This doctoral-level statistical analysis examines the application of frequency and descriptive statistics in healthcare and nursing research. The study analyzes a sample of 100 patients, presenting demographic data including age, education, race, ethnicity, and income through comprehensive statistical measures. The research demonstrates how descriptive statistics enable meaningful data interpretation in healthcare settings without making population inferences.
Statistical analysis has been applied to various fields. These are inclusive of, but they are not limited to; health sciences, social sciences, and physical sciences. The current paper seeks to assess the application of frequency and descriptive statistics in health science – more specifically in the nursing realm. Frequency and descriptive statistics are a branch of statistics that aims to describe data sets without making inferences on the sampled data about the population (Golami et al., 2020). Thus, descriptive statistics aim at describing the study\\\\\\\'s variables in a meaningful way/manner. In contrast, frequency analysis aims at summarizing data by depicting the number of times a data value occurs in the data table or output (Gray & Grove, 2020). This entails analyzing the data set, including where the data are concentrated or clustered, the range of values, observation of extreme values, and determining intervals for analysis that could make sense in categorizing variable values.
This section of the paper aids in providing the various procedures to be undertaken to conduct this study. The current study employed a quantitative technique in the analysis of the data sets through the use of summary statistics and frequency statistics. Therefore, the current study sought to apply frequency and descriptive statistics in nursing.
In selecting the data, purposive sampling was employed/utilized. A sample of 100 patients/observations was gathered - entailing the compiled vitals, pain scores, and medications for each patient.
This section entails the presentation of the study\\\\\\\'s data sets. The results were analyzed through descriptive statistics which, as Golami et al. (2020) observe “enable the researcher to describe the data sets and present them in a meaningful manner” (87). This section summarizes the data on the patient\\\\\\\'s age, highest school grade completed, race, ethnicity, and family income from the prior month.
The study\\\\\\\'s variables were summarized; including the respondents\\\\\\\' age, highest school grade completed, and family income. The results revealed that they had mean averages of 36.637, 11.28, and $1172.59, respectively. Their standard deviations were 6.199, 1.561, and $788.153, respectively. These were used to measure the variations/ dispersion from their means. The respondent\\\\\\\'s age depicted the highest maximum value of 49.430, while the highest grade depicted the lowest minimum value of 1.0.
In addressing race and ethnicity, frequency statistics were used. The result shows that the black, not Hispanic, was 80.5%, 12.8%, white (5.3%), and others were 1.4%. Those employed were 54.7% and not employed were 45.3%.
With regards to the study variable\\\\\\\'s distribution, the skewness statistics/ values were used to assess if the variables follow a normal distribution. Results show that their skewness values were -0.37, -0.727, and 2.030, respectively - indicating that they follow a normal distribution since they fall in the ±3 range (Aczel & Sounder Pandian, 1999).
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