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We used item response models to confirm the theoretically modeled levels and multilevel modeling to analyze influence factors on FK.
We used multilevel modeling to investigate the relative importance of each factor.
Third, we examine changes in scores over three periods, using multilevel modeling to consider the nested structure of the data.
We used Rasch analysis (to examine (a)), Pearson product moment correlations (to examine (b)), and multilevel modeling (to examine (c)).
We used Bayesian multilevel modeling to account for the hierarchical structure of the data and assess uncertainty in parameter estimates.
Consider a relatively recent example from my own work (Rutkowski et al. 2013), where my colleagues and I used multilevel modeling to understand whether an association exists internationally between bullying and educational achievement.
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We use multilevel models to address our research questions.
Therefore, we use a multilevel model to examine these effects simultaneously.
We used multilevel models to examine associations with late stage cancer diagnosis from predictors at person, county and state levels.
This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity.
In the literature, multilevel models include mixed-effects models, but to distinguish the two in this paper we use mixed-effects to refer to analyses containing both fixed and random effects (implemented using the R lmer function) and multilevel models to refer to our application of Gelman's approach – implemented using WinBUGS - that calculates variance components for all variables).
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