Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R
Buch, Englisch, 576 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1263 g
ISBN: 978-1-119-58304-2
Verlag: Wiley
AN UPDATED GUIDE TO STATISTICAL MODELING TECHNIQUES USED IN THE SOCIAL AND NATURAL SCIENCES
This revised and updated second edition of Applied Univariate, Bivariate, and Multivariate Statistics: Understanding Statistics for Social and Natural Scientists, with Applications in SPSS and R contains an accessible introduction to statistical modeling techniques commonly used in the social and natural sciences. The text offers a blend of statistical theory and methodology and reviews both the technical and theoretical aspects of good data analysis.
Featuring applied resources at various levels, the book includes statistical techniques using software packages such as R and SPSS®. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. The second edition has been designed to be more approachable by minimizing theoretical or technical jargon and maximizing conceptual understanding with easy-to-apply software examples. This important text:
- Offers demonstrations of statistical techniques using software packages such as R and SPSS®
- Contains examples of hypothetical and real data with statistical analyses
- Provides historical and philosophical insights into many of the techniques used in modern science
- Includes a companion website that features further instructional details, additional data sets, and solutions to selected exercises
Written for students of social and applied sciences, Applied Univariate, Bivariate, and Multivariate Statistics, Second Edition offers a thorough introduction to the world of statistical modeling techniques in the sciences.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Preface xviii
About the Companion Website xxi
1 Preliminary Considerations 1
1.1 The Philosophical Bases of Knowledge: Rationalistic Versus Empiricist Pursuits 1
1.2 What is a “Model”? 3
1.3 Social Sciences Versus Hard Sciences 5
1.4 Is Complexity a Good Depiction of Reality? Are Multivariate Methods Useful? 7
1.5 Causality 8
1.6 The Nature of Mathematics: Mathematics as a Representation of Concepts 8
1.7 As a Scientist How Much Mathematics Do You Need to Know? 10
1.8 Statistics and Relativity 11
1.9 Experimental Versus Statistical Control 12
1.10 Statistical Versus Physical Effects 12
1.11 Understanding What “Applied Statistics” Means 13
Review Exercises 14
Further Discussion and Activities 14
2 Introductory Statistics 16
2.1 Densities and Distributions 17
2.1.1 Plotting Normal Distributions 19
2.1.2 Binomial Distributions 21
2.1.3 Normal Approximation 23
2.1.4 Joint Probability Densities: Bivariate and Multivariate Distributions 24
2.2 Chi-Square Distributions and Goodness-of-Fit Test 27
2.2.1 Power for Chi-Square Test of Independence 30
2.3 Sensitivity and Specificity 31
2.4 Scales of Measurement: Nominal, Ordinal, Interval, Ratio 31
2.4.1 Nominal Scale 32
2.4.2 Ordinal Scale 32
2.4.3 Interval Scale 33
2.4.4 Ratio Scale 33
2.5 Mathematical Variables Versus Random Variables 34
2.6 Moments and Expectations 35
2.6.1 Sample and Population Mean Vectors 36
2.7 Estimation and Estimators 38
2.8 Variance 39
2.9 Degrees of Freedom 41
2.10 Skewness and Kurtosis 42
2.11 Sampling Distributions 44
2.11.1 Sampling Distribution of the Mean 44
2.12 Central Limit Theorem 47
2.13 Confidence Intervals 47
2.14 Maximum Likelihood 49
2.15 Akaike’s Information Criteria 50
2.16 Covariance and Correlation 50
2.17 Psychometric Validity Reliability: A Common Use of Correlation Coefficients 54
2.18 Covariance and Correlation Matrices 57
2.19 Other Correlation Coefficients 58
2.20 Student’s t Distribution 61
2.20.1 t-Tests for One Sample 61
2.20.2 t-Tests for Two Samples 65
2.20.3 Two-Sample t-Tests in R 65
2.21 Statistical Power 67
2.21.1 Visualizing Power 69
2.22 Power Estimation Using R and G*Power 69
2.22.1 Estimating Sample Size and Power for Independent Samples t-Test 71
2.23 Paired-Samples t-Test: Statistical Test for Matched-Pairs (Elementary Blocking) Designs 73
2.24 Blocking With Several Conditions 76
2.25 Composite Variables: Linear Combinations 76
2.26 Models in Matrix Form 77
2.27 Graphical Approaches 79
2.27.1 Box-and-Whisker Plots 79
2.28 What Makes a p-Value Small? A Critical Overview and Practical Demonstration of Null Hypothesis Significance Testing 82
2.28.1 Null Hypothesis Significance Testing (NHST): A Legacy of Criticism 82
2.28.2 The Make-Up of a p-Value: A Brief Recap and Summary 85
2.28.3 The Issue of Standardized Testing: Are Students in Your School Achieving More than the National Average? 85
2.28.4 Other Test Statistics 86
2.28.5 The Solution 87
2.28.6 Statistical Distance: Cohen’s d 87
2.28.7 What Does Cohen’s d Actually Tell Us? 88
2.28.8 Why and Where the Significance Test Still Makes Sense 89
2.29 Chapter Summary and Highlights 89
Review Exercises 92
Further Discussion and Activities 95
3 Analysis of Variance: Fixed Effects Models 97
3.1 What is Analysis of Variance? Fixed Versus Random Effects 98
3.1.1 Small Sample Example: Achievement as a Function of Teacher 99
3.1.2 Is Achievement a Function of Teacher? 100
3.2 How Analysis of Variance Works: A Big Picture Overview 101
3.2.1 Is the Observed Differ
