Re:Module 7 DQ 1
Considering your dissertation research interests, identify one continuous variable (Y) to be what you are trying to predict. Then identify three other continuous variables that you would want to evaluate as predictors of Y. State the null and alternative hypotheses based on this design (with a planned analysis using a multiple regression analysis). (Research support is not required for this question.)
In the area that I am interested in, I want to know if an individual’s Cultural Competence (CQ) score is impacted by education level, age, or years of experience working in international business environment.
In this situation, the Y variable is:
CQ score – the higher the score, the more culturally competent the individual is.
The three predictors of Y are:
Education level – education in years (includes all schooling)
Age – as a continuous variable
Years of experience working in international business environment
In this situation:
H?=Education level, Age, and Years of experience do not have an impact on CQ scores
H?= Education level, Age, or Years of experience (or a combination of any/all of them) are predictors of CQ scores.
A multiple regression analysis will allow me to determine first if any of the predictor variables have a statistically significant impact on the dependent variable. Once I determine that there is significant correlation in the variables, the multiple regression will allow me to determine which of the variable(s) are accounting for the statistically significant correlation. Because the goal is to find individuals with higher CQ scores, it will ultimately help me understand best practices for hiring.
Re:Module 7 DQ 1
Considering your dissertation research interests, identify one continuous variable (Y) to be what you are trying to predict. Then identify three other continuous variables that you would want to evaluate as predictors of Y.State the null and alternative hypotheses based on this design (with a planned analysis using a multiple regression analysis). (Research support is not required for this question.)
Y= levels of burnout after first 90 days working
Number of years in the field prior to the job
Number of years of related formal education
In this study the null hypothesis would be that age, number of years in the field, and number of years of formal education are not related to burnout after 90 days. H0: ?1 = ?2= ?3
The alternative hypotheses would be that at least one of the predictor variables is related to burnout after 90 days.
Using a planned analysis with multiple regression analysis would allow me to determine whether initial levels of the predictor variables are related to (or predict) values on the level of burnout as measured after 90 days of working. The information would be valuable because it could allow for a tracking system to prevent burnout for those at higher risk of it, or might even allow for preventative measures such as changing job descriptions or requirements to better reduce turnover and increase patient care outcomes.
MAREN Alitagtag 7,2
Considering the variables and design that you described in the first discussion question in this module, what information would a multiple regression analysis provide you? Why would this be significant to your research? (Research support is not required for this question.)
The first variable I did was socioeconomic status. If this was correlated with a predictive value of income, it would help me understand that further research would be needed in that area. For instance, is it because of other factors such as education, proper nutrition, more access to understanding different educational and career path choices, more time to study? What about socio economic status is correlated with higher income in job search. Next, I want to look at education level. If there is a positive correlation, is it limited to just some education, or all education? Is there actually some negative regarding education, for instance obtaining a degree that does not have much employment options in the future. This would help my research by helping career counselors understand which fields my produce better results for job seekers regarding which education and professions to choose. Finally, looking at age, is there a correlation between age and wage? Is it harder for younger workers to get a living wage? Or older displaced workers? Is there less of an issue for young people with a work gap in employment than older people? All of this would help me aIDress the many issues people face when returning to work after an absence, and help career counselors and job developers understand the barriers people are facing.