# Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models

@article{Singer1998UsingSP, title={Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth Models}, author={J. Singer}, journal={Journal of Educational Statistics}, year={1998}, volume={23}, pages={323 - 355} }

SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistical package makes it an ideal choice for empirical researchers and applied statisticians seeking to do data reduction, management, and analysis within a single statistical package. Because the program was developed from the perspective of a “mixed” statistical model with both random and fixed effects… Expand

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#### References

SHOWING 1-10 OF 30 REFERENCES

The Effect of Different Forms of Centering in Hierarchical Linear Models.

- Computer Science, Medicine
- Multivariate behavioral research
- 1995

It is concluded that centering rules valid for simple models, such as the fixed coefficients regression model, are no longer applicable to more complicated models,such as the random coefficient model. Expand

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

- Mathematics, Medicine
- Computer methods and programs in biomedicine
- 1996

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors, utilizing both the EM algorithm and a Fisher-scoring solution. Expand

Multilevel Analysis Methods

- Computer Science
- 1994

Some traditional approaches to the analysis of multilevel data and their statistical shortcomings are discussed and the random coefficient linear model is presented, which resolves many of these problems and the currently available software is discussed. Expand

Heterogeneous Variance-Covariance Structures for Repeated Measures

- Mathematics
- 1996

This article provides a unified discussion of a useful collection of heterogeneous covariance structures for repeated-measures data. The collection includes heterogeneous versions of the compound… Expand

Multilevel time series models with applications to repeated measures data.

- Mathematics, Medicine
- Statistics in medicine
- 1994

A time series model is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals and it is shown how the autcorrelation parameters can themselves be structured in terms of further explanatory variables. Expand

Covariance structure selection in general mixed models

- Mathematics
- 1993

This article describes a unified approach to variance modeling and inference in the context of a general form of the normal-theory linear mixed model. The primary variance modeling objects are… Expand

A note on the covariance structure in a linear model

- Mathematics
- 1997

Abstract The covariance structure of a random or mixed linear model is sensitive to minor changes in the characterization of random effects, and what appear to be inconsequential differences in the… Expand

Modeled Variance in Two-Level Models

- Mathematics
- 1994

The concept of explained proportion of variance or modeled proportion of variance is reviewed in the situation of the random effects hierarchical two-level model. It is argued that the proportional… Expand

A Unified Approach to Mixed Linear Models

- Mathematics
- 1991

Abstract The mixed model equations as presented by C. R. Henderson offers the base for a methodology that provides flexibility of fitting models with various fixed and random elements with the… Expand

Hierarchical Linear Models: Applications and Data Analysis Methods

- Computer Science, Sociology
- 1992

This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known. Expand