Introduction to Statistics with SPSS for Social Science
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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 Spearmans 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
- Includes examples from many fields of social sciences to help users understand the range of applications of statistics in data analysis
- Guidance on what kinds of data to enter and how, the SPSS steps needed to complete your analysis and how to understand and write-up the results of the analysis
- Comprehensive discussion and user-friendly guidance on the full range of statistical techniques, when it is used, when it should not be used , the sorts of data required for the analysis and common problems that cause users difficulty
- Explains step-by-step how to enter, analyse and interpret data using SPSS
- Gives detail about what to report and how to report it
- In-text features such as research design issue, calculation and the advice boxes help structure study into manageable chunks whilst overviews and key points help with revision
- Includes a comprehensive glossary
- Full colour, bringing illustrations and explanations to life.