A clear and comprehensive introduction to Statistics with step by step guidance on using SPSS to carry out statistical analysis. Understanding Statistics in Psychology with SPSS 7th edition is geared towards helping students to truly understand statistical techniques and gain the confidence to apply them with the help of SPSS.

1 Why statistics? Part 1 Descriptive statistics 2 Some basics: Variability and measurement 3 Describing variables: Tables and diagrams 4 Describing variables numerically: Averages, variation and spread 5 Shapes of distributions of scores 6 Standard deviation and z-scores: Standard unit of measurement in statistics 7 Relationships between two or more variables: Diagrams and tables 8 Correlation coefficients: Pearson's correlation and Spearman's rho 9 Regression: Prediction with precision Part 2 Significance testing 10 Samples from populations 11 Statistical significance for the correlation coefficient: Practical introduction to statistical inference 12 Standard error: Standard deviation of the means of samples 13 Related t-test: Comparing two samples of related/correlated/paired scores 14 Unrelated t-test: Comparing two samples of unrelated/ uncorrelated/independent scores 15 What you need to write about your statistical analysis 16 Confidence intervals 17 Effect size in statistical analysis: Do my findings matter? 18 Chi-square: Differences between samples of frequency data 19 Probability 20 One-tailed versus two-tailed significance testing 21 Ranking tests: Nonparametric statistics Part 3 Introduction to analysis of variance 22 Variance ratio test: F-ratio to compare two variances 23 Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA 24 ANOVA for correlated scores or repeated measures 25 Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one? 26 Multiple comparisons in ANOVA: A priori and post hoc tests 27 Mixed-design ANOVA: Related and unrelated variables together 28 Analysis of covariance (ANCOVA): Controlling for additional variables 29 Multivariate analysis of variance (MANOVA) 30 Discriminant (function) analysis - especially in MANOVA 31 Statistics and analysis of experiments Part 4 More advanced correlational statistics 32 Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 33 Factor analysis: Simplifying complex data 34 Multiple regression and multiple correlation 35 Path analysis 36 Analysis of a questionnaire/survey project Part 5 Assorted advanced techniques 37 Meta-analysis: Combining and exploring statistical findings from previous research 38 Reliability in scales and measurement: Consistency and agreement 39 Influence of moderator variables on relationships between two variables 40 Statistical power analysis: Getting the sample size right Part 6 Advanced qualitative or nominal techniques 41 Log-linear methods: Analysis of complex contingency tables 42 Multinomial logistic regression: Distinguishing between several different categories or groups 43 Binomial logistic regression Appendices Glossary References Index