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Analysis of Questionnaire Data with R

Publisher: 
Chapman & Hall/CRC
Number of Pages: 
269
Price: 
89.95
ISBN: 
9781439817667
Date Received: 
Thursday, October 20, 2011
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
Bruno Falissard
Publication Date: 
2011
Format: 
Hardcover
Category: 
Textbook

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

 


Publish Book: 
Modify Date: 
Tuesday, January 17, 2012

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