Multilevel modeling techniques and applications in institutional research
Source:New directions for institutional research ; no. 154, Jossey-Bass, San Francisco, p.132 (2012)
Call Number:Cubb LB1028 .N5 NO.154
Keywords:Education, Higher--Research--Statistical methods, Multilevel models (Statistics), Multivariate analysis, Social sciences--Research--Mathematical models, Sociology--Research--Mathematical models
Contents: Hierarchical data structures, institutional research, and multilevel modeling / Ann A. O'Connell, Sandra J. Reed -- Introduction to estimation issues in multilevel modeling / D. Betsy McCoach, Anne C. Black -- Using existing data sources/programs and multilevel modeling techniques for questions in institutional research / Joe P. King, José M. Hernandez, Joe L. Lott, II -- Multilevel models for binary data / Daniel A. Powers -- Cross-classified random effects models in institutional research / Laura E. Meyers -- Multilevel modeling: applications to research on the assessment of student learning, engagement, and developmental outcomes / Pu-Shih Daniel Chen, Kristina Cragg -- Multilevel modeling: presenting and publishing the results for internal and external constituents / Gary R. Pike, Louis M. Rocconi.; Summary: Multilevel modeling is an increasingly popular multivariate technique that is widely applied in the social sciences. Increasingly, institutional research (IR) practitioners are making instructional decisions based on results from their multivariate analyses, which often come from nested data that lend themselves to multilevel modeling techniques. As data-driven decision making becomes more critical to colleges and universities, multilevel modeling is a tool that will lead to more efficient estimates and enhance understanding of complex relationships. This volume illustrates both the theoretical underpinnings and practical applications of multilevel modeling in IR. It introduces the fundamental concepts of multilevel modeling techniques in a conceptual and technical manner. Providing a range of examples of nested models that are based on linear and categorical outcomes, it then offers important suggestions about presenting results of multilevel models through charts and graphs.