Paired sample T-Testing in nursing research
Paired sample T-Testing is also referred to as the dependent sample t-test. It is a statistical testing procedure that is often used to establish whether there is a zero mean difference between sets of data or observations ( Kim, 2015) . When conducting a paired sample t-test, each of the entries is measured two times leading to pairs of each observation. The Paired sample T-Testing is commonly used in nursing research when the adopted research involves a repeated-measures design or in case-control studies. For example, supposing nursing research is aimed at evaluating the effectiveness of evidence-based practice on the reduction of patients’ readmission rates. One appropriate approach would be to analyze readmission data before the practice was adopted and data after the adoption of the evidence-based practice. With the two sets of data, the paired sample t-test can then be used to analyze the difference if the mean difference between the paired samples is zero.
Descriptive Statistics in Nursing Research
Descriptive statistics are simply used to describe the aspects of a given set of information. They are useful in providing a vivid description of the basic features of the given data. They can be used to summarize and present large data into easy to understand forms of presentation like in graphs and tables. Descriptive statistics often used in nursing research include the measures of central tendency (mean, mode and median), the measure of skewness and kurtosis, measures of dispersion or variation (range, variance, and standard deviation) and the quartile and percentile measures ( George & Mallery, 2016) . For example, supposing the data on the various types of illnesses for patients received in the pediatric clinic is recorded for six months and a study is done to establish which the highest reported illness in the clinic is; descriptive statistics can be used to analyze the data to establish the percentages of each illness reported.
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Reference
George, D., & Mallery, P. (2016). Descriptive statistics . In IBM SPSS Statistics 23 Step by Step London: Routledge press, pp. 126-134.
Kim, T. K. (2015). T test as a parametric statistic. Korean journal of anesthesiology , 68 (6), 540.
