Quantitative Research Critique

Introduction

The theme of the reviews is the need for nurses to develop and implement an effective data collection plan. The context clearly outlines various methods of recording, collecting, and sampling observational data. However, it also identifies phenomena that lead researchers to use biophysiological measurements, observations, and self-reports. The dimensions along which data collection approaches vary are also explored.

When describing approaches to self-report data, for example, questionnaires versus interviews or composite scales, there’s a clear distinction between probability and non-probability samples.

Both their advantages and disadvantages are compared. An evaluation of the sampling method, along with the appropriateness of the sample size used in the study, is also carried out. The research holds high relevance for nurses.

Methodology

Data collection was conducted through direct interviews with doctoral doctors. Nurses were issued questionnaires to fill out. The overall finding was that nurses’ evaluation in research helps to maintain EBP guidelines and promotes proper patient attendance.

Literature review

The provision of both postdoctoral and pre-doctoral research fellowships facilitated the development of advanced study capabilities. With the present-day emphasis on EBP, it has emerged, making every nurse liable for greater interaction amongst people during research participation. The increased technology advancements boost intensive health research, thereby reducing the mortality rate.

Theoretical framework

Inside the United States of America, research competencies amongst nurses continued to improve in the 1970s. The number of nurses with earned doctorates gradually increased, especially during the later 1970s. The availability of both postdoctoral and pre-doctoral research fellowships facilitated the development of advanced study capabilities. The availability of computers and personnel in 1980 continued to enhance nursing until today. Some of the most used keywords are explained below.

Sampling is the selection of specific factors from a population, which is the central focus of a case study. The term “element” is the basic unit of population, typically used in human studies. Population characteristics are defined using exclusion and inclusion criteria or eligibility criteria (Polit, 2006). Typically, researchers draw their conclusions from a “convenience population”. However, using a broader “target population” is a group they would like to generalize their results to. “Representativeness” is the key criteria in assessing a quantitative study sample.

This refers to the degree to which the sample reflects the population without bias. Sampling bias involves the systematic overrepresentation or underrepresentation of parts of the population. Non-random methods of element selection include purposive, consecutive, quota, and convenience sampling—known as nonprobability sampling. Although nonprobability sampling is economical and practical, it is susceptible to bias. The group of people who are most easily available are chosen in convenience sampling (Polit, 2008).

Quota sampling involves dividing the population into homogenous subpopulations (strata) to ensure every subgroup is represented in the sample. For each stratum, the population is conveniently sampled. Consecutive sampling involves selecting everyone who meets the eligibility criteria over a particular period or for a specified sample size. In purposive sampling, individuals are chosen to be part of the sample based on the researcher’s understanding of the population.

Probability sampling involves randomly selecting elements from the population, yielding more representative samples than non-probability methods. This also allows for estimates of sampling error. One of the significant strengths of quantitative research is providing descriptive data—for instance, capturing a snapshot of a user population. However, interpreting this data can prove challenging. In a product development environment, this lack of information can lead to crucial errors in product design (Polit, 2004).

Furthermore, only someone with a solid understanding of how to use and interpret quantitative data should conduct such a study. There’s often an overreliance on the p-value and sample size for most tests. As for practice implications, nursing is expected to realize evidence-based practice—the actual use of clinical evidence in making patient care decisions. It is commonly agreed that research findings from rigorous studies provide the best type of evidence for informing nurse decision-making, client interactions, and actions.

Nurses strive to base their decisions and actions on evidence proving that such changes are clinically appropriate, fostering satisfactory outcomes for clients and cost-effectiveness. Nurses who incorporate research evidence into their clinical decisions are held accountable to their clients. This strengthens the professional identity of nursing. The primary motivation for nurses to engage in research includes the high health care costs.

Nurses now aspire to demonstrate the social effectiveness and relevance of their actions—not merely to assert their professionalism but also to provide quality care to clients.

Conclusion

Quantitative researchers gather their information from the accessible population, in the hope of generalizing it to a target population. According to PICO, the interest population is critical in all scientific studies. The target population refers to the entire population in which researchers are interested. The accessible population refers specifically to the target population that is available to the researcher.

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