Analysis of Questionnaire Data with R

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3rd Qu
A01=Bruno Falissard
About Statistical Modelling
advanced regression methods
Author_Bruno Falissard
avoidance
binary
Binary Variables
Binomial Family
Category=GPS
Category=JMB
Category=PBT
Category=UFM
composite score validation
Confidence Intervals and Statistical Tests of Hypothesis
data cleaning techniques
Data Frame
dependence
Description of Relationships between Variables
Description of Responses
Deviance Residuals
Dispersion Parameter
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Factor1 Factor2 Factor3
Freedom Residual Deviance
harm
Harm Avoidance
High Security Units
Int 1
Introduction to Structural Equation Modelling
Iw Ith
Linear Regression Model
logistic
MHP Study
Min 1Q Median 3Q Max
missing
Missing Data
Multiple Linear Regressions
novelty
Novelty Seeking
Null Deviance
Past Imprisonment
PCA Diagram
practical R questionnaire analysis
Psy
psychometrics
quantitative analysis
Regression Model
reward
seeking
Single Term Deletions
survey methodology
variable
Vice Versa

Product details

  • ISBN 9781439817667
  • Weight: 521g
  • Dimensions: 156 x 234mm
  • Publication Date: 21 Sep 2011
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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".

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