healthcare research quantitative methods

Quantitative Research Healthcare relies on quantitative research for evidence-based practice in nursing, for organizational structure, design, and marketing, for public health and value-based purchasing issues, safety, and a practically unlimited array of other uses. Using quantitative research methods generates numerical data: data that can be used to generate statistics, to track patterns reliably using metrics, and to make fairly accurate predictions based on quantitative modeling. The research methods used in quantitative studies are often scrutinized carefully, ensuring consistency, reliability, and validity.

Therefore, effective healthcare service delivery and operations depend on quantitative research. Implications for Knowledge Generation in Healthcare Both quantitative and qualitative research contributes to knowledge generation in healthcare. The implications of each depend on the ontological and epistemological frameworks used by stakeholders. For example, a positivist framework emphasizes the importance of numerical, quantifiable data for determining the efficacy of treatment interventions or marketing methods used in healthcare.

A phenomenological approach deems human subjective perceptions to be worthy of attention, important ways of knowing how patients perceive their interactions with nurses or how they respond to therapeutic interventions. Quantitative research might be attractive for its mathematical infallibility but also presents distinct limitations for knowledge generation in healthcare. It may therefore be important to combine qualitative with quantitative methods to generate knowledge in healthcare.

The advantage of quantitative methods is that it can be more attractive to policymakers, investors, and others who need to see metrics and numbers to make strategic decisions. Although they involve the self-assessment or self-report data that can seem subjective in scope, quantitative research includes surveys. Surveys are useful for healthcare administrators for a number of different reasons including patient satisfaction. When surveys are designed to have quantitative outcomes, researchers can also assess usage data and other knowledge relevant to policy or healthcare strategy.

Determining whether to form strategic alliances with other community organizations, or whether to increase staffing in specific departments all depends on access to quantitative data. For example, administrators can acquire knowledge about departments that are underperforming, which have underutilized technology, or which need additional equipment or staff to improve patient outcomes. The validity and reliability of quantitative research depends on how the studies are designed, and how the data is analyzed and applied in practice.

Small group studies yield preliminary knowledge that can be acted upon for minor or small-scale issues, or used to inspire future research. Generally, the larger the population sampled in quantitative research, the more reliable the study due to its being able to represent the general population better than a smaller sample, which tends to be less representative. Yet surveying a sample that is too large can also draw attention away from special populations that healthcare researchers actually have an ethical obligation to serve.

Healthcare administrators and policymakers tend to prefer “robust data sets” that are relevant to the specific population at stake (Penny & Atkinson, 2012, p. 2698). Current Barriers to.