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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]
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Cites
Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment
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- 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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Greenberg, D. H., Michalopoulos, C., & Robin, P. K. (2006) · Journal of Policy Analysis and Management · doi:10.1002/pam.20190 · Cited by 40
“To maintain comparability between experimental and nonexperimental studies, we followed Bloom (1984b) by dividing the estimated effects on earnings in the experimental evaluations by the program participation rate.”
MethodWhat Is a Summer Job Worth? The Impact of Summer Youth Employment on Academic Outcomes
Leos‐Urbel, J. (2014) · Journal of Policy Analysis and Management · doi:10.1002/pam.21780 · Cited by 41
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“35 We will use a maximum-likelihood mixture modelling approach to identify the optimal comparison group from the control arm for observed compliers in the intervention arm.”
MethodLudwig, J., Duncan, G. J., & Pinkston, J. C. (2000) · RePEc: Research Papers in Economics · Cited by 39
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Estimation and Identification of the Complier Average Causal Effect Parameter in Education RCTs
Schochet, P. Z., & Chiang, H. S. (2011) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998610375837 · Cited by 38
“The first is the complier average causal effect (CACE) parameter, defined as the average impact of intervention services on those who comply with their treatment assignments (Angrist, Imbens, & Rubin, 1996; Bloom, 1984).”
BackgroundRubin, D. B. (2007) · Handbook of statistics · doi:10.1016/s0169-7161(07)27002-6 · Cited by 39
Schochet, P. Z., & Burghardt, J. (2007) · Evaluation Review · doi:10.1177/0193841x06288736 · Cited by 36
“…participants only (that is, the treatment-on-the-treated parameter) is that outcomes conditional on participation status are the same whether the individual is in the treatment or control group, which implies that the program has no impacts on no-shows (Bloom 1984; Angrist, Imbens, and Rubin 1996).”
BackgroundMethodUnderstanding the effects of early motherhood in Britain: the effects on mothers
Kaplan, G., Walker, I., & Goodman, A. T. (2004) · Working paper series - Institute for Fiscal Studies/Working papers · doi:10.1920/wp.ifs.2004.0420 · Cited by 38
“…(1998) provides a good synthesis of this debate 4 the assumption that is equivalent to a family fixed-effects model results in estimates for family income-to-needs ratio8 and years of education from the NLSY that are significantly negative, and comparable to those in Geronimus and Korenman (1992).”
BackgroundMethodResultInfluentialEvidence from Maternity Leave Expansions of the Impact of Maternal Care on Early Child Development
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Lykens, K., & Jargowsky, P. A. (2002) · Journal of Policy Analysis and Management · doi:10.1002/pam.10024 · Cited by 37
Maynard, R. A. (2006) · Journal of Policy Analysis and Management · doi:10.1002/pam.20169 · Cited by 47
Myers, N. D., Prilleltensky, I., Lee, S., Dietz, S., Prilleltensky, O., McMahon, A., Pfeiffer, K. A., Ellithorpe, M. E., & Brincks, A. M. (2019) · BMC Public Health · doi:10.1186/s12889-019-7089-2 · Cited by 36
Frequency of examinations and student achievement in a randomized experiment
Paola, M. D., & Scoppa, V. (2011) · Economics of Education Review · doi:10.1016/j.econedurev.2011.07.009 · Cited by 37
“Therefore, the estimated effects are diluted by the fact that some treated students may actually not have undertaken the intermediate exam (see Angrist and Pischke, 2009; Bloom, 1984).”
Reducing children’s behavior problems through social capital: A causal assessment
Turley, R. N. L., Gamoran, A., Turner, A., & Fish, R. E. (2016) · Social Science Research · doi:10.1016/j.ssresearch.2016.06.015 · Cited by 41
“…on a compliance model (described below) and estimates the difference in outcomes between predicted compliers in the treatment schools and predicted would-be compliers in the control schools (Angrist et al., 1996; Bloom, 1984; Borman and Dowling, 2006; Jo et al., 2008; Muthen and Muthen, 1998e2010).”
BackgroundBraver, S. L., & Smith, M. C. (1996) · Evaluation and Program Planning · doi:10.1016/s0149-7189(96)00029-8 · Cited by 37
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de Matos, M. G., Ferreira, P., & Belo, R. (2018) · Marketing science (Providence, R.I.) · doi:10.1287/mksc.2018.1099 · Cited by 31
“As such, we have one-side non-compliance with treatment assignment (Bloom 1984) and thus our LATE estimates measure the Average E↵ect on the Treated (ATT) (Angrist et al. 1996).”
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Heaney, C. A. (1991) · Health Education Quarterly · doi:10.1177/109019819101800406 · Cited by 35
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Self-citationHow an Earnings Supplement Can Affect Union Formation among Low-Income Single Mothers
Harknett, K., & Gennetian, L. A. (2003) · Demography · doi:10.1353/dem.2003.0024 · Cited by 33
“If we assume that the earnings-supplement offer had no effect on nonrecipients, then “Treatment on the Treated” (TOT) estimates can be derived simply by dividing experimental effects by the rate of supplement receipt ( Bloom 1984 ).”
BackgroundMichalopoulos, C., Robins, P. K., & Card, D. (2000) · RePEc: Research Papers in Economics · Cited by 33
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“A simple correction would be to weight the treatment effect by the inverse take-up rate, see Bloom (1984). Assuming a positive impact of voucher”
BackgroundLatent class instrumental variables: a clinical and biostatistical perspective
Baker, S. G., Kramer, B. S., & Lindeman, K. S. (2015) · Statistics in Medicine · doi:10.1002/sim.6612 · Cited by 32
“In 1984, Bloom [23] formulated a restricted latent class IV method to estimate the mean difference in outcomes among compliers in a randomized trial involving controls (T0) and experimental group consisting of ‘no-shows’ (T0) and those receiving the intervention (T1).”
MethodExplaining the Consequences of Imprisonment for Union Formation and Dissolution in Denmark
Fallesen, P., & Andersen, L. H. (2016) · Journal of Policy Analysis and Management · doi:10.1002/pam.21933 · Cited by 32
“Following Bloom (1984), we can then calculate the average treatment effect on the treated as the Wald estimator: ATT = ITTP(EMi=1|Policyi=1) (see also Angrist & Pischke, 2009, pp. 163–164).”
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Bloom, H. S. (1990) · doi:10.17848/9780585283319 · Cited by 32
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What Should Be Our Human Capital Investment Policy?
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Page, L. C., Feller, A., Grindal, T., Miratrix, L. W., & Somers, M. (2015) · American Journal of Evaluation · doi:10.1177/1098214015594419 · Cited by 31
“In the program evaluation literature, the idea appears to originate in the analysis of the Job Training Partnership Act (Bloom et al., 1997), with further discussion in Orr (1999), and eventually as endogenous or program-related subgroups (Peck, 2003; see Peck, 2013, for a discussion). In statistics, the term principa…”
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“Under the Rubin’s causal model framework, several methods developed for handling nonadherence, including instrumental variable (IV) approach from the field of econometrics [22], complier average causal effect (CACE) by Rubin [14,23], and structural mean models (SMMs) by Robins [24].”
BackgroundMethodYu, W., Chen, K., Sobel, M. E., & Ying, Z. (2014) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12072 · Cited by 30
“To remedy this deficiency, various researchers have introduced similar frameworks, leading to estimation of what is now called the complier average causal effect CACE (Angrist et al., 1996; Bloom, 1984; Baker and Lindeman, 1994; Cuzick et al., 1997; Imbens and Angrist, 1994).”
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“The amount of unobserved compliers in the control group can be estimated using the proportion of compliers in the intervention group based on the assumption that the proportion of compliers is balanced across the treatment arms [5,6].”
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“the program and only estimates what happens to children who have been exposed to targeted pre-K in the sense that the program was available to them (Bloom, 1984).”
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“5 “the best learning conditions we can devise” (Bloom, 1984, p. 4).”
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Chingos, M. M., & Peterson, P. E. (2012) · Brookings Institution eBooks · Cited by 28
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Heinrich, C. J., Burkhardt, B. C., & Shager, H. M. (2011) · Journal of Policy Analysis and Management · doi:10.1002/pam.20599 · Cited by 28
“As a sensitivity test, we estimated a standard instrumental variables (IV) model of program impacts, and we also applied the Bloom (1984) correction for the proportion of experimental group members who enrolled in the program to the experimental and IV results.”
MethodCooperation in public good games. Calculated or confused?
Goeschl, T., & Lohse, J. (2018) · European Economic Review · doi:10.1016/j.euroecorev.2018.05.007 · Cited by 28
“, the effect of treatment assignment) we report above corresponds to a weighted average of the zero (or reduced) effect on non-compliers and the true treatment effect on compliers (Bloom, 1984).”
Anderson, D. M., Broton, K. M., Goldrick‐Rab, S., & Kelchen, R. (2020) · Journal of Policy Analysis and Management · doi:10.1002/pam.22190 · Cited by 38
Response Quality in Telephone Surveys
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“each) K to 12 1,077 30% 24% Reading; 25% Math 10% (1st year) 70% (initial) Original research team used Bloom (1984) adj.”
BackgroundMethodInfluentialAnother look at the regression discontinuity design
Rettore, E., & Battistin, E. (2003) · Cemmap working papers · doi:10.1920/wp.cem.2003.0103 · Cited by 28
“In a fully randomized experiment, Bloom (1984) deals with the case where some units assigned to the program do not actually participate (no-shows). Exploiting information on participants, eligible non-participants and non-eligibles the author proves that the mean impact on participants is identifiable. The result in t…”
BackgroundResultInfluentialDemocracy, Redistribution, and Political Participation: Evidence from Sweden 1919-1950
Hinnerich, B. T., & Pettersson‐Lidbom, P. (2010) · Cited by 26
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Ong-Dean, C., Huie Hofstetter, C., & Strick, B. R. (2011) · American Journal of Evaluation · doi:10.1177/1098214010376532 · Cited by 26
“A common method of estimating TOT effects is the Bloom adjustment (Bloom, 1984). This adjustment, which assumes no effect of the treatment on ‘‘noshows’’ (i.e., treatment-assigned subjects who do not participate in the treatment), increases the estimated treatment effect in proportion to the number of no-shows. Howeve…”
MethodEstimating causal effects of internet interventions in the context of nonadherence
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“(6) is known as the instrument variable estimator of CACE (Bloom, 1984; Imbens and Rubin, 1997; Little and Yau, 1998) and both quantities in Eq.”
BackgroundRiccio, J. A., & Bloom, H. S. (2002) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/1467-985x.0asp4 · Cited by 25
“During the past 25 years, the MDRC has conducted 30 major random assignment experiments involving nearly 300000 people (Gueron, 1999).”
BackgroundMethodInfluentialSelf-citationDesigning Probability Samples to Study Treatment Effect Heterogeneity
Tipton, E., Yeager, D. S., Iachan, R., & Schneider, B. (2019) · Wiley eBooks · doi:10.1002/9781119083771.ch22 · Cited by 26
WHO BENEFITS FROM JOB CORPS? A DISTRIBUTIONAL ANALYSIS OF AN ACTIVE LABOR MARKET PROGRAM
Eren, Ö., & Ozbeklik, S. (2013) · Journal of Applied Econometrics · doi:10.1002/jae.2345 · Cited by 24
“(i) obtaining the pre-treatment differences adjusted empirical CDFs for Y0 and Y1, defined as b FjNj (y) = PNj i=1 b ωi I(Yj ≤ y) PNj i=1 b ωi j = 0, 1 (6)”
BackgroundRavallion, M. D. (2011) · doi:10.1596/1813-9450-5804 · Cited by 24
Do Job Corps performance measures track program impacts?
Schochet, P. Z., & Burghardt, J. (2008) · Journal of Policy Analysis and Management · doi:10.1002/pam.20356 · Cited by 23
“For each center performance group, we also estimated impacts for program participants only using an instrumental variables approach in which the impacts per eligible applicant were divided by the difference between the program participation rate for the treatment group and the control group crossover rate (Bloom, 1984…”
MethodSensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions
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“One common way to achieve identifiability in this situation is to apply the instrumental variable approach (Angrist, Imbens, & Rubin, 1996; Bloom, 1984).”
MethodMiller, H., Valentine, J. L., Fish, R. E., & Robinson, M. (2016) · American Journal of Education · doi:10.1086/688167 · Cited by 23
Inference for Instrumental Variables: A Randomization Inference Approach
Kang, H., Peck, L. R., & Keele, L. J. (2018) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12353 · Cited by 22
“…of τ̂Y divided by τ̂D and the resulting 1−α confidence interval formula (15) is τ̂ ± z1−α=2 √{ v̂ar.τ̂Y |F , Z/ τ̂2D } : .18/ This simple approximation was proposed by Bloom (1984) who suggested it in the context of programme evaluation under non-compliance, and it is widely used in the…”
Treatment Effect Heterogeneity
Smith, J. A. (2022) · Evaluation Review · doi:10.1177/0193841x221090731 · Cited by 21
“See Bloom et al. (1993) for the 18-month impacts from the JTPA evaluation and Orr et al.”
BackgroundMethodInfluentialMyers, N. D., Prilleltensky, I., Hill, C. R., & Feltz, D. L. (2017) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2017.02.010 · Cited by 22
“In general terms, and assuming less than full compliance in at least some participants, a large effect for compliers may manifest as a less than large overall effect under an ITT only approach (e.g., Bloom, 1984).”
BackgroundLocal Average and Quantile Treatment Effects Under Endogeneity: A Review
Huber, M., & Wüthrich, K. (2018) · Journal of Econometric Methods · doi:10.1515/jem-2017-0007 · Cited by 21
“Furthermore, Assumption 2 is satisfied by construction in randomized experiments with so-called onesided non-compliance (see Bloom 1984): if no subject randomized out of a job training can manage to “sneak into” the training, then Pr(𝐷(0) = 1) = 0 such that defiers as well always takers do not…”
BackgroundScrivener, S., Weiss, M. J., Sommo, C., & Fresques, H. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2089460 · Cited by 22
A note on the impact evaluation of public policies: the counterfactual analysis
Loi, M., & Margarida, C. R. J. R. F. M. (2012) · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · doi:10.2788/50327 · Cited by 22
“The group of eligible firms that are not treated are often referred to as “no shows” (Bloom, 1984).”
BackgroundLimitless Regression Discontinuity
Sales, A. C., & Hansen, B. B. (2019) · Journal of Educational and Behavioral Statistics · doi:10.3102/1076998619884904 · Cited by 22
“However, the GPAs in the AP study are discrete, measured in 1/100s of a grade point; hence, limits of functions of GPA do not exist.(1) Further, reanalysis of LSO’s RDD uncovers evidence of “social corruption” (Wong & Wing, 2016)—some students appear to have finely manipulated their GPAs to avoid probation. This neces…”
BackgroundMethodInfluentialShirom, A., Vinokur, A. D., & Price, R. (2008) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.2008.00369.x · Cited by 21
“We followed Bloom’s (1984) estimation procedure and calculated an adjusted effect size for the difference between intervention participants and their control group counterparts.”
Seven kinds of randomization plans for designing field experiments
Boruch, R. F., & Wothke, W. (1985) · New Directions for Program Evaluation · doi:10.1002/ev.1413 · Cited by 21
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Bayer, A., Grossman, J. B., & DuBois, D. L. (2013) · RePEc: Research Papers in Economics · Cited by 21
Huang, S., Córdova, D., Estrada, Y. A., Brincks, A. M., Asfour, L. S., & Prado, G. J. (2014) · Family Process · doi:10.1111/famp.12068 · Cited by 20
Reassessing validity threats in experiments: Focus on construct validity
Conrad, K. J., & Conrad, K. M. (1994) · New Directions for Program Evaluation · doi:10.1002/ev.1680 · Cited by 20
Integrating Process with Outcome Data in a Randomized Controlled Trial of Sex Education
Strange, V., Allen, E., Oakley, A., Bonell, C. P., Johnson, A., Stephenson, J., & the RIPPLE Study Team (2006) · Evaluation · doi:10.1177/1356389006069138 · Cited by 19
“In order to assess the impact on outcomes of the one experimental school which did not implement the peer-led sex education programme, an approach suggested by Sommer and Zeger (1991) was employed (see Bloom, 1984, for a similar approach).”
MethodLauby, J. L., Kotranski, L., Feighan, K., Collier, K., Semaan, S., & Halbert, J. (1996) · Journal of Drug Issues · doi:10.1177/002204269602600310 · Cited by 19
Love, J. M., Kisker, E. E., Ross, C., Schochet, P. Z., Jeanne Brooks-gunn, B., & Boller, K. (2001) · PsycEXTRA Dataset · doi:10.1037/e303652003-001 · Cited by 19
Barrera‐Osorio, F., & Raju, D. (2011) · doi:10.1596/1813-9450-5638 · Cited by 19
Evaluating Local Economic and Employment Development
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On Analysis of Symmetrically Predicted Endogenous Subgroups
Peck, L. R. (2013) · American Journal of Evaluation · doi:10.1177/1098214013481666 · Cited by 18
“In the simple situation where the subgroup of interest is characterized by participation in the offered treatment—or ‘‘take-up’’ or ‘‘compliance’’ with treatment assignment—traditionally an IV approach is used (e.g., Angrist et al., 1996; Bloom, 1984).”
BackgroundMethodHarknett, K. (2006) · Evaluation Review · doi:10.1177/0193841x06293411 · Cited by 19
“This strategy of assuming zero effects for nonparticipants and inferring TOT effects in the context of program evaluations was developed by H. S. Bloom (1984).”
MethodUmansky, I. M. (2018) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2018.1490470 · Cited by 18
“The Wald estimator estimates the average effect of EL classification for marginal compliers, that is, those students near the margin whose language classification complies with district policy (Bloom, 1984).”
BackgroundSimple Tests for Selection Bias: Learning More from Instrumental VariablesPreprint
Black, D. A., Joo, J., LaLonde, R. J., Smith, J. A., & Taylor, E. J. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2663776 · Cited by 17
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Angrist, J. D. (2005) · National Bureau of Economic Research · doi:10.3386/t0314 · Cited by 18
Vélez, J. L., Rodríguez, C., & SSnchez, F. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2939722 · Cited by 18
“Moreover, in the absence of always-takers, the average effect for compliers (LATE) is equal to the average effect for the treated (TOT), since any student receiving the treatment is by definition a complier (Bloom, 1984).”
Schochet, P. Z. (2020) · Journal of Policy Analysis and Management · doi:10.1002/pam.22233 · Cited by 16
Angrist, J. D., & Hull, P. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311556120 · Cited by 18
Efficient Covariate Balancing for the Local Average Treatment Effect
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“In the case of one-sided noncompliance P(Di(0) = 1) = 0, LATE equals TT (Bloom 1984).”
BackgroundVinokur, A. D., Price, R., Caplan, R. D., van Ryn, M., & Curran, J. (1995) · American Psychological Association eBooks · doi:10.1037/10183-009 · Cited by 16
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Learning About Social Programs from Experiments with Random Assignment of TreatmentsPreprint
Manski, C. F. (1995) · RePEc: Research Papers in Economics · Cited by 16
Growing America Through Entrepreneurship: Findings from the Evaluation of Project GATEPreprint
Benus, J. M., McConnell, S., Bellotti, J., Shen, T., Fortson, K., & Kahvecioglu, D. C. (2008) · RePEc: Research Papers in Economics · Cited by 16
Impacts of Title I Supplemental Educational Services on Student AchievementPreprint
Deke, J., Dragoset, L., Bogen, K., & Gill, B. (2012) · RePEc: Research Papers in Economics · Cited by 16
Can Restorative Justice Conferencing Reduce Recidivism? Evidence From the Make-it-Right Program
Shem-Tov, Y., Raphael, S., & Skog, A. (2021) · National Bureau of Economic Research · doi:10.3386/w29150 · Cited by 15
Brandon, P. R., Smith, N. L., Trenholm, C. A., & Devaney, B. (2010) · American Journal of Evaluation · doi:10.1177/1098214010382769 · Cited by 15
“In this situation, the typical method used to adjust impact estimates for nonparticipation is to divide the impact estimates by the participation rate (Bloom, 1984).”
MethodHousing mobility and the intergenerational durability of neighborhood poverty
Owens, A. M. D., & Clampet‐Lundquist, S. (2016) · Journal of Urban Affairs · doi:10.1080/07352166.2016.1245083 · Cited by 16
“Treatment-on-the-treated results can be obtained by scaling ITT coefficients and standard errors by the proportion of the experimental group that complied (Bloom, 1984), a method employed by other MTO research (e.g., Ludwig et al., 2013).”
MethodBaker, S. G. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01451_1.x · Cited by 15
“Models for latent compliance classes with all-or-none compliance in one group were formulated by Bloom (1984) for continuous outcomes and Sommer and Zeger (1991) and Connor, Prorok, and Weed (1991) for binary outcomes.”
Method
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
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