The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Accounting for No-Shows in Experimental Evaluation Designs
Abstract
This article examines how to estimate the effect of a program in the presence of no- shows—persons who are assigned to the program but do not participate. The article briefly discusses the methodological problems involved, describes two current experimental evaluations that are subject to these problems, presents several estimators that overcome these problems, outlines the conditions necessary for these estimators to be feasible, and describes two extensions of the analysis that illustrate a potentially broad range of further applications.
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“One analytical approach to accounting for incomplete adoption is to use Complier Average Causal Effect (commonly known as CACE) modeling (3, 9, 60).”
From An Overview of Research and Evaluation Designs for Dissemination and Implementation (2017) · cites this paper as [9]
Citations
Cites
Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment
Hausman, J. A., & Wise, D. A. (1979) · Econometrica · doi:10.2307/1914193 · Cited by 483
Campbell, D. T. (1971) · Urban Affairs Quarterly · doi:10.1177/107808747100700202 · Cited by 47
Research works printed but not linked: 2
- Metcalf, C.E., 1979, A Research Design for the Employment Opportumty Pilot Projects
- Smith, D.A., 1983, The Downriver Community Conference Economic Readjustment Activity Program: Impact Findings from the First Phase of Operations
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Items 301–400 of 532
Estimating the causal effects of treatment
Dunn, G. (2002) · Epidemiologia e Psichiatria Sociale · doi:10.1017/s1121189x0000573x · Cited by 15
Youth Criminal Behavior in the Moving to Opportunity ExperimentPreprint
Kling, J. R., Ludwig, J., & Katz, L. F. (2004) · RePEc: Research Papers in Economics · Cited by 15
Goldklang, D. S. (1991) · American Journal of Community Psychology · doi:10.1007/bf00938045 · Cited by 15
“Similar strategies are discussed elsewhere ( Bloom, 1984; Vinokur, Price, & Caplan, in press).”
MethodTreatment Effect Heterogeneity in Theory and Practice
Angrist, J. D. (2003) · National Bureau of Economic Research · doi:10.3386/w9708 · Cited by 15
Myers, N. D., Lee, S., Bateman, A. G., Prilleltensky, I., Clevenger, K. A., Pfeiffer, K. A., Dietz, S., Prilleltensky, O., McMahon, A., & Brincks, A. M. (2019) · Pilot and Feasibility Studies · doi:10.1186/s40814-019-0455-0 · Cited by 14
Moving to Opportunity's Impact on Health and Well-Being Among High-Dosage Participants
Moulton, S. R., Peck, L. R., & Dillman, K.-N. (2014) · Housing Policy Debate · doi:10.1080/10511482.2013.875051 · Cited by 14
“…time in low-poverty neighborhoods, but “treatment on the treated” estimates of MTO’s impact on those who leased up are non-experimental in nature and rely on the assumption that the effect of the treatment occurs entirely through moving using an MTO program voucher (Bloom, 1984; Orr et al., 2003).”
BackgroundHomeownership subsidies and the marriage decisions of low-income households
Eriksen, M. D. (2010) · Regional Science and Urban Economics · doi:10.1016/j.regsciurbeco.2010.06.004 · Cited by 14
Mayer, A. K., Patel, R., & Gutierrez, M. (2016) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2015.1086914 · Cited by 14
Moerbeek, M., & van Schie, S. (2019) · Statistics in Medicine · doi:10.1002/sim.8351 · Cited by 14
The Effect of Workplace Pensions on Household Saving: Evidence From a Natural Experiment in Taiwan
Yang, T. (2018) · Journal of Risk & Insurance · doi:10.1111/jori.12254 · Cited by 14
Compliance Mixture Modelling with a Zero‐Effect Complier Class and Missing Data
Sobel, M. E., & Muthén, B. (2012) · Biometrics · doi:10.1111/j.1541-0420.2012.01791.x · Cited by 14
“This can differ from the ITT if subjects do not take up treatment when assigned to do so or take up treatment when not assigned to do so (Bloom, 1984) and/or if treatment assignment affects the outcome “directly.”
BackgroundUsing Experiments to Evaluate Performance Standards
Pepper, J. V. (2003) · The Journal of Human Resources · doi:10.3368/jhr.xxxviii.4.860 · Cited by 14
Statistical Theory for the RCT-YES Software: Design-Based Causal Inference for RCTsPreprint
Schochet, P. Z. (2015) · RePEc: Research Papers in Economics · Cited by 14
Bierens, H. J., & Carvalho, J. R. (2011) · The B E Journal of Economic Analysis & Policy · doi:10.2202/1935-1682.2361 · Cited by 16
“As to attrition, one could argue that ”no show” and ”drop–out” deserve separate treatment, as pointed out by Bloom (1984) and Heckman et al. (1998).”
BackgroundEarly childbearing and housing choices
Ermisch, J., & Pevalin, D. J. (2004) · Journal of Housing Economics · doi:10.1016/j.jhe.2004.07.001 · Cited by 13
Urban Infrastructure and Economic Development: Experimental Evidence from Street PavementPreprint
Gonzalez-Navarro, M., & Quintana‐Domeque, C. (2010) · SSRN Electronic Journal · doi:10.2139/ssrn.1720328 · Cited by 13
“In other words, there is only one-sided non-compliance (Bloom, 1984; Angrist, Imbens, and Rubin, 1996).”
BackgroundAssessing the External Validity of an Experimental Wage SubsidyPreprint
Kamionka, T., & Lacroix, G. (2005) · SSRN Electronic Journal · doi:10.2139/ssrn.678922 · Cited by 13
Weiss, M. J., Scrivener, S., Slaughter, A., & Cohen, B. (2021) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737211008901 · Cited by 12
“If someone is interested in the effect of CUNY Start on those who complied with their assignment (i.e., the complier average causal effect), they can take the presented estimated effects and divide by 0.80 (Angrist et al., 1996; Bloom, 1984; Imbens & Angrist, 1994).”
BackgroundInfluentialEXPERIMENTAL EVIDENCE ON THE USE OF EARNINGS SUPPLEMENTS AS A STRATEGY TO "MAKE WORK PAY"
Greenwood, J., & Voyer, J.-P. (2000) · Cited by 12
Master, B. K., Schwartz, H. L., Unlu, F., Schweig, J. D., Mariano, L. T., Coe, J., Wang, E. L., Phillips, B., & Berglund, T. (2021) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737211047256 · Cited by 11
“This is akin to the Wald estimator or Bloom adjustment for noncompliers in an experimental study (Bloom, 1984).”
MethodMyers, N. D., Dietz, S., Prilleltensky, I., Prilleltensky, O., McMahon, A., Rubenstein, C. L., & Lee, S. (2018) · Games for Health Journal · doi:10.1089/g4h.2017.0132 · Cited by 11
Wang, J., Yusa, N., Pan, H., Takagi, T., & Hashizume, H. (2012) · MATERIALS TRANSACTIONS · doi:10.2320/matertrans.m2012323 · Cited by 11
Harvill, E. L., Peck, L. R., & Bell, S. H. (2013) · American Journal of Evaluation · doi:10.1177/1098214013503201 · Cited by 11
“The Bloom (1984) correction is one approach to estimating the impact of the intervention on the participant subpopulation.”
BackgroundMethodEvidence of randomisation bias in a large-scale social experiment: The case of ERA
Sianesi, B. (2017) · Journal of Econometrics · doi:10.1016/j.jeconom.2017.01.003 · Cited by 11
Dunn, G. (2002) · Understanding Statistics · doi:10.1207/s15328031us0101_03 · Cited by 11
Performance Incentives and Organizational Behavior: Evidence from a Federal Bureaucracy
Marschke, G. (2002) · Cited by 11
Leake, M., & Lesik, S. A. (2007) · International Journal of Research & Method in Education · doi:10.1080/17437270701207827 · Cited by 11
“Another approach would be to use an instrumental variables estimation to determine the impact of the ‘treatment-on-the-treated’ (see Bloom, 1984; Lesik, 2005, 2006).”
MethodImproving the quality of randomized field experiments: Tricks of the trade
Dennis, M. L., & Boruch, R. F. (1994) · New Directions for Program Evaluation · doi:10.1002/ev.1687 · Cited by 11
Roads to Development: Experimental Evidence from Urban Road Pavement
Gonzalez-Navarro, M., & Quintana-Domeque, C. (2010) · doi:10.2139/ssrn.1558631 · Cited by 11
“Overall, our baseline balance findings suggest that: (i) we have a valid instrument to identify the ATET, (ii) ATET=ATE, and (iii) both OLS and IV estimates should provide similar estimated effects.”
MethodPeer Influence in the Diffusion of the iPhone 3G over a Large Social NetworkPreprint
de Matos, M. G., Ferreira, P., & Krackhardt, D. M. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2053420 · Cited by 11
Methods for measuring school effectiveness
Angrist, J. D., Hull, P., & Walters, C. (2023) · Handbook of the economics of education · doi:10.1016/bs.hesedu.2023.03.001 · Cited by 15
Setting up social experiments: the good, the bad, and the ugly
Barnow, B. S. (2010) · Journal for Labour Market Research · doi:10.1007/s12651-010-0042-6 · Cited by 10
“Bloom (1984) has shown that if one is willing to assume that the treatment has no impact on no-shows, the experimental impact estimator can be adjusted to provide an estimate of the impact on the treated.”
BackgroundInfluentialOn the Feasibility of Extending Social Experiments to Wider Applications
Bell, S. H., & Peck, L. R. (2016) · Journal of MultiDisciplinary Evaluation · doi:10.56645/jmde.v12i27.452 · Cited by 10
“Introduced by Bloom (1984), the no-show adjustment requires that the intervention has no effect on members of the treatment group who do not participate—for example, the students randomly assigned to a voluntary after-school program who never attend 3 This average effect can also be quite small and…”
BackgroundInterpersonal Understanding in Historical Context
Martens, M., Hartmann, U., Sauer, M., & Hasselhorn, M. (2009) · BRILL eBooks · doi:10.1163/9789460910685 · Cited by 10
Street Pavement: Results from an Infrastructure Experiment in MexicoPreprint
Gonzalez-Navarro, M., & Quintana‐Domeque, C. (2010) · RePEc: Research Papers in Economics · Cited by 10
Employment and Training Programs
Barnow, B. S., & Smith, J. A. (2015) · National Bureau of Economic Research · doi:10.3386/w21659 · Cited by 10
Breast cancer overdiagnosis in stop-screen trials: More uncertainty than previously reported
Baker, S. G., & Prorok, P. C. (2020) · Journal of Medical Screening · doi:10.1177/0969141320950784 · Cited by 9
“To adjust for refusers, we used the one-sided version of the method of latent class instrumental variables8 that was independently formulated in 1994 by Baker and Lindeman9 and Imbens and Angrist.10 The method of latent class instrumental variables estimates a quantity often called the local average treatment effect (…”
MethodA Field Experiment Assessing the Impact of the Focused Diagnosis Intervention on Job Autonomy
Shperling, Z., & Shirom, A. (2005) · The Journal of Applied Behavioral Science · doi:10.1177/0021886304271415 · Cited by 9
Huston, A. C., Gupta, A. E., Walker, J. T., Dowsett, C., Epps, S. R., Imes, A. E., & McLoyd, V. C. (2011) · Journal of Policy Analysis and Management · doi:10.1002/pam.20613 · Cited by 9
“This study does not satisfy the assumptions of a simple TOT correction for no-shows (Bloom, 1984; Gennetian et al., 2005).”
MethodCash transfers to the elderly and its spillover effects
de Oliveira, P. R., Kassouf, A. L., & Aquino, J. M. (2017) · Journal of Economic Studies · doi:10.1108/jes-04-2015-0059 · Cited by 9
Rubin, D. B. (2010) · Elsevier eBooks · doi:10.1016/b978-0-08-044894-7.01313-0 · Cited by 11
Optimal regimes for algorithm-assisted human decision-making
Stensrud, M. J., Laurendeau, J., & Sarvet, A. L. (2024) · Biometrika · doi:10.1093/biomet/asae016 · Cited by 8
“One strand of literature expands on the ATT by defining, identifying and estimating a general class of causal parameters defined by the values of patient’s natural treatment choices or intentions in the absence of intervention (Robins et al., 2004, 2006; Haneuse and Rotnitzky, 2013; Richardson and Robins, 2013; Young et…”
BackgroundInfluentialThe implications of noncompliance for randomized trials with partial nesting due to group treatment
Roberts, C. (2020) · Statistics in Medicine · doi:10.1002/sim.8778 · Cited by 8
Jung, H., & Pirog, M. A. (2014) · Journal of Policy Analysis and Management · doi:10.1002/pam.21764 · Cited by 8
“20 For more information of the JTPA impact assessment on earnings and employment, please see Bloom et al. (1993). 21 All appendices are available at the end of this article as it appears in JPAM online.”
BackgroundMethodRobson, K., & Pevalin, D. J. (2007) · Research in Social Stratification and Mobility · doi:10.1016/j.rssm.2007.08.002 · Cited by 8
“, those who have a birth regardless of the value of the instrument – occurrence of a miscarriage (Bloom, 1984).”
BackgroundRhodes, W. M. (2010) · Journal of Quantitative Criminology · doi:10.1007/s10940-010-9090-x · Cited by 8
Welbeck, R., Diamond, J., Mayer, A. K., & Richburg-Hayes, L. (2014) · MDRC · Cited by 8
Sobel, M. E. (2009) · SAGE Publications Ltd eBooks · doi:10.4135/9780857020994.n1 · Cited by 10
“To handle this, Bloom (1984) first proposed using Z as an instrument for D.”
MethodPragmatic trials of complex psychosocial interventions: methodological challenges
Dunn, G. (2013) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796013000048 · Cited by 8
“The important point is that in order to obtain a valid ITT effect one has to first estimate the CACE and then calculate the ITT effect from this.”
BackgroundInfluentialIowa's Family Investment Program Two-Year ImpactsPreprint
Fraker, T., Nixon, L. A., Jacobson, J., Gordon, A. R., & Martin, T. J. (1998) · RePEc: Research Papers in Economics · Cited by 8
Revised Design for the Mother and Infant Home Visiting Program EvaluationPreprint
Michalopoulos, C., DUGGAN, A. K., Knox, V., Filene, J. H., Lee, H., Snell, E. K., Crowne, S. S., Lundquist, E., Corso, P. S., & Ingels, J. B. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2273204 · Cited by 8
Weswasi, E. A., & Bäckman, O. (2024) · Journal of Quantitative Criminology · doi:10.1007/s10940-024-09595-2 · Cited by 7
“Following Bloom (1984) and Angrist and Pischke (2009), we can calculate the ATT with the aid of a Wald-estimator: where the ATT is simply a rescaling of the ITT effect based on the share of offenders in the target group who served their sentence under EM following the introduction of the 2005 EM…”
Foster, E. M. (2013) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0031980 · Cited by 7
Scrivener, S., Weiss, M. J., & Sommo, C. (2012) · MDRC · Cited by 7
Mousavi, N., Golara, S., & Bockstedt, J. C. (2024) · Information Systems Research · doi:10.1287/isre.2022.0353 · Cited by 10
On the “How” of Social Experiments: Analytic Strategies for Getting Inside the Black Box
Peck, L. R. (2016) · New Directions for Evaluation · doi:10.1002/ev.20211 · Cited by 7
“It is most common to use random assignment as an instrument to account for no-shows, individuals assigned to the treatment group who receive none of the intervention (e.g., Bloom, 1984) and also the combined effect of no-shows and crossovers (e.g., Angrist, Imbens, & Rubin, 1996).”
BackgroundChildren, Vaccines, and Carrots: How Do Financial Incentives Change Vaccination Behavior?
Erdem, O., Erdem, S., & Monson, K. (2022) · International Journal of Health Economics and Management · doi:10.1007/s10754-023-09343-2 · Cited by 8
Job‐search assistance using the internet: experiences from a Swedish randomised experiment
Hägglund, P. (2006) · International Journal of Manpower · doi:10.1108/01437720610683949 · Cited by 7
The Effect of Making a Voluntary Labour Market Programme Compulsory: Evidence from a UK Experiment
Dorsett, R., Smeaton, D., & Speckesser, S. (2013) · Fiscal Studies · doi:10.1111/j.1475-5890.2013.12016.x · Cited by 7
Evaluation of Program for Infant/Toddler Care (PITC): An On-site Training of Caregivers
Weinstock, P., Ortiz, L., Bos, J. M., Dowsett, C., & Associates, B. P. (2012) · Cited by 7
Promoting Student Success in Community College and Beyond
Brock, T., LeBlanc, A. J., & MacGregor, C. (2005) · Cited by 7
Public Health Insurance and Medical Spending: The Incidence of the ACA Medicaid Expansion
Shupe, C. (2022) · Journal of Policy Analysis and Management · doi:10.1002/pam.22435 · Cited by 7
Mills, G. B., Gale, W. G., & Patterson, R. (2005) · Cited by 7
Dealing with randomisation bias in a social experiment: the case of ERA
Sianesi, B. (2014) · doi:10.1920/wp.ifs.2014.1410 · Cited by 7
“Their formal refusers could be viewed as the program group “no-shows” considered by Bloom (1984), and indeed the approach followed by Dubin and Rivers builds upon the Bloom estimator.”
BackgroundComplier-Average Causal Effect (CACE) Estimation
Dunn, G. (2011) · International Encyclopedia of Statistical Science · doi:10.1007/978-3-642-04898-2_13 · Cited by 7
Impacts of vocational training for persons with disabilities: Experimental evidence from Cambodia
Takasaki, Y. (2024) · Journal of Development Economics · doi:10.1016/j.jdeveco.2024.103277 · Cited by 7
Schochet, P. Z., & Fortson, J. G. (2014) · Journal of Policy Analysis and Management · doi:10.1002/pam.21746 · Cited by 6
Gao, X., & Zheng, M. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2015.1076481 · Cited by 6
“Similar situations also emerged in a variety of randomized studies (Zelen, 1979; Bloom, 1984; Holland, 1988; Newcombe, 1988, Sommer and Zeger, 1991; Baker and Lindeman, 1994).”
BackgroundLessons From the Delaware Dislocated Worker Pilot Program
Bloom, H. S. (1987) · Evaluation Review · doi:10.1177/0193841x8701100202 · Cited by 6
Self-citationRandomized trials for the real world: making as few and as reasonable assumptions as possible
Baker, S. G., & Kramer, B. S. (2007) · Statistical Methods in Medical Research · doi:10.1177/0962280207080640 · Cited by 6
Simple Tests for Selection: Learning More from Instrumental Variables
Black, D. A., Joo, J., LaLonde, R., Smith, J. A., & Taylor, E. J. (2022) · Labour Economics · doi:10.1016/j.labeco.2022.102237 · Cited by 6
Sharp IV bounds on average treatment effects under endogeneity and noncompliancePreprint
Huber, M., & Mellace, G. (2010) · RePEc: Research Papers in Economics · Cited by 6
The Impacts of the Climate Change Levy on Manufacturing: Evidence from MicrodataPreprint
Martin, R., de Preux, L., & Wagner, U. J. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1367149 · Cited by 6
Kim, C.-O., Lee, T., Choi, A., Choi, E., Kim, H., Lee, J., Hong, J., Song, D., Jung, H., Park, J., et al. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05532-x · Cited by 5
Obstacles to and Limitations of Social Experiments: 15 False Alarms
Bell, S. H., & Peck, L. R. (2012) · Cited by 5
Supporting Community College Student Success: Evidence from a Randomized Controlled Trial
Hallberg, K., Hofmeister, K., Bertrand, M., & Morgan, B. (2022) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2022.2074929 · Cited by 5
“We estimate the TOT effects using assignment to treatment as an instrument for actual program participation (Angrist et al., 1996; Bloom, 1984).”
MethodUsing Impact Evaluation Tools to Unpack the Black Box and Learn What Works
Peck, L. R. (2015) · Journal of MultiDisciplinary Evaluation · doi:10.56645/jmde.v11i24.415 · Cited by 5
Does class size matter in postgraduate education?
Gaggero, A., & Haile, G. A. (2019) · Manchester School · doi:10.1111/manc.12305 · Cited by 5
“11 I.e., as in experimental framework where all subjects who receive treatment are compliers (see Bloom, 1984).”
BackgroundThe Complier Average Causal Effect Parameter for Multiarmed RCTs
Schochet, P. Z. (2020) · Evaluation Review · doi:10.1177/0193841x20979205 · Cited by 5
“Using an instrumental variables (IV) causal inference framework, Angrist et al. (1996) build on Bloom (1984) to derive the statistical assumptions required to identify (isolate) the CACE parameter for designs with a single treatment and control group, where compliance decisions are treated as…”
MethodLessons from the WIA Performance Measures
Barnow, B. S. (2011) · doi:10.17848/9780880994026.ch8 · Cited by 5
Social Pressure and Self-Selection in Experimental Policy Evaluation
Brandon, A. (2020) · Knowledge@UChicago (University of Chicago) · doi:10.6082/pw514-56j41 · Cited by 5
Eligible Training Provider Lists and Consumer Report Cards
Horn, C. E. V., & Fichtner, A. R. (2011) · doi:10.17848/9780880994026.ch6 · Cited by 5
Estimating Efficacy in a Proposed Randomized Trial With Initial and Later Non-Compliance
Baker, S. G., Frangakis, C., & Lindeman, K. S. (2007) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/j.1467-9876.2007.00574.x · Cited by 5
Regression Discontinuity in Prospective Evaluations
Klerman, J. A., Olsho, L. E. W., & Bartlett, S. (2014) · American Journal of Evaluation · doi:10.1177/1098214014553786 · Cited by 5
“Because we confined our analysis to the group of schools whose applications were accepted, no Bloom adjustment was necessary, no power/sample size penalty was imposed, the required window of FRPSL values in the estimation sample did not grow, and the overall plausibility of RD as a solution to issues of internal valid…”
BackgroundMethodInfluentialNathan, A. B. (2013) · ProQuest LLC eBooks · Cited by 5
The Causal Effect of Teen Motherhood on WorklessnessPreprint
Walker, I., & Zhu, Y. (2009) · RePEc: Research Papers in Economics · Cited by 5
The Power to Conserve: A Field Experiment on Electricity Use in Qatar
Al‐Ubaydli, O., Cassidy, A., Chatterjee, A., Khalifa, A., & Price, M. (2023) · National Bureau of Economic Research · doi:10.3386/w31931 · Cited by 5
“Therefore, the local average treatment effect also equals the average treatment effect on the treated (ATT), which is the causal effect of treatment for the entire population of compliers (Bloom, 1984).”
BackgroundEffect of early prison release with electronic monitoring
Weswasi, E. A. (2025) · Journal of criminal justice · doi:10.1016/j.jcrimjus.2025.102379 · Cited by 5
Rapid Communication: Delay between recruitment and participation impacts on preinclusion attrition
Walsh, E., & Brinker, J. K. (2015) · Quarterly Journal of Experimental Psychology · doi:10.1080/17470218.2015.1008019 · Cited by 4
“This can be mitigated somewhat by post hoc statistical weighting based upon potentially imbalanced characteristics (e.g., Bloom, 1984), though papers discussing such procedures recommend late random assignment to conditions and urge researchers to minimize the attrition whenever possible (e.g.,…”
BackgroundCausal inference for recurrent events data with all-or-none compliance
Gao, X., & Zheng, M. (2016) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2014.980515 · Cited by 4
“Similar situations also appeared in various kinds of randomized studies (Zelen, 1979; Bloom, 1984; Holland, 1988; Newcombe, 1988, Sommer and Zeger, 1991; Baker and Lindeman, 1994).”
What Can We Learn From A Doubly Randomized Preference Trial?—An Instrumental Variables Perspective
Wing, C., & Clark, M. H. (2016) · Journal of Policy Analysis and Management · doi:10.1002/pam.21965 · Cited by 4
Park, S., & Palardy, G. J. (2020) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998620908599 · Cited by 4
“There are several methods for estimating the average treatment effect among compliers including instrumental variablesbased (IV; Bloom, 1984), maximum likelihood (ML; Imbens & Rubin, 1997; Yau & Little, 2001), Bayesian (Imbens & Rubin, 1997), and propensity score methods (Ding & Lu, 2017; Jo & Stuart, 2009), which cou…”
MethodSuk, Y., & Kim, Y. (2024) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986241268902 · Cited by 4
“This independence (or exchangeability) assumption allows the LATE to be equal to the ATT under one-sided noncompliance (Angrist & Pischke, 2009, Chapter 4; Bloom, 1984).”
Multivariate summer school effects
Zvoch, K., & Robertson, M. (2017) · Studies In Educational Evaluation · doi:10.1016/j.stueduc.2017.10.003 · Cited by 5
Rubin, D. B. (2006) · Handbook of statistics · doi:10.1016/s0169-7161(06)26024-3 · Cited by 5
Cowen, J. M. (2012) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2012.698373 · Cited by 4
“To approximate this quantity, Bloom (1984) proposed a correction: ITTvoucher E(voucher|offer = 1) (5) which is the effect of offering the voucher divided by the proportion of students who actually use it.”
MethodIS THERE SOLID EVIDENCE OF POSITIVE EFFECTS FOR HIGH SCHOOL STUDENTS
Stern, D., & Wing, J. Y. (2004) · Cited by 4
Designing Education Voucher Experiments: Recommendations for Researchers, Funders, and Users
Doolittle, F., & Connors, W. (2018) · Routledge eBooks · doi:10.4324/9780429498015-14 · Cited by 4
Impact of the JOBS Intervention on Unemployed Workers Varying in Risk for Depression
Vinokur, A. D., Price, R., & Schul, Y. (2002) · Springer eBooks · doi:10.1007/978-1-4419-8646-7_22 · Cited by 4
Metadata
- Authors
- Bloom, H. S.
- Affiliations
- Harvard University (US)
- Corresponding author
- Bloom, Howard S.
- Venue
- Evaluation Review
- Publisher
- SAGE Publications
- Published
- 1984-04
- Volume
- 8
- Issue
- 2
- Pages
- 225-246
- ISSN
- 0145-4692 (ISSN-L)
- 0193-841X
- 1552-3926
- OpenAlex
- W2046165968
- Cited by
- 532
- References
- 2
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 15 · 2023: 9 · 2022: 18 · 2021: 13 · 2020: 15 · 2019: 14 · 2018: 14 · 2017: 17 · 2016: 22 · 2015: 22 · 2014: 29 · 2013: 19 · 2012: 38
- FWCI
- 1.35
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evaluation Review (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- missed appointments, program participation, program evaluation, experimental design, estimation bias, non-compliance, treatment effect estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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