Statistics And Experimental Design For Psychologists: A Model Comparison Approach
This is the first textbook for psychologists which combines the model comparison method in statistics with a hands-on guide to computer-based analysis and clear explanations of the links between models, hypotheses and experimental designs. Statistics is often seen as a set of cookbook recipes which must be learned by heart. Model comparison, by contrast, provides a mental roadmap that not only gives a deeper level of understanding, but can be used as a general procedure to tackle those problems which can be solved using orthodox statistical methods.Statistics and Experimental Design for Psychologists focusses on the role of Occam's principle, and explains significance testing as a means by which the null and experimental hypotheses are compared using the twin criteria of parsimony and accuracy. This approach is backed up with a strong visual element, including for the first time a clear illustration of what the F-ratio actually does, and why it is so ubiquitous in statistical testing.The book covers the main statistical methods up to multifactorial and repeated measures, ANOVA and the basic experimental designs associated with them. The associated online supplementary material extends this coverage to multiple regression, exploratory factor analysis, power calculations and other more advanced topics, and provides screencasts demonstrating the use of programs on a standard statistical package, SPSS.Of particular value to third year undergraduate as well as graduate students, this book will also have a broad appeal to anyone wanting a deeper understanding of the scientific method.
1125902474
Statistics And Experimental Design For Psychologists: A Model Comparison Approach
This is the first textbook for psychologists which combines the model comparison method in statistics with a hands-on guide to computer-based analysis and clear explanations of the links between models, hypotheses and experimental designs. Statistics is often seen as a set of cookbook recipes which must be learned by heart. Model comparison, by contrast, provides a mental roadmap that not only gives a deeper level of understanding, but can be used as a general procedure to tackle those problems which can be solved using orthodox statistical methods.Statistics and Experimental Design for Psychologists focusses on the role of Occam's principle, and explains significance testing as a means by which the null and experimental hypotheses are compared using the twin criteria of parsimony and accuracy. This approach is backed up with a strong visual element, including for the first time a clear illustration of what the F-ratio actually does, and why it is so ubiquitous in statistical testing.The book covers the main statistical methods up to multifactorial and repeated measures, ANOVA and the basic experimental designs associated with them. The associated online supplementary material extends this coverage to multiple regression, exploratory factor analysis, power calculations and other more advanced topics, and provides screencasts demonstrating the use of programs on a standard statistical package, SPSS.Of particular value to third year undergraduate as well as graduate students, this book will also have a broad appeal to anyone wanting a deeper understanding of the scientific method.
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Statistics And Experimental Design For Psychologists: A Model Comparison Approach

Statistics And Experimental Design For Psychologists: A Model Comparison Approach

by Rory Allen
Statistics And Experimental Design For Psychologists: A Model Comparison Approach

Statistics And Experimental Design For Psychologists: A Model Comparison Approach

by Rory Allen

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Overview

This is the first textbook for psychologists which combines the model comparison method in statistics with a hands-on guide to computer-based analysis and clear explanations of the links between models, hypotheses and experimental designs. Statistics is often seen as a set of cookbook recipes which must be learned by heart. Model comparison, by contrast, provides a mental roadmap that not only gives a deeper level of understanding, but can be used as a general procedure to tackle those problems which can be solved using orthodox statistical methods.Statistics and Experimental Design for Psychologists focusses on the role of Occam's principle, and explains significance testing as a means by which the null and experimental hypotheses are compared using the twin criteria of parsimony and accuracy. This approach is backed up with a strong visual element, including for the first time a clear illustration of what the F-ratio actually does, and why it is so ubiquitous in statistical testing.The book covers the main statistical methods up to multifactorial and repeated measures, ANOVA and the basic experimental designs associated with them. The associated online supplementary material extends this coverage to multiple regression, exploratory factor analysis, power calculations and other more advanced topics, and provides screencasts demonstrating the use of programs on a standard statistical package, SPSS.Of particular value to third year undergraduate as well as graduate students, this book will also have a broad appeal to anyone wanting a deeper understanding of the scientific method.

Product Details

ISBN-13: 9781786340641
Publisher: World Scientific Publishing Europe Ltd
Publication date: 10/24/2017
Pages: 472
Product dimensions: 6.00(w) x 9.10(h) x 1.20(d)

Table of Contents

Preface v

Technical Note xi

About the Author xv

Chapter 1 What is Science? 1

Summary 1

Need to Know 1

Models and hypotheses 10

Models in general 11

Causality 13

Causal hypotheses and models 16

Causal models 17

Variables 18

The language of science 21

Science and evidence 22

Going Deeper 23

Models and life 27

Chapter 2 Comparing Different Models of a Set of Data 31

Summary 31

Need to Know 31

Value for money versus mud polishing 32

Modelling rainfall data in Sri Lanka 35

The postman's distance 37

The sum of squares distance 39

Going Deeper 43

Model parameters 43

Comparing the null and saturated models 45

Models and hypotheses 56

Signal-to-noise ratio 56

Data, models and error: the basic equation 57

Looking Ahead 59

Chapter 3 Testing Hypotheses and Recording the Result: Types of Validity 63

Summary 63

Need to Know 63

How models arise 63

Going Deeper 72

Model construction and testing 72

Science and replicability 73

Operational definitions 77

Construct validity 79

Chapter 4 Basic Descriptive Statistics (and How Pierre Laplace Saved the World) 83

Summary 83

Need to Know 83

Measures of central tendency 85

Measures of dispersion 87

Reliability and validity for measurements 88

Estimating the population mean of a scale variable: the reliability of the sample mean 90

Basic descriptive statistics in SPSS: the mean, median, mode, standard deviation, variance, interquartile range, range and s.e.m. 93

Going Deeper 95

How to find "typical" numbers 95

How typical are typical numbers? 99

Why the mean is so important 105

Chapter 5 Bacon's Legacy: Causal Models, and How to Test Them 109

Summary 109

Need to Know 109

Bacon's big idea 113

Going Deeper 120

Strategy 2 is sometimes valuable 120

Criticisms of the causal approach 120

Chapter 6 How Hypothesis Testing Copes with Uncertainty: The Legacy of Karl Popper and Ronald Fisher 125

Summary 125

Need to Know 125

The Popperian revolution 125

"Falsification" of the null hypothesis 127

Going Deeper 130

p-Values 130

More on falsificationism 131

Fisher's Statistical Coup 136

Accentuate the positive: eliminate the negative 136

Fisher's approach 139

Fisher's tea party 140

The eightfold way 142

On error 144

Chapter 7 Gaussian Distributions, the Building Block of Parametric Statistics 147

Summary 147

Need to Know 147

Why Gaussian distributions are important 147

Histograms 148

Gaussian distributions characterised by mean and standard deviation 150

All normal distributions have certain things in common 152

Sampling from a population: distribution of M 158

Going Deeper 160

Areas under a histogram represent cases 160

Areas under a histogram represent probabilities 161

Histograms split between groups 163

Why do normal distributions occur so often in nature? 168

Non-normal distributions: their prevention and cure 170

The Gaussian formula, for the mathematically minded 173

The Central Limit Theorem 175

More on histograms and probability distribution functions (pdfs) 177

Chapter 8 Randomised Controlled Trials, the Model T Ford of Experiments 181

Summary 181

Need to Know 181

Introducing the RCT 181

Sampling and population validity 183

How the RCT concept is applied to a given sample of participants 184

Going Deeper 192

The origin of the RCT 192

The RCT today 194

Population validity and internal validity 196

Blinding 198

X-O diagram for the RCT 199

Controlled variables 200

Chapter 9 The Independent Samples t-Test, the Analytical Engine of the RCT 203

Summary 203

Need to Know 203

Analysing the data 207

Independent samples t-test in SPSS 211

Going Deeper 221

So why bother with model comparison? 224

Models and hypotheses: from hypotheses to models 225

Models and hypotheses: from models to hypotheses 230

Occam's principle revisited 233

Causality, counterfactuals and why comparing means is meaningful 239

Chapter 10 Generalising the f-Test: One-Way ANOVA 241

Summary 241

Need to Know 241

Summary of progress to date 241

Generalising the two group design to more than two groups 244

Statistical analysis: generalising the t-test 246

Going Deeper 262

Contrasts comparing one group of means with another group of means in SPSS 267

Inflation of type I error rates through multiple testing 270

Application to multiple contrast testing 273

Post hoc pairwise comparison of means 274

General comparisons of sets of means 281

Contrasts in trend analysis 282

Building the IV model from the null model using contrasts 288

Effect parameters 295

Chapter 11 Multifactorial Designs and Their ANOVA Counterparts 299

Summary 299

Need to Know 299

Using all the cell means: the full model 307

Main effects 311

The interaction term 317

Interpreting the results 318

Going Deeper 320

Simple main effects 321

Three-way and higher order ANOVAs 324

Sex, drugs, and rock 'n' roll 325

Contrasts 330

Models in multifactorial ANOVA 330

Qualitative and quantitative interactions 333

Chapter 12 Repeated Measures Designs, and Their ANOVA Counterparts 335

Summary 335

Need to Know 335

Basic implementation of RM ANOVA in SPSS 341

Going Deeper 357

Mixed ANOVA 357

Contrasts in RM ANOVA 366

Designs using "matching" or "blocking" 368

Model comparison in RM ANOVA 371

Appendix A On Finding the Right Effect Size 379

Appendix B Why Orthogonal Contrasts are Useful 385

Appendix C Mathematical Justification for the Occam Line 389

Glossary 395

Further Reading 435

References 439

Index 441

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