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Regression Analysis in R
Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers through bivariate correlation giving them a solid framework from which to expand into more complicated regression models. Concepts are demonstrated using R software and real data examples. Key Features: Full output examples complete with interpretation Full syntax examples to help teach R code Appendix explaining basic R functions Methods for multilevel data that are often included in basic regression texts End of Chapter Comprehension Exercises
Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidenc...
Regression Analysis in R
This book covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers through bivariate correlation.
Multilevel Modeling Using Mplus
This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.
Regression Analysis in R
Regression Analysis in R: A Comprehensive View for the Social Sciences covers the basic applications of multiple linear regression all the way through to more complex regression applications and extensions. Written for graduate level students of social science disciplines this book walks readers through bivariate correlation giving them a solid framework from which to expand into more complicated regression models. Concepts are demonstrated using R software and real data examples. Key Features: Full output examples complete with interpretation Full syntax examples to help teach R code Appendix explaining basic R functions Methods for multilevel data that are often included in basic regression texts End of Chapter Comprehension Exercises
Multilevel Modeling Using R
Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data. The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has bee...
Health and Well-being, Quality Education, Gender Equality, Decent work and Inequalities: The contribution of psychology in achieving the objectives of the Agenda 2030
The United Nations 2030 Agenda has defined 17 goals to promote sustainable development on a global scale; it's based on five critical dimensions, known as the 5Ps: people, prosperity, planet, partnership, and peace. Many of the goals can be connected to psychology or educational sciences, for example improving health and well-being (SDG3), ensuring quality education (SDG4), promoting gender equality (SDG5) and decent work (SDG8), and reducing inequality (SDG10). This means that researchers in the field of psychology or related sciences can give substantial contributions to support the achievement of the goals of Agenda 2030. Research on the contribution of psychology and educational sciences in achieving these goals should be encouraged.
China as Number One?
One of the most significant global events in the last forty years has been the rise of China— economically, technologically, politically, and militarily. The question on people's minds for decades has been whether China will replace the United States as a superpower in the near future. But for China, this power must be comprehensive — having strong economic and militant forces are only two pieces of the puzzle. China must also possess soft power, such as attractive ideologies, values, and culture. China as Number One? explores China’s soft powers through the eyes of Chinese citizens. Utilizing data from the World Values Survey, the contributors to this collection analyze the potential soft power of a rising China by examining its residents' social values. A comprehensive study of changes and continuities in the political and social values of Chinese citizens, the book examines findings in the context of evolutionary modernization theory and cross-national comparison.
Generalized Structured Component Analysis
Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized stru...
Handbook of Item Response Theory, Volume Two
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.
