Effectiveness of the Training Given According to Self-Care Deficit Nursing Theory in the Prevention of Peristomal Skin Complications.

NRSG 7400: Research to Improve Quality and Safety for

Graduate Nursing Roles

Review of Statistics for Nursing Research

Dr. Diann A. DeWitt, RN, CNE

Student Learning Outcomes

Upon completion of this session, the student will: Explain the purpose of descriptive statistics and provide

examples Describe the purpose of inferential statistics and provide

examples Discuss the significance of the p value in relationship to

inferential statistics

What is Statistics?

• Statistics is the collection, analysis, and interpretation of data.

• Categories of statistics: • Descriptive

• Inferential

Statistics Are Used in Data Analysis

• “Data analysis is the reduction, organization, and statistical testing of information obtained in the data collection phase” (Gray & Grove, 2021, p. 63)

Descriptive Statistics

Descriptive Statistics – organize and summarize numerical data gathered from populations and samples; summary statistics  In any study in which the data are numerical, data analysis begins with

descriptive statistics  Example: the sample had a mean birth weight of 7 lb 4 oz

 In simple descriptive studies, analysis may be limited to descriptive

Examples of Descriptive Statistics 1. Frequency Distribution 2. Percentages 3. Measures of central tendency

Mode Median Mean

4. Measures of variability Range Percentile Standard deviation Variance

5. Measure of relationships Correlation coefficients

Inferential Statistics

Inferential Statistics - concerned with populations and use sample data to make an inference about a population  commonly used to test hypotheses  Example: Multiple Regression with Beta Coefficient = 0.450 with a

p value of 0.001 demonstrating that nurses Spiritual Well Being (SWB) correlated positively with nurses’ attitudes toward spiritual care

Examples of Inferential Statistics

• 1. Pearson product-moment correlation

• 2. Factor analysis

• 3. Regression analysis

• Multiple regression

• 4. t-Tests

• 5. Chi-square

• 6. ANOVA

• 7. ANCOVA

More about Inferential Statistics

• With inferential statistic, regardless of the statistic used, there should be p values available.

• P values provide information about whether the statistic used to analyze data is significant or NOT significant!

Probability Theory

• Likelihood of accurately predicting an event or the extent of an effect

• Expressed as a lowercase p

• Values expressed as percentages or as a decimal value ranging from 0 to 1

• Probability of rejecting the null hypothesis when the null is actually true

• Nurse researchers typically consider a p = 0.05 value or less to indicate a real effect

Probability Theory

• Deductive

• Used to explain:

• Extent of a relationship

• Probability of an event occurring

• Probability that an event can be accurately predicted

• Expressed as lowercase p with values expressed as percentages

Probability Example

• If probability is 0.23, then p = 0.23

• There is a 23% probability that a particular event will occur

• Probability is usually expected to be p <0.05

Levels of Acceptable Significance (p values)

• 0.05

•0.01

•0.005

•0.001

Conclusion

• Statistics collect, analyze and test data that have been collected in a research study

• There are 2 categories of statistics • Descriptive

• Inferential

• p values determine if the results are statistically significant or NOT statistically significant

Reference

Gray, J. R., & Grove, S. K. (2021). Burns and Grove's The

practice of nursing research [9th Edition]. Elsevier.