1st Edition

Analysis of Questionnaire Data with R

By Bruno Falissard Copyright 2011
    280 Pages 64 B/W Illustrations
    by Chapman & Hall

    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.

    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
    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
    Statistical Inference
    Statistical Modelling
    Validation of a Composite Score


    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".

    "… useful for readers wishing to transfer knowledge of survey analysis and its application in other statistical packages to R. Insights in how a practitioner can use R to analyze one particular survey are very helpful and can be readily applied to one’s own work. … this text would be handy to have on my bookshelf to refer to when conducting survey analyses. … a good book to have …"
    —Gregory E. Gilbert, The American Statistician, November 2014

    "… excellently written and documented. The text covers many of the real-life concerns that arise when analyzing questionnaire data … . I recommend the book to any researchers and post-graduates embarking upon questionnaire design and analysis for the first time, especially in the field of social sciences."
    International Statistical Review, 80, 2012

    "[T]he book is nicely compact, well organized, and for the reader who is already familiar with R, sampling, and survey methodology, it is quite easy to jump from section to section and read through them quickly. … I have found myself already referring to portions of the text as I consider various survey analyses, and I have recommended at least portions of it to students and colleagues. … an interesting and well-written book … ."
    —Ronald D. Fricker, Jr., Journal of Statistical Software, Vol. 46, January 2012