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| Exploratory Data Analysis (EDA) | 1. '''Exploratory Data Analysis (EDA)''' |
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| • 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 |
* What is it? |
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| Regression | * Skew and kurtosis: definitions and magnitude rules of thumb |
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| • 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 |
* Pictorial representations - in particular histograms, boxplots and stem and leaf displays |
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| Between subjects analysis of variance | * Effect of outliers |
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| • 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 transformations |
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| Power analysis | * Rank transformations |
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| • 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 |
1. '''Regression''' |
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| Latent variable modelling – factor analysis and all that! | * What is it? |
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| • 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 |
* 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 1. '''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 1. '''Power analysis''' * Hypothesis testing * Boosting power * Effect sizes: definitions, magnitudes * Power evaluation methods:description and implementation using an examples * nomogram * power calculators * SPSS macros * spreadsheets * power curves * tables * quick formula 1. '''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 |
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
- nomogram
- power calculators
- SPSS macros
- spreadsheets
- power curves
- tables
- 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