Chapter 1. Why is My Evil Lecturer Forcing Me to Learn Statistics? What the hell am I doing here? I don't belong here Initial observation: finding something that needs explaining Generating and testing theories and hypotheses Collecting data: measurement Collecting data: research design Chapter 2. The Spine of Statistiscs What will this chapter tell me? What is the SPINE of statistics? E is for estimating parameters I is for (confidence) interval N is for null hypothesis significance testing Reporting significance tests Chapter 3. The Phoenix of Statistics NHST as part of wider problems with science A phoenix from the EMBERS Pre-registering research and open science Reporting effect sizes and Bayes factors Chapter 4. The IBM SPSS Statistics Environment Versions of IBM SPSS Statistics Windows, Mac OS, and Linux Entering data into IBM SPSS Statistics Extending IBM SPSS Statistics Chapter 5. Exploring Data With Graphs The art of presenting data Boxplots (box-whisker diagrams) Graphing means: bar charts and error bars Graphing relationships: the scatterplot Chapter 6. The Beast of Bias Normally distributed something or other Homoscedasticity/homogeneity of variance Spotting linearity and heteroscedasticity/heterogeneity of variance Chapter 7. Non-Parametric Models When to use non-parametric tests General procedure of non-parametric tests in SPSS Comparing two independent conditions: the Wilcoxon rank-sum test and Mann-Whitney test Comparing two related conditions: the Wilcoxon signed-rank test Differences between several independent groups: the Kruskal-Wallis test Differences between several related groups: Friedman's ANOVA Chapter 8. Correlation Data entry for correlation analysis Partial and semi-partial correlation Calculating the effect size How to report correlation coefficents Chapter 9. Linear Model (Regression) An introduction to the linear model (regression) Sample size and the linear model Fitting linear models: the general procedure Using IBM SPPS Statistics to fit a linear model with one predictor Interpreting a linear model with one predictor Interpreting a linear model with two or more predictors (multiple regression) Using IBM SPSS Statistics to fit a linear model with several predictors Interpreting a linear model with several predictors Chapter 10. Comparing Two Means An example: are invisible people mischievous? Categorical predictors in the linear model Assumptions of the t-test Comparaing two means: general procedure Comparing two independent means using IBM SPSS Statistics Comparing two related means using IBM SPSS Statistics Reporting comparisons between two means Between groups or repeated measures? Chapter 11. Moderation, Mediation and Multicategory Predictors Moderation: interactions in the linear model Categorical predictors in regression Chapter 12. GLM 1: Comparing Several Independent Means Using a linear model to compare several means Assumptions when comparing means Planned contrasts (contrast coding) Comparing several means using IBM SPSS Statistics Output from one-way independent ANOVA Robust comparisons of several means Bayesian comparisons of several means Calculating the effect size Reporting results from one-way independent ANOVA Chapter 13. GLM 2: Comparing Means Adjusted For Other Predictors (Analysis of Covariance) ANCOVA and the general linear model Assumptions and issues in ANCOVA Conducting ANCOVA using IBM SPSS Statistics Testing the assumption of homogeneity of regression slopes Bayesian analysis with covariates Calculating the effect size Chapter 14. GLM 3: Factorial Designs Independent factorial designs and the linear model Model assumptions in factorial designs Factorial designs using IBM SPSS Statistics Output from factorial designs Interpreting interaction graphs Robust models of factorial designs Bayesian models of factorial designs Reporting results of two-way ANOVA Chapter 15. GLM 4: Repeated-Measures Designs Introduction to repeated-measures designs Repeated-measures and the linear model The ANOVA approach to repeated-measures designs The F-statistics for repeated-measures designs Assumptions in repeated-measures designs One-way repeated-measures designs Chapter 16. GLM 5: Mixed Designs Assumptions in mixed designs Mixed designs using IBM SPSS Statistics Output for mixed factorial designs Reporting the results of mixed designes Chapter 17. Multivariate Analysis of Variance (MANOVA) Practical issues when conducting MANOVA MANOVA using IBM SPSS Statistics Reporting results from MANOVA Following up MANOVA with discriminant analysis Interpreting discriminant analysis Reporting results from discriminant analysis Chapter 18. Exploratory Factor Analysis When to use factor analysis Factor analysis uisng IBM SPSS Statistics Interpreting factor analysis How to report factor analysis Reliability analysis using IBM SPSS Statistics Interpreting reliability analysis How to report reliability analysis Chapter 19. categorical Outcomes: Chi-Square and Loglinear Analysis Analysing categorical data Associations between two categorical variables Associations between several categorical variables: loglinear analysis Assumptions when analysisng categorical data General procedure for analysing categorical outcomes Doing chi-square uisng IBM SPSS Statistics Interpreting the chi-square test Loglinear analysis using IBM SPSS Statistics Interpreting loglinear analysis Reporting the results of loglinear analysis Chapter 20. Categorical Outcomes: Logistic Regression What is logitsic regression? Theory of logistic regression Sources of bias and common problems Binary logistic regression Interpreting logistic regression Reporting logistic regression Testing assumptions: another example Predicting several categories: multinominal logistic regression Reporting multinominal logistic regression Chapter 21. Multilevel Linear Models Theory of multilevel linear models Multilevel modeling using IBM SPSS Statistics How to report a multilevel model A message from the octopus of inescapable despair Chapter 22. Epilouge SupplementsCompanion Website Companion Website Instructor Teaching Site SAGE EDGE FOR INSTRUCTORS supports your teaching by making it easy to integrate quality content and create a rich learning environment for students and includes:
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