Analysis of Employee AI Usage and Creativity Scores

Jan 3, 2025, 7:59 AM

模拟数据.xlsx

Missing file

帮我做描述性分析

The dataset contains 249 entries with the following columns:
  1. Employee_ID: A unique identifier for each employee.
  2. AI_Usage_Frequency: Categorical data indicating how frequently AI is used (High, Medium, Low).
  3. Job_Field: Categorical data indicating the job field (e.g., Marketing, Engineering, Sales, R&D).
  4. Creativity_Score: A numerical score representing the creativity level of the employee.

Descriptive Statistics:

  • Employee_ID: Ranges from 1 to 248.
  • AI_Usage_Frequency: Most frequent category is 'High'.
  • Job_Field: Most frequent job field is 'Sales'.
  • Creativity_Score:
    • Mean: 76.83
    • Standard Deviation: 10.88
    • Minimum: 58
    • Maximum: 95
There is one missing value in the Creativity_Score column.
Would you like to explore any specific aspect of this data further, such as visualizing the distribution of creativity scores or analyzing the relationship between AI usage frequency and creativity scores?

你帮我做个DID分析

Difference-in-Differences (DID) Analysis is a statistical technique used to estimate causal relationships. It is often used in observational studies to evaluate the effect of a treatment or intervention by comparing the changes in outcomes over time between a treatment group and a control group.
To perform a DID analysis, we need:
  1. Treatment Group: A group that receives the intervention or treatment.
  2. Control Group: A group that does not receive the intervention.
  3. Pre-Treatment Period: Time period before the intervention.
  4. Post-Treatment Period: Time period after the intervention.

Steps to Perform DID Analysis:

  1. Identify the treatment and control groups in your data.
  2. Define the pre-treatment and post-treatment periods.
  3. Calculate the average outcome for each group in both periods.
  4. Compute the DID estimator as:
    DID=(Yˉpost, treatment−Yˉpre, treatment)−(Yˉpost, control−Yˉpre, control)\text{DID} = (\bar{Y}_{\text{post, treatment}} - \bar{Y}_{\text{pre, treatment}}) - (\bar{Y}_{\text{post, control}} - \bar{Y}_{\text{pre, control}})

Assumptions:

  • Parallel Trends: The treatment and control groups would have followed the same trend over time in the absence of treatment.
  • No Spillover Effects: The treatment does not affect the control group.
Please provide information on how the treatment and control groups are defined in your dataset, as well as the time periods for pre-treatment and post-treatment. This will allow us to proceed with the DID analysis.
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