While theoretical statistics relies primarily on mathematics and hypothetical situations, statistical practice is a translation of a question formulated by a researcher into a series of variables linked by a statistical tool. As with written material, there are almost always differences between the meaning of the original text and translated text. Additionally, many versions can be suggested, each with their advantages and disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations. As indicated in the title, the syntax of these statistical formulations is based on the well-known R language, chosen for its popularity, simplicity, and power of its structure. Although syntax is vital, understanding the semantics is the real challenge of any good translation. In this book, the semantics of theoretical-to-practical translation emerges progressively from examples and experience, and occasionally from mathematical considerations. Sometimes the interpretation of a result is not clear, and there is no statistical tool really suited to the question at hand.
Sometimes data sets contain errors, inconsistencies between answers, or missing data. More often, available statistical tools are not formally appropriate for the given situation, making it difficult to assess to what extent this slight inadequacy affects the interpretation of results. Analysis of Questionnaire Data with R tackles these and other common challenges in the practice of statistics.
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".
Introduction 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
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