Consider using fixed effect models for small sample sizes to explain cluster-level effects.
Disaggregate within and between group effects in multi-level modeling for focused analysis.
Utilize Bayesian approaches and centering techniques to handle uncertainties and obtain accurate estimates in multi-level modeling.
Deep dives
Planning a Study with Multi-Level Models
When planning a study with multi-level models, considerations include power analysis, choice between multi-level models and models with corrections, and dealing with small sample sizes at level two clusters. Fixed effect models can be a last resort for small sample sizes, as they explain cluster-level effects and focus on within-group estimates.
Disaggregating within and between group effects
Disaggregating within and between group effects in multi-level modeling helps in focusing on the effects at different levels of the hierarchy. Fixed effect models are useful for explaining away cluster-level effects, allowing researchers to concentrate on lower-level questions.
Bayesian Approaches and Centering in Multi-Level Models
Bayesian approaches are beneficial for small sample sizes, with the use of priors to handle uncertainties. Centering approaches in multi-level modeling aid in obtaining pure within or between-group estimates, avoiding conflating effects across levels.
Diagnostics in Multi-Level Models
Diagnostics in multi-level models, involving residual plots, trend analysis, and identifying outliers, are crucial for empirically evaluating model assumptions. Utilizing traditional regression diagnostics can ensure model robustness and validity.
Choosing Between Multi-Level and Fixed Effect Models
Choosing between multi-level and fixed effect models depends on sample sizes, level of aggregation, and focus on within-group effects. Fixed effect models can serve as a practical option for addressing small sample sizes and providing clearer within-group estimates.
Patrick and Greg fulfill a legal obligation to interview the unnecessarily ubiquitous Dr. Dan McNeish of Arizona State University about why you probably don't need to use multilevel modeling even when you have multilevel data. Along the way they also mention MacNair, safety schools, the Green Monster, driving a Corvette across the country, Compensation Club, anklet shocks, endogeneity, frunks, Tom Brady's middle name, and de-meaning.
