Statistics Explained A Guide for Social Science Students

ISBN-10: 0415332850

ISBN-13: 9780415332859

Edition: 2nd 2004 (Revised)

Authors: Perry R. Hinton

List price: $53.95
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Do you hate statistics? Are you confused by the wide range of statistical tests and their uses? This book clearly outlines the major statistical tests used by undergraduates in psychology and the social sciences, and provides easy-to-understand explanations of how and why they are used.
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Book details

List price: $53.95
Edition: 2nd
Copyright year: 2004
Publisher: Routledge
Publication date: 8/31/2004
Binding: Paperback
Pages: 400
Size: 6.75" wide x 9.50" long x 0.75" tall
Weight: 1.738
Language: English

List of figures
Preface to the Second Edition
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
Standard scores
Comparing scores from different distributions
The Normal Distribution
The Standard Normal Distribution
Introduction to hypothesis testing
Testing an hypothesis
The logic of hypothesis testing
One- and two-tailed predictions
Populations and samples
Selecting a sample
Sample statistics and population parameters
Hypothesis testing with one sample
An example
When we do not have the known population standard deviation
Confidence intervals
Hypothesis testing with one sample: in conclusion
Selecting samples for comparison
Designing experiments to compare samples
The interpretation of sample differences
Hypothesis testing with two samples
The assumptions of the two sample t test
Related or independent samples
The related t test
The independent t test
Confidence intervals
Significance, error and power
Type I and Type II errors
Statistical power
The power of a test
The choice of [alpha] level
Effect size
Sample size
Introduction to the analysis of variance
Factors and conditions
The problem of many conditions and the t test
Why do scores vary in an experiment?
The process of analysing variability
The F distribution
One factor independent measures ANOVA
Analysing variability in the independent measures ANOVA
Rejecting the null hypothesis
Unequal sample sizes
The relationship of F to t
Multiple comparisons
The Tukey test (for all pairwise comparisons)
The Scheffe test (for complex comparisons)
One factor repeated measures ANOVA
Deriving the F value
Multiple comparisons
The interaction of factors in the analysis of variance
Dividing up the between conditions sums of squares
Simple main effects
Calculating the two factor ANOVA
The two factor independent measures ANOVA
The two factor mixed design ANOVA
The two factor repeated measures ANOVA
A non-significant interaction
An introduction to nonparametric analysis
Calculating ranks
Two sample nonparametric analyses
The Mann-Whitney U test (for independent samples)
The Wilcoxon signed-ranks test (for related samples)
One factor ANOVA for ranked data
Kruskal-Wallis test (for independent measures)
The Friedman test (for related samples)
Analysing frequency data: chi-square
Nominal data, categories and frequency counts
Introduction to X[superscript 2]
Chi-square (X[superscript 2]) as a 'goodness of fit' test
Chi-square (X[superscript 2]) as a test of independence
The chi-square distribution
The assumptions of the X[superscript 2] test
Linear correlation and regression
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 r[subscript S] correlation coefficient
Multiple correlation and regression
Introduction to multivariate analysis
Partial correlation
Multiple correlation
Multiple regression
Complex analyses and computers
Undertaking data analysis by computer
Complex analyses
Factor analysis
Multivariate analysis of variance (MANOVA)
Discriminant function analysis
An introduction to the general linear model
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
Acknowledgements and statistical tables
The standard normal distribution tables
Critical values of the t distribution
Critical values of the F distribution
Critical values of the Studentized range statistic, q
Critical values of the Mann-Whitney U statistic
Critical values of the Wilcoxon T statistic
Critical values of the chi-square (X[superscript 2]) distribution
Table of probabilities for X[superscript 2 subscript r] when k and n are small
Critical values of the Pearson r correlation coefficient
Critical values of the Spearman r[subscript S] ranked correlation coefficient
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