# Employee Engagement Survey 2024

## Section 1: Role & Work Environment (8-10 questions)
1. I know what is expected of me at work
2. I have the materials and equipment to do my work right
3. My workload is manageable
4. I have opportunities to learn and grow

## Section 2: Management & Leadership (10-12 questions)
5. My supervisor or someone at work seems to care about me as a person
6. Someone at work has talked to me about my progress in the last six months
7. My opinions seem to count at work
8. Senior leadership clearly communicates company direction

## Section 3: Team & Culture (8-10 questions)
9. My fellow employees are committed to doing quality work
10. I have a best friend at work
11. The company culture aligns with my values
12. I feel included and valued for who I am

## Section 4: Recognition & Development (6-8 questions)
13. In the last seven days, I have received recognition or praise
14. There is someone at work who encourages my development
15. I see a clear path for career advancement

## Section 5: Engagement Outcomes (6-8 questions)
16. I would recommend this company as a great place to work
17. I plan to be working here one year from now
18. I am proud to work for this organization
19. I rarely think about looking for a job at another company (reverse)

## Demographics (Optional)
- Department/Business Unit
- Tenure (< 1 year, 1-3 years, 3-5 years, 5+ years)
- Management Level (Individual Contributor, Team Lead, Manager, Director+)
- Work Location (Office, Remote, Hybrid)

## Pre-Survey Communication Template

Subject: Your Voice Matters - Annual Engagement Survey

Dear Team,

We're launching our annual engagement survey from [DATE] to [DATE]. This is your opportunity to share honest feedback about your experience working here.

**Key Details:**
- Survey takes 10-15 minutes to complete
- All responses are completely anonymous
- Results will be shared transparently
- Action plans will be developed based on your feedback

**What happens next:**
- Week 3: Results shared with all employees
- Week 4: Department discussions on findings
- Week 6: Action plans communicated

Your participation drives positive change. Thank you for your time.

# Engagement Score Calculation Example
import pandas as pd
import numpy as np

def calculate_engagement_scores(df):
    # Define engagement question columns (1-5 scale)
    engagement_cols = ['role_clarity', 'growth_opportunities', 'recognition', 
                      'manager_support', 'recommend_company']

    # Calculate individual engagement score (% favorable)
    # Favorable = 4 or 5 on 5-point scale
    df['engagement_score'] = df[engagement_cols].apply(
        lambda x: (x >= 4).mean() * 100, axis=1
    )

    # Calculate overall engagement percentage
    overall_engagement = (df['engagement_score'] >= 80).mean() * 100

    # Calculate dimension scores
    dimension_scores = {
        'Role Clarity': (df['role_clarity'] >= 4).mean() * 100,
        'Growth': (df['growth_opportunities'] >= 4).mean() * 100,
        'Recognition': (df['recognition'] >= 4).mean() * 100,
        'Management': (df['manager_support'] >= 4).mean() * 100,
        'Advocacy': (df['recommend_company'] >= 4).mean() * 100
    }

    return overall_engagement, dimension_scores

# Statistical significance testing
from scipy.stats import chi2_contingency

def test_demographic_differences(df, question_col, demographic_col):
    """Test if engagement differs significantly by demographic"""
    contingency_table = pd.crosstab(
        df[demographic_col], 
        df[question_col] >= 4  # Favorable vs unfavorable
    )
    chi2, p_value, dof, expected = chi2_contingency(contingency_table)
    return p_value < 0.05  # Significant if p < 0.05

## Q2 Engagement Action Plan - Engineering Department

### Key Findings
- Overall Engagement: 68% (Target: 75%)
- Lowest Scores: Career Development (45%), Recognition (52%)
- Highest Scores: Team Collaboration (82%), Work-Life Balance (79%)

### Priority Actions

#### Quick Wins (0-30 days)
1. **Manager Recognition Training**
   - Owner: HR & Engineering Managers
   - Action: 2-hour workshop on effective recognition
   - Measure: Recognition score increase to 65%

2. **Monthly Team Spotlights**
   - Owner: Engineering Leadership
   - Action: Highlight team achievements in all-hands
   - Measure: Track recognition mentions

#### Strategic Initiatives (90+ days)
1. **Career Development Framework**
   - Owner: HR & Engineering Leadership
   - Action: Define technical and leadership tracks
   - Measure: Career development score to 70%
   - Budget: $15K for external career coaching

### Success Metrics
- Pulse survey in 90 days
- Target 10-point improvement in focus areas
- Manager effectiveness scores

# Regression analysis to identify engagement drivers
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler

def engagement_driver_analysis(df):
    # Define predictor variables
    predictors = ['manager_effectiveness', 'growth_opportunities', 
                 'recognition', 'workload_balance', 'team_collaboration']

    X = df[predictors]
    y = df['overall_engagement']

    # Standardize features
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    # Fit model
    model = LinearRegression()
    model.fit(X_scaled, y)

    # Get driver importance
    driver_importance = dict(zip(predictors, abs(model.coef_)))
    return sorted(driver_importance.items(), key=lambda x: x[1], reverse=True)

# Sentiment and theme analysis
from textblob import TextBlob
import re

def analyze_open_responses(comments_list):
    themes = {
        'management': ['manager', 'supervisor', 'leadership', 'boss'],
        'workload': ['stress', 'overwhelmed', 'workload', 'busy'],
        'culture': ['culture', 'values', 'environment', 'atmosphere'],
        'growth': ['development', 'career', 'learning', 'promotion']
    }

    results = {}
    for theme, keywords in themes.items():
        theme_comments = []
        avg_sentiment = 0

        for comment in comments_list:
            if any(keyword in comment.lower() for keyword in keywords):
                theme_comments.append(comment)
                avg_sentiment += TextBlob(comment).sentiment.polarity

        if theme_comments:
            results[theme] = {
                'count': len(theme_comments),
                'sentiment': avg_sentiment / len(theme_comments),
                'sample_comments': theme_comments[:3]
            }

    return results