
Multilevel modeling, also known as hierarchical regression, generalizes ordinary regression modeling to allow explicit and flexible compromises between simple and complex models. This article provides an elementary introduction to multilevel modeling as a model-averaging technique. Model averaging provides an alternative to model selection, and it emphasizes the role of prior information in finding good models.
Adult, Models, Statistical, Water Pollution, Reproducibility of Results, Bayes Theorem, Confounding Factors, Epidemiologic, Abortion, Spontaneous, Bias, Pregnancy, Risk Factors, Data Interpretation, Statistical, Humans, Regression Analysis, Female
Adult, Models, Statistical, Water Pollution, Reproducibility of Results, Bayes Theorem, Confounding Factors, Epidemiologic, Abortion, Spontaneous, Bias, Pregnancy, Risk Factors, Data Interpretation, Statistical, Humans, Regression Analysis, Female
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 19 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
