Choosing between growth curve modeling (latent growth modeling) and multilevel modeling for repeated measures depends on several factors, including the structure of your data, the research questions you want to address, and the specific characteristics of your longitudinal study. Here are some key considerations to help you decide:

Growth Curve Modeling (Latent Growth Modeling)

Pros:

• Explicit Modeling of Growth Trajectories: Latent growth models explicitly model the growth trajectories over time, allowing you to estimate initial status (intercept) and rate of change (slope).


• Flexibility in Modeling Growth Patterns: You can model various growth patterns, including linear, quadratic, or piecewise growth.


• Handling Missing Data: Latent growth models can handle missing data more effectively using full information maximum likelihood (FIML) estimation.


• Incorporation of Latent Variables: You can incorporate latent variables to account for measurement error and model complex relationships.

• Complexity: Latent growth models can be more complex to specify and interpret, especially for those not familiar with structural equation modeling (SEM).


• Software Requirements: Typically requires specialized software like Mplus, AMOS, or the lavaan package in R.

Multilevel Modeling for Repeated Measures

Pros:

• Simplicity and Flexibility: Multilevel models are generally easier to specify and interpret, especially for those familiar with regression analysis.


• Handling Nested Data: Multilevel models are well-suited for handling nested data structures, such as repeated measures within individuals.


• Incorporation of Random Effects: You can model random effects to account for individual differences in growth trajectories.


• Wide Software Availability: Can be implemented in a variety of statistical software, including R (lme4 package), SAS, SPSS, and Stata.

• Less Explicit Growth Modeling: While you can model growth trajectories, the focus is more on the hierarchical structure of the data rather than explicit growth patterns.


• Handling Missing Data: Multilevel models can handle missing data, but they may not be as robust as latent growth models in this regard.

Key Considerations

• Research Questions:


• If your primary interest is in modeling and understanding the growth trajectories over time, latent growth modeling might be more appropriate.


• If you are more interested in the hierarchical structure of the data and the variability within and between individuals, multilevel modeling might be more suitable.


• Data Structure:


• If your data has a clear hierarchical structure (e.g., repeated measures within individuals), multilevel modeling is a natural choice.


• If your data has a more complex structure with multiple waves and you want to model growth patterns explicitly, latent growth modeling might be better.


• Software and Expertise:


• Consider your familiarity with the statistical software and the complexity of the models you are comfortable with.


• If you are more comfortable with regression-based approaches, multilevel modeling might be easier to implement.


• Missing Data:


• If missing data is a significant concern, latent growth modeling with FIML estimation might be more robust.

Example Scenarios

• Scenario 1: You have longitudinal data with multiple waves and want to model linear growth over time. You are interested in estimating the initial status and rate of change.


• Recommendation: Latent growth modeling.


• Scenario 2: You have longitudinal data with multiple waves and want to understand the variability in growth trajectories between individuals. You are also interested in the hierarchical structure of the data.


• Recommendation: Multilevel modeling.

Conclusion

Both approaches have their strengths and weaknesses, and the choice depends on your specific research questions, data structure, and familiarity with the statistical methods. If you are primarily interested in modeling growth trajectories and handling missing data robustly, latent growth modeling might be the better choice. If you are more interested in the hierarchical structure of the data and the variability within and between individuals, multilevel modeling might be more appropriate.