Executive Summary
Various health outcomes measurement levels are used when appraising epidemiologic and biostatistical research methods in the healthcare sector. The nominal level is the least precise, assigning a label or category to each group member. The ordinal level is more precise and allows for the ranking of group members (Fisher & Bloomfield, 2019). The interval or ratio level is precise, allowing for equal intervals between group members. The essay will discuss how these levels can be used to appraise research methods concerning their health outcomes in the community of San Jose in Santa Clara County. This type of research is crucial for enhancing a community’s health and well-being.
Measurement of the Health Outcome at the Nominal Level
Definition of Variables
The nominal level variable is the application of these methods to the San Jose community in Santa Clara County. This variable is used to measure the community’s health outcomes in terms of the prevalence of diseases and the incidence of health problems.
Attainment of the Measure
The nominal level measurement of research methods is obtained by asking the San Jose community how often they use these methods. The responses are then tallied, and the total number of responses for each method is divided by the total number of residents surveyed. Data is collected on the community’s health outcomes and then analyzed to see how the methods have been applied.
Recording of the Measurement
Both research methods can be recorded at the nominal level by simply counting the number of times each method is used. The activity can be tracked by keeping a record of the frequency of method usage in research studies, whether in published papers or conference presentations. The measurement should be recorded as the percentage of residents.
Descriptive Statistics
A percentage is the most appropriate descriptive statistic for this data because it would allow for easy comparisons between different groups of residents. By recording the data at the nominal level, it is possible to get a general idea of how many people in the community are using these research methods.
Graphical Display
One possible graphical display that could be used to depict the health outcome variable at the nominal level is a bar chart. This would allow for a quick and easy visual comparison of the different levels of the health outcome variable.
Measurement of the Health Outcome at the Ordinal Level
Definition of Variables
Biostatistical and epidemiologic research methods can be measured on an ordinal scale. This means that the methods can be ranked according to how well they are applied. For instance, the researcher could rank the methods from 1 to 10, with one being the least effective and ten being the most effective.
Attainment of the Measure
The rating of methods on a scale is applied to reveal the health outcome within the community.
Recording of the Measurement
There are various ways to measure and record research methods at the ordinal level. One way to do this would be to ask people how often they use these methods when conducting research. Another way would be to ask people to rate the usefulness of these methods on a scale from 1 to 10 (Langley & McKenna, 2020). Nevertheless, another way to measure this would be to ask people to rate the methods on a scale of 1 to 5, with one being “not helpful” and five being “very useful.”
Descriptive Statistics
To be specific, descriptive statistics should be used to describe the variable. Simple descriptive statistics, such as frequency counts, can be used to determine the frequency of a particular event (MacCannell et al., 2022). For example, if we examined the frequency of epidemiological and biostatistical research methods used in the San Jose community, we could tally the usage of each method and then report the results as a percentage.
Graphical Display
A graph that would be appropriate for representing health outcomes on an ordinal scale is a box plot. This would provide a good indication of the data’s range and any potential outliers in the dataset. A box plot is suitable for ordinal-level data because it displays the range of the data and any outliers that may be present. Thus, the box plot helps determine the overall health of the population being studied.
Measurement of the Health Outcome at the Interval Level
Definition of Variables
The interval or ratio level measurement is obtained by measuring the rate of occurrence of specific health outcomes in a population. This measurement is usually expressed as a rate per 100,000 population (Mokkink et al., 2020). The research methods are used to study the health outcomes of this community. This research is fundamental in identifying risk factors for specific health outcomes and in developing interventions to prevent or reduce the occurrence of low-quality health outcomes.
Attainment of the Measure
It explores the application of these research methods to a population to obtain quantitative data about that population’s health. This data can then be used to understand the population’s health and make comparisons between different populations. To obtain this data, research methods must be applied to a large number of people to obtain a representative sample. Once this data is collected, it can be analyzed.
Recording of the Measurement
The level measurement should be recorded using a tool such as the Epidemiologic Research Scale or the Biostatistical Research Scale. This tool will help ensure that the data collected is of the highest quality and will provide the researcher with an unbiased and reliable record of the measured health outcome.
Descriptive Statistics
There are several methods for measuring the approaches to epidemiologic and biostatistical research. One way is to measure them at intervals, which allows the collected data to be categorized and a precise order to be maintained. For example, if we were examining the number of people who fell ill after exposure to a new virus, we could categorize them based on the severity of their illness. We could then use descriptive statistics to describe the data.
The application of epidemiologic and biostatistical research methods has been measured at the nominal, ordinal, and interval levels. The study’s results showed that health outcomes differed significantly at the different measurement levels. The nominal level showed the slightest difference, while the ordinal and interval levels showed the most significant difference. This study provides valuable insights into measuring health outcomes in the community and can be used to enhance the population’s health.
Graphical Display
There are many different ways to display data at the interval level, but some standard options include line graphs, bar graphs, and histograms. In this case, a line graph would be an excellent option to show the trend of the variable over time. A line graph is a suitable choice for this data because it effectively illustrates how the variable changes over time. Data presented in a line graph is fundamental for understanding health outcomes, as it can reveal trends and patterns that may not be immediately obvious.
References
Fisher, M. J., & Bloomfield, J. (2019). Understanding the research process. Journal of the Australasian Rehabilitation Nurses Association, 22(1), 22–27. Web.
Langley, P. C., & McKenna, S. P. (2020). Measurement, modeling, and Qualys. F1000Research, 9(8), 217. Web.
Lavallee, D. C., Austin, E., & Franklin, P. D. (2018). How can health systems advance patient-reported outcome measurement? Joint Commission journal on quality and patient safety, 44(8), 439. Web.
MacCannell, T., Batson, J., Bonin, B., Astha, K. C., Quenelle, R., Strong, B.,… & Villarino, M. E. (2022). Genomic Epidemiology and Transmission Dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Congregate Healthcare Facilities in Santa Clara County, California. Clinical Infectious Diseases, 74(5), 829–835. Web.
Mokkink, L. B., Boers, M., Van Der Vleuten, C. P. M., Bouter, L. M., Alonso, J., Patrick, D. L.,… & Terwee, C. B. (2020). COSMIN Risk of Bias tool to assess the quality of studies on reliability or measurement error of outcome measurement instruments: a Delphi study. BMC medical research methodology, 20(1), 1-13. Web.