Addressing the problem of analysis of questionnaire data and interpretation of results, this book proposes a methodology that uses R software. The author takes a practical approach to the subject, presenting principles first followed by detailed examples of their application to real data. He discusses principles of data management and manipulation, and covers descriptive statistics, statistical modeling, reliability, and missing data. The book focuses on R packages that are readily available and integrates discussion of their implementation and code throughout. All the data sets and R code packages are provided on a website.…mehr
Addressing the problem of analysis of questionnaire data and interpretation of results, this book proposes a methodology that uses R software. The author takes a practical approach to the subject, presenting principles first followed by detailed examples of their application to real data. He discusses principles of data management and manipulation, and covers descriptive statistics, statistical modeling, reliability, and missing data. The book focuses on R packages that are readily available and integrates discussion of their implementation and code throughout. All the data sets and R code packages are provided on a website.
After studying mathematics and getting his Ph.D. in biostatistics, the author graduated as a child and adolescent psychiatrist. He is now professor in biostatistics in Paris-Sud University, head of a master in public health and of the research lab "public health and mental health".
Inhaltsangabe
Introduction. Description of Responses. Description of Relationships between Variables. Confidence Intervals and Statistical Tests of Hypothesis. Introduction to Linear, Logistic, Poisson, and Other Regression Models. About Statistical Modelling. Principles for the Validation of a Composite Score. 8 Introduction to Structural Equation Modelling. Introduction to Data Manipulation using R. Appendix A: The Analysis of Questionnaire Data using R: Memory Card. References. Index.
Introduction. Description of Responses. Description of Relationships between Variables. Confidence Intervals and Statistical Tests of Hypothesis. Introduction to Linear, Logistic, Poisson, and Other Regression Models. About Statistical Modelling. Principles for the Validation of a Composite Score. 8 Introduction to Structural Equation Modelling. Introduction to Data Manipulation using R. Appendix A: The Analysis of Questionnaire Data using R: Memory Card. References. Index.
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