Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Journal of Educational and Behavioral Statistics
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Rogosa, D.
Right arrow Articles by Saner, H.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Articles

Longitudinal Data Analysis Examples With Random Coefficient Models

David Rogosa

Stanford University

Hilary Saner

RAND Corporation

Longitudinal panel data examples are used to illustrate estimation methods for individual growth curve models. These examples constitute one of the basic multilevel analysis settings, and they are used to illustrate issues and concerns in the application of hierarchical modeling estimation methods, specifically, the widely advertised HLM procedures of Bryk and Raudenbush. One main expository purpose is to demystify these analyses by showing equivalences with simpler approaches. Perhaps more importantly, these equivalences indicate useful data analytic checks and diagnostics to supplement the multilevel estimation procedures. In addition, we recommend the general use of standardized canonical examples for the checking and exposition of the various multilevel procedures; as part of this effort, methods for the construction of longitudinal data examples with known structure are described.

Key Words: longitudinal data analysis • hierarchical linear models

Journal of Educational and Behavioral Statistics, Vol. 20, No. 2, 149-170 (1995)
DOI: 10.3102/10769986020002149


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
Applied Psychological MeasurementHome page
J. R. Rausch
Investigating Change in Intraindividual Factor Structure Over Time
Applied Psychological Measurement, June 1, 2009; 33(4): 266 - 284.
[Abstract] [PDF]


Home page
Eval Health ProfHome page
A. A. O'Connell and D. B. McCoach
Applications of Hierarchical Linear Models for Evaluations of Health Interventions: Demystifying the Methods and Interpretations of Multilevel Models
Eval Health Prof, June 1, 2004; 27(2): 119 - 151.
[Abstract] [PDF]


Home page
JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
E.S. Tan, A.W. Ambergen, R.J.M.M. Does, and Tj. Imbos
Approximations of Normal IRT Models for Change
Journal of Educational and Behavioral Statistics, January 1, 1999; 24(2): 208 - 223.
[Abstract] [PDF]


Home page
EDUCATIONAL EVALUATION AND POLICY ANALYSISHome page
D. P. Mayer
Do New Teaching Standards Undermine Performance on Old Tests?
Educational Evaluation and Policy Analysis, January 1, 1998; 20(2): 53 - 73.
[Abstract] [PDF]



AER home page RER home page JEB home page EPA home page RRE home page