Multiple Regressions

Question one

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). My research interest is motivation in human service organizations.

            In light of my dissertation research interests; multiple regressions would be a crucial tool to use in the analysis of the findings. The research interest that guides the study is motivation in the human services organizations. The continuous variable (Y) intended to predict is how to motivate employees in the workplace. The other continuous variables to evaluate as predictors of employee motivation would be the effect of; staff training and development, flexible working hours, and good working environment on the employee motivation in the workplace.

            The null hypothesis that has a basis of the planned analysis using multiple regression analysis research design would be; there is no relationship between staff training and development, flexible working hours, and good working environment on the dependent variable employee motivation. The alternative hypothesis for the study would be that there is a positive relationship between the independent variables; staff training and development, flexible working hours, and good working environment, and the dependent variable of employee motivation in the workplace.

             The three independent variables identified for research helps to predict employee motivation. The level of motivation varies by staff training and development, flexible working hours, and good working environment. As such, a researcher would be interested in examining the relationship between the three variables and employee motivation in the workplace.  

Question 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?

            The design of using planned analysis by multiple regression analysis is appropriate for the research study since it involves the use of several independent variables to predict the outcome of employee motivation. The extent of employee motivation depends on the three variables in which every factor is a predictor factor. The information likely to be generated by multiple regressions is the relationship between staff training and development on motivation; having flexible working hours and motivation, as well as the effect of a good working environment to motivation. Thus, the level of motivation is a factor of different but related variables.

            The likely results from the first independent variable would be that there is a relationship or no relationship between the staff training and employee motivation. It predicts employee motivation since a staff that undergoes for training is likely to be motivated to work due to increased efficiency. However, the results would not be the same for all the participants in the study. The second variable of having flexible working hours in the workplace is likely to predict a positive correlation with employee motivation. Many of the working staff opt to work in a flexible schedule that meets their needs. The last variable of having a good working environment is also likely to have a positive correlation with employee motivation due to the benefit of job satisfaction.

            The information obtained from the relationship between the independent variables, and the dependent variable is essential in informing a decision about the factors of consideration in improving employee motivation. Many human resource leaders conduct such studies in assessing the level of job satisfaction, employee motivation, the impact of change within an organization, and the cause of high turnover among other factors. The research findings are essential in reporting the relationship or correlation of a particular factor of interest with others.          

Question 3

Multiple Regressions in Research

            The main purpose of using multiple regressions in research is to learn about the relationship between different independent or predictor variables and a dependent variable. Multiple regression procedures have wide acceptance as tools of research in the social and natural sciences. The general question that multiple regressions help to address is the most likely predictor of a particular variable. It is an instrument of choice to a researcher by the premise that there are several independent variables that interact to predict the value of a dependent variable (Cohen, Cohen, West & Aiken, 2013).

            The technique can be through stepwise regression or backward stepwise regression. Stepwise regression starts by the measurement of the extent to which an independent variable correlates with the dependent variable. Other independent variables are added to the equation and their level of prediction noted. The backward stepwise regression starts with the examination of the combined impact of all the independent variables on the dependent variable. The independent variables are then removed progressively as well as performing a new analysis (Cohen, Cohen, West & Aiken, 2013). 

            Multiple regression in some types of research questions helps to examine the extent to which a particular set of predictors explain the differences in the outcome. In other instances, regression helps to examine the impact of some specific factor while accounting for the other factors that influence the results. In such a case, the researcher uses algebraic methods to hold a group of factors as a constant, except one and observe the account of the factor to the net result.

The major assumptions in the use of multiple regressions are that the independent variables do not have a high correlation with each others, and the independent variables predict the dependent variable and not the reverse.       

Reference

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2013) Applied multiple regression/correlation   analysis for the behavioral sciences: Routledge.

Sherry Roberts is the author of this paper. A senior editor at MeldaResearch.Com in best custom research papers if you need a similar paper you can place your order for custom college essay services.