Description
Taylor & Francis Ltd Introduction To Statistics With Spss For Social Science 2012 Edition by Faiza Qureshi
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 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