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About this product
- Author(s)Bruno Falissard
- PublisherTaylor & Francis Ltd
- Date of Publication26/09/2011
- GenreSoftware Packages
- Country of PublicationUnited States
- ImprintChapman & Hall/CRC
- Content Note64 black & white illustrations, 1 black & white tables
- Weight521 g
- Width156 mm
- Height234 mm
- Format DetailsUnsewn / adhesive bound
- Table Of ContentsIntroduction About Questionnaires Principles of Analysis The Mental Health in Prison (MHP) Study If You Are a Complete R Beginner Description of Responses Description using Summary Statistics Summary Statistics in Subgroups Histograms Boxplots Barplots Pie Charts Evolution of a Numerical Variable across Time (Temperature Diagram) Description of Relationships between Variables Relative Risks and Odds-Ratios Correlation Coefficients Correlation Matrices Cartesian Plots Hierarchical Clustering Principal Component Analysis A Spherical Representation of a Correlation Matrix Focused Principal Component Analysis Confidence Intervals and Statistical Tests of Hypothesis Confidence Interval of a Proportion Confidence Interval of a Mean Confidence Interval of a Relative Risk or an Odds-Ratio Statistical Tests of Hypothesis: Comparison of Two Percentages Statistical Tests of Hypothesis: Comparison of Two Means Statistical Tests of Hypothesis: The Correlation Coefficient Statistical Tests of Hypothesis: More than Two Groups Sample Size Requirements: The Survey Perspective Sample Size Requirement: The Inferential Perspective Introduction to Linear, Logistic, Poisson, and Other Regression Models Linear Regression Models for Quantitative Outcomes Logistic Regression for Binary Outcome Logistic Regression for a Categorical Outcome with More than Two Levels Logistic Regression for an Ordered Outcome Regression Models for an Outcome Resulting from a Count About Statistical Modelling Coding Numerical Predictors Coding Categorical Predictors Choosing Predictors Interaction Terms Assessing the Relative Importance of Predictors Dealing with Missing Data The Bootstrap Random Effects and Multilevel Modelling Principles for the Validation of a Composite Score Item Analysis (1): Distribution Item Analysis (2): The Multi trait Multi-method Approach to Confirm a Subscale Structure Assessing the Unidimensionality of a Set of Items Factor Analysis to Explore the Structure of a Set of Items Measurement Error (1): Internal Consistency and the Cronbach Alpha Measurement Error (2): Inter-rater Reliability 8 Introduction to Structural Equation Modelling Linear Regression as a Particular Instance of Structural Equation Modelling Factor Analysis as a Particular Instance of Structural Equation Modelling Structural Equation Modelling in Practice Introduction to Data Manipulation using R Importing and Exporting Datasets Manipulation of Datasets Manipulation of Variables Checking Inconsistencies Appendix A: The Analysis of Questionnaire Data using R: Memory Card Data Manipulations Importation Exportation of Datasets Manipulation of Datasets Manipulation of Variables Descriptive Statistics Univariate Bivariate Multidimensional Statistical Inference Statistical Modelling Validation of a Composite Score References Index
- Author BiographyAfter 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 .
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