An adventure in statistics : the reality enigma
 Responsibility
 Andy Field.
 Publication
 London ; Thousand Oaks, California : SAGE Publications, 2016.
 Physical description
 xvi, 746 pages : illustrations (some color) ; 25 cm.
 Series
 Sage edge
At the library
Science Library (Li and Ma)
Stacks
Call number  Status 

QA276 .F54 2016  Unknown 
More options
Description
Creators/Contributors
 Author/Creator
 Field, Andy P. author.
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 Prologue: The Dying Stars1 Why You Need Science: The Beginning and The End 1.1. Will you love me now? 1.2. How science works 1.2.1. The research process 1.2.2. Science as a life skill 1.3. Research methods 1.3.1. Correlational research methods 1.3.2. Experimental research methods 1.3.3. Practice, order and randomization 1.4. Why we need science2 Reporting Research, Variables and Measurement: Breaking the Law 2.1. Writing up research 2.2. Maths and statistical notation 2.3. Variables and measurement 2.3.1. The conspiracy unfolds 2.3.2. Qualitative and quantitative data 2.3.3. Levels of measurement 2.3.4. Measurement error 2.3.5. Validity and reliability3 Summarizing Data: She Loves Me Not? 3.1. Frequency distributions 3.1.1. Tabulated frequency distributions 3.1.2. Grouped frequency distributions 3.1.3. Graphical frequency distributions 3.1.4. Idealized distributions 3.1.5. Histograms for nominal and ordinal data 3.2. Throwing Shapes4 Fitting Models (Central Tendency): Somewhere In The Middle 4.1. Statistical Models 4.1.1. From the dead 4.1.2. Why do we need statistical models? 4.1.3. Sample size 4.1.4. The one and only statistical model 4.2. Central Tendency 4.2.1. The mode 4.2.2. The median 4.2.3. The mean 4.3. The 'fit' of the mean: variance 4.3.1. The fit of the mean 4.3.2. Estimating the fit of the mean from a sample 4.3.3. Outliers and variance 4..4. Dispersion 4.4.1. The standard deviation as an indication of dispersion 4.4.2. The range and interquartile range5 Presenting Data: Aggressive Perfector 5.1. Types of graphs 5.2. Another perfect day 5.3. The art of presenting data 5.3.1. What makes a good graph? 5.3.2. Bar graphs 5.3.3. Line graphs 5.3.4. Boxplots (boxwhisker diagrams) 5.3.5. Graphing relationships: the scatterplot 5.3.6. Pie charts6 ZScores: The wolf is loose 6.1. Interpreting raw scores 6.2. Standardizing a score 6.3. Using zscores to compare distributions 6.4. Using zscores to compare scores 6.5. Zscores for samples7 Probability: The Bridge of Death 7.1. Probability 7.1.1. Classical probability 7.1.2. Empirical probability 7.2. Probability and frequency distributions 7.2.1. The discs of death 7.2.2. Probability density functions 7.2.3. Probability and the normal distribution 7.2.4. The probability of a score greater than x 7.2.5. The probability of a score less than x: The tunnels of death 7.2.6. The probability of a score between two values: The catapults of death 7.3. Conditional probability: DeathscotchInferential Statistics: Going Beyond the Data 8.1. Estimating parameters 8.2. How well does a sample represent the population? 8.2.1. Sampling distributions 8.2.2. The standard error 8.2.3. The central limit theorem 8.3. Confidence Intervals 8.3.1. Calculating confidence intervals 8.3.2. Calculating other confidence intervals 8.3.3. Confidence intervals in small samples 8.4. Inferential statistics9 Robust Estimation: Man Without Faith or Trust 9.1. Sources of bias 9.1.1. Extreme scores and nonnormal distributions 9.1.2. The mixed normal distribution 9.2. A great mistake 9.3. Reducing bias 9.3.1. Transforming data 9.3.2. Trimming data 9.3.3. Mestimators 9.3.4. Winsorizing 9.3.5. The bootstrap 9.4. A final point about extreme scores10 Hypothesis Testing: In Reality All is Void 10.1. Null hypothesis significance testing 10.1.1. Types of hypothesis 10.1.2. Fisher's pvalue 10.1.3. The principles of NHST 10.1.4. Test statistics 10.1.5. One and twotailed tests 10.1.6. Type I and Type II errors 10.1.7. Inflated error rates 10.1.8. Statistical power 10.1.9. Confidence intervals and statistical significance 10.1.10. Sample size and statistical significance11 Modern Approaches to Theory Testing: A Careworn Heart 11.1. Problems with NHST 11.1.1. What can you conclude from a 'significance' test? 11.1.2. Allornothing thinking 11.1.3. NHST is influenced by the intentions of the scientist 11.2. Effect sizes 11.2.1. Cohen's d 11.2.2. Pearson's correlation coefficient, r 11.2.3. The odds ratio 11.3. Metaanalysis 11.4. Bayesian approaches 11.4.1. Asking a different question 11.4.2. Bayes' theorem revisited 11.4.3. Comparing hypothesis 11.4.4. Benefits of bayesian approaches12 Assumptions: Starblind 12.1. Fitting models: bringing it all together 12.2. Assumptions 12.2.1. Additivity and linearity 12.2.2. Independent errors 12.2.3. Homoscedasticity/ homogeneity of variance 12.2.4. Normally distributed something or other 12.2.5. External variables 12.2.6. Variable types 12.2.7. Multicollinearity 12.2.8. Nonzero variance 12.3. Turning ever towards the sun13 Relationships: A Stranger's Grave 13.1. Finding relationships in categorical data 13.1.1. Pearson's chisquare test 13.1.2. Assumptions 13.1.3. Fisher's exact test 13.1.4. Yates's correction 13.1.5. The likelihood ratio (Gtest) 13.1.6. Standardized residuals 13.1.7. Calculating an effect size 13.1.8. Using a computer 13.1.9. Bayes factors for contingency tables 13.1.10. Summary 13.2. What evil lay dormant 13.3. Modelling relationships 13.3.1. Covariance 13.3.2. Pearson's correlation coefficient 13.3.3. The significance of the correlation coefficient 13.3.4. Confidence intervals for r 13.3.5. Using a computer 13.3.6. Robust estimation of the correlation 13.3.7. Bayesian approaches to relationships between two variables 13.3.8. Correlation and causation 13.3.9. Calculating the effect size 13.4. Silent sorrow in empty boats14 The General Linear Model: Red Fire Coming Out From His Gills 14.1. The linear model with one predictor 14.1.1. Estimating parameters 14.1.2. Interpreting regression coefficients 14.1.3. Standardized regression coefficients 14.1.4. The standard error of b 14.1.5. Confidence intervals for b 14.1.6. Test statistic for b 14.1.7. Assessing the goodness of fit 14.1.8. Fitting a linear model using a computer 14.1.9. When this fails 14.2. Bias in the linear model 14.3. A general procedure for fitting linear models 14.4. Models with several predictors 14.4.1. The expanded linear model 14.4.2. Methods for entering predictors 14.4.3. Estimating parameters 14.4.4. Using a computer to build more complex models 14.5. Robust regression 14.5.1. Bayes factors for linear models15 Comparing Two Means: Rock or Bust 15.1. Testing differences between means: The rationale 15.2. Means and the linear model 15.2.1. Estimating the model parameters 15.2.2. How the model works 15.2.3. Testing the model parameters 15.2.4. The independent ttest on a computer 15.2.5. Assumptions of the model 15.3. Everything you believe is wrong 15.4. The pairedsamples ttest 15.4.1. The pairedsamples ttest on a computer 15.5. Alternative approaches 15.5.1. Effect sizes 15.5.2. Robust tests of two means 15.5.3. Bayes factors for comparing two means16 Comparing Several Means: Faith in Others 16.1. General procedure for comparing means 16.2. Comparing several means with the linear model 16.2.1. Dummy coding 16.2.2. The Fratio as a test of means 16.2.3. The total sum of squares (SSt) 16.2.4. The model sum of squares (SSm) 16.2.5. The residual sum of squares (SSr) 16.2.6. Partitioning variance 16.2.7. Mean squares 16.2.8. The Fratio 16.2.9. Comparing several means using a computer 16.3. Contrast coding 16.3.1. Generating contrasts 16.3.2. Devising weights 16.3.3. Contrasts and the linear model 16.3.4. Post hoc procedures 16.3.5. Contrasts and post hoc tests using a computer 16.4. Storm of memories 16.5. Repeatedmeasures designs 16.5.1. The total sum of squares, SSt 16.5.2. The withinparticipant variance, SSw 16.5.3. The model sum of squares, SSm 16.5.4. The residual sum of squares, SSr 16.5.5. Mean squares and the Fratio 16.5.6. Repeatedmeasures designs using a computer 16.6. Alternative approaches 16.6.1. Effect sizes 16.6.2. Robust tests of several means 16.6.3. Bayesian analysis of several means 16.7. The invisible manFactorial Designs 17.1. Factorial designs 17.2. General procedure and assumptions 17.3. Analysing factorial designs 17.3.1. Factorial designs and the linear model 17.3.2. The fit of the model 17.3.3. Factorial designs on a computer 17.4. From the pinnacle to the pit 17.5. Alternative approaches 17.5.1. Calculating effect sizes 17.5.2. Robust analysis of factorial designs 17.5.3. Bayes factors for factorial designs 17.6. Interpreting interaction effectsEpilogue: The Genial Night: SI Momentum Requiris, Circumspice.
 (source: Nielsen Book Data)
 Publisher's Summary
 ["Shortlisted for the British Psychological Society Book Award 2017 Shortlisted for the British Book Design and Production Awards 2016 Shortlisted for the Association of Learned & Professional Society Publishers Award for Innovation in Publishing 2016 An Adventure in Statistics: The Reality Enigma by bestselling author and awardwinning teacher Andy Field offers a better way to learn statistics. It combines rocksolid statistics coverage with compelling visual storytelling to address the conceptual difficulties that students learning statistics for the first time often encounter in introductory courses  guiding students away from rote memorization and toward critical thinking and problem solving. Field masterfully weaves in a unique, actionpacked story starring Zach, a character who thinks like a student, processing information, and the challenges of understanding it, in the same way a statistics novice would. Illustrated with stunning graphic novelstyle art and featuring Socratic dialogue, the story captivates readers as it introduces them to concepts, eliminating potential statistics anxiety. The book assumes no previous statistics knowledge nor does it require the use of data analysis software. It covers the material you would expect for an introductory level statistics course that Field's other books (Discovering Statistics Using IBM SPSS Statistics and Discovering Statistics Using R) only touch on, but with a contemporary twist, laying down strong foundations for understanding classical and Bayesian approaches to data analysis. In doing so, it provides an unrivalled launch pad to further study, research, and inquisitiveness about the real world, equipping students with the skills to succeed in their chosen degree and which they can go on to apply in the workplace. The Story and Main Characters The Reality Revolution In the City of Elpis, in the year 2100, there has been a reality revolution. Prior to the revolution, Elpis citizens were unable to see their flaws and limitations, believing themselves talented and special. This led to a selfabsorbed society in which hard work and the collective good were undervalued and eroded. To combat this, Professor Milton Grey invented the reality prism, a hat that allowed its wearers to see themselves as they really were  flaws and all. Faced with the truth, Elpis citizens revolted and destroyed and banned all reality prisms. The Mysterious Disappearance Zach and Alice are born soon after all the prisms have been destroyed. Zach, a musician who doesn't understand science, and Alice, a geneticist who is also a whiz at statistics, are in love. One night, after making a worldchanging discovery, Alice suddenly disappears, leaving behind a song playing on a loop and a file with her research on it. Statistics to the Rescue! Sensing that she might be in danger, Zach follows the clues to find her, as he realizes that the key to discovering why Alice has vanished is in her research. Alas! He must learn statistics and apply what he learns in order to overcome a number of deadly challenges and find the love of his life. As Zach and his pocket watch, The Head, embark on their quest to find Alice, they meet Professor Milton Grey and Celia, battle zombies, cross a probability bridge, and encounter Jig:Saw, a mysterious corporation that might have something to do with Alice's disappearance... Author News \"Eight years ago I had the idea to write a fictional story through which the student learns statistics via a shared adventure with the main character...\" Read the complete article from Andy Field on writing his new book Times Higher Education article: \"Andy Field takes statistics adventure to a new level\" Stay Connected Connect with us on Facebook and share your experiences with Andy's texts, check out news, access free stuff, see photos, watch videos, learn about competitions, and much more. Video Links Go behind the scenes and learn more about the man behind the book: Watch Andy talk about why he created a statistics book using the framework of a novel and illustrations by one of the illustrators for the show, Doctor Who. See more videos on Andy's YouTube channel Available with Perusallan eBook that makes it easier to prepare for class Perusall is an awardwinning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more.", {"source"=>"(source: Nielsen Book Data)"}, "9781446210444", "20170907"]
Subjects
 Subject
 Statistics.
 Statistics.
Bibliographic information
 Publication date
 2016
 ISBN
 9781446210444 (hbk.)
 1446210448 (hbk.)
 9781446210451 (pbk.)
 1446210456 (pbk.)
 9781473943926 (ePub ebook)
 9781473943933 (ebook)