Data Analysis in Nursing

Data Analysis in Nursing

            Evidence-based practice cannot be underrated in the nursing practice. Statistics is one of the tools used for data analysis. Nurses need to comprehend the most common statistical tests, when and how they are used, and how to determine significance.

Testing Correlations and Power Analysis and Effect Size

Power analysis involves finding a statistically significant variation where the null hypothesis is actually false. The effect size, alpha level, and sample size determines a study’s power. Researchers have regard for power issues if a study is proposed before any data is collected. In such a case, a researcher’s intention is determining the most appropriate sample size or justifying the proposed sample size. Therefore, the researcher needs to find out about the effect size and alpha level (Polit & Beck, 2012). Some of the tests include t-test, z-test, ANOVA, correlation of simple regression, logistic regression, proportions, non parametric analyses, survival analysis, and chi-square contingency tables.

ES (Effect size) refers to an indices’ family which measures a treatment effect’s magnitude. The indices are sample size-independent. ES measures are common meta-analysis studies’ currency, summarizing findings from a particular research area, for instance, influential meta-analysis of behavioral, educational, and psychological treatments. ES is measured as the standardized variation between two means or the correlation between individual dependent variable’s scores and independent variable classification. ES may also be interpreted as the nonoverlap percent of the treated and untreated groups’ scores (Polit & Beck, 2012).

Correlation is a very useful and common statistics. A correlation defines a single number that elaborates the level of relationship existing between two variables. A correlation can be positive or negative (Polit & Beck, 2012). An example of correlation is the relationship between self-esteem and height. Correlation statistics are used for binary or continuous variables or a combination of both.

Reference

Polit, D. F., & Beck, C. T. (2012). Nursing research: Generating and assessing evidence for          nursing practice. Philadelphia, PA: Laureate Education, Inc., custom ed.).

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