Effectiveness of conducting Correlational Research

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Correlational research is vital when exploring relationships among associated variables. Understanding this relationship among variables is a vital aspect of healthcare research. In correlational research, one or more variables are measured while the researcher is unaware of which variable will be the outcome. Nursing research has significantly increased in the last decade, with a call for more nursing research being put forward by nursing executives and professional organizations. Nursing leaders have called for more nursing research that collects evidence-based results that provide empirical evidence to support findings (Chen et al., 2019). This article gives an overview of conducting correlational research in nursing and its importance to healthcare conversations and decisions.

Correlational research is usually used in healthcare and social research since it can be applied in any research that does not require the manipulation of the independent variable. Correlational research is also an appropriate tool for nursing, which may assist with the determination of certain outcomes and the relationships among them. The researcher normally selects two or more variables from the population and gathers information through a collected data set. The researcher must then perform statistical analysis on these variables to identify findings.

Correlational research utilizes qualitative, quantitative and mixed methods for data collection. In nursing, correlational research is normally conducted in a large sample size that can comprise over 100 participants (Chen et al., 2019). This allows the researchers to draw conclusions and make recommendations to patient care practices based on their findings.

The key considerations in correlational research are sampling, variables, and outcomes. Sampling is the process of systematically selecting a data set that is representative of the whole population. This includes considering the sampling techniques such as convenience, probability and non-probability sampling. The researcher must also choose between quantitative or qualitative methods in data collection. Quantitative methods collect numeric data through instruments such as questionnaires and surveys, while qualitative methods involve in-depth interviews and observational studies.

The variables that are used in correlational research derive from the construct theory by differentiating characteristics, attributes, dimensions, or elements of a system with respect to variables based on their properties. Variables are observable, measurable, or quantifiable characteristics of a system that can change, unlike constants. There are at least three types of variables: qualitative and quantitative variables and one categorical variable. Qualitative data is non-numeric data that describes the characteristics of a population. Quantitative data describes numerical quantities or attributes by specifying the quantity with units and values. Categorical variables are defined as nominal data or ordinal data that only has distinct categories and does not have any quantitative measurement. Outcomes in correlational research may be described as the result of an event like a clinical sign, symptom, disease state (Friedman et al., 2022).

References

Chen, J. H., Björkman, A., Zou, J. H., & Engström, M. (2019). Self–regulated learning ability, metacognitive ability, and general self-efficacy in a sample of nursing students: A cross-sectional and correlational study. Nurse Education in Practice, 37, 15-21.

Friedman, C. P., Wyatt, J. C., & Ash, J. S. (2022). Designing and Carrying Out Correlational Studies Using Real-World Data. In Evaluation Methods in Biomedical and Health Informatics (pp. 289-325). Springer, Cham.