This book is a successful attempt for providing a generic introduction and outline of univariate through multivariate statistical modelling techniques along with their applications in the field of social, behavioural and associated sciences. Although the book is specifically designed to target undergraduate and graduate students, it should be beneficial to anybody searching for a compact and concise overview of statistical approaches for data analysis in related fields. One of the most important features of this book is that the keywords are highlighted by bold texts, which is beneficial for readers. Moreover, summary and highlights, review exercises, and further discussion and activities are included at the end of each chapter.

This book contains an excellent balance of theory and application. The book is a compilation of 15 chapters and every chapter is supplemented with real-world applications that include R commands and SPSS syntax. Computer outputs produced by R and SPSS are displayed and explained which make it much easier to grasp the concepts. After an introductory chapter reviewing the preliminary concepts, the subsequent chapters discussed different models applied in analysis of variance and experimental designs; different regression models and generalized linear model; multivariate techniques such as MANOVA, discriminant analysis, principal component, factor analysis and path analysis along with structural equation modelling.

Chapter 2 gives a straightforward and brief review of basic statistical concepts such as summary statistics, measures of association, common probability densities and hypothesis testing, as well as power estimation using G*Power. From Chapter 3 to Chapter 5, the details of fixed, random and mixed effect models used in the analysis of variance are covered with applications. Chapter 6 addresses randomized block designs as well as repeated measures, incorporating the estimate and interpretation of R and SPSS outputs. A variety of broadly applicable regression models, for example as simple, multiple, logistic and generalized linear models, along with a succinct introduction of penalized regression methods like ridge and lasso regression are discussed from Chapter 7 to Chapter 10. A brief introduction and overview of different multivariate techniques are presented from Chapter 11 to Chapter 14 by including R and SPSS commands for dealing with real-life problems to make effective and efficient decisions. Finally, the last chapters in the book focus on the path and structural equation models.

Overall, the book is well-organized and written in a straightforward and concise manner. I heartily suggest it as a textbook for social and natural science starting courses and advanced students. This book will be valuable to applied statisticians and scientists working in the social and natural sciences. I also think that this book would be a welcome addition to the library.

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