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
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