Assessing the impact of a business intelligence program on the employability and well-being of low-income women: a quasi-experimental study protocol
Year: 2025
Type: article
Study design: Study Protocol
Abstract: program, a data analytics and business intelligence course sequence that aims to address this gender gap by providing free business intelligence training to disadvantaged women in Ecuador. The program offers both technical (Business Intelligence) and soft skills training, including Excel, Power BI, SQL, GitHub, R, Tableau, statistics, Python, and workshops on empowerment, employability, and public speech. The purpose of this quasi-experimental study is to assess the impact of this training program on employability and other well-being outcomes of the participants. A total of 80 individuals will be part in the study, of which 70 will be selected to participate in the program, 50 will receive both hard and soft skills training, and 20 only soft skills training. Ten individuals will form part of the control group with no intervention. The study design involves a nonrandomized control group composed of rejected applicants. Data will be collected through an online application form and a computer-based exam. The outcome measures are participants' labor market outcomes, income, food security, and economic stratification, among others. This protocol will prospectively evaluate the program's potential effectiveness; findings will inform future, larger randomized studies focused on employability and well-being in underrepresented groups.
Authors: Marco Faytong-Haro, Alonso Quijano-Ruiz, Daniel Sanchez-Pazmiño, Patricio Alvarez-Muñoz, Jose Diaz-Montenegro, María José Delgado-Rendon, Stephanie Gallegos-Caamaño, Andrea Angulo-Prado, Manuel Murrieta-Vásquez, Karla Robles-Velasco, Ivan Cherrez-Ojeda, Angélica-María Sánchez-Riofrío, Mónica Izurieta Guevara, Claudia Reytor-González, Daniel Simancas-Racines
Language: English
Topic: Labour Market and Migration
Subfield: Gender Studies
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,077
OpenAlex ID: W4416160848
