Project Activities
The CC-SEM library was developed as an extension to OpenMx, an open source SEM project integrated into the R statistical modeling language. Integration of CC-SEM with R and OpenMX is to make the software: (1) fully programmable and extensible; (2) free and opensource; (3) multiprocessor and cluster enabled; and (4) user-friendly with multiple front-ends (e.g., matrix, simple script based and graphical interfaces). The library used sparse matrix methods for formulating CC-SEM models as restricted linear mixed-effects models and for computing the likelihood and its derivatives. Potential users of CC-SEM include substantive users interested in analyzing data, methodologists interested in evaluating models and methods, and statistical developers interested in further extending its functionality.
In addition, the project team analyzed a number of large educational datasets with common methodological issues that are of interest to educational researchers. The methodological issues include: (1) longitudinal (multiple years, grades, and cohorts) student language and literacy outcome data with multiple within-year and end-of-year assessments with cross-classification of responses within teachers from different grades; (2) multiple teachers within a grade; (3) repeated teachers across years;(4) pullout instruction for a subset of students; (5) multiple latent student constructs of interest; and (6) teacher, school and district level constructs of interest.
The project fully documented every aspect of the software library as well as the CC-SEM modeling framework using the R documentation standards. A user-friendly manual for the CC-SEM software was developed that includes examples from publicly available datasets as well as the datasets used in the secondary data analysis.
People and institutions involved
IES program contact(s)
Products and publications
Book chapter
Branum-Martin, L. (2013). Multilevel Modeling: Practical Examples to Illustrate a Special Case of SEM. In Y. Petscher, C. Schatschneider, and D. Compton (Eds.), Applied Quantitative Analysis in the Social Sciences (pp. 95-124). New York: Routledge.
Mehta, P.D. (2013). nLevel Structural Equation Modeling. In Y. Petscher, C. Schatschneider, and D.L. Compton (Eds.), Applied Quantitative Analysis in Education and the Social Sciences (pp. 329-362). New York: Routledge.
Mehta, P. D., & Petscher, Y. (2016). N-level structural equation model of student achievement data nested with repeated teachers, schools, and districts. In J. R. Harring, L. M. Stapleton, & S. N. Beretvas (Eds.), Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applications (pp. 193-228). IAP Information Age Publishing.
Journal article, monograph, or newsletter
Brunson, J.A., Øverup, C.S., and Mehta, P.D. (2016). A Social Relations Examination of Neuroticism and Emotional Support. Journal of Research in Personality, 63, 67-71.
Mehta, P. D. (2018). Virtual Levels and Role Models: N-Level Structural Equations Model of Reciprocal Ratings Data. Multivariate Behavioral Research, 1-20.
Øverup, C. S., Brunson, J. A., & Mehta, P. D. (2021). A Social Relations Model of need supportiveness. Journal of Research in Personality, 94, 104142.
Porter, B., Øverup, C. S., Brunson, J. A., & Mehta, P. D. (2018). Meta-accuracy and perceived reciprocity from the perception-meta-perception social relations model. Social Psychology, 50, 24-37.
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