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Implementing Structural Equation Modeling on Public Data

Implementing Structural Equation Modeling on Public Data

Yair Cohen, Elsa-Sofia Morote
Copyright: © 2018 |Pages: 18
ISBN13: 9781522536161|ISBN10: 1522536167|EISBN13: 9781522536178
DOI: 10.4018/978-1-5225-3616-1.ch008
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MLA

Cohen, Yair, and Elsa-Sofia Morote. "Implementing Structural Equation Modeling on Public Data." Maximizing Social Science Research Through Publicly Accessible Data Sets, edited by S. Marshall Perry, IGI Global, 2018, pp. 178-195. https://doi.org/10.4018/978-1-5225-3616-1.ch008

APA

Cohen, Y. & Morote, E. (2018). Implementing Structural Equation Modeling on Public Data. In S. Perry (Ed.), Maximizing Social Science Research Through Publicly Accessible Data Sets (pp. 178-195). IGI Global. https://doi.org/10.4018/978-1-5225-3616-1.ch008

Chicago

Cohen, Yair, and Elsa-Sofia Morote. "Implementing Structural Equation Modeling on Public Data." In Maximizing Social Science Research Through Publicly Accessible Data Sets, edited by S. Marshall Perry, 178-195. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3616-1.ch008

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Abstract

The researchers used structural equation modeling (SEM) to create a model predicting fourth-grade student achievement in math by exploring the relationships among: student, household, school, and teacher factors. Public data from the New York State Education Department (NYSED) 2012–13 school report card data, NYSED fiscal reporting system, Census 2010 School District Demographics System, and 2011 Civil Right Data Collection were used from 1,263 schools in New York excluding New York City. Variables were chosen using this convenient sample and supported by our conceptual rationale. The model predicted fourth-grade math achievement with 67 percent of effect size. Household factors had strong predictive, while school attendance rate had medium predictive value for student achievement.

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