We contribute to debate about causal inferences in educational research in two ways. First, we quantify how much bias there must be in an estimate to invalidate an inference. Second, we utilize Rubin's causal model to interpret the bias necessary to invalidate an inference in terms of sample replacement. We apply our analysis to an inference of a positive effect of Open Court Curriculum on reading achievement from a randomized experiment, and an inference of a negative effect of kindergarten retention on reading achievement from an observational study. We consider details of our framework, and then discuss how our approach informs judgment of inference relative to study design. We conclude with implications for scientific discourse.

Academic entrepreneurship, the establishment of new companies based on technologies derived from university research, is a well-recognized driver of regional and national economic development. For more than a decade, scholars have conceptualized individual university faculty as the primary agents of academic entrepreneurship. Recent research suggests that graduate students also play a critical role in the establishment and early development of university spinoff companies, but the nature of their involvement through the entrepreneurial process is not yet fully understood. Employing a case study approach, this paper investigates the role of graduate students in early-stage university spinoff companies from the Massachusetts Institute of Technology. We find that graduate students play role similar to that of individual faculty entrepreneurs in university spinoffs, both in terms of making the initial establishment decision and in reconfiguring the organization for marketable technology development. We also find that student entrepreneurs face unique challenges involving conflicts with faculty advisors and other students.

Agent-based modeling and network analysis can help integrate knowledge on “micro-level” mechanisms and “macro-level” effects.

Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.