In the intervention group receiving the educational program, there was a 50% increase in performance scores compared to the control group (p<0.05)". Provide an interpretation of these results?
Statistical Tests
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Due Thursday
This week you have gained insight about bivariate and multivariate inferential statistical tests as well as statistical significance. Review the following examples.
Bivariate:
An example of a bivariate relationship is the effect of age on compassion fatigue in nurses. Most phenomena of interest to nurses have complex causes: readmission for heart failure, exclusive breastfeeding up to 6 months postpartum, changing lifestyle to reduce chance of another MI.
Multivariate:
Multivariate tests allow for combining more than 2 variables in 1 analysis to see the combined effect of variables together. A multivariate test such as multiple regression could include many independent variables (age, severity of heart failure, adherence to medications) to test their combined effects on days to readmission.
Interpretation:
When describing results of data analysis, this refers to statistical analysis and not general conclusions: Pearson correlation was utilized to test the relationship between age and compassion fatigue. The r value for the relationship is 0.42 and it is a positive relationship. The relationship is statistically significant at the 0.01 level. This means that the older the nurse, the greater the compassion fatigue and the degree of strength of the relationship could occur by chance only 1 in 100 samples.
You have read a research article that reports the following: “In the intervention group receiving the educational program, there was a 50% increase in performance scores compared to the control group (p<0.05)”. Provide an interpretation of these results? Discuss whether the results are statistically significant.
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