Dear David,
R-sig-mixedmodels is a better mailing list for this kind of question.
1) yes
2) use (Treatment | Random_Assignment_Block) instead of (1 |
Random_Assignment_Block)
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
2016-01-29 7:10 GMT+01:00 David Roy <dmr02...@gmail.com>:
> I am conducting a multilevel regression analysis on the effect of an
> intervention on student test results, and am not sure how to implement the
> necessary R code to correctly capture the nested structure.
>
>
>
> The outcome measure for the study is whether a student passed or failed a
> final exam. The structure of the data is students nested within schools,
> and then schools nested within random assignment blocks. Treatment (i.e.,
> the intervention) was implemented at the school-level. The covariates that
> I am planning to use are prior year test scores (this is also a binary
> variable for pass or fail), race, and gender.
>
>
>
> My ideal output would show the impact of the treatment for each of the
> random assignment blocks, and then the weighted average of the impact
> across all of the random assignment blocks.
>
>
>
> Based on my research thus far, it seems like the **lmer** function from the
> **lme4** package would be the best route to go.
>
>
>
> This is the code that I have tried:
>
>
>
> # Fit multilevel regression with random assignment blocks
>
> glmer2 <- glmer(Post_Test_Score ~ Treatment +
>
> Pre_Test_Score +
>
> (1 | School) +
>
> (1 | Random_Assignment_Block),
>
> data = StudyData,
>
> family = binomial("logit"))
>
>
>
> My two questions are the following:
>
>
>
> 1.) Given the nested structure of my data, would the above regression
> output the correct coefficient for the impact of treatment across all
> random assignment blocks?
>
>
>
> 2.) How would I code the interaction effect between Treatment and
> Random_Assignment_Block in order to generate separate impact estimates for
> each of the random assignment blocks?
>
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.
>
[[alternative HTML version deleted]]
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