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.
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 Variance Chapter 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 techniques Chapter 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