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"Applied Linear Regression for Longitudinal Data: With an Emphasis on Missing Observations" by Frans E. S. Tan and Shahab Jolani is a book that focuses on the analysis of quantitative longitudinal outcomes using linear regression models. It is designed for researchers and students in the fields of epidemiology, health, social, and behavioral sciences. The book emphasizes interpretation rather than mathematics and provides practical examples to help readers understand the concepts. It also addresses the treatment of missing data, which is common in longitudinal studies. The book is well-written and suitable for those interested in longitudinal data analysis, although it could benefit from providing more details on the mathematical aspects of statistical methods. [Extracted from the article]

R for Political Data Science: A Practical Guide: edited by Francisco Urdinez and Andrés Cruz, Chapman and Hall/CRC, Taylor & Francis Group, Boca Raton, FL, 2021, ISBN 978-0-367-81889-0, xix + 440 pp., 209 b/w illustrations, $99.95 (Hardback) Chapter 2 presents data management tools with various operations and commands, data transformation and pivoting - all in numerous practical examples. The monograph belongs to I The R Series i , and presents a reference textbook on R language with a semester course on statistics with application to estimations on real political data. [Extracted from the article]

Chapter 6 is about the analysis of clustered count data, which means, data where the response variable takes on only non-negative integer values (0, 1, 2, 3, etc.). In Chapter 10, the author discussed propensity score analysis methods and explained that these are methods used to remove bias-related events such as bias in treatment effect estimates. There are several numerical computing software packages available to students and researchers for the analysis of statistical data. [Extracted from the article]

A broader class of missing data is called incomplete data, which includes data with measurement error, multilevel data with latent variables, and potential outcomes in causal inference. Chapters 3 and 4 cover imputation methods for univariate missing data and multivariate missing data, respectively. Chapter 7 is a new chapter discussing multiple imputation for nested data accounting for the multilevel data structure. [Extracted from the article]