Introduction to Statistics with SPSS for Social SciencePaperback
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- Publisher: LONGMAN
- Format: Paperback | 496 pages
- Dimensions: 194mm x 264mm x 18mm | 921g
- Publication date: 31 May 2012
- Publication City/Country: London
- ISBN 10: 1408237598
- ISBN 13: 9781408237595
- Edition statement: New.
- Sales rank: 1,735,002
This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, tests and procedures. It is also a guide to getting started with SPSS, and includes screenshots to illustrate explanations. With examples specific to social sciences, this text is essential for any student in this area.
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Table of contents
Part One -- Descriptive Statistics.Chapter 1 -- Why you need statistics: types of data Chapter 2 -- Describing variables: Tables and diagrams Chapter 3 -- Describing variables numerically: averages, variation and spread Chapter 4 -- Shapes of distributions of scores Chapter 5 - Standard deviation, z-scores and standard error: the standard unit of measurement in statistics Chapter 6 -- Relationships between two or more variables: diagrams and tables Chapter 7 -- Correlation coefficients: Pearson correlation and Spearman's rho Chapter 8 -- Regression and standard error Part Two: Comparing Two or More Variables and the Analysis of Variance.Chapter 9 - The analysis of a questionnaire/survey project Chapter 10 -- The related t-test: Comparing two samples of correlated/related scores Chapter 11 -- the unrelated t-test: comparing two samples of unrelated/uncorrelated scores Chapter 12 -- Chi-square: Differences between samples of frequency data Part Three: Introduction to Analysis of VarianceChapter 13 -- Analysis of variance (ANOVA): introduction to one-way unrelated or uncorrelated ANOVA Chapter 14 -- Two way analysis of variance for unrelated/uncorrelated scores: two studies for the price of one? Chapter 15 -- Analysis of covariance (ANCOVA): controlling for additional variables Chapter 16 -- Multivariate analysis of variance (MANOVA)Part Four: More advanced correlational statistics and techniquesChapter 17 - Partial correlation: spurious correlation, third or confounding variables (control variables), suppressor variables Chapter 18 -- Factor analysis: simplifying complex data Chapter 19 -- Multiple regression and multiple correlation Chapter 20 -- Multinomial logistic regression: Distinguishing between several different categories or groups Chapter 21 - Bionomial logistic regression Chapter 22 - Log-linear methods: The analysis of complex contingency tables