Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer.
Chapter 16, “Descriptive Statistics”
This chapter introduces descriptive statistics, which, as the name implies, describe characteristics of the data. There is an overview of the different levels of measurement—nominal, ordinal, interval, and ratio—and how they are used. The chapter also defines frequency distributions and distinguishes between univariate and bivariate descriptive statistics. develop a 1-page study sheet that includes the following:The key concepts of the chapter: Focus on the basic concepts that are important for nurses to understand as they review research studies.A description of the statistical methods covered in the chapter, what they measure, and under what circumstances they are used. Identify examples of how the statistical methods have been used in research studies.An explanation of the key statistical tests and how they measure significance (if applicable).
Expert Solution Preview
Introduction:
In Chapter 16 of “Nursing research: Generating and assessing evidence for nursing practice” by Polit and Beck (2017), the focus is on descriptive statistics. Descriptive statistics are fundamental tools used to describe characteristics of data. This study sheet aims to provide a concise overview of the key concepts, statistical methods, and tests covered in this chapter, highlighting their significance in nursing research.
Key Concepts:
1. Levels of measurement: The chapter discusses four levels of measurement – nominal, ordinal, interval, and ratio. These levels determine the type of statistical analysis that can be applied to the data.
2. Frequency distributions: Descriptive statistics often involve creating frequency distributions, which display the number or percentage of cases falling into different categories or intervals.
3. Univariate and bivariate descriptive statistics: Univariate descriptive statistics focus on analyzing a single variable, while bivariate descriptive statistics examine the relationship between two variables.
Statistical Methods and Examples:
1. Measures of central tendency: Statistical methods such as mean, median, and mode are used to determine central tendencies within a dataset. For instance, researchers may calculate the mean age of a sample population or the mode of a symptom severity scale.
2. Measures of dispersion: These methods, including range, variance, and standard deviation, provide insights into the spread or variability of data. They help nurses assess the consistency of outcomes or differences in patient responses.
3. Measures of association: Statistical techniques like correlations and contingency tables are employed to measure the strength and direction of relationships between variables. For instance, researchers may examine the association between smoking status and the incidence of lung cancer.
Statistical Tests and Significance:
1. Hypothesis testing: This chapter introduces the concept of tests of significance, which help determine whether observed differences or relationships in the data are statistically significant or due to chance. Examples include t-tests and chi-square tests.
2. P-values: Statistical tests generate p-values, which indicate the probability that the observed result occurred by chance. A lower p-value suggests stronger evidence against the null hypothesis and greater statistical significance.
Conclusion:
Chapter 16 of “Nursing research” provides a comprehensive overview of descriptive statistics, their applications, and their significance in nursing research. Understanding the key concepts and statistical methods covered in this chapter is essential for nurses to critically appraise research studies and utilize evidence-based practices in their clinical settings.
