Synopsis of the Graduate Statistics Course 2007
Exploratory Data Analysis (EDA)
• What is it? • Skew and kurtosis: definitions and magnitude rules of thumb • Pictorial representations - in particular histograms, boxplots and stem and leaf displays • Effect of outliers • Power transformations • Rank transformations
Regression
• What is it? • Expressing correlations (simple regression) in vector form • Scatterplots • Assumptions in regression • Restriction of range of a correlation • Comparing pairs of correlations • Multiple regression • Least squares • Residual plots • Stepwise methods • Synergy • Collinearity
Between subjects analysis of variance
• What is it used for? • Main effects • Interactions • Simple effects • Plotting effects • Implementation in SPSS • Effect size • Model specification • Latin squares • Balance • Venn diagram depiction of sources of variation
Power analysis
• Hypothesis testing • Boosting power • Effect sizes: definitions, magnitudes • Power evaluation methods:description and implementation using an examples a) nomogram b) power calculators c) SPSS macros d) spreadsheets e) power curves f) tables g) quick formula
Latent variable modelling – factor analysis and all that!
• Path diagrams – a regression example • Comparing correlations • Exploratory factor analysis • Assumptions of factor analysis • Reliability testing (Cronbach’s alpha) • Fit criteria in exploratory factor analysis • Rotations • Interpreting factor loadings • Confirmatory factor models • Fit criteria in confirmatory factor analysis • Equivalence of correlated and uncorrelated models • Cross validation as a means of assessing fit for different models • Parsimony : determining the most important items in a factor analysis