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Analysis of Questionnaire Data with R - Bruno Falissard

Analysis of Questionnaire Data with R


Published: 26th September 2011
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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

Prefacep. vii
Acknowledgmentsp. ix
Introductionp. 1
About Questionnairesp. 1
Principles of Analysisp. 2
Overviewsp. 2
Specific Aspects of Questionnaire Data Analysisp. 3
The Mental Health in Prison (MHP) Studyp. 3
If You are a Complete R Beginnerp. 4
First Stepsp. 4
Functions from Optional Packagesp. 7
When Assistance is Neededp. 7
Importing a Datasetp. 7
More about the R Languagep. 8
Description of Responsesp. 9
Description Using Summary Statisticsp. 9
Summary Statistics in Subgroupsp. 13
Histogramsp. 17
Boxplotsp. 21
Barplotsp. 23
Pie Chartsp. 24
Evolution of a Numerical Variable across Time (Temperature Diagram)p. 25
Description of Relationships between Variablesp. 29
Relative Risks and Odds Ratiosp. 29
Correlation Coefficientsp. 33
Correlation Matricesp. 34
Cartesian Plotsp. 37
Hierarchical Clusteringp. 39
Principal Component Analysisp. 42
A Spherical Representation of a Correlation Matrixp. 47
Focused Principal Component Analysisp. 48
Confidence Intervals and Statistical Tests of Hypothesisp. 51
Confidence Interval of a Proportionp. 51
Confidence Interval of a Meanp. 55
Confidence Interval of a Relative Risk or an Odds Ratiop. 56
Statistical Tests of Hypothesis: Comparison of Two Percentagesp. 58
Statistical Tests of Hypothesis: Comparison of Two Meansp. 61
Statistical Tests of Hypothesis: Correlation Coefficientp. 64
Statistical Tests of Hypothesis: More than Two Groupsp. 66
Sample Size Requirements: Survey Perspectivep. 71
Sample Size Requirements: Inferential Perspectivep. 72
Introduction to Linear, Logistic, Poisson, and Other Regression Modelsp. 75
Linear Regression Models for Quantitative Outcomesp. 75
Logistic Regression for Binary Outcomep. 89
Logistic Regression for a Categorical Outcome with More than Two Levelsp. 97
Logistic Regression for an Ordered Outcomep. 101
Regression Models for an Outcome Resulting from a Countp. 104
About Statistical Modellingp. 113
Coding Numerical Predictorsp. 113
Coding Categorical Predictorsp. 120
Choosing Predictorsp. 130
Interaction Termsp. 139
Assessing the Relative Importance of Predictorsp. 148
Dealing with Missing Datap. 155
Bootstrapp. 165
Random Effects and Multilevel Modellingp. 170
Principles for the Validation of a Composite Scorep. 177
Item Analysis (1): Distributionp. 177
Item Analysis (2): The Multi-Trait Multi-Method Approach to Confirm a Subscale Structurep. 180
Assessing the Unidimensionality of a Set of Itemsp. 185
Factor Analysis to Explore the Structure of a Set of Itemsp. 191
Measurement Error (1): Internal Consistency and the Cronbach Alphap. 197
Measurement Error (2): Inter-Rater Reliabilityp. 199
Introduction to Structural Equation Modellingp. 205
Linear Regression as a Particular Instance of Structural Equation Modellingp. 205
Factor Analysis as a Particular Instance of Structural Equation Modellingp. 209
Structural Equation Modelling in Practicep. 212
Introduction to Data Manipulation Using Rp. 223
Importing and Exporting Datasetsp. 223
Manipulation of Datasetsp. 227
Manipulation of Variablesp. 230
Checking Inconsistenciesp. 234
Appendix: The Analysis of Questionnaire Data Using R: Memory Cardp. 241
Data Manipulationsp. 241
Importation/Exportation of Datasetsp. 241
Manipulation of Datasetsp. 241
Manipulation of Variablesp. 241
Descriptive Statisticsp. 242
Univariatep. 242
Bivariatep. 242
Multidimensionalp. 243
Statistical Inferencep. 243
Statistical Modellingp. 243
Validation of a Composite Scorep. 244
Referencesp. 247
Indexp. 253
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781439817667
ISBN-10: 1439817669
Audience: Tertiary; University or College
Format: Hardcover
Language: English
Number Of Pages: 280
Published: 26th September 2011
Publisher: Taylor & Francis Ltd
Country of Publication: GB
Dimensions (cm): 23.5 x 15.9  x 1.78
Weight (kg): 0.52
Edition Number: 1