniques to test hypotheses and find the correct answer. The research process involves drawing a representative sample of participants from a known population and measuring the variables of interest. Typically, researchers begin by testing the null hypothesis, which posits no connection between independent and dependent variables. The hypothesis is scrutinised, and the findings are assessed through statistical analysis. Ultimately, the null hypothesis is deemed acceptable or unacceptable based on the results of the statistical evaluations. Once the null hypothesis is accepted or rejected, inferences or generalisations can be applied to the population of interest. The research design must be reliable and have internal and external validity, collectively known as rigour, to allow confident generalisations about a population (Watson, 2015).
Rigour in quantitative research can be described as the amount of control the researcher exerts to prevent the effects of extraneous or confounding variables on the dependent (test or outcome) variable.
To assess the effect of a single factor on a particular outcome, the researcher must account for any other variables or external factors that could potentially affect the outcome. This can help isolate the specific impact of the independent variable being studied.
For example, to create a fall risk profile, a researcher needs to compare the characteristics of a sample of patients who have experienced a fall with those of a sample of patients who have not.
In this situation, if there is an error in the sample selection of the non-falling patient group and this group accidentally consists of a higher mean for age, then the difference – or not – between the two groups may be due to age due to the sampling error.
Quantitative research uses a range of different study designs. These may vary in how they are categorised according to four significant types of quantitative research: descriptive, correlational, quasi-experimental, and experimental (Tab. 6).
Table 6. Type of Quantitative Research Design
Source: Adapted from Bloomfield and Fisher (2019).
