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This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest.RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.Support RAND Make a tax-deductible charitable contribution at www.rand.org/giving/contribute www.rand.org Library of Congress Cataloging-in-Publication Data is available for this publication.ISBN: 978-1-9774-0020-8 For more information on this publication, visit www.rand.org/t/RR2364Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2018 RAND CorporationR® is a registered trademark. Cover: Photo courtesy of U.S. Army Recruiting Command.iii PrefaceThis report documents research and analysis conducted as part of a project entitled Resources Needed to Meet Army's Enlisted Accession Requirements, sponsored by the Assistant Secretary of the Army for Manpower & Reserve Affairs. The purpose of the project was to enhance the effectiveness and efficiency of the Army's use of the suite of available recruiting resources and policies by optimizing the required resource levels and mix to support future recruiting under changing enlisted accession requirements, varying labor market conditions and recruiting environments, and alternative recruit eligibility policies, and by enabling the assessment of alternative courses of action.The Project Unique Identification Code (PUIC) for the project that produced this document is HQD146830.This research was conducted within RAND Arroyo Center's Personnel, Training, and Health Program. RAND Arroyo Center, part of the RAND Corporation, is a federally funded research and development center (FFRDC) sponsored by the United States Army.RAND operates under a "Federal-Wide Assurance" (FWA00003425) and complies with the Code of Federal Regulations for the Protection of Human Subjects Under United States Law (45 CFR 46), also known as "the Common Rule," as well as with the implementation guidance set forth in DoD Instruction 3216.02. As applicable, this compliance includes reviews and approvals by RAND's Institutional Review Board (the Human Subjects Protection Committee) and by the U.S. Army. The views of sources utilized in this study are solely their own and do not represent the official policy or position of DoD or the U.S. government. SummaryThe research described in th...

here is abundant research suggesting that peer collaboration is an important component of teachers' long-term career development and capacity to support student learning. A growing body of evidence reveals that teachers' satisfaction and career pathways are affected more by workplace environment, including aspects of teachers' ability to collaborate with peers, than by student characteristics (

Do quasi‐experimental (QE) studies conducted with baseline covariates that are typically available in the longitudinal administrative state databases yield unbiased effect estimates? This paper conducts a within‐study comparison (WSC) study that compares experimental impacts of early college high school (ECHS) attendance with QE impacts drawn from the state and locales. We find that (1) QE models for outcomes with natural (matching) pretests replicated the randomized benchmarks quite well; (2) the replication bias is not sensitive to type of propensity score model or method; and (3) imposing locational restrictions (i.e., local matching) on the comparison students––specifically choosing them from among non‐treatment students who came from the same feeder middle schools as the treatment students––does not decrease the QE bias; on the contrary, it performed worse than the models that did not impose this restriction for most outcomes. The first two findings are generally consistent with other education WSCs while the third one is not, suggesting that in cases where selection may be driven by individual‐level factors, such as this one, local matching may yield biased treatment effect estimates by greatly reducing the pool of potential comparison units and distorting balance on unobservable confounders while prioritizing balance on observable factors.