Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

By Stephanie K. Riegg. 2008

116 citations recorded by the source corpus.

Subfields: Education, Statistics and Probability

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, background): “Omitted variable bias4 occurs when one or more important variables are left out of the model [38, 114, 131].”
State Higher Education Performance Funding: Data, Outcomes, and Policy Implications* (2014, methodology): “technique is often used in policy analysis and program evaluation and is becoming more widely used in higher education policy scholarship (Cellini 2008).”
A Review of the Theories Developed to Describe the Process of College Persistence and Attainment (2011, background): “3 For a detailed description of the selection problem see ( Cellini, 2008; Melguizo, 2008).”
Reducing Income Inequality in Educational Attainment: Experimental Evidence on the Impact of Financial Aid on College Completion1 (2016, background): “…the true benefits of that aid, partly depending on whether the estimates are based on aid eligibility or aid receipt among other factors ðAlon 2005; Cellini 2008; Goldrick-Rab et al. 2009; Harris and GoldrickRab 2012;Castleman andLong 2013Þ.Mostfinancial aid research uses basic regression…”
If Life Happened but a Degree Didn’t: Examining Factors That Impact Adult Student Persistence (2014, background): “As Cellini (2008) notes, endogeneity—caused by reverse causality or self-selection bias within models—limits the ability to make causal inference.”
Investigating the Impact of Financial Aid on Student Dropout Risks: Racial and Ethnic Differences (2010, background): “As several researchers (Alon, 2005; Cellini, 2008; DesJardins, 2005) have pointed out, one reason for the inconsistent findings in the prior student aid studies is the difficulty in controlling for the relationship between aid eligibility and college outcomes.”
College on Credit: A Multilevel Analysis of Student Loan Default (2013, background): “This unobserved heterogeneity can bias parameter estimates, and readers should take caution—interpreting results as descriptive rather than causal (Cellini, 2008).”
An Investigation of the Contingent Relationships Between Learning Community Participation and Student Engagement (2011, background): “This ‘‘omitted variable bias’’ does not allow causal attributions ( Cellini 2008; DesJardins et al. 2002).”