Understanding Statistics in Psychology

Dennis Howitt is a reader in Psychology at Loughborough University, a chartered forensic psychologist and fellow of the British Psychological Society, with a specific interest in the study of mass communications and the application of psychology to social issues.Duncan Cramer is an emeritus professor at Loughborough University with a specific interest in topics such as mental health, personality, personal relationships, organizational commitment, psychotherapy and counselling.

• Preface Why statistics?Part 1 Descriptive statistics Some basics: Variability and measurementDescribing variables: Tables and diagramsDescribing variables numerically: Averages, variation and spreadShapes of distributions of scoresStandard deviation and z-scores: Standard unit of measurement in statisticsRelationships between two or more variables: Diagrams and tablesCorrelation coefficients: Pearson’s correlation and Spearman's rhoRegression: Prediction with precisionPart 2 Significance testing Samples from populationsStatistical significance for the correlation coefficient: Practical introduction to statistical inferenceStandard error: Standard deviation of the means of samplesRelated or paired-samples t-test: Comparing two samples of related/correlated/paired scoresUnrelated or independent-samples t-test: Comparing two samples of unrelated/uncorrelated/independent scoresWhat you need to write about your statistical analysisConfidence intervalsEffect size in statistical analysis: Do my findings matter?Chi-square: Differences between samples of frequency dataProbabilityOne- versus two-tailed or -sided significance testingRanking tests: Nonparametric statisticsPart 3 Introduction to analysis of variance Variance ratio test: F-ratio to compare two variancesAnalysis of variance (ANOVA): One-way unrelated or uncorrelated ANOVAANOVA for correlated scores or repeated measuresTwo-way or factorial ANOVA for unrelated/uncorrelated scores: Two studies for the price of one?Multiple comparisons in ANOVA: A priori and post hoc testsMixed-design ANOVA: Related and unrelated variables togetherAnalysis of covariance (ANCOVA): Controlling for additional variablesMultivariate analysis of variance (MANOVA)Discriminant (function) analysis – especially in MANOVAStatistics and analysis of experimentsPart 4 More advanced correlational statistics Partial correlation: Spurious correlation, third or confounding variables, suppressor variablesFactor analysis: Simplifying complex dataMultiple regression and multiple correlationPath analysisAnalysis of a questionnaire/survey projectPart 5 Assorted advanced techniques Meta-analysis: Combining and exploring statistical findings from previous researchReliability in scales and measurement: Consistency and agreementInfluence of moderator variables on relationships between two variablesStatistical power analysis: Getting the sample size rightPart 6 Advanced qualitative or nominal techniques Log-linear methods: Analysis of complex contingency tablesMultinomial logistic regression: Distinguishing between several different categories or groupsBinomial logistic regressionPart 7 Bringing things together Data mining and Big DataTowards a masterplanAppendicesGlossaryReferencesIndex