For nurses, it is paramount to be able to properly select a research design for their study. It is often needed to choose between quantitative, qualitative, and mixed methods. The current paper compares these methods.
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Quantitative research involves studying phenomena that can be operationalized as quantifiable variables. Generally, these phenomena can be precisely defined, and measurement instruments are used to assess the magnitude of these phenomena. Quantitative studies may be experimental (or quasi-experimental), correlational, or descriptive (Barnham, 2015). Descriptive studies measure the strength of a phenomenon, correlational research assesses the strength of association between the target variables (but does not allow for causal inference), whereas experimental (or quasi-experimental) research assesses the impact of an intervention (treatment) on the dependent variable (and usually allows for drawing causal conclusions).
To carry out quantitative research, it is needed to formulate a research question, operationally define the phenomena to be measured, identify the target population, select a proper sample, measure the variables (provide intervention, if any, and measure again), analyze the data, and interpret the results.
Quantitative research is useful to identify the strength of association (or to establish causation, in experimental studies) between phenomena in question, and these phenomena are already studied well enough to be operationalized and measured with instruments. It does not allow for studying non-quantifiable phenomena (e.g., subjective experiences). Some of the strengths and advantages include the high degree of generalizability and precision of such studies, whereas weaknesses and disadvantages include the fact that quantitative research often simplifies phenomena and works with artificial constructs (McCusker & Gunaydin, 2014).
Examples of quantitative studies include measuring the association between nurse understaffing and medication error in hospitals (correlational) or assessing the effectiveness of a new drug in comparison to placebo (experimental).
Quantitative studies are paramount for ARNPs and APNs because, e.g., they allow for developing and implementing evidence-based practice.
Quantitative research involves studying phenomena that cannot be operationalized, precisely defined, and measured (for instance, subjective experiences of an individual, reasons for making decisions, etc.). Research designs include case study, grounded theory, ethnography, and phenomenology (Lewis, 2015); an approach labeled “generic qualitative research” has also been proposed (Percy, Kostere, & Kostere, 2015).
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Generally, qualitative research includes formulating research questions, choosing an appropriate design, selecting relevant subjects, collecting data from them (usually using observation, interviews, etc.), interpreting the data, and making conclusions (Norris, Plonsky, Ross, & Schoonen, 2015).
The differences between qualitative and quantitative studies include the fact that in contrast to quantitative research, qualitative studies can examine poorly defined and/or non-operationalizable phenomena. Qualitative studies are useful in particular because they might provide the groundwork for future quantitative studies. However, they are limited because they do not provide supply exact information and are difficult to generalize (Noble & Smith, 2015).
Strengths and advantages of qualitative studies include the fact that they allow for attaining an in-depth understanding of phenomena, the reasons for them, etc. Weaknesses and disadvantages include the fact that qualitative studies might easily become biased or misinterpreted unless the researcher is extremely careful (McCusker & Gunaydin, 2014).
Examples of qualitative research include studying experiences of elderly people in a nursing home, or a case study of a child with autism who is cared for to minimize the adverse consequences of the condition.
Qualitative studies are pivotal for ARNPs and APNs because, e.g., they allow for identifying the particular reasons for a certain problem (not simply confirming that there is a problem, or assessing if it is associated with any particular reasons that are already known).
Mixed research combines qualitative and quantitative methods in a single study. It is used when there exists a need for a study of multiple dimensions of a single phenomenon, overcoming the limitations of utilizing only one design and combining the strengths of both (Schoonenboom & Johnson, 2017). The mixed research designs include sequential explanatory (quantitative to measure + qualitative to explain), sequential exploratory (qualitative to explore + quantitative to measure), sequential transformative (collect one type of data first, integrate the results later), concurrent triangulation (two methods cross-validate one another), concurrent nested (one method is prioritized and provides extra data), concurrent transformative (Ingham-Broomfield, 2016). Concrete steps for a mixed study depend on the selected research design, and may somewhat vary from one design to another.
It may be irrelevant to speak of differences between mixed research and the previous two types of research, for mixed design combines both. Mixed research is very useful when it is needed to investigate several aspects of a phenomenon simultaneously; to explain previously gained unexpected results; etc. (Venkatesh, Brown, & Sullivan, 2016). Its strengths and advantages include the possibility to generalize qualitative conclusions, to create and validate an instrument for measurement, to avoid the weaknesses of a single design, etc. Its weaknesses and disadvantages include the fact that it is time-consuming, may generate discrepancies between qualitative and quantitative results, that it might be difficult to appropriately select a research design, etc. (Neale & Strang, 2015).
Examples of mixed research include studying the satisfaction of customers with a service (combining qualitative and quantitative questions in a single questionnaire) or exploring the effectiveness of exercise for losing weight and its impact on participants’ subjective feelings.
Mixed studies are essential for ARNPs and APNs because they allow for approaching multi-faceted problems that are difficult to study otherwise.
Thus, quantitative and qualitative research designs are different, and they have different strengths and weaknesses. However, these may be partially mitigated by using a mixed study. It is paramount to select a research method in a way that would allow for using the required strengths and not harming a study by the weaknesses of its methodology.
Barnham, C. (2015). Quantitative and qualitative research: Perceptual foundations. International Journal of Market Research, 57(6), 837-854.
Ingham-Broomfield, R. (2016). A nurses’ guide to mixed methods research. The Australian Journal of Advanced Nursing, 33(4), 46-52.
Lewis, S. (2015). Qualitative inquiry and research design: Choosing among five approaches. Health Promotion Practice, 16(4), 473-475.
McCusker, K., & Gunaydin, S. (2014). Research using qualitative, quantitative or mixed methods and choice based on the research. Perfusion, 30(7), 537-542.
Neale, J., & Strang, J. (2015). Blending qualitative and quantitative research methods to optimize patient reported outcome measures (PROMs). Addiction, 110(8), 1215-1216.
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Noble, H., & Smith, J. (2015). Issues of validity and reliability in qualitative research. EvidenceBased Nursing, 18(2), pp. 3435.
Norris, J. M., Plonsky, L., Ross, S. J., & Schoonen, R. (2015). Guidelines for reporting quantitative methods and results in primary research. Language Learning, 65(2), 470-476.
Percy, W. H., Kostere, K., & Kostere, S. (2015). Generic qualitative research in psychology. The Qualitative Report, 20(2), 76-85.
Schoonenboom, J., & Johnson, R. B. (2017). How to construct a mixed methods research design. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 69(2), 107-131.
Venkatesh, V., Brown, S. A., & Sullivan, Y. W. (2016). Guidelines for conducting mixed-methods research: An extension and illustration. Journal of the Association for Information Systems, 17(7), 435-494.