1st Edition

Introduction to Statistics with SPSS for Social Science

490 Pages

by Routledge

490 Pages

by Routledge

496 Pages

by Routledge

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Description

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... Read more

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