Introduction to Multi-Level Modeling

**Chapter Three: Random coefficient models: When intercepts and coefficients vary****Robson, K., & Pevalin, D. (2016). Random coefficient models: When intercepts and coefficients vary.***Multilevel modeling in plain language*(pp. 67-114). SAGE Publications Ltd.

This chapter discusses multilevel models with lines that are allowed to have different slopes. These models are known as random coefficient models. You should also be aware that random coefficient models also assume random intercepts in most cases.**Chapter 6: Longitudinal models****Luke, D. (2020). Chapter 6 Longitudinal models.***Multilevel modeling: Second Edition*(pp. 79-93). SAGE Publications, Inc.

This chapter focuses on longitudinal models, which are examples of random coefficient models. When we consider multilevel models, it is not unusual to think first of individual objects nested within a physical or social context, such as persons in neighborhoods or clinics in hospitals. However, mixed-effects models can be applied to multiple observations nested within a single object. In particular, mixed-effects models can be applied to longitudinal data where the primary interest is in modeling the structure and predictors of change over time.**Random intercept models****Centre for Multilevel Modelling. (n.d.).***Random intercept models.*University of Bristol.

This is a very rich resource for multilevel modeling and it breaks down complex concepts of multilevel modeling into simpler forms which are easy to understand, and it also provides concrete examples which students can relate to.

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