Reference no: EM133671167
Assessment - HR Policy Recommendation Project
ASSESSMENT DESCRIPTION
The HR Policy Recommendation Project is an extension of Assignment 2. and will be undertaken individually. The Project needs to propose possible correlations between each of the business outcome metrics (dependent variables) and other metrics (independent variables) you have chosen in Assignment 2, test these relationships using predictive analytics techniques (regression). and provide recommendations on how the organisation should improve staffing practices to enhance organisational performance (when you run regression with profitability being the dependent variable, you also need to include other business outcome metrics such as productivity as additional independent variables). in writing up the Project, you are advised to refer to what are discussed In the seminar on 'PredictiveAnalytices in Action` and Chapter 6 of the textbook, and apply predictive analylics methods (regression) to Justify yourpolicy recommendations, The Project should be 2.000 words In length (plus or minus 10%), excluding cover page, reference list and appendix (you must include the original regression output in the appendix). If you need to use Charts/TablesiDashboarl already presented in Assignment 2, please put them in the appendix. You need to submit the Project in Microsoft Word banal via LMS. An assignment cover sheet must be attached to the assignment.
The Project should include the following,
a. Introduction
b. Describe the key metrics and propose possible causal relationships between them.
c. Analyse relationship between key HR metrics using predictive analytics methods (you must specify the regression models you use, and explain the key regression output. The original regression output must be attached as an appendix).
d. Provide recommendations on HR policy to enhance organisational performance.
e. Conclusion
GUIDELINES for SUCCESS
To do well in this assignment. you need to work independently on the basis of the team-based Assignment 2, apply regression method to analyse possible correlations between the chosen metrics, and make HR policy recommendations based on the regression results and relevant academic sources.
HOW IT IS ASSESSED (summary)
You will be assessed on these key criteria,
- Quality of your policy recommendations
- Logic of policy recommendations
- Justification for your policy recommendations
- Evidence from HR data and regression results to support policy recommendations
- Application of appropriate predictive analytic& methods
Basic predicthse analyties skills using regression in Excel,
Writing skills and editorial care
Clear structure. A minimum at 5 academic references cited correctly using APA style, a Correct grammar and spelling.
The best Ht t Policy Recommendation Project will be awarded the is-Best HR Analytics Project Award The Award winners will be announced when the mark of the Project is released. and will be given a Certificate.
FEEDBACK FOR LEARNING
You will receive the feedback via LMS together with the assessment outcome when the University releases your final mark of the unit.
Just some pointers for assignment 3.
1. You can use around 3-4 independent variables based on some scientific evidence, as seen in the previous lecture where we predicted the employee engagement. You may or may not include a dummy variable as a part of the 3-4 independent variables.
Followed by 2 control variables.
2. You have to provide the correlation analysis matrix
3. In regression output, make sure your adjusted r square is between 0.40 to 1.0
4. The "Significance F" in the regression output should be less than or equal to 0.05. This suggests that your overall model is significant. This is different from the p-value which reflects the significance for each individual independent variables.
5. Lastly, for significant relationship the p-value for independent variables should be less than or equal to 0.05.
6. There should be at least 5 scientific references (in APA citation style) for conceptual framework/hypothesis and HR policy recommendations.