This book, "Analysis of Longitudinal Data" by P. L. Diggle, K.-L. Liang, and S. L. Zeger, focuses on statistical models and methods for analyzing longitudinal data, primarily illustrated in the fields of biology and health sciences. Longitudinal data are considered as a large number of short series, each corresponding to an individual subject, with measurements taken at multiple time points. The book distinguishes itself from other types of repeated data, such as cross-sectional studies, clinical trials, survival analysis, and time series, which are not covered. The central issue addressed is modeling the observations for each subject, acknowledging the intercorrelation of these measurements. The studies are formulated as regression problems, where the response variable's dependence on explanatory variables is described. The book begins with the design choice, emphasizing the sample size required to achieve a specified statistical power. It then moves to exploratory methods, including graphical representation of time series, curve fitting, and correlation structure exploration. The extensive presentation of Generalized Linear Models (GLMs) for this type of data is contrasted with the popular Analysis of Variance (ANOVA) for repeated measures. The statistical treatment of binary and frequency response variables is illustrated in marginal, random effects, and transition (Markov chain) models. The book concludes with a discussion on missing data, a common issue in longitudinal studies. Through six initial examples covering biological and health science problems, the authors apply the theoretically presented models to practical situations. The book maintains rigor and elegance in statistical model presentations, using a structured approach with small introductions leading to detailed theoretical explanations and concrete examples. The language is accessible, requiring only basic regression model understanding and elementary matrix calculations. While the book's focus on biology and health science may limit its applicability to psychology, the authors suggest that a deeper reflection on these models for repeated measures in psychology is justified.This book, "Analysis of Longitudinal Data" by P. L. Diggle, K.-L. Liang, and S. L. Zeger, focuses on statistical models and methods for analyzing longitudinal data, primarily illustrated in the fields of biology and health sciences. Longitudinal data are considered as a large number of short series, each corresponding to an individual subject, with measurements taken at multiple time points. The book distinguishes itself from other types of repeated data, such as cross-sectional studies, clinical trials, survival analysis, and time series, which are not covered. The central issue addressed is modeling the observations for each subject, acknowledging the intercorrelation of these measurements. The studies are formulated as regression problems, where the response variable's dependence on explanatory variables is described. The book begins with the design choice, emphasizing the sample size required to achieve a specified statistical power. It then moves to exploratory methods, including graphical representation of time series, curve fitting, and correlation structure exploration. The extensive presentation of Generalized Linear Models (GLMs) for this type of data is contrasted with the popular Analysis of Variance (ANOVA) for repeated measures. The statistical treatment of binary and frequency response variables is illustrated in marginal, random effects, and transition (Markov chain) models. The book concludes with a discussion on missing data, a common issue in longitudinal studies. Through six initial examples covering biological and health science problems, the authors apply the theoretically presented models to practical situations. The book maintains rigor and elegance in statistical model presentations, using a structured approach with small introductions leading to detailed theoretical explanations and concrete examples. The language is accessible, requiring only basic regression model understanding and elementary matrix calculations. While the book's focus on biology and health science may limit its applicability to psychology, the authors suggest that a deeper reflection on these models for repeated measures in psychology is justified.