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雷达卡
I would like to specify a multilevel model including the following variables:
- DV: count data, i.e. a score value between 0 and 13 (resulting from an additive index) for each individual
- Individual level IVs: 4 socioeconomic variables
- Group level IVs: 2 neighborhood characteristics
Data comprise approximately 2000 observations nested in 150 neighborhoods. Of interest are the context effects of the neighborhoods, i.e., their mediating role on the individual level variables. I would thus like to specify a varying slope, varying intercept multilevel model including cross-level interaction terms.
My question however relates to the DV. Due to the property of the DV as being count data, I thought of testing for overdispersion and, in case of overdispersion, I would have to take it into consideration when choosing an appropriate distribution. However, I am quite confused regarding the problem of overdispersion in multilevel models and the implementation in R, even after having read a lot in this forum and relevant literature.
I am currently using the lme4 package and have two rather basic questions:
- How do I test for overdispersion?
- How do I account for it in the model?
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