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xxM is a software package for multilevel structural equation modeling with complex dependent data structures. xxM implements a modeling framework called n-Level Structural Equation Modeling (NL-SEM) and can estimate models with any number of levels. Observed and latent variables are allowed at all levels.
Data-structures- Hierarchically nested data (e.g., students << classrooms << schools << districts).
- Cross-classified data (e.g., students crossed within schools and neighborhoods).
- Partial nesting (e.g., only at-risk students in a classroom receive additional instruction by a tutor).
- Longitudinal data (long or wide).
- Longitudinal data with switching classification (e.g., students changing classrooms/teachers over time).
- Round-robin design (e.g., each person rates every other person in a small group).
- 360 performance evaluation data.
- A combination of above structures for different level pairs.
- Multilevel models (MLM) with random-effects of observed variables (MLM, HLM).
- Structural equation models (SEM) with observed and latent variables at all levels. (Mplus, GLLAMM)
- Linear Mixed-Effects Models (LME) with constraints on both G and R side of the model. (lme4: lmer, SAS: HPMixed)
- Linear Growth Curve (LGC) Models.
- David Kenny’s Social Relations Model (SRM) for reciprocal dyadic ratings.
- Efficient maximum Likelihood (ML) estimation.
- Profile-likelihood confidence intervals (CI).
- Fixed-to-constant and equality constraints on model parameters.
- Works well with small datasets as well.
- ‘If you can draw the model, you can build the model’. Graphical representation of NL-SEM models is intuitive.
- ‘If you can build a toy-model, you can build a skyscraper’. Models are constructed using a ‘building-block’ approach.
- xxM reference manual documents all commands and packaged datasets.
- xxM user’s guide provides examples of NL-SEM models.
- Additional examples of ‘live’ xxM model fitting sessions are available on the site.
- Discussion forum.
- Priceless!
Development of xxM was made possible by a generous grant (R305D090024) from the Institute of Education Sciences, U.S. Department of Education awarded to Paras D. Mehta.
Contact- paras dot mehta at times dot uh dot edu
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