This week you are gaining insight about bivariate and multivariate inferential statistical tests. 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, such as readmission for heart failure, exclusive breastfeeding up to 6 months postpartum, and adherence to psychotropic medications.
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 (e.g., age, adherence to medications, educational level) 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 .42 and it is a positive, moderate strength relationship. The relationship is significant at the p =.01 level. This means that the older the nurse, the greater the compassion fatigue, and the likelihood the relationship could occur by chance was only 1 in 100 samples.
Select an article (report of research study) that you have already retrieved in a previous week. Identify 1 statistical test used in the study. Share the following in your response:
Identify the problem, purpose, research question, and/or hypothesis.
Identify the statistical test (i.e., descriiptive or inferential) that was used to answer the research question and/or test the hypothesis.
Discuss whether a power analysis was performed.
Describe how Type I and Type II errors were or could have been avoided.
Provide references for all sources cited.
