Kirjahaku

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your dataMatch substantive theory and statistical modelsApply statistical procedures hands-on, with example data analysesDevelop and use graphs to understand data and fit models to dataWork with statistical modeling principles using any software packageLearn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your dataMatch substantive theory and statistical modelsApply statistical procedures hands-on, with example data analysesDevelop and use graphs to understand data and fit models to dataWork with statistical modeling principles using any software packageLearn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.

Looking to extend your understanding of statistical methods and data analysis skills? This text shows you how to apply advanced statistical models to data across the behavioural and social sciences. This approachable book explains how to develop, estimate, and test statistical models with demonstrations using R. It gives you a strong grounding in key techniques such as multiple regression, multilevel modelling, factor analysis, and structural equation modelling, and helps you move beyond rigid statistical tests to confidently choose an appropriate model for reaching strong conclusions from your data. This second edition includes: Two new chapters on logistic regression and missing data. Updated and improved content on causal models, mediation and modelling change over time with longitudinal data. Examples of real-world research from psychology, sociology, business and management, education, and more. Accompanied by datasets and R code for you to practice techniques at your own pace, this book is an accessible guide for anyone looking to advance their statistical understanding.

Looking to extend your understanding of statistical methods and data analysis skills? This text shows you how to apply advanced statistical models to data across the behavioural and social sciences. This approachable book explains how to develop, estimate, and test statistical models with demonstrations using R. It gives you a strong grounding in key techniques such as multiple regression, multilevel modelling, factor analysis, and structural equation modelling, and helps you move beyond rigid statistical tests to confidently choose an appropriate model for reaching strong conclusions from your data. This second edition includes: Two new chapters on logistic regression and missing data. Updated and improved content on causal models, mediation and modelling change over time with longitudinal data. Examples of real-world research from psychology, sociology, business and management, education, and more. Accompanied by datasets and R code for you to practice techniques at your own pace, this book is an accessible guide for anyone looking to advance their statistical understanding.