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The brief describes how STRIVE developed tools to monitor program quality, strengthened data systems to track engagement and outputs, standardized historical data from 2018–2025, and improved data-entry procedures to enhance accuracy. It emphasizes using data to learn, set benchmarks, and support rigorous evaluation readiness.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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addressed gaps in program data by investing in data-system improvements, connecting logic-model inputs and outputs to CRM measures, standardizing historical data, and strengthening data-entry procedures and redundancies.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},586332,1792210499,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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Evidence  \nFEBRUARY 2026  \nAnnabel Utz Edith Yang  \nExpanding a program while maintaining its quality is a challenge  \nfor many sector-focused training organizations (organizations that train people for sectors of the labor market in which there is strong local demand). STRIVE, a sector-based training organization that began as a small organization in East Harlem and is now operating nationally, is committed to expanding its programs responsibly while building its evidence base. In partnership with MDRC, STRIVE is drawing insights from its historical program data, developing tools to improve its data systems, and advancing its research and development agenda. If STRIVE meets its expansion targets, these conditions could support a rigorous evaluation.  \nThis brief explores how STRIVE has developed tools to monitor and strengthen its programs as it expands nationally.  \nThe STRIVE Program Model  \nFor over 40 years, STRIVE has offered students both career and personal development services. In 2023, it launched a national expansion plan to serve 10,000 people annually by 2033, from 2,000 annually. As part of this effort, STRIVE is replicating WorkAdvance—an evidence-based sector  \nEXPANDING WITH INSIGHT: STRIVE’S INVESTMENTS IN DATA-INFORMED PRACTICES TO STRENGTHEN PROGRAMS AND EVIDENCE  \n2  \ntraining model—in New York City, Atlanta, Birmingham, New Orleans, and northern New Jersey. STRIVE has adjusted its program model to align with central features of WorkAdvance, and has partnered with MDRC to assess whether STRIVE’s programs are being carried out as designed and whether STRIVE is making progress on building evidence of its program effectiveness.  \nThis work, called STRIVE Forward, follows STRIVE’s evaluation roadmap, which outlines steps to build the organization’s ability to evaluate its own programs and its readiness to participate in a rigorous evaluation conducted by an outside party.1 STRIVE Forward uses data on students in New York City who have participated in two of STRIVE’s programs: Career Path, which includes job readiness training and occupational skills training, career coaching, job placement, and lifelong services; and Fresh Start, a program for people reentering the community following incarceration that incorporates holistic services to support students in their learning pathways.2  \nLessons for Expansion  \nUsing a common framework for assessing evaluation readiness, MDRC and STRIVE developed logic models for Career Path and Fresh Start, assessed data practices, and built data tools to support expansion and inform program improvement.3  \nDevelop Logic Models to Connect Actions to Outcomes  \n• Discussions about how to describe STRIVE’s programs in a logic model revealed that it valued—and needed to measure—its commitment to providing comprehensive services to its communities’most vulnerable populations.  \nLogic models illustrate how a program’s activities (“inputs”) connect to immediate changes in conditions (“outputs”) and participants’ outcomes. While many sector programs offer supportive services to their participants, in developing its logic model, STRIVE sought to integrate those services at each step in the logic represented, from needs assessments being a part of the application, to services being integrated with training workshops, to expected life-skills achievements among the outputs and outcomes. Fresh Start also includes reentry-focused services such as support for posttraumatic stress disorder.  \nAssess Data System Capabilities  \n• A review of STRIVE’s program data revealed some important gaps, which prompted new data-system investments that have improved STRIVE’s data quality and enabled better service delivery.  \nThe discussions about the logic model made it clear that STRIVE needed reliable data systems to track program engagement and outputs. MDRC worked with STRIVE to connect the logic model’s i","cbCaiqpU8U37PzIA","https://ap.wps.com/l/cbCaiqpU8U37PzIA","pdf",824185,"English","# STRIVE’s Investments and Expansion Approach\n## The STRIVE Program Model\n## Lessons for Expansion\n### Develop Logic Models to Connect Actions to Outcomes\n### Assess Data System Capabilities\n### Use Data to Learn","[{\"question\":\"What is STRIVE Forward, and what is its purpose?\",\"answer\":\"STRIVE Forward follows STRIVE’s evaluation roadmap to build the organization’s ability to evaluate its own programs and to prepare for a rigorous outside evaluation.\"},{\"question\":\"Which programs does the brief focus on for student data and evaluation tools?\",\"answer\":\"It focuses on Career Path and Fresh Start, using data from New York City participants who took part in two STRIVE programs.\"},{\"question\":\"How did STRIVE improve its data systems during the expansion?\",\"answer\":\"STRIVE addressed gaps in program data by investing in data-system improvements, connecting logic-model inputs and outputs to CRM measures, standardizing historical data, and strengthening data-entry procedures and redundancies.\"}]","Expanding With Insight - STRIVE’s Investments in Data-Informed Practices to Strengthen Programs and Evidence | PDF",1792057608]