Levels of Measurement: Categorical vs. Continuous Data; Descriptive Statistics and Probability Theory Basics
What is the incidence of blood clots from COVID-19 in females over the age of 35?
The above question is an example of a research question. A research question consists of three parts and guides the methods and approaches in which you will study the question to find answers. The research question includes the question, the topic, and the population or variables. In the example provided above, the question examines the prevalence of blood clots from severe COVID-19 in a selected population. From this question, the variables can be assessed, considerations can be analyzed, and populations can be sampled in order to guide the research.
For this Discussion, you will analyze a selected work to identify and analyze the variables, comparisons, and sample sizes. You will explore the potential levels of measurement for your variables and the rationale for the labels, as well as consider the advantages and challenges that you might experience in the statistical analysis.
Reference: Gray, J. R., & Grove, S. K. (2020).
Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
Learning Resources
· Bullen, P. (n.d.).
How to choose a sample size (for the statistically challenged)Links to an external site.
. tools4dev.
· Centers for Disease Control and Prevention. (2024, March).
The NHSN standard infection ratio (SIR)Links to an external site.
.
· “Overview of the Standard Infection Ratio (SIR)” (pp. 4–5)
· Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021).
Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
· Chapter 6, “Evidence of Appraisal: Research” (pp. 147–157)
· Salkind, N., & Frey, B. (2019).
Statistics for people who (think they) hate statistics (7th ed.). SAGE Publications.
· Chapter 3, “Computing and Understanding Averages: Means to an End” (pp. 65–68)
· Chapter 5, “Creating Graphs: A Picture Really Is Worth a Thousand Words” (pp. 88–118)
· Chapter 8, “Hypotheticals and You: Testing Your Questions” (pp. 167–180)
· Chapter 9, “Probability and Why It Counts: Fun With a Bell-Shaped Curve” (pp. 181–200)
· Niedz, B. (2024).
Descriptive statistics [Video]. Walden University Canvas.
· Document:
Descriptive Statistics (PowerPoint presentation)
Download Descriptive Statistics (PowerPoint presentation)
Required Resources for Topic: Infections
· Beydoun, A. S., Koss, K., Nielsen, T., Holcomb, A. J., Pichardo, P., Purdy, N., Zebolsky, A. L., Heaton, C. M., McMullen, C. P., Yesensky, J. A., Moore, M. G., Goyal, N., Kohan, J., Sajisevi, M., Tan, K., Petrisor, D., Wax, M. K., Kejner, A. E., Hassan, Z., … Zenga, J. (2022).
Perioperative topical antisepsis and surgical site infection in patients undergoing upper aerodigestive tract reconstructionLinks to an external site..
JAMA Otolaryngology-Head & Neck Surgery, 148(6), 547–554.
· Sood, N., Lee, R. E., To, J. K., Cervellione, K. L., Smilios, M. D., Chun, H., & Ngai, I. M. (2022).
Decreased incidence of cesarean surgical site infection rate with hospital‐wide perioperative bundleLinks to an external site.. B
irth: Issues in Perinatal Care, 49(1), 141–146.
· Sauer, K. (2023).
Testing for the treatment of urinary tract infections in symptomatic adult patients residing in long-term care facility: An evidence-based quality improvement projectLinks to an external site.
(Publication No. 30569808) [Doctoral dissertation, Phoenix University]. ProQuest Dissertations and Theses Global.
To prepare:
· View the required media.
· It is recommended you complete the quiz prior to constructing your initial response.
By Day 3 of Week 3
Post a response including the following:
· Choose a research study, QI article, or EBP DNP project and interpret at least one continuous demographic variable and one categorical variable.
· Differentiate between comparisons made using descriptive statistics (e.g., the mean and standard deviation) and comparisons based on inferential statistics (e.g., a
t test).
· Compare and contrast the sample sizes used in the research study, the QI project, and the DNP project in terms of type 1 and type 2 errors.
· Explain the SIR rate, how it is developed, and how organizations use it.
· Using the same articles, pick one and differentiate between one descriptive and one inferential statistic used in any one of the three studies/projects.
By Day 6 of Week 3
Read a selection of your colleagues’ posts and
respond to
at least two of your colleagues on
two different days by expanding upon their reflections, making connections to your perceptions, and offering additional insights.
RESPOND TO THIS DISCUSSION
Evidence-based practice (EBP) is driven by statistical analysis, ensuring interventions are effective in improving patient outcomes (Dang et al., 2021). This discussion focuses on the Quality Improvement (QI) project by Sood et al. (2022), which examined the impact of a hospital-wide perioperative bundle on cesarean surgical site infection (SSI) rates.
Demographic Variables: Continuous vs. Categorical
In Sood et al. (2022), patient age serves as a continuous demographic variable since it is measured numerically and can take on a range of values. This variable is important as older patients may be at greater risk for infections due to comorbidities. Meanwhile, the presence or absence of a surgical site infection (SSI) is a categorical variable, as patients are grouped into two distinct categories: those who developed an infection and those who did not. These variables guide the statistical tests used in the study, influencing both descriptive summaries and inferential comparisons (Salkind & Frey, 2019).
Descriptive vs. Inferential Statistical Comparisons
Descriptive statistics provide an overview of the dataset, while inferential statistics determine the significance of observed differences (Salkind & Frey, 2019). In Sood et al. (2022), descriptive statistics were used to calculate the mean and standard deviation of patient ages, summarizing the general characteristics of the study population. Meanwhile, inferential statistics, such as a chi-square test, were used to compare infection rates before and after implementing the perioperative bundle. The chi-square test determined whether the reduction in SSI rates was statistically significant or occurred by chance. This differentiation highlights the importance of using both descriptive and inferential methods to support data-driven conclusions in quality improvement projects.
Sample Size Considerations and Type I & II Errors
Sample size directly impacts the risk of Type I and Type II errors in research and quality improvement studies (Bullen, n.d.). The research study by Beydoun et al. (2022) had a large sample size, which reduced the likelihood of Type I error (false positives) by ensuring that any detected differences in infection rates were not due to random variability. In contrast, the QI project by Sood et al. (2022) had a moderate sample size, increasing the risk of Type II error (false negatives) if the study lacked enough power to detect a true effect. The DNP project by Sauer (2023), which focused on urinary tract infection (UTI) testing in long-term care facilities, had a smaller sample size, making both Type I and Type II errors more probable due to reduced generalizability and statistical power. In QI projects, balancing statistical rigor with practical implementation constraints is essential to ensure reliable findings.
Standardized Infection Ratio (SIR): Definition and Application
The Standardized Infection Ratio (SIR) is a widely used metric in infection control that enables hospitals to assess their infection rates relative to national benchmarks (Centers for Disease Control and Prevention [CDC], 2024). It is calculated by dividing the observed number of infections by the expected number, adjusting for risk factors such as patient demographics, surgical procedures, and hospital characteristics. In Sood et al. (2022), the SIR decreased following the implementation of the perioperative bundle, indicating that the intervention effectively reduced cesarean SSIs. Healthcare organizations use SIR data to monitor infection trends, evaluate infection control programs, and implement evidence-based strategies to improve patient
safety.
Descriptive vs. Inferential Statistics in the QI Project
In Sood et al. (2022), a descriptive statistic used was the mean length of hospital stay for patients before and after the intervention, summarizing patient recovery trends. An inferential statistic employed was the chi-square test, which assessed whether the reduction in SSI rates post-intervention was statistically significant. This combination of statistical approaches ensures that both data trends and meaningful differences are analyzed, allowing for evidence-based decision-making in quality improvement initiatives (Salkind & Frey, 2019).
Understanding statistical methods is crucial in evaluating research and applying findings to practice. The QI project by Sood et al. (2022) demonstrates the importance of analyzing continuous and categorical variables, utilizing both descriptive and inferential statistics, and considering sample size implications on Type I and Type II errors. Additionally, the SIR metric serves as a valuable tool for infection control, enabling healthcare organizations to assess infection rates and improve patient outcomes.
References
Beydoun, A. S., Koss, K., Nielsen, T., Holcomb, A. J., Pichardo, P., Purdy, N., Zebolsky, A. L., Heaton, C. M., McMullen, C. P., Yesensky, J. A., Moore, M. G., Goyal, N., Kohan, J., Sajisevi, M., Tan, K., Petrisor, D., Wax, M. K., Kejner, A. E., Hassan, Z., … Zenga, J. (2022). Perioperative topical antisepsis and surgical site
infection in patients undergoing upper aerodigestive tract reconstruction.
JAMA Otolaryngology-Head & Neck Surgery, 148(6), 547–554.
Links to an external site.
Bullen, P. (n.d.). How to choose a sample size (for the statistically challenged).
tools4dev.
Links to an external site.
Centers for Disease Control and Prevention. (2024, March).
The NHSN standard infection ratio (SIR).
Links to an external site.
Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021).
Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
Salkind, N., & Frey, B. (2019).
Statistics for people who (think they) hate statistics (7th ed.). SAGE Publications.
Sauer, K. (2023). Testing for the treatment of urinary tract infections in symptomatic adult patients residing in long-term care facilities: An evidence-based quality improvement project (Publication No. 30569808) [Doctoral dissertation, Phoenix University]. ProQuest Dissertations and Theses Global.
Links to an external site.
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