Project Description:

Save the Children is committed to addressing gender discrimination and promoting gender equality to ensure the safety, development, and participation of all children. The Gender Equality Self-Assessment (GESA) is a key tool designed to drive organizational change and deepen commitment to gender equality across Save the Children.

GESA Objectives:

The GESA enables Save the Children to self-assess progress in embedding gender equality within its systems, processes, practices, and programs. The assessment results in a comprehensive action plan aimed at fostering a global organization that truly embodies gender equality principles.

Key Domains of GESA:­

Capacity: Evaluates staff specialization and the institutionalization of gender-equitable and inclusive processes.

Accountability: Measures the effectiveness of mechanisms to ensure gender mainstreaming within policies, procedures, practices, and programming.

Leadership: Assesses the commitment and actions of leaders in championing gender equality.

Safeguarding: Examines mechanisms to prevent and respond to sexual harassment, abuse, gender-based violence, and exploitation.

Culture: Analyzes the organizational culture, including attitudes, beliefs, values, and behaviors that influence equality and inclusion.

Data Collection:

The GESA survey includes Likert-scale questions and a few open-ended questions to gather in-depth responses. The survey, conducted online using Microsoft Forms, covers the five domains of GESA.

Analysis Approach

Three types of analysis are employed to interpret the survey data:

Univariate Analysis: Focuses on individual questions to highlight response variations.

Bivariate Analysis: Examines the relationship between variables (e.g., gender and age) and key GESA domains using chi-square tests, Fisher’s Exact Test, and Odds Ratio (OR) analysis.

Composite Analysis: Calculates average scores for the GESA domains by scoring survey responses, aggregating them, and averaging the results.

Tools for Analysis

All data analysis is conducted using R Programming Statistics, ensuring rigorous and reliable interpretation of the survey results.