Multilevel analysis is a statistical method used to analyze data with a hierarchical or nested structure, such as individuals within groups, repeated measures within individuals, or respondents within clusters. This book provides an introduction to multilevel analysis for students and researchers, covering two main types of models: multilevel regression models and multilevel structural equation models. The book explains how to estimate and test hypotheses in multilevel regression, discusses important methodological and statistical issues, and presents various applications, including longitudinal data analysis, categorical and count data analysis, survival analysis, cross-classified models, multivariate models, and meta-analysis. It also addresses advanced topics such as sample size and power analysis, estimation methods, and Bayesian approaches. The book emphasizes the importance of understanding the hierarchical structure of data and the implications of aggregation and disaggregation. It also discusses common statistical pitfalls, such as the ecological fallacy and the atomistic fallacy, and provides guidance on how to avoid them. The book is intended as an introduction to multilevel analysis, but it also serves as a reference for researchers and methodologists working in various fields. The second edition includes updates and new chapters on multilevel models for ordinal and count data, and multilevel survival analysis. The book is written in a clear and accessible manner, with a focus on understanding the methodological and statistical issues involved in using these models. It assumes a basic knowledge of social science statistics, including analysis of variance and multiple regression analysis. The book is accompanied by a website with data sets, software examples, and PowerPoint slides for instructors.Multilevel analysis is a statistical method used to analyze data with a hierarchical or nested structure, such as individuals within groups, repeated measures within individuals, or respondents within clusters. This book provides an introduction to multilevel analysis for students and researchers, covering two main types of models: multilevel regression models and multilevel structural equation models. The book explains how to estimate and test hypotheses in multilevel regression, discusses important methodological and statistical issues, and presents various applications, including longitudinal data analysis, categorical and count data analysis, survival analysis, cross-classified models, multivariate models, and meta-analysis. It also addresses advanced topics such as sample size and power analysis, estimation methods, and Bayesian approaches. The book emphasizes the importance of understanding the hierarchical structure of data and the implications of aggregation and disaggregation. It also discusses common statistical pitfalls, such as the ecological fallacy and the atomistic fallacy, and provides guidance on how to avoid them. The book is intended as an introduction to multilevel analysis, but it also serves as a reference for researchers and methodologists working in various fields. The second edition includes updates and new chapters on multilevel models for ordinal and count data, and multilevel survival analysis. The book is written in a clear and accessible manner, with a focus on understanding the methodological and statistical issues involved in using these models. It assumes a basic knowledge of social science statistics, including analysis of variance and multiple regression analysis. The book is accompanied by a website with data sets, software examples, and PowerPoint slides for instructors.