Analysis of Questionnaire Data with R: 1st Edition (Hardback) book cover

Analysis of Questionnaire Data with R

1st Edition

By Bruno Falissard

Chapman and Hall/CRC

280 pages | 64 B/W Illus.

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pub: 2011-09-21
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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.


"… 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

Table of Contents


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



About the Author

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".

Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General