NURS 8201 Levels of Measurement

NURS 8201 Levels of Measurement

NURS 8201 Levels of Measurement

My research question which has been adjusted is “Does the level of prenatal care in African American women ages 18-40 influence the maternal mortality rate?”  The variables involved which are the independent variable and dependent variable play an essential part in the prospective research study. These study variables can take on different values and vary which makes it a part of an empirical phenomenon. The purpose of independent and dependent variables is to be used as tools to formulate and design the research study (Flannelly, Flannelly, Jankwoski, 2014). The independent variable from the research question is the level of prenatal care. The dependent variable from the research question is the maternal mortality rate. The independent variable is the variable that will have the presumed effect on the dependent variable.

The independent variable will be displayed as a nominal measurement which implies numbers that aim to categorize reactions into discreet, jointly, and selected categories. The basis for these categories will serve to identify and separate information using numbers (Prion,2013). In this case, African American women would be the known nominal variable in this research study. The dependent variable is demonstrated as the ordinal measurement which is known as rankings that are prioritized based on the criterion. The maternal mortality rate will be evaluated based on a chart, score, or survey data system.

            Factors to consider when analyzing each variable based on the level of measurement includes the purpose of the study, hypotheses, questions, or objectives, research design, level of measurement, previous experience in statistical analysis, statistical knowledge level,  availability of statistical consultation,  financial resources; and access to statistical software (Gray, Grove, & Sutherland, 2017). Out of these factors, I would say the hypothesis is one of the most important elements because it clearly determines what statistics are needed to test the variables. A decision tree is also imperative to put together when analyzing data based on the variables because it can guide your decisions by narrowing alternatives presented. A challenge in making a decision tree, however, is that if you make an incorrect or uninformed decision (guess), you can be led down a path where you might select an inappropriate statistical procedure for your study (Gray, Grove, & Sutherland, 2017).

References

Flannelly, L.T., Flannelly, K.J., & Jankwoski, K.R. (2014). Independent, Dependent, and Other Variables in Healthcare and Chaplaincy Research, Journal of Health Care Chaplaincy, 20(4), 161-170.

Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burn’s and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.), St. Louis, MO: Saunders Elsevier

Prion, S. (2013). Making Sense of Methods and Measurement: Levels of Measurement for Quantitative Research. Clinical Simulation in Nursing, 9(1), 35-36.

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Your discussion post on levels of measurement is outstanding, informative, and well done. Essentially, selecting a

NURS 8201 Levels of Measurement
NURS 8201 Levels of Measurement

suitable statistical method is essential in the analysis of research questions and data. Inappropriate selection of statistical methods leads to serious challenges during the interpretation of findings and compromises the study conclusion. In statistics, every situation is associated with an existing statistical method to analyze and interpret the data (Daniel & Cross, 2018). Therefore, researchers must acknowledge the assumptions and state of the statistical methods to identify an appropriate statistical method to facilitate data analysis. Apart from understanding the statistical methods, it is also important to consider the aspects of type and nature of the collected data and the aims of the study because, based on the objective of the study, the resultant statistical methods are identified which are appropriate on given data (Mishra et al., 2019).  Understanding and applying appropriate statistical methods is essential in ensuring accurate data analysis and helping in avoiding the practice of inappropriate statistical methods, which is prevalent in published research articles (Grove & Gray, 2018).

References

Daniel, W. W., & Cross, C. L. (2018). Biostatistics: a foundation for analysis in the health sciences. Wiley.

Grove, S. K., & Gray, J. R. (2018). Understanding Nursing Research E-Book: Building an Evidence-Based Practice. Elsevier Health Sciences.

Mishra, P., Pandey, C. M., Singh, U., Keshri, A., & Sabaretnam, M. (2019). Selection of appropriate statistical methods for data analysis. Annals of cardiac anaesthesia22(3), 297. doi: 10.4103/aca.ACA_248_18

Research problem: How will the implementation of a sepsis protocol be beneficial among the treatment for the critically ill patient population (i.e. Covid-19)?

Independent variable– sepsis protocol- Interval level

Dependent variable– critically ill population- Ordinal level

In the expansive world of research, it is important to understand the level of measurement of variables in research, because the level of measurement determines the type of statistical analysis that can be conducted and therefore the type of conclusions that can be drawn from research. In my research problem, my independent variable would be the sepsis protocol and is classified as an interval level of measurement, because it is considered a common and constant unit of measurement. According to Statistic Solutions (2020), the interval level not only classifies observations into categories that are mutually exclusive but also have some relationship among them. It is also called a continuous level variable and a successful implementation of sepsis protocols should include demographical data (age, race, income, and co-morbidity), distribution (frequency and pattern), and population (school, neighbor, city, state or country). Numerical values can be assigned to arbitrary measurements that may or may not have differences.

In a data collection performed by Sakr et.al (2018), as sepsis remains a major health problem in ICU patients worldwide and is associated with high mortality rates; there is a wide variability and range in the sepsis rate and outcomes in the ICU patients globally. This interval measurement’s advantages include  data is crucially important to increase awareness of the global impact of sepsis, highlight the need for continued research into potential preventive and therapeutic interventions, and help guide resource allocations. Furthermore, information on patterns of sepsis around the globe is also of interest, including causative microorganisms, primary source of infection, associated outcomes, and international differences in occurrence rates. The interval scale of measurement provides equal intervals among different categories or variables associated within a sepsis protocol and can take negative or positive values among statistical analysis.

The main challenge of interval levels of measurements is that there is no absolute zero with no fixed beginning. This in part that sepsis provides large multiple data collection in the ICU would not provide precise information on subtypes of microorganisms needed for appropriate antimicrobial coverage, cannot be monitored for errors, and no exact measurement of appropriate sepsis definition. As a result, there is no true definition of sepsis measurement as there can be a variety of measurements which do not have exact or relative values. Interval level of measurements does not always generate more useful data if its uses are highly arbitrary. Therefore, research selection about what should constitute a proper sepsis protocol requires a selection of right statistical technique and data analysis that depends heavily on the variables to be studied and the measurement scales used in the research (Sahifa, 2017)

My dependent variable of the critically ill population can be categorized as in the ordinal level of measurement. This type of measurement depicts some ordered relationship among the variable’s observation and measurements can be in ranked order  but without a degree of difference between categories (Goff, 2021) . The critically ill population of the ICU can be categorized into six diagnostic groups based on their severity: traumatic brain injury, multiple trauma, respiratory, neurological, surgery, and medical. These groups are then statistically analyzed and compared to draw inferences and conclusions about the surveyed population with regards to the specific variable and have ordered relationships among them. The critically ill population groups can be assigned numbers for ranking (i.e. the highest number being the worse and the least number represents the least critically ill in the ICU patient population). Ordinal measurements make it easy for the collection, comparison, and categorizing for statistic conversion. The values are indicated in a relative manner and become more informative. It maintains the description qualities  within intrinsic order but does not void the origins of scale. The ordinal measurements disadvantages include responses in research that can be too narrow and create or magnify biases. The numerical ranking of each critically ill diagnostic group has no standardized scale of differences of how each score is measured or how each group is different from each other (Allen, 2017).

 

Reference(s):

Allen, M. (2017). Measurements level ordinals. SAGE Journal of Communication research

Methods. Retrieved from https://methods.sagepub.com/reference/the-sage-encyclopedia-of-communication-research-methods/i8473.xml

Goff, S. (2021). What are the advantages and disadvantages of ordinal measurement? Sciencing

Probability and Statistics. Retrieved from https://sciencing.com/advantages-disadvantages-using-ordinal-measurement-12043783.html

Sahifa (2017). Interval scale in research methodology. Reading Craze Article. Retrieved from http://readingcraze.com/index.php/interval-scale-in-research-methodology/#:~:text=Disadvantages,not%20have%20an%20absolute%20zero.

Sakr, Y., Jaschinski, U., Wittebole, X., Szakmany, T., Lipman, J., Namendys-Silva, S…Vincent,

J.L. (2018). Sepsis in intensive care unit patients: Worldwide data from the intensive care over nations audit. Open Forum Infectious Disease. 5(12). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289022/

Statistic Solutions (2020). Data levels and measurements. Complete Dissertation Consulting.

Retrieved from https://www.statisticssolutions.com/free-resources/directory-of-statistical- https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/data-levels-and-measurement/analyses/data-levels-and-measurement/