peer response


First, I commend you for writing an informative and precise post. Secondly, I have learned some additional components for both bivariate and multivariate statistical tests. For instance, I had overlooked what direction of variables in analyzing bivariate statistics implies, and from your post, I have revisited the information. The direction of a variable is crucial since it defines the correlation of variables. For instance, whether a movement of an independent variable in an upward direction causes the same, opposite, or no movement.

I agree with you that in nursing, statistical significance is mostly determined using Chi-square. It is crucial for us always to recall that statistically significant results are represented for all values of P where P>0.05 and not significant where P≤0.05. Also, nursing researchers prefer Chi-square since it is designed to test the relationship between categorical variables, which is often the nature of variables in nursing. However, Johnson et al. (2015) explain that Chi-square should not be used on population size <50.

I feel that you have taken your time to understand the multivariate statistical test, which is commendable. It was not apparent to me that factor analysis in multivariate analysis reduces problem dimensionality. I went ahead to find more, and now I understand that dimensionality (or what I can call noise) is reduced by extracting the maximum common variance from the available variables and using them as scores for in-depth analysis (Usman, Ahmed, Ferzund, Mehmood & Rehman, 2017). Lastly, I agree with you that multivariate analysis is essential for an easy understanding of nursing problems and that conclusions are more realistic due to the in-depth analysis of each variable.


Johnson, W., Beyl, R., Burton, J., Johnson, C., Romer, J., & Zhang, L. (2015). Use of Pearson’s Chi-Square for Testing Equality of Percentile Profiles across Multiple Populations. Open Journal Of Statistics05(05), 412-420. doi: 10.4236/ojs.2015.55043

Usman, M., Ahmed, S., Ferzund, J., Mehmood, A., & Rehman, A. (2017). Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data. International Journal Of Advanced Computer Science And Applications8(5). doi: 10.14569/ijacsa.2017.080551