By Jos W. R. Twisk

ISBN-10: 0521849756

ISBN-13: 9780521849753

This can be a useful creation to multilevel research appropriate for all these doing learn. such a lot books on multilevel research are written by means of statisticians; these books are tricky for non-mathematical researchers. against this, this quantity offers an obtainable account at the sensible program of multilevel research in study. Many labored examples, with computing device output, are given to demonstrate and clarify this topic. Datasets of the examples can be found on the net, so the reader can reanalyze the knowledge. This technique can help bridge the conceptual and communique hole that exists among researchers and statisticians.

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Additional info for Applied Multilevel Analysis: A Practical Guide for Medical Researchers (Practical Guides to Biostatistics and Epidemiology)

Example text

This is a relatively small odds ratio, with a narrow interval, which is due to the fact that it reflects the odds ratio related to only 1 year difference in age. e. the value of 1, which reflects no relationship, does not lie within the interval). e. the actual p-value of the odds ratio) can be derived from the Wald test. Again, the Wald statistic is defined by the regression coefficient divided by its standard error, and this magnitude squared follows a Chi-square distribution with one degree 1 To perform a logistic multilevel analysis in MLwiN software, the variable bcons must be only random at the lowest level (not fixed), while the variable cons must not be random at the lowest level (fixed, and if necessary random on the levels other than the lowest level).

6 Multilevel analysis with more than two levels Up to now, only a relatively simple situation has been considered, in which the patient observations were clustered within the medical doctors. 9). It is not surprising that this clustering within a higher level can be treated in the same way as has been described for the clustering of the patient observations within the medical doctor. e. the slopes) can also be estimated. 6 shows the descriptives of the example dataset described in the earlier sections, but it also includes the institution variable.

The higher the ICC, the less new information provided by a patient ‘belonging’ to the same medical doctor, and the higher the standard error of the multilevel analysis, compared to the ‘naive/disaggregated’ analysis. When the analysis is performed on the average health outcomes of the patients of the 20 medical doctors, it can be seen that the regression coefficient of this ‘aggregated’ analysis is exactly the same as for the other two analyses. e. 5). 34 What do we gain by applying multilevel analysis?

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Applied Multilevel Analysis: A Practical Guide for Medical Researchers (Practical Guides to Biostatistics and Epidemiology) by Jos W. R. Twisk


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