Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)

 
H  HLM workshops

Location: Gleacher Center, Chicago
Instructors: Steve Raudenbush, University of Michigan & Tony Bryk, Stanford University

For more detail information about Spring Session, please click here.
For more detail information about Fall Session, please click here.

Research applications of HLM analysis of hierarchical data structures continue to expand as new models are incorporated in the computing procedures. The models make possible a highly efficient and informative analysis of complex survey data, including those from two- and three-stage unbalanced sampling designs, possibly with explanatory variables and random effects at all three stages. For example, in studies of factors that influence year-to-year gains in the achievement of school students, the level-1 model may describe the course-of-growth in the achievement of individual students over a number of years, the level-2 model represents differences between classrooms, and the level-3 model incorporates effects between schools.

From variables specified at each level, the program generates the linear model with the respective explanatory variables that account for response variability at each level. The hierarchical linear analysis not only estimates the model coefficients at each level, but it also predicts the random effects associated with each sampling unit at each level.

These analyses can now be carried out by full-information maximum likelihood methods using a combination of EM and Fisher scoring algorithms for fast and stable convergence to the solution optimum. These procedures provide standard errors for both the fixed effects and the variance-covariance components that describe the random effects.

Previously, these analyses were available only for data in which the errors of prediction at each level could be assumed approximately normally distributed. New statistical developments now allow Bernoulli and binomial models for binary data to be analyzed with a logit link function, and a Poisson model allows count data to be analyzed with a log link function.

The session will cover the principles and assumptions of hierarchical linear analysis, applications to a wide variety of practical problems, and the interpretation and reporting of results. The presentation will include many worked examples and computer demonstrations. A prerequisite for the workshop is a working knowledge of simple regression analysis.

If you are completely new to multilevel analysis, it is recommended as a preparation for the workshop to download the free student edition of HLM and work through some of the examples as described in HLM's online Help system. The free HLM student edition may be downloaded from the HLM downloads page.

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