Demographic Table - Essay Sample

Decision making is critical to the diverse sectors of the global economy, considering its influence on the enactment and implementation of effective and resourceful measures and strategies. Therefore, the decision-making process should be built and based on facts to facilitate the achievement of sound and effective outcomes. Data collection and analysis promote sound decisions, particularly because it helps identify the existing gaps in the respective fields of operation, thus strategizing on suitable solutions (Geher & Hall, 2014). In the healthcare sector, data collection and analysis are an integral part of its diverse operations because it helps understand the area of interests while building a robust foundation for decision making.

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In particular, collecting and analyzing quantitative data in the health care sectors is important because it provides numerical data that reflects measurement and observation that define and represent features and characteristics of the sample demographics (Quantitative Specialists, 2015). Also, the collection and analysis of quantitative data provide a detailed summary and understanding of the utility, efficacy, and cost of medical services (Heavey, 2019). Data collection and analysis also facilitates evidence-based practices in the healthcare sector, thus implementing data-driven quality and continuous care programs to optimize efficiency. Overall, collection and analysis of quantitative data help healthcare utilization, product and service development, resource allocation, and quality improvement.

Statistical results vary based on their category, use, and functionality. The significance of statistical results is relative based on the subject and area of interest in a given sample or population. Some of the common statistical components include mean, mode, median, standard deviation, and variance. Each component serves a unique function and purpose n different data sets. For instance, is statistics mean is used to measure the central tendency (Chapter 1). Mode establishes the most occurring value or variable in a data set. Median is the figure or value separating the higher value from the lower values in a statistical data set (Osherson & Lane, n.d). Further, standard deviation and variance are measures of variation or dispersion in a data set.

The Yoga Stress Study facilitated the establishment of different statistical measures whose incorporation in the healthcare sector would play an important in implementing sound decisions. The study sampled a data set of 20 individuals that comprised of 10 males and 10 females. The researcher anticipated the relationship between stress and aspects such as age, race, education, and occupation (military status) through the collected information. Based on the study results, 30 percent of the sample data represented African-American, 15 percent represented Caucasians, 20 percent Asians, 15 percent Native Americans, 15 percent Hispanic, and 1 percent representing two or more races.

Also, the study results indicated that 40 percent of the sampled population were college graduates, while 15 percent had graduate education and above. Besides, 10 percent were high school graduates, not to mention that 10 percent were pursuing education in institutions below the high school level. Ultimately, 25 percent of the overall sample size represented some college kind of education. All participants in the study represented a 50 percent active duty and 50 percent US civilians in their military status.

The sampled data had a mean of 39.45 and a dispersion rate/ standard deviation of 12.6. The mode in the sample data was 33, while the median was 40. The range of the sampled information was between 18 and 60 years. Also, from the sampled information, 20 individuals (50 percent of the sample size) represented individuals in the pre-pss category with a mean of 20.35 and a standard deviation of 7.0208. Ultimately, the post-pss data was tested from a 50 percent of the collected information generating a mean of 16.15 and a standard deviation of 6.49.

Typically, demographic tables play a significant role in research by aiding in the study results' contemplation and reporting. A demographic table summarizes the study's primary features and characteristics numerically, including the gender, race, and age of the participants. As such, one understands the research's scope and nature through a detailed analysis of the collected information. In a clinical setting, demographic tables reflect the issues affecting the execution and delivery of services in the institutions while understanding the general population and its conditions. Also, from the collected data, a clinician understands the adversity of the highlighted issues, the people affected based on age, race, gender, and, most importantly, establishing suitable measures to address the issue (Heavey, 2019). Overall, demographic tables are critical to a clinical setting, considering their role in numerically summarizing information from a study, thus understanding the sample size in detail while establishing adversity of the issue.

Finally, the purpose of this study was to develop a demographic table based on the collected data. From the demographic table, the sampled information assessed the effectiveness of yoga as a stress intervention amongst the military. Based on the study results, clinicians would assess yoga's viability and effectiveness while establishing the primary aspects contributing to increased military stress levels while establishing suitable measures and interventions.

References

Heavey, E. (2019). Statistics for nursing: A practical approach (3rd ed.). Burlington, MA: Jones & Bartlett. Available in the course room via the VitalSource Bookshelf link.

Chapter 1, "Introduction to Statistics and Levels of Measurement."

Geher, G., & Hall, S. (2014). Straightforward statistics: Understanding the tools of research. New York, NY: Oxford University Press.

Osherson, D., & Lane, D. (n.d.). Levels of measurement. http://onlinestatbook.com/2/introduction/levels_of_measurement.html

Quantitative Specialists. (2015). Scales of measurement - Nominal, ordinal, interval, ratio (Part 1) - Introductory statistics [Video]. https://www.youtube.com/watch?v=KIBZUk39ncI