Introduction to statistics in psychology [with SPSS]

1. Why statistics?Part 1: Descriptive statistics  2. Some basics: Variability and measurement3.    Describing variables: Tables and diagrams 4.    Describing variables numerically: Averages, variation and spread 5.    Shapes of Distributions of Scores6.    Standard deviation and z-scores: The standard unit of measurement in statistics7.    Relationships between two or more variables: Diagrams and tables8.    Correlation coefficients: Pearson correlation and Spearman's rho9.    Regression: Prediction with precisionPart 2: Significance testing 10.  Samples and populations: Generalising and inferring11.  Statistical significance for the correlation coefficient: A practical introduction to statistical inference12.  Standard error: The standard deviation of the means of samples13.  The t-test: Comparing two samples of correlated/related scores14.  The t-test: Comparing two samples of unrelated/uncorrelated scores15.  Chi-square: Differences between samples of frequency data16.  Probability17.  Reporting significance levels succinctly18.  One-tailed versus two-tailed significance testing19.  Ranking tests: Nonparametric statisticsPart 3: Introduction to analysis of variance 20.  The variance ratio test: The F-ratio to compare two variances21.  Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA22.  Analysis of variance for correlated scores or repeated measures23.  Two-way analysis of variance for unrelated/uncorrelated scores: Two studies for the price of one? 24.  Multiple comparisons in ANOVA: Just where do the differences lie?25.  Mixed-design ANOVA: Related and unrelated variables together26.  Analysis of covariance (ANCOVA): Controlling for additional variables27.  Multivariate Analysis of Variance (MANOVA)28.  Discriminant (Function) analysis especially in MANOVA29.  Statistics and the analysis of experimentsPart 4: More advanced correlational statistics 30.  Partial correlation: Spurious correlation, third or confounding variables, suppressor variables 31.  Factor analysis: Simplifying complex data32.  Multiple regression and multiple correlation33.  Path analysis34.  The analysis of a questionnaire/survey projectPart 5: Assorted advanced techniques 35.  The size of effects in statistical analysis: Do my findings matter?36.  Meta-analysis: Combining and exploring statistical findings from previous research37.  Reliability in scales and measurement: Consistency and agreement38.  Confidence intervals39.  The influence of moderator variables on relationships between two variables40.  Statistical power analysis: getting the sample size rightPart 6: Advanced qualitative or nominal techniques41.  Log-Linear Methods: The analysis of complex contingency tables42.  Multinomial logistic regression: Distinguishing between several different categories or groups43.  Binomial Logistic Regression AppendicesAppendix A: Testing for excessively skewed distributionsAppendix B1: Large sample formulae for the nonparametric testsAppendix B2: Nonparametric tests for three or more groupsAppendix C: Extended table of significance for the Pearson correlation coefficientAppendix D: Table of significance for the Spearman correlation coefficientAppendix E: Extended table of significance for the t-testAppendix F: Table of significance for Chi-squareAppendix G: Extended table of significance for the sign testAppendix H: Table of significance for the Wilcoxon Matched Pairs TestAppendix I: Table of significance for the Mann-Whitney U-testAppendix J: Table of significance values for the F-distributionAppendix K: Table of significant values oft when making multiple t-testsGlossaryReferencesIndex