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Related Experiment Video

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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Published on: September 17, 2019

Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects.

Daniel J Bauer¹, Nisha C Gottfredson, Danielle Dean

¹Department of Psychology, University of North Carolina, Chapel Hill, NC 27599-3270, USA. dbauer@email.unc.edu

Psychological Methods

|November 15, 2012

Summary

This summary is machine-generated.

This study introduces multilevel linear models to analyze how dynamic group effects change over time. These models capture evolving group influences on individual trajectories, such as in schools or families.

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Area of Science:

Multilevel modeling
Longitudinal data analysis
Group dynamics

Background:

Repeated measures data are common in research, often involving individuals nested within groups (e.g., students in schools).
Groups can be dynamic, with changing structures and functions over time, affecting individual experiences.

Purpose of the Study:

To demonstrate the application of multilevel linear models for analyzing time-varying group effects.
To account for evolving group characteristics in longitudinal studies of nested data.

Main Methods:

Utilizing multilevel linear models to analyze repeated measures data.
Modeling time-varying effects of groups on individual outcomes.

Main Results:

The proposed method effectively recovers time-varying group effects.
Examples illustrate dynamic school effects on student achievement and family effects on behavior.

Conclusions:

Multilevel models are effective for analyzing nested data with evolving group structures.
This approach enhances understanding of how dynamic group environments influence individual development over time.