Understanding Statistics in Psychology

<div class="c-section-headers-number-list_container"> <p>Preface</p> <ol> <li>Why statistics?</li> </ol> <h4>Part 1 Descriptive statistics</h4> <ol start="2"> <li>Some basics: Variability and measurement</li> <li>Describing variables: Tables and diagrams</li> <li>Describing variables numerically: Averages, variation and spread</li> <li>Shapes of distributions of scores</li> <li>Standard deviation and z-scores: Standard unit of measurement in statistics</li> <li>Relationships between two or more variables: Diagrams and tables</li> <li>Correlation coefficients: Pearson’s correlation and Spearman's rho</li> <li>Regression: Prediction with precision</li> </ol> <h4>Part 2 Significance testing</h4> <ol start="10"> <li>Samples from populations</li> <li>Statistical significance for the correlation coefficient: Practical introduction to statistical inference</li> <li>Standard error: Standard deviation of the means of samples</li> <li>Related or paired-samples t-test: Comparing two samples of related/correlated/paired scores</li> <li>Unrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scores</li> <li>What you need to write about your statistical analysis</li> <li>Confidence intervals</li> <li>Effect size in statistical analysis: Do my findings matter?</li> <li>Chi-square: Differences between samples of frequency data</li> <li>Probability</li> <li>One- versus two-tailed or -sided significance testing</li> <li>Ranking tests: Nonparametric statistics</li> </ol> <h4>Part 3 Introduction to analysis of variance</h4> <ol start="22"> <li>Variance ratio test: F-ratio to compare two variances</li> <li>Analysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVA</li> <li>ANOVA for correlated scores or repeated measures</li> <li>Two-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?</li> <li>Multiple comparisons in ANOVA: A priori and post hoc tests</li> <li>Mixed-design ANOVA: Related and unrelated variables together</li> <li>Analysis of covariance (ANCOVA): Controlling for additional variables</li> <li>Multivariate analysis of variance (MANOVA)</li> <li>Discriminant (function) analysis – especially in MANOVA</li> <li>Statistics and analysis of experiments</li> </ol> <h4>Part 4 More advanced correlational statistics</h4> <ol start="32"> <li>Partial correlation: Spurious correlation, third or confounding variables, suppressor variables</li> <li>Factor analysis: Simplifying complex data</li> <li>Multiple regression and multiple correlation</li> <li>Path analysis</li> <li>Analysis of a questionnaire/survey project</li> </ol> <h4>Part 5 Assorted advanced techniques</h4> <ol start="37"> <li>Meta-analysis: Combining and exploring statistical findings from previous research</li> <li>Reliability in scales and measurement: Consistency and agreement</li> <li>Influence of moderator variables on relationships between two variables</li> <li>Statistical power analysis: Getting the sample size right</li> </ol> <h4>Part 6 Advanced qualitative or nominal techniques</h4> <ol start="41"> <li>Log-linear methods: Analysis of complex contingency tables</li> <li>Multinomial logistic regression: Distinguishing between several different categories or groups</li> <li>Binomial logistic regression</li> </ol> <h4>Part 7 Bringing things together</h4> <ol start="44"> <li>Data mining and Big Data</li> <li>Towards a masterplan</li> </ol> <p>Appendices</p> <p>Glossary</p> <p>References</p> <p>Index</p> </div>