Data Analysis in Nursing

As a nurse engaged in evidence-based practice, it is important to recognize how statistics
and other data analysis tools are used to generate and assess evidence. Most nurses need
only a foundational understanding of statistical tools and terminology to understand the
majority of research studies. As a nurse, you should be able to recognize the most
commonly used statistical tests, how and when they are used, and how significance is
determined.
In this Discussion, you examine different types of statistics and statistical tests, when and
why these particular tests would be selected for use, and, most importantly, what the
results indicate. To this end, you will be assigned to a group by Day 1 of this week. Each
group will be assigned one of the five chapters listed in this week’s Learning Resources and
will develop a study sheet on their chapter that will be shared with the other groups.
To prepare:
�Review the information in your assigned chapter.
�As a group, 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).
The topics for this group discussion I have to write on are Testing Correlations and Power
Analysis and Effect Size. It does not have to necessarily be 275 words as it is part of a group
assignment. The text we use is Polit, D. F., & Beck, C. T. (2012). Nursing research:
Generating and assessing evidence for nursing practice (Laureate Education, Inc., custom
ed.). Philadelphia, PA

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

DATA ANALYSIS IN NURSING 2
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.

DATA ANALYSIS IN NURSING 3

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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