代做ELE000172M Research Methods Theory & Data Analysis 2024-5代写Python语言

ELE000172M

BEng/MEng Degree Examinations 2024-5

Department:

School of Physics, Engineering and Technology

Title of Exam:

Research Methods Theory & Data Analysis

Question: [40 marks]

Write a consultancy report for GreenBuild Materials investigating factors influencing employee productivity. Your report should include the following sections:

A. Introduction (200 to 250 Words):                                                    [6 marks]

This section should explain the report's purpose and value to the Executive Board.

•    It should clearly outline your specific hypotheses relevant to the research question.                      [3 marks]

•    You  need  to  identify  relevant  variables  from  your  hypotheses  and provide reasons for your chosen research direction.                  [3 marks]

B. Methodology (200 to 250 Words): [11 marks]

•   You  need  to  explain  the  methods  /  tests  you  used  to  conduct  the research process - including data cleaning procedures.             [5 marks]

•    You need to provide reasons for each of your choices.             [6 marks]

C. Results (250 words): [10 marks]

•    Present  the findings  using  charts  or tables created  in SPSS,  Excel, Python, or another statistical software.           [5 marks]

•     Use diagrams, graphs, or charts to present the data clearly and visually illustrate productivity scores across shifts, noise levels, and other variables. You should provide a complete picture of the empirical analysis and not pick-and-choose findings based on their significance. [5 marks]

D. Discussion and Reflection (100 words to 150 words):                 [10 marks]

•    Interpret the statistical findings to address the research questions, highlighting factors that significantly affect productivity.

E. Conclusion (50 words to 100 words): [3 marks]

•    Provide actionable recommendations for GreenBuild to improve scheduling, work environments, or employee engagement based on the findings.

Supplementary Evidence Requirement

You must include supplementary evidence, such as graphical data or tables generated in SPSS or another statistical software, to enhance clarity and support  your findings. Evidence may include histograms, bar charts, box plots, correlation matrices, or ANOVA output tables.

Note: The instructions are outlined as follows:

Case: GreenBuild Materials - Manufacturing Efficiency

Consultancy Report for GreenBuild Materials: Investigating the Effect of Workplace Factors on Employee Productivity

GreenBuild  Materials  is  a  leading  company  in  sustainable construction,  operating multiple manufacturing facilities across the region. Recently, the Board of Directors has  raised  concerns  about  variable  productivity  levels  among  employees  across different shifts and environmental conditions. They believe that factors such as shift type, noise exposure, workplace temperature, employee satisfaction, and salary may significantly impact productivity, affecting overall efficiency.

To gain a clearer understanding of these factors, GreenBuild conducted a survey among their  manufacturing  workforce.  The  data  collected  will  help  the  company identify the most influential factors on productivity, enabling the Board to implement targeted changes to optimize  employee  performance  and satisfaction.  Some  key questions they are interested in exploring include:

•     How do different shift types (morning, afternoon, night) impact productivity?

•     Is   there   a   relationship   between   noise   level   exposure   and   employee productivity?

•     Does workplace temperature affect productivity levels?

•    What is the correlation between employee satisfaction and productivity?

•     How does annual salary relate to productivity scores?

You may be able to think of other important questions the company should be asking that can be answered by this data set. Marks will be awarded for deeper insights and additional exploration of the dataset.

Dataset overview:

The dataset comprises responses from 120 employees across GreenBuild’s facilities, with data collected on the following key variables:

• Employee ID: Unique identifier for each participant.

• Age: Age of the employee.

• Gender: Gender of the employee.

• Shift Type: Type of shift (Morning, Afternoon, Night) worked by the employee.

• Noise Level Exposure: Noise exposure level in the workplace (Low, Medium, High).

• Workplace Temperature (°C): Average temperature in the employee’s work area.

• Employee  Satisfaction:   Satisfaction  level  on  a  scale  of   1  to  5,  with 5 indicating “highly/Very satisfied.”

• Productivity Score: Productivity score based on performance evaluations on a scale of 1 to 10, where 10 is the highest productivity score.

• Years at Company: Duration of employment at GreenBuild.

• Training Hours Last Year: Training hours completed in the past year.

• Workload Intensity: Perceived job intensity (Low, Moderate, High).

• Annual Salary (£): Annual salary of the employee in British pounds (£).

Instructions for Candidates:

Your task is firstly to explore which variables relating to salary are the most important,  then  statistically  analyse  the dataset gathered by  the Board of Directors  and  use the findings from your  analysis to write a  report for the Executive Board.  Your statistical analysis should follow these steps:

Instruction:

Step 1: Formulate Appropriate Hypotheses:

•   To answer the research question, you will need to select relevant hypotheses

- there is a strong recommendation to propose three hypotheses in total.

This step will be important in shaping your statistical analysis and guiding the investigative journey.

Step 2: Identify relevant variables from your hypotheses:

•   You  need  to   identify  what   the  relevant  variables  are  for  your  selected hypotheses. These variables will play a crucial role in shaping the study's focus and analytical framework.

Step 3: Complete Data Cleaning:

•    Before the analysis phase, rigorous data cleaning procedures are essential to ensure the dataset is accurate, reliable, and ready for scrutiny. This preparatory step should fix any dataset discrepancies or anomalies that might affect the validity of the findings.

Step 4: Identifying and applying statistical tests for analysis:

•   The core of the research process involves systematically conducting statistical tests and analysis to assess the validity of the formulated hypotheses. This step is central to uncovering empirical evidence and gaining a better understanding of the research question.

Step 5: Interpret the results:

•   The insights derived from the analysis, whether significant or not, will require effective interpretation. Use diagrams, graphs, or charts to clarify / visualise the findings.

Step 6: Write your Report.

•   The report must be between 800 to 1000 words. This word count ensures a concise  yet  thorough  presentation  of  the  research  process,  findings,  and implications.

Note: Graphical evidence such as graphs and tables are excluded from the word count.

Additional information:

●   No references are required.

●   You must add supplementary evidence such as graphical data from SPSS or any statistical tools you used for this data analysis.

●   Cover  page, contents list, figures, appendices, and references are excluded from the total word count.