Detecting selection bias, using propensity score matching, and estimating treatment effects: an application to the private returns to a master’s degree

By Marvin A. Titus. 2007

135 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Education, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b79764607071b03ccf647b58127fa3170be7d400

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Citations in the corpus

Emergence of Financial Intermediaries in Electronic Markets: The Case of Online P2P Lending (2009, unknown): “The propensity score is usually determined using Logit or Probit models (Titus 2007).”
The Effect of Study Abroad on Intercultural Competence Among Undergraduate College Students (2013, background): “” Although some postsecondary education researchers have attempted to show the importance of this method for examining a range of important research questions using observational data (Brand & Halaby, 2006; Reynolds & DesJardins, 2009; Titus, 2007), other scholars have questioned the perceived unifo”
Comparing the Educational Attainment of Community College Transfer Students and Four-Year College Rising Juniors Using Propensity Score Matching Methods (2011, background): “The limitations of PsM methods in this analysis are understood; thus, the results from this study should not be interpreted as causal. nevertheless, as heckman, lalonde, and smith (1999) noted, non-parametric methods, if rigorously applied, force analysts only to compare comparable people.”
Non-farm work and food security among farm households in Northern Ghana (2011, background): “However, under the assumption of unconfoundedness, Titus (2007) and Millimet and Tchernis(2008) point out that an appropriate approach of evaluating policy-relevant outcomes in a counterfactual framework is to examine not only the ATT, but also the Average Treatment Effect on the Untreated (ATU),…”
The Impact of Developmental Education on Community College Persistence and Vertical Transfer (2014, methodology): “According to Titus (2007), PSM can be used by educational researchers using national databases to produce more accurate parameter estimates and make stronger inferences of cause and effect by allowing for the comparison of outcomes among students who are not comparable.”
If and When Money Matters: The Relationships Among Educational Expenditures, Student Engagement and Students’ Learning Outcomes (2011, unknown): “Although there are a variety of statistical techniques designed to account for this type of bias, including instrumental-variable analysis (Angrist et al. 1996) and propensity-score matching ( Titus 2007 ), there are serious issues associated with the identification of variables that can serve as in”
If Life Happened but a Degree Didn’t: Examining Factors That Impact Adult Student Persistence (2014, background): “or endogeneity, a persistent and growing concern in education research (DesJardins et al., 1999; Dowd & Coury, 2006; Titus, 2007).”
A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark (2010, background): “…that matching is a superior way to estimate causal effects from observational data (Gilligan and Sergenti 2008:91; Morgan and Harding 2006:3; Titus 2007:487; VanderWeele 2006:95), with some going even further to argue that matching ‘‘mimic[s] randomization’’ (Hahs-Vaughn and Onwuegbuzie…”
An Investigation of the Contingent Relationships Between Learning Community Participation and Student Engagement (2011, background): “Although a variety of statistical techniques, including instrumental-variable analysis (Angrist et al. 1996) and propensity-score matching ( Titus 2007 ), attempt to account for this type of bias, there are serious issues associated with the identification of variables to serve as instruments or cal”