Respond to at least one of your colleagues’ posts and comment on the following:

    September 15, 2022

This is my classmates article
Respond to at least one of your colleagues’ posts and comment on the following:

Make recommendations for the design choice.
Explain whether you think that this is the appropriate correlation or bivariate regression to use for the research question. Why or why not?
As a lay reader, were you able to understand the results and their implications? Why or why not?

In this week’s article, the comparison was between the effectiveness of leadership within the healthcare industry and the decrease of accidents in the healthcare industry. The authors in this study used methods such as correlation, regression, and bivariate regression to calculate these results to show how effective leadership does in fact reduce accidents within the workplace. According to Frankfort-Nachmias et.al, 2020 he describes correlation as the relationship between variables to determine the existence and strength of these variables. There are two types of regression, bivariate regression, and multiple regression. The text describes regression as a” linear model that uses one or more independent variables to predict the dependent variable” (Frankfort-Nachmias,2020). Frankfort-Nachmias et.al,2020 examines bivariate regression as how one independent variable changes the dependent and multiple regression is defined as how several independent variables affect one dependent variable.
In the article presented by May et.al,2019, she describes that having the most appropriate and most effective leadership style will improve the performance levels within the healthcare workplace. She adds that applying and adapting the “seven steps of leadership and worker involvement” has proven that a reduction in accidents with injuries were 50% or more prior to adapting and applying the seven steps of leadership and worker involvement plan. Another reason this plan was implemented into this study was because the healthcare and occupational industry is steady evolving due to the advancement of technology and equipment and it is important to have effective leadership that is “aimed in protecting the health, safety, and well-being of workers within the workplace which helps in reducing risks, preventing damages, and to prevent the arising of illnesses within the workplace” (May,2019).
The research designs the authors used in this study is the correlation and regression design which was used to show evidence between “safety leadership behavior and the reduction of errors within the workplace” (May,2019).  The authors used bivariate regression because they used and adapted the “seven steps of leadership and worker involvement” which would be named the independent variable that changed the dependent variable results which the dependent variable in this study would be the reduction of errors and risks, preventing damages, and the prevention of arises illnesses. Yes, using the correlation and the bivariate regression was the correct method to use because it shows the cause and effect of two variables within this study in the workplace. Yes, the author did display the data results which were identified as 5% as the level of significance which shows that the null hypothesis “is equal to average occurrences before and after the method was applied” (May,2019). Yes, the results stand alone because this add empirical evidence amongst the occurrences that happened before the method was applied and the occurrences that happened after the method was applied. Bakker et. al, 2019, defines effect size as one that provides quantitative measures between two groups which helps strengthens the findings of the test that statistical significance cannot determine. Therefore, the authors in this case study did not report the effect size because the p value is <0.05 which is not enough evidence to support the data for it to be meaningful. Below are the data from the case study as listed above.
References
Bakker, A., Cai, J., English, L. et al. Beyond small, medium, or large: points of consideration when interpreting effect sizes. Educ Stud Math 102, 1–8 (2019). https://doi.org/10.1007/s10649-019-09908-4
Frankfort-Nachmias, C., Leon-Guerrero, A.,

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