Statistics Explained is an accessible introduction to statistical concepts and ideas. It makes few assumptions about the reader’s statistical knowledge, carefully explaining each step of the analysis and the logic behind it. The book:
• provides a clear explanation of statistical analysis and the key statistical tests employed in analysing research data
• gives accessible explanations of how and why statistical tests are used
• includes a wide range of practical, easy-to-understand worked examples.
Building on the international success of earlier editions, this fully updated revision includes developments in statistical analysis, with new sections explaining concepts such as bootstrapping and structural equation modelling. A new chapter - ‘Samples and Statistical Inference’ - explains how data can be analysed in detail to examine its suitability for certain statistical tests. The friendly and straightforward style of the text makes it accessible to all those new to statistics, as well as more experienced students requiring a concise guide. It is suitable for students and new researchers in disciplines including Psychology, Education, Sociology, Sports Science, Nursing, Communication, and Media and Business Studies. Presented in full colour and with an updated, reader-friendly layout, this new edition also comes with a companion website featuring supplementary resources for students. Unobtrusive cross-referencing makes it the ideal companion to Perry R. Hinton’s SPSS Explained, also published by Routledge. Perry R. Hinton has many years of experience in teaching statistics to students from a wide range of disciplines and his understanding of the problems students face forms the basis of this book.
Author(s): Perry R. Hinton
Edition: 3
Publisher: Routledge | Taylor & Francis Group
Year: 2014
Language: English
Commentary: TruePDF
Pages: 377
Tags: Social Sciences: Statistical Methods; Psychometrics; Statistics; Statistics As Topic
Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
List of figures
Preface
Chapter 1 Introduction
Accompanying Website
Chapter 2 Descriptive Statistics
Measures of ‘Central Tendency’
Measures of ‘Spread’
Describing a Set of Data: In Conclusion
Comparing Two Sets of Data with Descriptive Statistics
Some Important Information about Numbers
Chapter Recap
Chapter 3 Standard Scores
Comparing Scores from Different Distributions
The Normal Distribution
The Standard Normal Distribution
Chapter Recap
Chapter 4 Introduction to Hypothesis Testing
Testing a Hypothesis
One- and Two-Tailed Predictions
Chapter Recap
Chapter 5 Sampling
Populations and Samples
Selecting a Sample
Sample Statistics and Population Parameters
Chapter Recap
Chapter 6 Hypothesis Testing with One Sample
An Example
When We Do Not Have the Known Population Standard Deviation
Confidence Intervals
Chapter Recap
Chapter 7 Selecting Samples for Comparison
Comparing Samples
The Interpretation of Sample Differences
Chapter Recap
Chapter 8 Hypothesis Testing with Two Samples
The Assumptions of the Two Sample t Test
Paired Samples or Independent Samples
The Paired Samples (Related) t Test
The Independent t Test
Chapter Recap
Chapter 9 Significance, Error and Power
Type I and Type II Errors
Statistical Power
The Power of a Test
The Choice of α Level
Effect Size
Sample Size
Chapter Recap
Chapter 10 Samples and Statistical Inference
Examining a Sample
Normally Distributed Populations and Samples
Skew
Kurtosis
Tests of Normality
Outliers
Sample Variation
Parametric Tests and Measurement
Bootstrapping
Chapter Recap
Chapter 11 Introduction to the Analysis of Variance
Factors and Conditions
The Limitations of the t Test
Why Do Scores Vary in a Set of Data?
The Process of Analysing Variability
The F Distribution
Chapter Recap
Chapter 12 One Factor Independent Anova
Analysing Variability in the Independent Anova
Rejecting the Null Hypothesis
Unequal Sample Sizes
The Relationship of F to t
Chapter Recap
Chapter 13 Multiple Comparisons
The Tukey Test (For All Pairwise Comparisons)
The Scheffé Test (For Complex Comparisons)
Chapter Recap
Chapter 14 One Factor Repeated Measures Anova
Deriving the F Value
Multiple Comparisons
Chapter Recap
Chapter 15 The Interaction of Factors in the Analysis of Variance
Interactions
Dividing Up the Between Conditions Sums of Squares
Simple Main Effects
Chapter Recap
Chapter 16 The Two Factor Anova
The Two Factor Independent Anova
The Two Factor Mixed Design Anova
The Two Factor Repeated Measures Anova
A Non-Significant Interaction
Chapter Recap
Chapter 17 Two Sample Nonparametric Analysis
Ordinal Data
Calculating Ranks
The Mann–Whitney u Test (For Independent Samples)
The Wilcoxon Signed-Ranks Test (For Paired Samples)
Chapter Recap
Chapter 18 One Factor Anova for Ranked Data
Kruskal–Wallis Test (For Independent Measures)
The Friedman Test (For Related Samples)
Chapter Recap
Chapter 19 Analysing Frequency Data: Chi-Square
Nominal Data, Categories and Frequency Counts
Introduction to X²
Chi-Square (X²) As a ‘Goodness of Fit’ Test
Chi-Square (X²) As a Test of Independence
The Chi-Square Distribution
The Assumptions of the X² Test
Chapter Recap
Chapter 20 Linear Correlation and Regression
Introduction
Pearson r Correlation Coefficient
Linear Regression
The Interpretation of Correlation and Regression
Problems with Correlation and Regression
The Standard Error of the Estimate
The Spearman rs Correlation Coefficient
Chapter Recap
Chapter 21 Multiple Correlation and Regression
Introduction to Multivariate Analysis
Partial Correlation
Multiple Correlation
Multiple Regression
The Significance of R²
Chapter Recap
Chapter 22 Complex Analyses
Complex Analyses and the Analysis of Variation
Reliability
Factor Analysis
Multivariate Analysis of Variance (Manova)
Discriminant Function Analysis
Structural Equation Modelling
Chapter Recap
Chapter 23 An Introduction to the General Linear Model
Models
An Example of a Linear Model
Modelling Data
The Model: The Regression Equation
Selecting a Good Model
Comparing Samples (The Analysis of Variance Once Again)
Explaining Variations in the Data
The General Linear Model
Chapter Recap
Chapter 24 Postscript
Appendix: Acknowledgements and Statistical Tables
A.1 The standard normal distribution tables
A.2 Critical values of the t distribution
A.3 Critical values of the F distribution
A.4 Critical values of the Studentized range statistic, q
A.5 Critical values of the Mann–Whitney U statistic
A.6 Critical values of the Wilcoxon T statistic
A.7 Critical values of the chi-square (X²) distribution
A.8 Table of probabilities for X²r when k and n are small
A.9 Critical values of the Pearson r correlation coefficient
A.10 Critical values of the Spearman rs ranked correlation coefficient
A.11 Excel commands for the D’Agostino–Pearson omnibus K² test
Glossary
References
Index
Choosing a statistical test
