43 research outputs found
Data for: Discovery, dissemination, and information diversity in networked groups
No full textExperimental data associated with pape
Supplemental Material - Mechanisms Underlying Choice-Set Formation: The Case of School Choice in Chile
No full textSupplemental Material for Mechanisms Underlying Choice-Set Formation: The Case of School Choice in Chile by Catalina Canals, Spiro Maroulis, Enrique Canessa, Sergio Chaigneau, and Alejandra Mizala in Social Science Computer Review</p
School enrollment model
No full textPlease refer to the updated version of this model:
Catalina Canals, Enrique Canessa, Spiro Maroulis, Alejandra Mizala, Sergio Chaigneau (2024, July 08). “School Enrollment Model” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/a03bcb4e-733c-41d0-b3ec-e4035b06faf4/releases/1.0.0/
The School Enrollment Model is a spatially-explicit computational model that depicts a city, with schools and households located within the space. The model represents the Chilean school system, a market-based educational system, where people are free to choose among public, private voucher, or unsubsidized private schools. In the model, households become aware of some schools, apply to schools, switch schools, pass or fail grade levels, and eventually either graduate or dropout. Schools select students, update their tuition, test scores, and other characteristics.
The model was implemented using the Netlogo multi-agent programmable modeling environment v.6.0.4. It uses GIS data about schools, areas of the city (blocks or neighborhoods) and city boundaries as an input
School enrollment model
No full textPlease refer to the updated version of this model:
Catalina Canals, Enrique Canessa, Spiro Maroulis, Alejandra Mizala, Sergio Chaigneau (2024, July 08). “School Enrollment Model” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/a03bcb4e-733c-41d0-b3ec-e4035b06faf4/releases/1.0.0/
The School Enrollment Model is a spatially-explicit computational model that depicts a city, with schools and households located within the space. The model represents the Chilean school system, a market-based educational system, where people are free to choose among public, private voucher, or unsubsidized private schools. In the model, households become aware of some schools, apply to schools, switch schools, pass or fail grade levels, and eventually either graduate or dropout. Schools select students, update their tuition, test scores, and other characteristics.
The model was implemented using the Netlogo multi-agent programmable modeling environment v.6.0.4. It uses GIS data about schools, areas of the city (blocks or neighborhoods) and city boundaries as an input
School enrollment model
No full textPlease refer to the updated version of this model:
Catalina Canals, Enrique Canessa, Spiro Maroulis, Alejandra Mizala, Sergio Chaigneau (2024, July 08). “School Enrollment Model” (Version 1.0.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/a03bcb4e-733c-41d0-b3ec-e4035b06faf4/releases/1.0.0/
The School Enrollment Model is a spatially-explicit computational model that depicts a city, with schools and households located within the space. The model represents the Chilean school system, a market-based educational system, where people are free to choose among public, private voucher, or unsubsidized private schools. In the model, households become aware of some schools, apply to schools, switch schools, pass or fail grade levels, and eventually either graduate or dropout. Schools select students, update their tuition, test scores, and other characteristics.
The model was implemented using the Netlogo multi-agent programmable modeling environment v.6.0.4. It uses GIS data about schools, areas of the city (blocks or neighborhoods) and city boundaries as an input
Quantifying strength of evidence in education research : accounting for spillover, heterogeneity, and mediation
Get PDF"It is very rare that education studies have constant intervention effects through simple mechanisms to independent individuals. It is well-documented that schooling is a complex process because teachers, students, and administrators interact with each other in a diverse set of social contexts (e.g., An, 2018; Frank, 1998; Hong, 2015; Kim, Frank, & Spillane, 2018; Maroulis et al., 2010). As such, considering potential bias due to unobserved or uncontrolled spillover, heterogeneity and alternative mediators is important to making an inference for policy implications. Additionally, since the ultimate goal of education research is to inform decision-makings in the allocation of educational resources regarding curricula, pedagogy, practices or school organizations (e.g., Bulterman-Bos, 2008; Cook, 2002), education research must be accessible to practitioners. Consequently, a sensitivity framework that can account for all potential sources of bias, including spillover, heterogeneity and alternative mediators, is required to allow all stakeholders to conceptualize the quality of evidence independently so that the debate for future policy manipulations can take place in a more transparent, effective and equitable way.Drawn on the work by Frank, Maroulis, Duong, and Kelcey (2013), Chapters 1 and 2 in this dissertation propose a non-parametric case replacement approach to quantify the robustness of inference in multisite randomized control trials and value-added measures for teacher effectiveness, accounting for spillover and heterogeneity. Throughout, the Tennessee class size experiment (Project STAR) is applied to demonstrate the case replacement approach. Chapters 3 and 4 focus on unobserved mediators in a single-mediator model. Specifically, Chapter 3 examines whether and how omitting an alternative mediator can bias causal mediation effect estimates in a crosssectional single-mediator model. Further, a sensitivity analysis approach is proposed to evaluate the robustness of causal mediation inference to missing a potential confounding mediator. Chapter 4 continues the discussion in Chapter 3 and a parameter framework is developed to characterize inconsistency in mediation models. This parameter framework is also applied to a longitudinaldesign for a post-treatment confounder."--Pages ii-iii.Thesis (Ph. D.)--Michigan State University. Measurement and Quantitative Methods, 2019Includes bibliographical references (pages 131-137
Identification, estimation, and sensitivity analysis of contagion effects using longitudinal social network data
No full text"Contagion effects, also known as peer effects or social influence process, refer to the phenomenon whereby people tend to assimilate the behavior of those with whom they have interaction in a social network. With the availability of longitudinal social network data, studies of contagion effects have become more and more central to social science, with many applications in the field of education, such as the diffusion of innovation, change of health behaviors, academic outcomes among adolescents, and the implementation of practices among teachers (Valente, 1995, 1996; Christakis et al., 2007, 2008; Sacerdote, 2000; Frank et al, 2004). However, contagion effects are usually difficult to identify as they are often entangled with other factors such as homophily in the selection process, an individual's preference for the same social settings, etc. Methods currently available either do not solve these problems or require strong assumptions. Furthermore, there is still a significant degree of misconception about why identifying contagion effects is a problem, and when these methods should be applied. For this dissertation, in the first chapter I will clarify why and when we will encounter problems identifying contagion effects. Specifically I will frame this in terms of an omitted variable bias problem; and then I will explore the magnitude of bias in the estimation of contagion effects in various situations, and possible remedies under an OLS framework. In the second chapter I will propose some alternative estimation methods that have the potential to correctly identify contagion effects under weaker assumptions when there are unobserved variables present. In the third chapter I will propose a set of simulation-based sensitivity analysis methods that can test the robustness of inferences made in social network analysis, especially inferences about contagion effects."--Page ii.Thesis (Ph. D.)--Michigan State University. Measurement and Quantitative Methods, 2016Includes bibliographical references (pages 110-117
Stable processes of exchange
No full textIn this paper we address a long standing gap in economic theory--the gap between claims for the dynamic efficiency of trading in markets, and the findings of formal economic theory, which justify those claims only under restrictive assumptions. We use agent-based methods to study the dynamics of exchange with trading agents who are characterized by several different preference relations. We see that outcomes converge with high probability to Pareto optima in the cases studied, including the well-known example due to Scarf.General equilibrium Agent-based modeling
