Multilevel analysis: An introduction to basic and advanced multilevel modeling. Snijders T., Bosker R.

Multilevel analysis: An introduction to basic and advanced multilevel modeling
ISBN: 0761958908,9780761958901 | 275 pages | 7 Mb

Multilevel analysis: An introduction to basic and advanced multilevel modeling Snijders T., Bosker R.
Publisher: Sage

Analysis of continuous outcome data was done taking repeated measurements into account by using multilevel modeling. The final chapter provides an introduction to programming. Because of unequal intervals between follow up visits, time was additionally used as categorical variable. Advanced search · Site index These include summary statistics and tables, ANOVA, linear regression (and diagnostics), robust methods, nonlinear regression, regression models for limited dependent variables, complex survey data, survival analysis, factor analysis, cluster analysis, structural equation modeling, multiple imputation, time series, and multilevel mixed-effects models. In this report, the relationship between poverty and malaria in Nigeria was analyzed using a multi-level logistic model. Multilevel Analysis: Techniques and Applications (Quantitative Methodology Series) Ebook By J. This book is an introduction to multilevel analysis for applied researchers featuring models for hierarchical or nested data. €�This is a clearly written book on longitudinal analysis, multilevel models, and survival analysis by two outstanding classroom teachers. Hox Language: English Publish Year : 2002 Info: E-Book readable online or download on PDF DJVU "The author has chosen a technical level for the presentation that allows precision in the model specifications and the discussion of more advanced issues without detracting from its applied focus. This book presents two types of models: The multilevel regression and multilevel covariance The basic models and examples are discussed in non-technical terms; the emphasis is on understanding the methodological and statistical issues involved in using these models. This approach requires a multilevel analysis incorporating variables at different levels of aggregation.

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