Sampling and Empirical Research Studies Quantitative Scientific

Sampling and Empirical Research Studies

Quantitative scientific studies typically require sample sizes that are sufficiently large enough to produce valid interpretable data, and using a questionnaire-based survey to poll a large group of respondents is a traditionally accepted methodology within the realm of scholarly research. The objective of any survey-based sampling experiment is to translate raw statistical data on a testable subject of interest, obtained from a sizable sample of relevant respondents, into a viable theory. The strength of this approach lies in the fact that social phenomena are most effectively studied through a systematic empirical investigation of statistical data. Because surveys are generally designed to include an array of multiple choice, yes-or-no, and essay questions, the varied level of detail provided in each recorded answer is expected to provide substantial data. Statistical sampling is used within the field of nursing to identify patient acuity rates, trends concerning mortality during certain circumstances, optimal hospital staffing ratios, and a diverse array of other extremely useful data points necessary to improve the profession's overall level of efficiency and effectiveness. Indeed, it has been consistently observed that "as a cost-effective and useful methodology, work sampling warrants more in-depth exploration of the various techniques involved to ensure nurse managers, clinicians and researchers appreciate the complexities of the approach and its potential to contribute to an understanding of nursing work. (Pelletier & Duffield, 2003).

However, a potential weakness tied to the use of survey-based sampling lies in the fact that any sample size, no matter how significant in terms of quantity, is inherently limited by the respondents' tendency towards selection bias. Whenever a researcher administers a survey to a randomized sample of individuals or organizations, the segment which chooses to respond inevitably shares similar characteristics, attitudes, and indeed, biases. Simply put, if 150 major technology companies are surveyed, the 100 -- 120 expected respondents will generally be highly likely to share similar attributes, such as firm size, headquarters location and executive structure, which will inevitably skew the resulting data. Any information survey respondents provide must always be couched in the knowledge that their experiences are not wholly representative of the wider subject being studied, nor the specific hypothesis being tested. This risk can be significantly mitigated through the use of a technique known as power analysis, because "power, by definition, is the ability to find a statistically significant difference when the null hypothesis is in fact false & #8230; or the ability to find a difference when a real difference exists & #8230; (and) the power of a study is determined by three factors: the sample size, the alpha level, and the effect size" (Henriques, 2011). As is the case with every survey-based quantitative study, the inadequate measurement of confounding variables that can potentially invalidate data is one of the most common threats to internal and external validity. The fact that survey recipients are far more likely to respond when the questionnaire contains fewer questions is partially responsible for the influence of confounding variables. Power analysis is used by responsible and rigorous researchers to ensure that the massive amount of statistical data their sampling surveys generate are effectively gathered, sorted and interpreted to allow for viable extrapolations.