Intended for psychology students, whether at introductory, intermediate or advanced level, this book is a useful companion to their statistics course. It is a guide to understanding a range of statistics, and teaches how to choose appropriate statistical tests. It also describes how to analyse data of various sorts.
Introduction to Statistics in Psychology by Dennis Howitt
Statistics can be tricky, but the 3rd edition of Howitt and Cramer's popular statistics text makes it much easier. Users of statistics at all levels find this comprehensive and modern approach indispensable. This new edition has been redesigned for maximum clarity with difficult concepts explained in simple steps using a wide variety of examples. This book can be used on its own or in conjunction with 'Introduction to SPSS 12 in Psychology' and Introduction to Research Methods in Psychology' by the same authors.
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Introduction Part 1: Descriptive statistics 1. Why you need statistics: Types of data Overview 1.1 Introduction 1.2 Variables and measurement 1.3 Major types of measurement Key points 2. Describing variables: Tables and diagrams Overview 2.1 Introduction 2.2 Choosing tables and diagrams 2.3 Errors to avoid Key points 3. Describing variables numerically: Averages, variation and spread Overview 3.1 Introduction 3.2 Typical scores: mean, median and mode 3.3 Comparison of mean, median and mode 3.4 The spread of scores: variability Key points 4. Shapes of distributions of scores Overview 4.1 Histograms and frequency curves 4.2 The normal curve 4.3 Distorted curves 4.4 Other frequency curves Key points 5. Standard deviation: The standard unit of measurement in statistics Overview 5.1 Introduction 5.2 Theoretical background 5.3 Measuring the number of standard deviations - the z-score 5.4 A use of z-scores 5.5 The standard normal distribution 5.6 An important feature of z-scores Key points 6. Relationships between two or more variables: Diagrams and tables Overview 6.1 Introduction 6.2 The principles of diagrammatic and tabular presentation 6.3 Type A: both variables numerical scores 6.4 Type B: both variables nominal categories 6.5 Type C: one variable nominal categories, the other numerical scores Key points 7. Correlation coefficients: Pearson correlation and Spearman's rho Overview 7.1 Introduction 7.2 Principles of the correlation coefficient 7.3 Some rules to check out 7.4 Coefficient of determination 7.5 Significance testing 7.6 Spearman's rho - another correlation coefficient 7.7 An example from the literature Key points 8. Regression: Prediction with precision Overview 8.1 Introduction 8.2 Theoretical background and regression equations 8.3 Standard error: how accurate are the predicted score and the regression equations? 8.4 Notes and recommendations Key points Part 2: Significance testing 9. Samples and populations: Generalising and inferring Overview 9.1 Theoretical considerations 9.2 The characteristics of random samples 9.3 Confidence intervals Key points 10. Statistical significance for the correlation coefficient: A practical introduction to statistical inference Overview 9.4 Theoretical considerations 9.5 Back to the real world: the null hypothesis 10.3 Pearson's correlation coefficient again 10.4 The Spearman's rho correlation coefficient Key points 11. Standard error: The standard deviation of the means of samples Overview 11.1 Theoretical considerations 11.2 Estimated standard deviation and standard error Key points 12. The t-test: Comparing two samples of correlated/related scores Overview 12.1 Introduction 12.2 Dependent and independent variables 12.3 Some basic revision 12.4 Theoretical considerations 12.5 Cautionary note Key points 13. The t-test: Comparing two samples of unrelated/uncorrelated scores Overview 13.1 Introduction 13.2 Theoretical considerations 13.3 Standard deviation and standard error 13.4 Cautionary note Key points 14. Chi-square: Differences between samples of frequency data Overview 14.1 Introduction 14.2 Theoretical issues 14.3 Partitioning chi-square 14.4 Important warnings 14.5 Alternatives to chi-square 14.6 Chi-square and known populations 14.7 Chi-square for related samples - the McNemar Test 14.8 Example from the literature Key points 15. Probability Overview 15.1 Introduction 15.2 The principles of probability 15.3 Implications Key points 16. Reporting significance levels succinctly Overview 16.1 Introduction 16.2 Shortened forms 16.3 Examples from the published literature Key points 17. One-tailed versus two-tailed significance testing Overview 17.1 Introduction 17.2 Theoretical considerations 17.3 Further requirements Key points 18. Ranking tests: Nonparametric statistics Overview 18.1 Introduction 18.2 Theoretical considerations 18.3 Nonparametric statistical tests 18.4 Three or more groups of scores 18.5 Notes and recommendations Part 3: Introduction to analysis of variance 19. The variance ratio test: The F-ratio to compare two variances Overview 19.1 The research problem 19.2 Theoretical issues and an application Key points 20. Analysis of variance (ANOVA): Introduction to the one-way unrelated or uncorrelated ANOVA Overview 20.1 Introduction 20.2 Some revision and some new material 20.3 Theoretical considerations 20.4 Degrees of freedom 20.5 The analysis of variance summary table 20.6 Quick calculation methods for ANOVA Key points 21. Analysis of variance for correlated scores or repeated measures Overview 21.1 Introduction 21.2 Theoretical considerations 21.3 Examples Key points 22. Two-way analysis of variance for unrelated/uncorrelated scores: Two experiments for the price of one? Overview 22.1 Introduction 22.2 Theoretical considerations 22.3 Steps in the an. 22.4 More on interactions 22.5 Calculation of two-way ANOVA using quick method 22.6 Three or more independent variables Key points 23. Multiple comparisons in ANOVA: Just where do the differences lie? Overview 23.1 Introduction 23.2 Methods 23.3 Planned versus a posteriori (post hoc) comparisons 23.4 The Scheffe Test for one-way ANOVA 23.5 Multiple comparisons for multifactorial ANOVA Key points 24. More analysis of variance designs: Mixed-design ANOVA and analysis of covariance (ANCOVA) Overvie.2 Mixed designs and repeated measures 24.3 Analysis of covariance Key points 25. Statistics and the analysis of experiments Overview 25.1 Introduction 25.2 The Patent Stats Pack 25.3 Checklist 25.4 Special cases Key points Part 4: More advanced correlational statistics 26. Partial correlation: Spurious correlation, third or confounding variables, suppressor variables Overview 26.1 Introduction 26.2 Theoretical considerations 26.3 The calculation 26.4 Interpretation 26.5 Multiple control variables 26.6 Suppressor variables 26.7 An example from the research literature 26.8 An example from a student's work Key points 27. Factor analysis: Simplifying complex data Overview 27.1 Introduction 27.2 A bit of history 27.3 Concepts in factor analysis 27.4 Decisions, decisions, decisions 27.5 Exploratory and confirmatory factor analysis 27.6 An example of factor analysis from the literature 27.7 Reporting the results Key points 28. Multiple regression and multiple correlation Overview 28.1 Introduction 28.2 Theoretical considerations 28.3 Stepwise multiple regression example 28.4 Reporting the results 28.5 An example from the published literature Key points 29. Path analysis Overview 29.1 Introduction 29.2 Theoretical considerations 29.3 An example from published research 29.4 Reporting the results Key points 30. The analysis of a questionnaire/survey project Overview 30.1 Introduction 30.2 The research project 30.3 The research hypothesis 30.4 Initial variable classification 30.5 Further coding of data 30.6 Data cleaning 30.7 Data analysis Key points Part 5:Assorted advanced techniques 31. Statistical power analysis: Do my findings matter? Overview 31.1 Statistical significance 31.2 Method and statistical power 31.3 Size of the effect in studies 31.4 An approximation for nonparametric tests 31.5 Analysis of variance (ANOVA) Key points 32. Meta-analysis: Combining and exploring statistical findings from previous research Overview 32.1 Introduction 32.2 The Pearson correlation coefficient as the effect size 32.3 Other measures of effect size 32.4 Effects of different characteristics of studies 32.5 First steps in meta-analysis 32.6 Illustrative example 32.7 Comparing a study with a previous study 32.8 Reporting the results Key points 33. Reliability in scales and measurement: Consistency and agreement Overview 33.1 Internal consistency of scales and measurements 33.2 Item-analysis using item-total correlation 33.3 Split-half reliability 33.5 Agreement between raters Key points 34. Confidence intervals Overview 34.1 Introduction 34.2 The relationship between significance and confidence intervals 34.3 Regression 34.4 Other confidence intervals Key points Part 6: Advanced qualitative or nominal techniques 35. The analysis of complex contingency tables: Log-linear methods Overview 35.1 Introduction 35.2 A two-variable example 35.3 A three-variable example 35.4 Reporting the results Key points 36. Multinomial logistic regression: Distinguishing between several different categories or groups Overview 36.1 Introduction 36.2 Dummy variables 36.3 What can multinomial logistic regression do? 36.4 Worked example 36.5 Accuracy of the prediction 36.6 How good are the predictors? 36.7 The prediction 36.8 What have we found? 36.9 Reporting the findings Key points 37. Binomial Logistic Regression Overview 37.1 Introduction 37.2 Typical example 37.3 Applying the logistic regression procedure 37.4 The regression formula 37.5 Reporting the findings Key points Appendices Appendix A: Testing for excessively skewed distributions Appendix B: Large sample formulae for the nonparametric tests Appendix B2: Nonparametric tests for three or more groups Appendix C: Extended table of significance for the Pearson correlation coefficient Appendix D: Table of significance for the Spearman correlation coefficient Appendix E: Extended table of significance for the t-test Appendix F: Table of significance for Chi-square Appendix G: Extended table of significance for the sign test Appendix H: Table of significance for the Wilcoxon Matched Pairs Test Appendix I: Table of significance for the Mann-Whitney U-test Appendix J: Table of significance values for the F-distribution Appendix K: Table of significant values of t when making multiple t-tests Index
Introduction to Statistics in Psychology by Dennis Howitt
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Pearson Education (US)
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